This document serves as a transcript for a video tutorial focused on Microsoft Power BI, a business intelligence tool. The tutorial, led by Kevin, explains how to download and install Power BI, import data from various sources like Excel spreadsheets and the web, and transform that data for analysis. It then guides users through creating various visualizations such as bar charts, line charts, and maps, and demonstrates how to interact with and slice the data within the reports. Finally, the document covers customizing the report’s appearance and the process of saving and publishing the report for sharing and collaboration within the Power BI service.
Power BI: From Data to Insightful Reports
Microsoft Power BI is a tool used to gain insights from data. It was utilized at Microsoft to analyze business performance and make decisions based on that performance. Power BI Desktop is entirely free to download and install, regardless of whether you have an enterprise or commercial account.
The general workflow for using Power BI, as introduced in a tutorial, involves:
Downloading and installing Power BI.
Importing sample data.
Creating visualizations and reports.
Saving, publishing, and sharing these reports with others.
This overview serves as a “101” or introduction to Power BI.
Installation Methods The easiest and recommended way to install Power BI is by clicking the “download free” button, which opens the Microsoft Store to the Power BI download page. Benefits of installing via the Microsoft Store include automatic updates, quicker downloads of only changed components, and the ability for any user (not just an admin) to install it. Alternatively, you can click “see download or language options” to download an executable (.EXE) file and install it manually, though this method does not use the Microsoft Store.
Getting Started and Interface After installation, you can launch Power BI, which first displays a welcome screen. The most crucial initial step is to “get data,” as visualizations cannot be created without it. The welcome screen also shows recent data sources and previously created reports for quick access. Power BI offers training content, including videos and tutorials, to help users get up to speed.
The main interface of Power BI Desktop includes several views:
Report View: This is the default view, a blank canvas where visuals like charts, tables, or maps are created. On the right side, there are “fields” (all available data columns) and “visuals” (different types of visuals that can be built) panes.
Data View: Clicking this option displays a spreadsheet-like view of all imported and transformed data.
Model View: This view shows the relationships between different data tables. For example, if two tables are joined based on a common field like “country name,” a line will connect them, highlighting the relationship when hovered over.
Data Import and Transformation Power BI can pull data from an extensive list of sources, including Excel spreadsheets, SQL databases, web sources (like Wikipedia articles), and Kusto queries. For example, data can be imported from an Excel spreadsheet containing revenue, cost, and profit data, along with details like country, product, sales, and dates. Additionally, data from the web, such as a Wikipedia article listing countries and their populations, can be pulled in.
Data transformation is a key step, allowing users to modify and select data before it’s brought into Power BI. This process opens the Power Query editor, where data is “shaped” and a data model is built. Examples of transformations include:
Filtering out specific data, such as removing “Fortune cookies” from product analysis. These filtered steps can also be undone.
Changing data types, like converting “units sold” from decimals to whole numbers.
Renaming columns for conciseness, such as changing “month name” to “month”.
Removing unnecessary columns, like “percent of world population,” “date,” “source,” or “rank” from imported web data.
Filtering rows to include only relevant data, such as specific countries where a company has locations (e.g., Canada, France, Germany, Mexico, United States).
Replacing values within columns, like removing an extra “D” from “United StatesD”.
Connecting Data Sources Independent data tables can be connected or joined. This is done using the “merge queries” function, allowing tables to be linked based on common fields, such as “country name” between cookie sales data and country populations data. This enables the association of data from one source (e.g., population) with another (e.g., cookie sales).
Creating and Formatting Visualizations After data is loaded and modeled, visualizations can be created on the report canvas. Users can insert a text box to add a title to the report. To create a visual, users can simply click on a data field (e.g., “profit” and “date”) and Power BI will suggest a default chart type (e.g., a bar chart). This can then be changed to another type, such as a line chart for profit by date. Other common visualizations include:
Map visualization: Automatically inserted when country data is selected, showing locations and allowing profit data to be displayed on the map, with dot sizes indicating profit levels. Can be switched to a treemap to show profit by country hierarchy.
Table: Allows presentation of data like country, population, and units sold in a structured format.
Bar chart: Used to show sales or profit by product, easily illustrating which products generate the most profit.
Visualizations can be formatted by clicking on the “format” option (paint roller icon) in the visualization pane. This allows adjustment of various elements, such as increasing title text size, to match company branding or preference. Reports can also have multiple pages.
Slicing and Sharing Data Power BI reports allow for easy data slicing (filtering). A “slicer” visual can be added to a report, where users can select specific categories (e.g., country name) to filter all other visuals on the page. Clicking directly on elements within other visuals, such as a country on a map or in a table, can also serve as a quick way to slice the data.
Once a report is complete, it can be saved. The “power” of Power BI comes from its ability to share reports with others. Reports are published to the Power BI service (powerbi.com). From there, the report can be opened in the Power BI service, where it can still be filtered. The share dialog allows granting access to specific individuals via email, setting permissions (like allowing sharing or creating new content based on datasets), and sending email notifications.
Power BI: Data Transformation and Modeling with Power Query
Data transformation in Power BI is a crucial step that allows users to modify and select data before it is loaded into the Power BI environment. This process is carried out in the Power Query editor, where data is “shaped” and a data model is built.
Here are the key aspects and examples of data transformation discussed:
Purpose of Transformation
It enables users to modify their data and choose exactly what data they want to bring into Power BI.
It helps in building a structured data model suitable for analysis and visualization.
Accessing the Power Query Editor
After selecting data from a source (e.g., an Excel spreadsheet), users can choose “Transform data” instead of “Load” to open the Power Query editor.
Common Transformation Actions
Filtering Data: Users can filter out specific rows or values that are not relevant to the analysis. For example, a product line like “Fortune cookies” might be removed from the analysis if it’s not profitable or is distracting from other products. These filtered steps can also be undone later if needed.
Changing Data Types: Data types can be adjusted to ensure accuracy and usability. For instance, “units sold” might be changed from decimal numbers to whole numbers if fractional sales don’t make sense.
Renaming Columns: Columns can be renamed for conciseness or clarity, such as changing “month name” to simply “month”.
Removing Unnecessary Columns: Columns that are not needed for the analysis can be removed, such as “percent of world population,” “date,” “source,” or “rank” from a web-imported dataset.
Filtering Rows to Specific Subsets: Users can filter down rows to include only relevant data, such as selecting only countries where a company has locations (e.g., Canada, France, Germany, Mexico, United States).
Replacing Values: Specific values within columns can be replaced to correct inconsistencies, like removing an extra “D” from “United StatesD”.
Tracking Transformations (Applied Steps)
As changes are made in the Power Query editor, each transformation is recorded in a section called “applied steps” on the right-hand side of the interface. This allows users to see all the modifications made to the data and also provides the option to remove a step if it was made unintentionally.
Connecting Independent Data Sources (Merging Queries)
Power BI allows users to connect or join independent data tables, such as linking cookie sales data with country population data from a Wikipedia article.
This is done using the “merge queries” function, where tables are joined based on a common field (e.g., “country name”).
The “Model View” in Power BI Desktop visually represents these relationships between data tables, showing lines connecting tables that are joined.
Once all transformations are complete and the data model is built, users click “close and apply” to load the refined data into Power BI, ready for report creation.
Power BI: Crafting Interactive Reports and Visualizations
After data transformation and modeling, Power BI Desktop provides a Report View, which serves as a blank canvas where users create and arrange various visuals such as charts, tables, or maps. This blank area is referred to as the report editor.
On the right side of the Power BI Desktop interface, there are two key panes that facilitate report visualization:
Fields Pane: This pane displays all available data columns (called fields) from the imported and transformed data. Users can drag and drop these fields onto the canvas or select them to build visuals.
Visuals Pane: Located to the left of the fields pane, this section offers various types of visuals that can be built using the data.
Here’s a breakdown of how report visualization works:
Creating Visualizations
Starting a Visual: To create a visual, users can simply click on relevant data fields in the “fields” pane, such as “profit” and “date”.
Default Suggestions: Power BI often predicts and inserts a default chart type that it deems most likely suitable for the selected data, like a bar chart for profit by date.
Changing Visual Types: Users can easily change the chart type from the “visualizations” pane if the default doesn’t align with their needs (e.g., switching a bar chart to a line chart for profit by date).
Defining Visual Elements: The visualizations pane also allows users to define different elements of the chart, such as what fields serve as the axis, values, or legend.
Examples of Visualizations:
Text Box: Can be inserted to add a title to the report, providing context (e.g., “Kevin Cookie Company performance report”).
Line Chart: Useful for showing trends over time, such as profit by date.
Map Visualization: Automatically inserted when geographical data like “country” is selected. It shows locations with dots, and profit data can be dragged onto the map to represent profit levels by dot size.
Treemap: An alternative to the map view, it can display hierarchical data like profit by country, illustrating which country had the most or least profit.
Table: Allows presentation of data in a structured, spreadsheet-like format, such as country, population, and units sold. Users can drag and drop fields into the table.
Bar Chart: Used to show comparisons, such as sales or profit by product, clearly indicating top-performing products.
Formatting and Appearance
Themes: The “View” tab in the ribbon provides different themes (e.g., “executive” theme) that can be applied to change the overall look and feel of the report, including color schemes, to make it appear more professional.
Individual Visual Formatting: Each visual can be formatted individually by clicking on the “format” option (represented by a paint roller icon) within the visualization pane. This allows users to adjust elements like title text size or other visual properties to match company branding or preference.
Multiple Pages: Reports can span multiple pages, allowing for comprehensive data presentation.
Slicing and Interacting with Data
Slicer Visual: A “slicer” visual can be added to the report, typically based on a categorical field like “country name”. Selecting a specific category in the slicer will filter all other visuals on the page to reflect only that selection.
Direct Interaction with Visuals: Users can also slice data by directly clicking on elements within other visuals, such as clicking on a country on a map or in a table. This provides a quick way to filter the entire report based on that selection. Clicking a blank area or re-clicking a selection can undo the filter.
Saving and Sharing Reports Once a report with visualizations is complete, it can be saved locally. The “power” of Power BI is realized when reports are published to the Power BI service (powerbi.com), enabling sharing and collaboration. In the Power BI service, reports remain interactive and can still be filtered. The share dialog allows users to grant access to specific individuals via email, set permissions (e.g., allowing sharing or creating new content based on datasets), and send email notifications.
Power BI: Collaborative Data Sharing Essentials
Data sharing in Power BI is a fundamental aspect that unlocks the full potential of the platform, moving beyond individual analysis to collaborative insights. While reports can be created and saved locally for personal use, the true “power” of Power BI lies in its ability to enable collaboration and allow others to interact with the created visualizations.
Here’s a discussion on data sharing:
Purpose of Sharing: The primary goal of sharing is to allow other individuals to view and interact with the visualizations and reports you’ve created. This facilitates collective analysis and decision-making based on the data.
The Sharing Process:
Local Saving: After creating a report and its visualizations, it is initially saved locally on your desktop as a .pbix file. At this stage, it can be used for individual analysis.
Publishing to Power BI Service: To share the report, it must first be “published”. This is done by navigating to the “file” menu and selecting the “publish” option, then choosing “publish to Power BI”.
Power BI Service (powerbi.com): The Power BI service is the online platform where all published reports are housed. Once published successfully, the report becomes accessible on powerbi.com. Reports opened in the Power BI service remain interactive, allowing users to filter data just as they would in the Power BI desktop application.
Sharing Options and Permissions:
From the Power BI service, you can click on the “share” button, typically found in the top right-hand corner.
This opens a “share dialog” that provides various options for granting access.
You can grant access to specific individuals by entering their email addresses.
Crucially, you can define permissions for those you share with:
You can allow recipients to share the report with others.
You can enable them to create new content based on the underlying datasets.
An option to send an email notification to the recipients is also available, which can include any changes made to the report.
Power BI Report Customization Guide
Report customization in Power BI allows users to refine the appearance and layout of their reports to enhance clarity, professionalism, and alignment with specific branding or preferences. This process goes beyond merely creating visualizations and focuses on making the report aesthetically pleasing and user-friendly.
Key aspects of report customization include:
Adding Contextual Elements:
Titles: Users can insert text boxes to add a main title to the report, providing immediate context (e.g., “Kevin Cookie Company performance report”). These titles can be resized and positioned to span the entire report.
Formatting Visuals:
Changing Chart Types: While Power BI often suggests a default chart type (e.g., bar chart) for selected data, users can easily switch to other visual types (e.g., line chart, treemap, map, table, bar chart) from the “visualizations” pane to better represent their data.
Defining Visual Elements: Within the visualization pane, users can explicitly define what fields should serve as the axis, values, or legend for a chart. They can also add secondary values.
Individual Visual Formatting: Each visual can be formatted independently. By selecting a visual and clicking on the “format” option (represented by a paint roller icon) in the visualizations pane, users can adjust various elements. For instance, the title text size of a visual can be increased to make it stand out. This allows users to match the visuals to their company’s brand, look, and feel.
Applying Themes:
Power BI provides different themes (e.g., “executive” theme) under the “View” tab on the ribbon. Applying a theme changes the overall color scheme and appearance of the report, contributing to a more professional look.
Organizing Layout:
Users can drag and drop visuals around the report editor (the blank canvas) to organize them as desired.
Reports are not limited to a single page; users can add multiple pages to their report to accommodate extensive data and different views. Pages can also be renamed.
By leveraging these customization features, users can transform raw data visualizations into polished, insightful reports that effectively communicate their findings. Once satisfied with the customization, the report can be saved locally and then published to the Power BI service for sharing.
How to use Microsoft Power BI – Tutorial for Beginners
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This comprehensive guide provides an in-depth look into Power BI, a powerful business intelligence tool from Microsoft. It details the step-by-step process of installing and utilizing Power BI Desktop, covering essential data manipulation techniques such as text, numerical, date, and time transformations. The sources further explore advanced concepts like merging and appending queries, managing data relationships through primary and foreign keys, and understanding different cardinalities. Finally, the guide concludes with a focus on data visualization, demonstrating the creation of various charts and filters, and the process of publishing dashboards to Power BI service.
Mastering Power BI: Data Analysis and Visualization
Power BI, developed by Microsoft, is a powerful business analytics tool designed for analyzing and visualizing data in insightful and interactive ways. It has gained popularity due to its user-friendly interface and robust features. Power BI is suitable for business analysts, data analysts, data scientists, or anyone who wants to work efficiently with data, providing necessary skills and knowledge to become proficient in data handling.
Key Capabilities and Features Power BI allows users to transform, clean, analyze, and visualize data. It enables effortless data gathering from various platforms, including Excel, CSV files, different databases like MySQL, Postgres, Oracle, or other datasets. It is noted for its strong visualization capabilities, offering a wide range of charts such as bar plots, pie charts, and stack plots. Unlike Excel, Power BI has the capacity to work with large datasets and offers numerous deployment options. The end result of working with Power BI is often the creation of interactive and visually appealing dashboards.
Installation and Interface To install Power BI Desktop for Windows, users typically download the executable file from Microsoft’s website. Once installed, its user interface is very similar to Excel, making it easy for Excel users to adapt. Power BI also offers tutorials, blogs, and forums for support. While desktop usage is common, Power BI reports can also be created and viewed on mobile phones. A company domain email address is generally required for login, though free business emails can be created for this purpose.
Data Handling and Transformation Power BI provides various data connectors to import data from diverse sources. These include:
Files: Excel workbooks, Text/CSV files, XML, JSON, and PDF. Data can also be pulled from folders.
Databases: SQL Server, Oracle, Postgres, MySQL, and other databases.
Power Platform: Existing datasets loaded in Power Platform can be accessed.
Cloud Services (Azure): Azure SQL Database and other Azure options are available.
Online Services: Google Analytics, GitHub, LinkedIn Sales Navigator, and many more.
Other: Data can be scrapped from the web, or connected to Hadoop, Spark, R script, and Python script.
Power BI offers extensive tools for data transformation:
Text Tools: Used for text manipulations like converting to lower/upper case, trimming whitespace, replacing values, combining values (concatenate), finding specific text, formatting text, and extracting specific parts of text using delimiters (e.g., username from an email address). These tools can either transform the existing column or add a new column with the transformed data.
Numerical Tools: Used for mathematical operations, statistics (maximum, median, average, standard deviation, count), rounding values, and applying filters. These can be applied by adding a new column or transforming an existing one.
Date and Time Tools: Essential for analyzing time-based patterns, such as identifying peak order times or days. They allow extraction of year, month, day, age calculations, and conversion of time formats (e.g., 24-hour to 12-hour). Regional settings may need adjustment for proper date parsing.
Pivoting and Unpivoting: These techniques allow converting rows to columns (pivoting) and columns to rows (unpivoting) to restructure data for easier analysis.
Conditional Columns: New columns can be created based on specified conditions, similar to conditional statements in programming.
Creating Tables: Users can manually create tables within Power BI by entering data directly.
DAX (Data Analysis Expressions) DAX is a collection of functions, operators, and constants used in Power BI to create new data or transform existing data.
Purpose: DAX is used to calculate complex formulas, create measures, develop time intelligence calculations, and dynamically or statically analyze data.
Calculated Columns vs. Measures:
Calculated Columns: Create a new column in the data model, adding static data that consumes memory and updates when new data is added. They work row by row.
Measures: Dynamically calculate values at runtime, primarily for aggregations like sum, count, or average, and are used to create visual reports. They do not consume memory for each row. Measures can be implicit (automatically created by Power BI) or explicit (user-defined).
DAX Functions: Broadly categorized into:
Date and Time: Work on date-related calculations (e.g., NOW, YEAR, WEEKDAY).
Text Functions: Manipulate text strings (e.g., CONCATENATE, FIND, FORMAT, LEFT, LEN, LOWER, REPLACE, RIGHT, TRIM, UPPER).
Informative Functions: Provide information about data types and handle errors (e.g., IFERROR, IFNA).
Filter Functions: Filter data based on conditions (e.g., FILTER, CALCULATETABLE).
Math and Trigonometric Functions: Perform mathematical calculations (e.g., ABS, SIN, COS, TAN).
Statistical Functions: Used for statistical calculations (e.g., percentile, standard deviation).
Financial Functions: Aid in financial computations.
DAX Syntax: Typically involves a column name, an equals sign, a function, and then references to table and column names (e.g., ColumnName = Function(TableName[ColumnName])).
Operators: Used in DAX formulas for various purposes:
Arithmetic: +, -, *, / for mathematical operations.
Logical: AND, OR, NOT for combining or negating conditions.
Concatenation: & for joining text from multiple columns.
Reference: TableName[ColumnName] for referencing specific columns.
Parentheses: () for controlling execution order of formulas.
Miscellaneous: : (colon) for separating elements in date and time.
Data Modeling and Relationships Data modeling is crucial for connecting different tables and sources of data within Power BI, especially in companies with diverse datasets (e.g., product, sales, customer details).
Merge and Append Queries:
Merge: Combines two tables based on a common key (like a primary key and foreign key), increasing the number of columns, similar to SQL joins (inner, left, right, full, anti-joins).
Append: Stacks rows from multiple tables with similar columns into one table, increasing the number of rows.
Keys:
Primary Key: A unique identifier for each record in a table (e.g., product ID, Aadhaar card number).
Foreign Key: A column in one table that refers to the primary key in another table, allowing for duplicate values.
Cardinality: Describes the nature of the relationship between two tables based on primary and foreign keys.
One-to-one (1:1): Both tables have unique primary keys related to each other.
One-to-many (1:*): One table has a primary key, and the other has a foreign key that can be repeated multiple times.
Many-to-one (*:1): The reverse of one-to-many, where the foreign key is on the “many” side and the primary key is on the “one” side.
Many-to-many (:): Both tables have foreign keys that can be repeated.
Cross-Filter Direction: Defines the flow of data filtering between related tables (single or double direction).
Managing Relationships: Power BI can automatically detect relationships. Users can manually manage and edit these relationships, including setting cardinality and cross-filter direction, and activating/deactivating multiple relationships between tables.
Data Visualization Visualization is a critical step in Power BI, revealing patterns and trends that are not apparent in raw row and column data.
Dashboard Elements: The report section is where visuals are built using fields (columns from tables) that can be dragged and dropped.
Visual Types: Power BI offers a wide array of built-in visuals:
Charts: Stacked bar, stacked column, clustered bar, clustered column, line, area, pie, scatter, donut, funnel, map, tree map.
Matrices: Powerful tools for visualizing data across different parameters and dimensions, allowing drill-down into subcategories.
Cards: Number cards (for highlighting single large numbers) and multi-row cards (for multiple pieces of information).
KPI Visuals: Show key performance indicators, often with trend lines, useful for comparing current and past performance.
Custom Visuals: Users can import additional visuals from the Power BI marketplace (e.g., boxplot, flow map, calendar).
Formatting and Customization: Visuals can be extensively formatted, including changing font size, colors, titles, background, borders, data labels, and themes.
Filtering:
Filter Pane: Allows applying filters on a specific visual, on the current page, or across all pages. Advanced filtering options like “greater than” or “less than” are available.
Slicers: Interactive tools for filtering data across the entire dashboard or different pages. They can display data as lists, dropdowns, or ranges (e.g., date sliders).
Sync Slicers: Allows the same filter to be applied consistently across multiple pages.
Interactivity Tools:
Buttons: Can be added to navigate between pages or trigger other actions.
Bookmarks: Capture the current state of a report page (e.g., filters applied, visuals visible) allowing users to return to that view.
Images: Can be inserted for branding (e.g., logos) or icons.
Publishing and Sharing Once a dashboard is complete, it can be published to Power BI service, which typically requires a user to be signed in. Published reports retain their interactivity and can be viewed online, shared with co-workers, or even published to the web without security if desired. Power BI also allows creating a mobile layout for dashboards, optimizing them for phone viewing.
Power BI: Data Analysis from Gathering to Visualization
Data analysis is a critical process for extracting insights and patterns from raw data to inform decision-making, and Power BI serves as a powerful business analytics tool to facilitate this. It involves several key steps, from data gathering and cleaning to sophisticated analysis and visualization.
The Role of a Data Analyst
A data analyst’s primary responsibility is to gather, interpret, process, and clean data, ultimately representing it in a graphical format. This graphical representation allows business strategists to understand the information better and use it to grow their business. Power BI is designed to provide the necessary skills and knowledge to become proficient in working efficiently with data.
Key Steps in Data Analysis using Power BI
Data Gathering (Data Connectors): Power BI offers extensive data connectors that allow users to effortlessly gather data from various platforms. These sources include:
Files: Excel workbooks, Text/CSV files, XML, JSON, and PDF. Data can also be pulled from folders.
Databases: SQL Server, Oracle, Postgres, and MySQL are among many databases from which data can be extracted.
Power Platform: Existing datasets loaded in Power Platform can be directly accessed.
Cloud Services (Azure): Azure SQL Database and other Azure options enable data retrieval from the cloud.
Online Services: Google Analytics, GitHub repositories, and LinkedIn Sales Navigator are examples of online services that can connect to Power BI.
Other: Data can be obtained by scrapping from the web, or connecting to Hadoop, Spark, R scripts, and Python scripts.
Data Transformation and Cleaning: Once data is gathered, Power BI provides robust tools for cleaning and processing it. This includes:
Text Tools: Used for manipulations such as converting text to lower or upper case, trimming whitespace, replacing values, combining values (concatenate), finding specific text, formatting text, and extracting parts of text using delimiters (e.g., username from an email address). These tools can either transform an existing column or add a new one with the transformed data.
Numerical Tools: Applicable for mathematical operations, statistics (maximum, median, average, standard deviation, count), rounding values, and applying filters. Like text tools, they can transform existing columns or create new ones.
Date and Time Tools: Essential for analyzing time-based patterns (e.g., peak order times or days). They allow extraction of year, month, day, and age calculations, and conversion of time formats (e.g., 24-hour to 12-hour). Regional settings may need adjustment for proper date parsing.
Pivoting and Unpivoting: These techniques allow restructuring data by converting rows to columns (pivoting) or columns to rows (unpivoting) for easier analysis.
Conditional Columns: New columns can be created based on specified conditions, similar to conditional statements in programming.
Creating Tables: Users can manually create tables within Power BI by entering data directly.
Data Analysis Expressions (DAX): DAX is a collection of functions, operators, and constants used in Power BI to create new data or transform existing data.
Purpose: DAX is used to calculate complex formulas, create measures, develop time intelligence calculations, and dynamically or statically analyze data.
Calculated Columns vs. Measures:
Calculated Columns: Create a new column in the data model, adding static data that consumes memory and updates when new data is added. They work row by row.
Measures: Dynamically calculate values at runtime, primarily for aggregations like sum, count, or average, and are used to create visual reports. They do not consume memory for each row. Measures can be implicit (automatically created by Power BI) or explicit (user-defined).
DAX Functions: Broadly categorized into Date and Time, Text, Informative, Filter, Aggregation, Time Intelligence, Logical, Math and Trigonometric, Statistical, and Financial functions.
DAX Syntax: Typically involves a column name, an equals sign, a function, and then references to table and column names (e.g., ColumnName = Function(TableName[ColumnName])).
Operators: Used in DAX formulas, including arithmetic (+, -, *, /), comparison (>, <, =, >=, <=, <>), logical (AND, OR, NOT), concatenation (&), reference (TableName[ColumnName]), and parentheses () for controlling execution order.
Data Modeling and Relationships: Data modeling is crucial for connecting different tables and sources, especially in companies with diverse datasets (e.g., product, sales, customer details).
Merge and Append Queries:
Merge: Combines two tables based on a common key, increasing the number of columns, similar to SQL joins (inner, left, right, full, anti-joins).
Append: Stacks rows from multiple tables with similar columns into one table, increasing the number of rows.
Keys: Primary keys are unique identifiers, while foreign keys can be duplicated and refer to a primary key in another table.
Cardinality: Describes the relationship type between tables (one-to-one, one-to-many, many-to-one, many-to-many).
Cross-Filter Direction: Defines the flow of data filtering between related tables (single or double direction).
Managing Relationships: Power BI can automatically detect relationships, and users can manually manage and edit them, including setting cardinality and cross-filter direction.
Data Visualization: Visualization is a critical step in data analysis within Power BI, as it reveals patterns and trends not apparent in raw row and column data.
Dashboard Elements: Visuals are built in the report section by dragging and dropping fields (columns from tables).
Visual Types: Power BI offers a wide range of built-in visuals, including stacked bar, stacked column, clustered bar, clustered column, line, area, pie, scatter, donut, funnel, map, tree map, matrices, cards (number and multi-row), and KPI visuals. Users can also import custom visuals from the Power BI marketplace.
Formatting and Customization: Visuals can be extensively formatted, including changing font size, colors, titles, background, borders, data labels, and themes.
Filtering: Filters can be applied via the filter pane (on specific visuals, pages, or all pages) or interactive slicers (displaying data as lists, dropdowns, or ranges). Slicers can also be synced across multiple pages.
Interactivity Tools: Buttons can be added for page navigation or other actions, and bookmarks capture report states to allow users to return to specific views. Images can be inserted for branding or icons.
Publishing and Sharing: Completed dashboards can be published to Power BI service, requiring login, to be viewed online, shared with co-workers, or published to the web without security. Power BI also supports creating mobile layouts for dashboards, optimizing them for phone viewing.
Power BI: Mastering Data Visualization and Reporting
Data visualization is a crucial step in data analysis, transforming raw data into insightful and interactive visual representations to reveal patterns and trends that are not apparent in simple rows and columns. Power BI, a business analytics tool developed by Microsoft, is designed to facilitate this process, offering powerful features for visualizing data.
The Importance of Data Visualization
Visualizing data helps users see new things and discover patterns that might otherwise be missed. When data is presented in a graphical format, business strategists can better understand the information and use it to grow their business. Power BI provides the necessary skills and knowledge to become proficient in efficiently working with and visualizing data.
Key Aspects of Data Visualization in Power BI
Report Section and Visuals:
The primary area for creating visuals in Power BI is the report section.
Users can build visuals by dragging and dropping fields (columns from tables) from the “Fields” pane on the right-hand side.
Power BI offers a user-friendly interface with a wide range of interactive and powerful features for visualization.
Types of Visuals: Power BI includes many built-in chart types and allows for the import of custom visuals:
Bar and Column Charts: Stacked bar, stacked column, clustered bar, and clustered column charts are available for comparing values across categories.
Line and Area Charts: Used to show trends over time or categories.
Pie and Donut Charts: Represent parts of a whole. A donut chart can become a pie chart by reducing its inner radius to zero.
Scatter Plot: Displays relationships between two numerical variables.
Funnel Chart: Shows stages in a linear process.
Maps: Allows visualization of data geographically, using locations like countries or continents. Bubbles on the map can represent values, with their size corresponding to a measure like population. A “flow map” visual can also be imported to show destinations and origins or flows between regions.
Tree Maps: Display hierarchical data in a set of nested rectangles, where the size of each rectangle is proportional to its value. An existing chart, like a donut chart, can easily be converted into a tree map.
Matrices: A powerful tool for visualizing data on different parameters and dimensions, allowing for hierarchical drilling down from categories (e.g., continents) to subcategories (e.g., countries).
Cards: Used to highlight specific numeric information or text.
Number Cards: Display a single large number, such as total population or average values.
Multi-row Cards: Show multiple pieces of information, like sum of population, average life expectancy, and average GDP, in one visual.
Text Cards: Display textual information, such as the top-performing category based on an order quantity filter.
KPI (Key Performance Indicator) Visuals: Allow for showing performance metrics, often with a trend graph in the background, like the sum of population over time or company profit/loss.
Slicers: Interactive filtering tools that allow users to filter data across the entire dashboard or specific pages. Slicers can display data as a list, a dropdown, or a range slider (e.g., for years). They can also be synchronized across multiple pages.
Tables: Simple tabular representations of data.
Custom Visuals: Users can import additional visuals from the Power BI marketplace (AppSource) to enhance their dashboards.
Formatting and Customization: Power BI provides extensive options for customizing the appearance of visuals and dashboards:
Canvas Settings: Users can change the background color or add images to the canvas background to match a particular theme. Transparency can also be adjusted.
Themes: Different built-in themes are available, and users can also create their own custom themes.
Gridlines: Can be added to help arrange visuals neatly on the canvas.
Object Locking: Visuals can be locked in place to prevent accidental movement.
Axis Formatting: Users can change font size, colors, define ranges (minimum/maximum), and customize titles for X and Y axes.
Data Labels: Can be turned on or off to display specific values directly on the chart, with customizable colors and positions.
Colors: Colors of bars, slices (in donut charts), and text can be customized. Conditional formatting can be applied, for instance, to show a gradient of colors based on value (e.g., light blue for lowest to dark blue for highest).
Borders and Shadows: Visuals can have customizable borders and shadows to make the dashboard more interactive and visually appealing.
Spacing and Padding: Adjusting inner and outer padding for elements within charts helps control visual spacing.
Titles: Visual titles can be customized in terms of text, color, and font.
Filtering and Interactivity:
Filter Pane: Filters can be applied to individual visuals, to all visuals on a specific page, or to all visuals across all pages. Advanced filtering options include operators like “less than” or “greater than”.
Buttons: Can be added to dashboards for various actions, such as page navigation. Users can define the destination page for a button.
Bookmarks: Capture the current state of a report (including filters, sort order, and visible visuals), allowing users to return to specific views easily. Bookmarks can be linked to buttons for navigation.
Images: Logos or other icons can be added to the dashboard for branding or aesthetic purposes.
Publishing and Mobile View:
Mobile Layout: Dashboards created on desktops can be optimized for phone viewing by arranging elements within a mobile grid layout. This allows for scrolling and resizing visuals to fit mobile screens.
Publishing: Once a dashboard is complete and satisfactory, it can be published to the Power BI service for online viewing and sharing with co-workers. Reports can also be published to the web without security for public viewing.
Power BI Data Modeling: Relationships and Cardinality
Data modeling is a crucial aspect of data analysis in Power BI, particularly when dealing with information from various sources. It involves connecting different tables and managing the relationships between them to enable comprehensive and accurate data visualization and analysis.
Purpose and Importance of Data Modeling
Data modeling is essential because companies often have data stored in separate tables or databases, such as sales, product, and customer details. Creating relationships between these disparate tables allows for a unified view and accurate visualization of the data, which is vital for data analysis. Without proper data modeling, tables remain independent, and it becomes difficult to see relationships between them, leading to inaccurate or incomplete data display.
Key Concepts in Data Modeling
Primary Key: A column that contains unique values and is not repeated or duplicated within a table. For example, a product ID in a product table or an Aadhaar card number are primary keys because each is unique to a single entity.
Foreign Key: A column that can contain duplicate values and acts as a clone of a primary key from another table. For instance, a customer key in a sales table might appear multiple times if a customer buys several products, making it a foreign key, whereas the same customer key in the customer data table would be a primary key.
Relationships and Cardinality
Relationships are built between tables based on common primary and foreign keys. Power BI can automatically detect these relationships upon data load. The type of relationship between tables is known as cardinality:
One-to-One (1:1): Occurs when both tables involved in the relationship have unique primary keys in the joined columns. For example, an employee ID in an employee details table and the same employee ID in a bonus table, where both IDs are unique in their respective tables, form a one-to-one relationship.
One-to-Many (1:N): This is a common relationship where one table contains a primary key, and the related column in another table is a foreign key with multiple occurrences. An example is a product table with unique product IDs (primary key) linked to a sales table where product IDs can repeat for multiple sales (foreign key). The data flow typically goes from the ‘one’ side (primary key) to the ‘many’ side (foreign key).
Many-to-One (N:1): This is the inverse of one-to-many, where the foreign key is in the first table and the primary key is in the second.
Many-to-Many (N:N): This relationship occurs when both related columns in two tables are foreign keys, meaning values can repeat in both. It is generally advised to create this type of relationship rarely.
Cross-Filter Direction: This refers to the direction of data flow between tables in a relationship.
Single Direction: Data flow is from the primary key side to the foreign key side (1 to Many).
Double Direction (Both): Data flow is bidirectional, allowing filtering from either side (primary key to foreign key and vice versa). This enables a third connected table to access data more easily, even if it doesn’t have a direct relationship.
Managing and Editing Relationships in Power BI
Power BI offers tools to manage and edit relationships:
Automatic Detection: Power BI can automatically detect and create relationships between tables when data is loaded, especially if common column names or keys exist.
Manual Creation: Users can manually create relationships by dragging and dropping common keys between tables in the ‘Model’ view.
Editing Relationships: Existing relationships can be edited to change their type (cardinality) or cross-filter direction. For instance, a user can modify a relationship from one-to-many to many-to-many or change its filter direction.
Activation/Deactivation: Only one active relationship can exist between two tables at any given time. If multiple potential relationships exist, others will appear as dotted lines, indicating they are deactivated. To activate a deactivated relationship, another active relationship between the same tables must be deactivated first.
Proper data modeling ensures that relationships are correctly defined, leading to accurate data analysis and visualization in dashboards.
DAX Functions for Data Analysis and Power BI
DAX, which stands for Data Analysis Expressions, is a powerful functional language used in Power BI to create custom calculations for data analysis and visualization. It includes a library of functions, operators, and constants that can be used to perform dynamic aggregations and define new computed columns and measures within your data models.
Purpose and Application of DAX Functions
DAX functions are essential for transforming and analyzing data beyond what simple transformations can achieve. They allow users to:
Create calculated columns: These are new columns added to a table, where each row’s value is computed based on a DAX formula. Calculated columns are static and consume memory, updating when new data is added to the model.
Create measures: Measures are dynamic calculations that aggregate data, such as sums, averages, or counts, and are evaluated at query time, making them efficient for reporting and dashboard interactions. They do not consume memory until used in a visual.
Calculate complex formulas: DAX enables the creation of sophisticated calculations, including time intelligence calculations, to group data and derive insights.
Analyze data dynamically and statically: DAX expressions provide flexibility for various analytical needs.
Categories of DAX Functions
DAX functions are broadly categorized to handle different types of data and analytical needs:
Date and Time Functions: Used for operations on date and time data, such as extracting parts of a date (year, month, day), calculating age, or finding differences between dates. Examples include NOW(), YEAR(), WEEKDAY(), DATE_DIFFERENCE().
Text Functions: Used to manipulate text strings, such as concatenating text, changing case, trimming whitespace, or finding specific substrings. Examples include CONCATENATE(), FIND(), FORMAT(), LEFT(), RIGHT(), LEN(), LOWER(), UPPER(), REPLACE(), and TRIM().
Informative Functions: Provide information about data types or handle errors, like checking for text, even/odd numbers, or missing data. Examples include ISERROR() or ISNA().
Filter Functions: Work based on specified conditions to filter data, often used with CALCULATE or FILTER to modify contexts. Examples include SUMX (sum if condition) or COUNTX (count if condition).
Aggregation Functions: Used to summarize data, such as SUM, COUNT, AVERAGE, MIN, and MAX.
Time Intelligence Functions: Specialized functions that enable calculations over time periods, essential for trend analysis.
Logical Functions: Implement conditional logic, evaluating expressions based on true/false conditions. Examples include IF(), AND(), OR(), NOT(), and SWITCH().
Math and Trigonometric Functions: Perform mathematical operations like absolute value, square root, exponents, or trigonometric calculations such as sine, cosine, and tangent. Examples include ROUNDUP(), ROUNDDOWN().
Statistical Functions: Used for statistical calculations like percentile or standard deviation.
Financial Functions: Help compute financial calculations.
Other Functions: A category for functions that don’t fit into the above, such as NOW() or GOOD().
DAX Syntax
The general syntax for a DAX expression typically involves:
Column Name: The name of the new calculated column or measure being created.
Equals Sign (=): Indicates that the column or measure is defined by the subsequent expression.
Function: The DAX function to be used (e.g., SUM, COUNT, IF).
Table Name (optional for measures, often needed for calculated columns): Specifies the table containing the data.
Column Reference: The specific column on which the function operates, often enclosed in square brackets [].
Example: Total Price = SUM(‘Order Items'[Price])
Practical Examples of DAX Functions
LEN(): To find the number of digits or characters in a column, such as digit count of ID = LEN(‘Zomato Asia Africa'[Restaurant ID]).
LEFT() / RIGHT(): To extract a specified number of characters from the beginning or end of a text string. For instance, creating a “Short Day” column from “Day Name” using short day = LEFT(‘Customer Data'[Day Name], 3) to get “THU” from “Thursday”.
LOWER() / UPPER(): To convert text in a column to lowercase or uppercase. For example, LOWER(‘Customer Data'[Day Name]) converts “THU” to “thu”.
Concatenation (&): To combine values from multiple columns into one, like creating a full name: ‘Customer Data'[Prefix] & ” ” & ‘Customer Data'[First Name] & ” ” & ‘Customer Data'[Last Name].
DATE_DIFFERENCE(): To calculate the difference between two dates, useful for determining age. For example, DATE_DIFFERENCE(‘Customers Data'[Birth Date], TODAY(), YEAR) to get age in years.
IF(): To apply conditional logic. For instance, creating a payment data column: IF(‘O list order payments'[Payment Value] > 100, “High Price”, “Low Price”).
Arithmetic Operators (+, -, *, /): Used for mathematical calculations on column values.
Comparison Operators (>, <, =, etc.): Used to compare values, yielding true/false results, often within conditional statements.
DAX functions are fundamental for performing advanced data manipulation and aggregation, enabling users to derive deeper insights from their data in Power BI.
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John Michaloudis of MyExcelOnline.com presents a free, 10-hour Excel PivotTable course comprising 230 tutorials. The course covers a wide range of PivotTable functionalities, from basic customization to advanced techniques like calculated fields, PivotCharts, and macros. Step-by-step instructions and practical exercises are included, using sample datasets for hands-on learning. The course also promotes Michaloudis’s paid MyExcelOnline Academy, offering more comprehensive Excel training. A link to download accompanying workbooks is provided.
Pivot Table Mastery: A Comprehensive Study Guide
Quiz
Instructions: Answer the following questions in 2-3 sentences each.
How can you change field names in a Pivot Table, and what is one reason you might want to do so?
Explain the process of formatting values in a Pivot Table, including how to ensure the formatting is maintained even when new data is added.
What happens when you drop multiple metrics into the “values” area of a Pivot Table, and how can you adjust the view?
Describe the “compact form” layout of a Pivot Table, and explain how to modify the indentation of row labels while maintaining this layout.
How can you alter the layout of a report filter in a Pivot Table, including changing the display order and fields per column?
What are two ways you can handle error values in a Pivot Table?
How can you customize the display of empty cells in a Pivot Table, and why might you choose to display “no transactions” instead of “0”?
Explain how to prevent column widths in a Pivot Table from automatically resizing when you refresh the data.
What are the steps to change a calculation to average instead of sum and how do you format the result as a number with a comma and zero decimal places?
How can the PRODUCT function be used with binary values (1 and blank) to analyze datasets?
Answer Key
You can change field names by clicking directly on the name in the Pivot Table or through the options menu, choosing the active field. Changing field names can make the Pivot Table more readable and user-friendly by using custom names.
To format values, right-click a value, select “number format,” and adjust decimal places and separators. To ensure formatting is maintained, select “entire Pivot Table” under options, then values, and format from there to retain format when new data is added.
When multiple metrics are added to the “values” area, they appear as columns by default. To adjust the view, you can move the “values” field to row labels, creating a different perspective of the data.
Compact form keeps data labels in a single column, which can be modified using layout options. To maintain compact form but indent the labels, use the “indent row labels” option under the layout and format tab and specify number of characters.
You can modify the layout by going into options and changing the display order (“down then over” or “over then down”). Additionally, you can set the number of report filter fields per column to better organize filter options.
Error values can be addressed in the Pivot Table options, where you can choose to display a blank, zero, or specific text (like “error”) in place of the error. This makes the table easier to read.
Empty cells can be customized in the Pivot Table options to show a zero amount or custom text such as “no transactions”. Displaying “no transactions” is appropriate for situations where no data is present.
To prevent auto-fitting column widths on updates, you must uncheck “auto-fit column widths on update” in the options tab, This will maintain your customized layout when the table is refreshed.
To change to average, click on the dropdown for that value, then “value field settings.” Select “average.” Then, click “number format”, choose “number,” 0 decimal places, and enable the separator.
The PRODUCT function can be used with binary data to show if all values in a selection are blank, or if a 1 exists which means there is a defect. A result of 0 would indicate all blank (no defects) and a result of 1 means at least one defect.
Essay Questions
Instructions: Answer each of the following questions in a well-structured essay format. Your response should demonstrate a comprehensive understanding of Pivot Tables as presented in the source material.
Discuss the various ways that Pivot Tables allow users to customize the presentation of data. Your response should include examples from the source material of changes to layout, formatting, error handling, and calculated values.
Explain how Pivot Tables can be used to perform various statistical calculations, including average, maximum, minimum, standard deviation, and variance. How does each calculation help to gain deeper insights from the raw data?
Discuss the different ways that you can group data in a pivot table and explain how that grouping can be used to create different views of the data, and describe how to create calculated items within grouped data.
Explain how slicers can enhance the interactivity of Pivot Tables. Your answer should cover how slicers function, options for styling slicers, and how slicers can be used to control various elements of a dashboard, including calculated fields and conditional formatting.
Describe and discuss calculated fields and items, explaining the differences between them, the limitations of calculated items and how they can be utilized to solve real world business challenges using formulas and functions.
Glossary of Key Terms
Calculated Field: A virtual field created in a Pivot Table that uses a formula to perform calculations on other fields within the data source. These are defined by the columns of the source data. Calculated Item: A virtual data item created within a Pivot Table using existing row or column label items. These use formulas against the row and column headings of a pivot table. Compact Form: A layout option for Pivot Tables that keeps all row labels within a single column and provides indentation for hierarchical levels. Conditional Formatting: A feature in Excel that allows you to automatically format cells based on specific criteria. Data Model: A feature in Excel that allows for creating relationships between multiple tables. External Data Source: Data that is imported into Excel from external files, such as databases or text files. Field Settings: Options that allow you to change the summarization method (e.g., sum, average, count) for data in the values area, as well as choose custom subtotal calculations. GETPIVOTDATA: An Excel function that retrieves data from a Pivot Table based on specified criteria, making it useful for creating dynamic reports. Grand Total: The final sum of all data within a Pivot Table. Grouping: A feature that allows you to combine row or column label items into broader categories or ranges, often used for dates or numbers. Macro: A recorded sequence of steps in Excel that can be run automatically. Pivot Table: A tool in Excel that summarizes and analyzes data, allowing users to dynamically rearrange and view information. Pivot Table Cache: An internal memory in Excel which stores data retrieved from a data source for a pivot table. Report Filter: A way to filter data in a Pivot Table by selecting specific items or groups for a field, allowing different views of the data. Slicer: A visual tool used to filter data in a Pivot Table, allowing for interactive data exploration. Solve Order: Defines the sequence in which calculated items are calculated. Sparklines: Small charts within a cell that visually represent trends in data. Standard Deviation: A statistical measure of the amount of variation or dispersion of a set of values. Timeline: An interactive slicer that filters data within a pivot table based on date ranges. Variance: The measure of how far a data set is spread out from the average. Value Field Settings: The settings used to change the summarization method, number formatting, and custom calculations within the values area of a Pivot Table.
Excel Pivot Table Mastery
Okay, here is a detailed briefing document summarizing the key themes and ideas from the provided source (“01.pdf”):
Briefing Document: Excel Pivot Table Mastery
Document Overview:
This document summarizes a series of techniques and best practices for working with Pivot Tables in Excel, as presented in the source document “01.pdf.” The document covers a wide range of topics, from basic formatting and layout adjustments to advanced calculations and data analysis methods. It is designed for users who want to enhance their Pivot Table skills to create more insightful and customized reports.
Key Themes & Ideas:
Customizing Pivot Table Field Names:
Users can rename field names directly within the Pivot Table or via the Options menu.
Changing the name in either location will update the name displayed in the Pivot Table.
“Another way we can change the field names is to click in the Pivot Table and go to options. And because we’re in now sales, we get the active field as sales. We can click in there and we can see that the change that we made.”
Formatting Values:
Direct formatting (e.g., number formats, decimal places) of values in a Pivot Table can be lost when new fields are added.
To maintain formatting across changes, users should apply formatting using the “Format Cells” dialog box, accessible through the Options > Actions > Entire Pivot Table > Values menu or Ctrl+1 shortcut.
“And what we need to do is go into our Pivot Table tools and under options in the actions group, select entire Pivot Table, then select values. Press Control + 1 to bring in the format cells dialog box. And from in here, you can make your formatting changes.”
Manipulating Pivot Table Layout:
Users can rearrange metrics between row and column labels.
“Compact Form” can be adjusted to include indentation, mimicking an “Outline Form” appearance.
“Now under options and on the left-hand side options in layout and format tab, you have here when in compact form indent row labels. So let’s move it to the right by 10 characters and press okay. And as you can see, our salespeople have moved to the right, but they’re maintained in column A.”
Report filters can be customized with different layouts (down then over or over then down) and fields per column.
“We have here display field and report filter area. Our default is down then over. And in the dropdown box, we can choose over then down.”
Handling Errors & Empty Cells:Pivot Tables can display custom text instead of error values (e.g. “#DIV/0!”) by using the Options > Format > For error values show option.
“Now under format, there’s an option that says, for error values show. Let’s tick that. And we can put anything in there.”
Empty cells can be replaced with a custom text or numeric value, using Options > Format > For empty cells show.
“So we tick that box and in there we can put in there a zero amount, and that feels in our Pivot Table blank cells with zero.”
Column Width Preservation:
The “auto-fit column widths on update” feature can be disabled under Options > Options to prevent column widths from reverting to default on refresh.
“Under the options tab and options, there’s an option at the bottom here that says, auto-fit column widths on update. So upon a refresh it auto-fits that back to where it was previously. Well, let’s uncheck this and press okay.”
Refreshing Pivot Table Data:
Users can refresh a pivot table manually through the options.
There are issues with shared workbooks with refreshing and remembering to do so.
Understanding Common Calculations:Average: Demonstrates calculating average sales by product, salesperson, year and order date.
Maximum: Shows finding the largest sale transaction by product, salesperson and year.
“So as you can see here, for each product and each salesperson, we have the maximum sales transaction that I made for that year.”
Minimum: Illustrates finding the smallest sale transaction by product, salesperson and year.
“In 2012, for bottles, Homer’s smallest sale was 10,780. Ian Wright’s was 20,650, John Michaloudis’ was a 48,378, and Michael Jackson’s smallest sale was $17,030 out of all his sales in 2012 for the bottles product.”
Product: Demonstrates using a product function to identify months with flawless (zero defects) defect rates.
“So if we give a zero that means we had our flawless month and that’s a great result for our company.”
Percentage: Explores finding out the percentage of overdue transactions.
“So 55% of our total transactions are past due, which is a bad result.”
Standard Deviation: Explains the concept of standard deviation and its relationship with data distribution. It provides step by step guide on how to generate and analyze a standard deviation graph and determine the volatility of the data.
“So what it means is you can go either way to the left or right of 44,000 by about 20,000. So we have a high volatility there. So therefore, as you can see, our graph is pretty flat.”
Variance: Show how to generate and analyse variance, demonstrating both a low and high variance.
“So as you can see here, we have a very high variance and also a very high standard deviation.”
Subtotals and Grand Totals:Users can customize subtotal calculations.
Shows users how to add multiple grand total calculations (average, max, min, etc.) by adding a blank column to the data table, then creating new grand total rows using the field settings.
“So what wanna do is put in there some extra grand total. Now there’s a way around this. First of all, we need to go to our data table and in our table, just add another column field named grand total, press enter.”
Accessing Field Settings and Value Field Settings
Demonstrates the multiple ways to access both field settings and value field settings.
Explores doing this via the field list, right clicking in the pivot table, and using the options tab from the ribbon.
Advanced Calculations and Analysis
Demonstrates how to create a bonus scheme using sales data to determine the bonus amount paid based on the channel sales made in different zones.
Shows how to create a group of customers to do further analysis.
Provides guidance on grouping dates on a Monday.
Explores how to isolate dates to do further analysis.
Explains how to group by fiscal year or quarter, using custom formulas in the data source.
Slicers
Explores how to format and style slicers.
Explains what slicer elements are.
Demonstrates how to copy slicer styles to other workbooks.
Provides insight on how to change a slicer name.
Explains how to use a customer list in slicer settings to sort data.
Camera Tool and Dynamic Pictures
Provides guidance on how to add the camera tool to the quick access tool bar.
Explains how to use the camera tool with an offset range to create dynamic pictures.
Profit and Loss Statements
Demonstrates how to create a P&L statement using slicers.
Provides step by step guide on how to compare the actual and plan numbers and create a variance.
Shows how to set up three different scenarios: base, best, and worst.
Explores the use of calculated fields.
Details how to correct calculate the sales margin, how to edit and modify it.
Calculated Fields and Items
Show how to create calculated fields with IF Statements.
Explains how to set up and use a calculated item.
Explains calculated item shortcomings.
Shows how to use solve order with calculated items.
Provides guidance on how to view calculated field and item formulas.
Demonstrates how to remove a calculated field temporarily.
Explores order of operations.
Explains how to create a P&L using sparklines and calculated items.
Shows how to set up a variance report using calculated fields.
Conditional Formatting
Explains how to keep conditional formatting alive when changing a field.
Shows how to create a conditional formatting using slicers.
Demonstrates how to show text values using conditional formatting.
Explores how to highlight blank cells.
Financial Reporting
Explains how to create an accounts receivable aging report.
Shows how to highlight highest and lowest values using conditional formatting.
Advanced Formulas
Demonstrates how to use the end of month date to show actual or plan data based on today’s date.
Shows how to use the GETPIVOTDATA to generate complex reports and also the use of slicers for comparison reports.
Macros
Provides guidance on how to record macros.
Explains how to assign macros to shapes.
File Optimization and Data Source
Provides guidance on how to reduce file memory by saving the file as a Excel Binary Workbook.
Shows how to use Microsoft Access to create a pivot table when there is over a million rows of data.
Explores compatibility issues with Excel 2007 and 2010.
Cloud Sharing and Forecasting
Demonstrates how to share a pivot table via Microsoft’s OneDrive.
Shows how to create sales forecasts based on a percent increase using calculated fields.
Graphing and Analysis
Demonstrates how to use a Pivot Table to create a frequency distribution graph.
Explains how to do a break-even analysis using a Pivot Table and a slicer.
Additional Slicers, Dashboards, and Reports
Shows different types of slicers and the flexibility when creating them.
Explores how to create a balance sheet using slicers and Pivot Tables.
Provides guidance on how to create a sales manager performance report showing variances using conditional formatting.
Demonstrates how to reconcile customer payments.
Provides information on the pivot power add-in.
Timelines and Data Models
Explains how to use the timeline feature in Excel 2013
Introduces the data model and how it works by combining data from different tables to create a Pivot Table.
Conclusion:
The source document “01.pdf” provides a comprehensive overview of Pivot Table functionality in Excel, covering essential techniques for data manipulation, formatting, and analysis. By following the practices and methods explained in this document, users can create more informative, dynamic, and visually appealing reports to support better decision-making.
Pivot Table FAQs
How can I change the field names in a Pivot Table? There are a couple of ways to change field names. First, you can click just after the last character in the existing name in the Pivot Table field list, press the space bar, and then press “Okay.” This makes it recognized as a different name. Alternatively, select the field within the Pivot Table, go to “Options” in the PivotTable Tools menu, find the active field, and you can directly edit it there. Changes will reflect both in the field list and in the Pivot Table itself.
How can I maintain number formatting in a Pivot Table after adding more fields? To ensure your formatting remains consistent, do not format directly within the values of the pivot table. Instead, click anywhere in the Pivot Table, then within the “Pivot Table Tools” menu, go to the “Options” tab, then to the “Actions” group and select the entire Pivot Table, then select values. Press Control + 1 to open the “Format Cells” dialog, and apply your desired formatting here. This formatting will be maintained even when you drop additional fields into the values area.
Why does adding multiple metrics to the values area change the Pivot Table layout, and how can I adjust it? When you add multiple metrics to the values area, the Pivot Table automatically places them under “Column Labels.” To change this, drag the “Values” field from the column labels into either the row labels or place it at the top of the row labels area. This lets you display metrics side-by-side or stacked, as desired.
How can I create an outline-like view in a compact format Pivot Table? Although the report layout can be changed to outline form, you can achieve a similar effect with the compact form by going to “Options”, then “Layout and Format”. In “when in compact form indent row labels”, you can set an indent value. This moves the labels to the right while maintaining the compact layout.
How do I customize the layout of my report filters? To adjust report filter layout, go to “Options,” then “Options.” Under the “Display” field in “Report Filter Area” you can choose to show filter fields “down then over” or “over then down”. You can also change the “report filter fields per column” setting, where you can specify how many filter options show per column. This is useful for more readable and manageable report filters.
How do I manage errors or blank cells within a Pivot Table? To handle error values, go to “Options” and then “Options,” and in “Format”, select “For error values show”. There you can either leave it blank, show a zero or type something like “error” to handle the errors in a way that’s useful to you. To handle empty cells, go to “Options”, then “Options” and format, then tick “For empty cells show”. Here, you can enter a value, like “0” or text like “no transactions”. This allows you to differentiate between missing data and zero values.
How can I prevent column widths from resetting after refreshing the Pivot Table? To stop column widths from auto-adjusting after a refresh, go to “Options” and then “Options” and uncheck the option called “AutoFit column widths on update”. After unchecking this, you can manually adjust column widths, and they will remain fixed, even after refreshing the data.
How do I access and use different calculation functions like average, maximum, minimum, product, and percentages in Pivot Tables? To change the calculation function applied to a value field, right-click on any value in your Pivot Table and select “Summarize Values By.” Here you can choose from sum, count, average, max, min, product, or other calculations. For custom formatting, click “Value Field Settings” and format it as desired. For example, you can format as a date, percentage, or number with separators and decimal places. You can use functions like average, maximum, minimum, and product by dropping your value into the values area, then selecting value field settings and choosing the function you want. You can do other types of calculations, like show a percentage of the total, or the percentage difference from previous, in the value field settings dialog. You can use the Product function within a calculated field, or you can show a percentage of total transactions by creating a calculated field that counts overdue transactions divided by total transactions.
Mastering Excel Pivot Tables
Excel Pivot Tables are a powerful tool for analyzing data [1]. They allow users to analyze thousands of rows of data with drag-and-drop ease [1]. With Pivot Tables, you can create reports that analyze your data and make business decisions [1].
Here are some key concepts and features of Excel Pivot Tables discussed in the sources:
Data Preparation: Before creating a Pivot Table, data should be in a tabular format, with no gaps, and with labeled columns [1]. Column names should be at the top, showing a distinct category of information [1].
Creating a Pivot Table:
You can create a Pivot Table by selecting your data and going to Insert > Pivot Table [1].
Alternatively, if your data is in an Excel table, you can go to Table Tools > Design > Summarize with Pivot Table [1].
Pivot Tables can be placed in a new worksheet or an existing one [1].
Pivot Table Fields:
The Pivot Table field list shows all the column names from your data source [1].
Fields can be dragged and dropped into four different areas:
Row Labels: Shows unique values from chosen fields on the left side of the Pivot Table [2]. This is used for grouping items such as products or company names [2].
Column Labels: Shows a trend of data across the top of the Pivot Table, such as time periods or years [2].
Values: Where fields to be calculated or quantified are placed, including sum, count, average, maximum, or minimum [2].
Report Filter: Optional field to drill down on and focus on, such as regions or staff [2].
The layout of the Pivot Table can be changed by dragging fields between the different areas [2].
Refreshing Pivot Tables:
When the original data is updated, the Pivot Table must be refreshed to reflect those changes by right-clicking and choosing refresh or by going to the Pivot Table tools tab under options, and in the data group, press refresh [3].
Changes to the data range must be updated in the Pivot Table data source unless the source is an Excel table [4].
You can set a Pivot Table to automatically refresh when the Excel workbook is opened [4].
Formatting Pivot Tables:
You can sort the field list alphabetically to make it easier to find the fields [3].
You can format specific parts of the Pivot Table by enabling selection, which allows you to select subtotals, columns, or unique row entries [4].
You can also format number values, for example, to show currency, by selecting value field settings and number format [5].
You can also change the names of the value fields to your liking [6].
You can use the “select entire Pivot Table” to ensure formatting applies to new fields [6].
Layout Options:Pivot Tables can be displayed in compact form, outline form, or tabular form [7]. Compact form displays multiple fields in one column, while outline and tabular forms separate the row fields into separate columns [7].
You can also add or remove blank rows after each item to make the Pivot Table easier to read [7].
Drill Down and Expand/Collapse:
Double-clicking on a value in the Pivot Table will provide a snapshot of the underlying data that makes up that value [3].
The expand and collapse options allow you to drill down on specific rows or columns or summarize at a higher level [7].
You can expand or collapse entire fields, or individual items [7].
Moving Items:
Items within a Pivot Table can be moved by right-clicking and selecting move [5].
You can also move items by typing the item name, which will bring it to the top of the list, or moving fields by dragging and dropping them within the area sections [5].
Fields can also be removed by right-clicking and selecting remove field [5].
Filtering:
You can filter data using report filters, which are located at the top left of the Pivot Table [8].
You can also filter data using label filters and value filters by right-clicking on the row labels and choosing filter, or from the Pivot Table field list [9].
You can select multiple items by using the “select multiple items” option in the report filter [8].
You can also keep only selected items by highlighting your selection and right-clicking [8].
Slicers:
Slicers are buttons that show what has been selected in your filters [8].
To insert a slicer, you must click inside your Pivot Table and go to the Pivot Table tools tab under options or the Analyze tab and choose insert slicer [8].
Slicers can be moved and resized and can have multiple columns [10].
You can connect multiple Pivot Tables with a single slicer to filter them simultaneously [10].
Calculated Fields and Items:Calculated fields allow you to perform calculations using the data within the Pivot Table, such as adding or subtracting fields, or applying a percentage [11].
You can create calculated items to see the difference between, for example, revenue and COGS [12].
Show Values As:
The “show values as” function allows you to display values in different ways, such as a percentage of the grand total or a difference from a base value [12].
Conditional Formatting:
You can apply conditional formatting to the cells of a Pivot Table to highlight certain values or trends [13].
Pivot Charts:
A pivot chart is a visual extension of a Pivot Table that changes as the Pivot Table is modified [14].
Pivot charts are created by selecting a Pivot Table, going to the options tab, and clicking Pivot Chart [14].
You can filter a pivot chart directly from the chart or by using a slicer [14].
GETPIVOTDATA Formula:
The GETPIVOTDATA formula allows you to extract specific values from a Pivot Table into your own customized reports that are outside of the Pivot Table layout [13].
This formula can be used to create custom reports that reference cells for items to enhance reporting [13].
Consolidating Data:
The Pivot Table wizard can be used to consolidate data from multiple sources into a single Pivot Table [11].
The wizard can also be used to transform data from a tabular format into a Pivot Table [11].
Excel Versions:
In Excel 2013, the “options” tab is renamed to “analyze” [15].
Excel 2013 also introduced the recommended Pivot Tables, distinct count calculation, and the timeline slicer [15].
Excel 2016 has auto-grouping for date columns, multi-select for slicers, and the ability for pivot charts to expand or collapse its data [16].
Excel 2019 introduced 3D maps [16].
This information should give you a comprehensive understanding of the features, uses, and nuances of Excel Pivot Tables, as described in the sources provided.
Excel Pivot Tables: Data Analysis and Visualization
Data analysis is a key function of Excel Pivot Tables, allowing users to summarize and interpret large datasets to make informed decisions [1-8]. Pivot Tables can perform various calculations and statistical analyses, offering multiple ways to view and understand data [1]. Here are some ways that Pivot Tables can support data analysis:
Summarizing Data: Pivot Tables summarize data by grouping similar items together and applying calculations, such as sums, averages, counts, minimums, and maximums, to these groups [1, 4, 9].
For example, you can calculate the total sales for each product over different years [1].
You can also calculate the average, maximum, and minimum sales for each product and salesperson [9].
The count function can show the number of transactions that occurred within a specific time frame or sales range [1, 3, 5, 10].
Filtering Data: Pivot Tables allow users to filter data by specific criteria, focusing on relevant subsets of information [1, 6, 11, 12].
Report filters can be used to drill down on specific fields such as regions, time periods, business units, and staff [1].
Label filters and value filters can be used to select specific items, or to show the top or bottom performers [6].
Slicers provide visual buttons to quickly filter the data in a Pivot Table [7, 11, 13, 14].
Timelines filter date fields, allowing you to quickly select specific time periods [12].
Calculating Variance and Trends: Pivot Tables can be used to calculate variances between different data points and identify trends over time [7, 14-16].
You can calculate the difference between the actual and planned sales, both in terms of dollar value and percentage [15].
You can calculate percentage differences from a base field, such as a previous year or a planned target [7, 15].
Calculated fields allow you to create new metrics by using formulas with existing fields [7]. For example, you could calculate profit margin as a percentage of revenue [7].
Statistical Analysis: Pivot Tables provide tools for statistical analysis of data [3, 4].
You can calculate the standard deviation of your data to understand its volatility [3]. A low standard deviation means that data points tend to be very close to the average [3]. A high standard deviation means that the data points are more spread out [3].
The variance calculation measures how far a set of numbers are spread out from the average and from each other [4].
Pivot Tables can help you understand data distribution, such as whether it is normally distributed, or has a higher volatility [3].
Grouping Data: Pivot Tables can group numerical and text data to summarize trends across categories [5, 10].
Numerical data can be grouped by range to see the number of transactions or sales that fall within a certain range [3, 5].
Date data can be grouped by days, weeks, months, quarters, or years [10].
Text data can be grouped into new categories to consolidate data [5].
Time data can be automatically grouped into hours and minutes [17].
Visualizing Data: Pivot Tables allow you to create visualizations to aid in data analysis [8, 11, 13].
Pivot charts are visual extensions of Pivot Tables, and update as you make changes to the Pivot Table [11].
You can customize charts by changing the chart type, layout, style and colors [11, 13].
You can insert different chart types, including column charts, pie charts, or scatter charts [11, 13].
Sparklines are small charts that fit within a cell to quickly show trends across data [16].
Consolidating Data: Pivot Tables can consolidate data from multiple sources, such as different spreadsheets or databases [18].
You can use the Pivot Table wizard to consolidate data from different salespersons into one report [18].
You can also use the wizard to convert data from a tabular format into a Pivot Table layout [18].
Data Models: Excel 2013 and later versions can create a data model by relating multiple tables based on common fields [17, 19].
You can create a relationship between different tables when there is a one to many relationship using a primary key [19].
You can then create Pivot Tables by combining data from these related tables [17, 19].
By utilizing these features, Pivot Tables can transform raw data into actionable insights for effective business analysis [1, 2].
Mastering Excel Pivot Tables
Excel Pivot Tables offer a wide array of features that enable users to analyze and manipulate data effectively. These features can be broadly categorized into data preparation, creation, layout, filtering, calculations, visualization, and more [1-18].
Here’s a detailed look at some of the key features of Pivot Tables, as discussed in the sources:
Data Preparation: Before creating a Pivot Table, the source data should be in a tabular format, with no gaps, and with labeled columns [1]. Column names should be at the top, representing distinct categories of information [1].
Creating Pivot Tables:
A Pivot Table is created by selecting the data and going to Insert > Pivot Table [1].
If the data is in an Excel table, you can go to Table Tools > Design > Summarize with Pivot Table [1].
Pivot Tables can be placed in a new worksheet or an existing worksheet [1].
Pivot Table Fields:
The Pivot Table field list shows all column names from the data source [1].
Fields are dragged and dropped into four areas:
Row Labels: Shows unique values on the left side of the Pivot Table, used for grouping items [1].
Column Labels: Shows trends across the top of the Pivot Table, such as time periods [1].
Values: Where fields to be calculated are placed, such as sums, counts, or averages [1].
Report Filter: An optional area to filter and focus on specific fields [1].
Refreshing Pivot Tables:
When the original data is updated, the Pivot Table must be refreshed by right-clicking and choosing refresh, or by using the Pivot Table tools tab [1].
You can set a Pivot Table to automatically refresh when the workbook is opened [7].
A pivot cache stores a snapshot of the data, and is updated when you refresh the Pivot Table [2].
Formatting Pivot Tables:
The field list can be sorted alphabetically [1].
You can format specific parts of the Pivot Table by enabling selection, allowing formatting of subtotals, columns, or unique row entries [1].
Number formats can be changed (e.g., to show currency) by selecting value field settings and number format [1].
The names of value fields can be customized [1, 5].
Use “select entire Pivot Table” to ensure formatting applies to new fields [6].
Layout Options:
Pivot Tables can be displayed in compact form, outline form, or tabular form [4]. Compact form displays multiple fields in one column, while outline and tabular forms separate row fields into columns [4].
Blank rows can be added or removed after each item [4].
Drill Down and Expand/Collapse:
Double-clicking on a value in the Pivot Table will provide a snapshot of the underlying data [2].
The expand and collapse options allow drilling down on specific rows or summarizing at a higher level [5].
Entire fields or individual items can be expanded or collapsed [5].
Moving Items:
Items within a Pivot Table can be moved by right-clicking and selecting move [5].
Items can also be moved by typing the name, or by dragging and dropping fields within the area sections [5].
Fields can be removed by right-clicking and selecting remove field [5].
Filtering:
Report filters are located at the top left of the Pivot Table [2, 10].
Label filters and value filters can be used by right-clicking on the row labels or from the Pivot Table field list [10].
You can select multiple items by using the “select multiple items” option in the report filter [10].
You can also keep only selected items by highlighting your selection and right-clicking [10].
Slicers:
Slicers are visual buttons that show what has been selected in the filters [11].
To insert a slicer, click inside the Pivot Table and go to the Pivot Table tools tab > options or Analyze tab > insert slicer [11].
Slicers can be moved, resized, and can have multiple columns [11].
Multiple Pivot Tables can be connected to a single slicer to filter them simultaneously [11].
Calculated Fields and Items:
Calculated fields allow performing calculations using data within the Pivot Table [15].
Calculated items can show the difference between revenue and COGS [8].
Show Values As:
This function allows displaying values in different ways, such as percentage of grand total or a difference from a base value [8].
Conditional Formatting:
You can apply conditional formatting to highlight specific values or trends in a Pivot Table [14].
Pivot Charts:
A pivot chart is a visual extension of a Pivot Table that changes as the Pivot Table is modified [12].
Pivot charts are created by selecting a Pivot Table and choosing Pivot Chart from the options tab [12].
You can filter a pivot chart directly from the chart or by using a slicer [12].
GETPIVOTDATA Formula:
This formula allows you to extract values from a Pivot Table into customized reports outside the Pivot Table [14].
The formula can reference cells for items to enhance reporting [14].
Consolidating Data:
The Pivot Table wizard can consolidate data from multiple sources into a single Pivot Table [15].
The wizard can also transform data from a tabular format into a Pivot Table [15].
Excel Versions:
In Excel 2013, the “options” tab is renamed to “analyze” [17].
Excel 2013 introduced recommended Pivot Tables, a distinct count calculation, and the timeline slicer [17].
Excel 2016 has auto-grouping for date columns, multi-select for slicers, and the ability for pivot charts to expand or collapse its data [18].
Excel 2019 introduced 3D maps [18].
These features collectively make Excel Pivot Tables a versatile tool for data analysis, reporting, and visualization.
Mastering Excel Pivot Tables
Excel skills can be enhanced through the use of Pivot Tables, which are a powerful tool for data analysis, reporting, and visualization [1, 2]. The sources describe a variety of Excel skills related to Pivot Tables, including:
Data Preparation:
Organizing data in a tabular format with labeled columns and no gaps is a fundamental skill for using Pivot Tables effectively [1, 2].
Data should be in a list with labeled columns, also known as tabular format [1].
Data must be arranged so that column names are on the top, showing a distinct category of information, with the rows below containing the corresponding data [1].
Creating Pivot Tables:
Inserting a Pivot Table is done by selecting the data and going to Insert > Pivot Table [1, 2].
If the data is in a table format, a Pivot Table can be created using Table Tools > Design > Summarize with Pivot Table [1, 2].
Pivot Tables can be created in either a new worksheet or an existing worksheet [1, 2].
Understanding the Pivot Table Field List:
The Pivot Table field list contains all the column names from the data source and is used to drag and drop the fields into the different areas of the Pivot Table [1, 2].
The four areas of a Pivot Table are row labels, column labels, values, and report filter [1, 2].
Understanding which fields to place in each area is crucial for effective data analysis [1, 2].
Manipulating Pivot Table Layout:
You can move fields to different areas to get different views of the data [1].
Items in the Pivot Table can be moved by right-clicking and selecting move or by dragging and dropping [1, 2].
Fields can be removed from the Pivot Table by right-clicking and selecting “remove field” [1, 2].
Pivot Tables can be displayed in compact, outline, or tabular form [2, 3]. You can also adjust the indentations for row labels when in compact form [3].
Report filter layouts can be changed to display fields down then over, or over then down [3].
Data Analysis Skills:
Summarizing data by using sum, average, count, min, max calculations in the values area [1, 2].
Filtering data using report filters, label filters, and value filters to focus on relevant information [1, 2].
Using slicers to filter Pivot Table data visually [1, 2, 4].
Using timelines to filter data based on dates [5].
Calculating variance between different data points [6].
Identifying trends over time by grouping data by time periods [1].
Performing statistical analysis, such as calculating standard deviation [7].
Grouping data by range to see how sales or costs are distributed [7, 8].
Understanding how to use calculated fields and calculated items [1, 6, 9].
Using the “show values as” option to display values as percentages, differences, or running totals [1, 6, 10].
Formatting Pivot Tables:
Applying number formats to values to improve readability [1, 3, 11].
Customizing the names of value fields [1, 11].
Applying conditional formatting to highlight specific values or trends in a Pivot Table [1, 10].
Removing field headers to present a cleaner table [11].
Visualizing Data:
Creating Pivot Charts to visualize data from the Pivot Table [1, 2, 12].
Customizing charts by changing the chart type, layout, and colors [1, 12, 13].
Inserting sparklines to quickly see trends [1, 12].
Advanced Skills:
Using the GETPIVOTDATA formula to extract data for customized reports [1, 14].
Consolidating data from multiple sources using the Pivot Table wizard [1, 8].
Creating data models by relating multiple tables for complex analysis [1, 5, 15, 16].
Working with 3D maps to visualize geographical data [15].
Improving Efficiency:
Using Pivot Power add-in to set default styles, apply formatting quickly, and clear filters with one click [14].
Saving files as an Excel Binary Workbook to reduce file memory [9].
Using Microsoft Access with over a million rows of data [9].
Sharing a Pivot Table via Microsoft OneDrive to view and make changes [9].
Version-Specific Skills:
Being aware of the new features in different Excel versions such as recommended Pivot Tables, timelines, distinct count calculations, auto grouping dates, and the data model [1, 5, 15].
By mastering these Excel skills, users can effectively analyze data, create insightful reports, and make informed business decisions using Pivot Tables [1, 2].
Mastering Excel Pivot Tables: A Free 10-Hour Course
The sources describe a free Excel Pivot Table course that includes the following:
Course Overview: The course is a comprehensive, 10-hour long training program, consisting of 230 short and precise tutorials designed to help users learn Excel and Pivot Tables efficiently [1].
Course Goal: The course aims to help users become more efficient and skillful, gain confidence, and potentially get promotions and pay raises [1].
Cost: The course is available for free, having previously been sold for $300. This decision was made because many people need to learn Excel but cannot afford expensive online courses [1].
Target Audience: The course is suitable for any Excel user, regardless of their skill level, whether they are a beginner, intermediate, or advanced. It is also for people of all ages and employment statuses [1].
Course Content:
The course focuses on Pivot Tables and their various functionalities [1].
Specific topics covered include:
Customizing Pivot Table layouts [1].
Summarizing values and showing values as calculations [1].
Grouping, sorting, and filtering data [1].
Using slicers [1].
Working with calculated fields and items [1].
Creating Pivot Charts [1].
Using conditional formatting with Pivot Tables [1].
Using the GETPIVOTDATA formula [1].
Integrating Pivot Tables with macros [1].
Exploring new features introduced in Excel 2013, 2016, 2019, and Office 365 [1].
Learning Approach:
The course uses a start and finish format for each tutorial, with downloadable workbooks for practice [1].
Users are encouraged to practice along with the tutorials to improve their skills [1].
The course is designed to allow users to jump to specific sections they want to learn, without having to complete the entire course [1].
Support: The course instructor will monitor and respond to questions posted in the comments area below the videos [1].
Downloadable Workbooks: There is a link to download all 230 tutorials so users can practice along with the instructor [1].
Additional Resources: The course also invites users to join the MyExcelOnline Academy for more advanced Excel training, which includes over 1000 video tutorials covering various topics such as formulas, macros, Power BI, and more [1].
This free course is presented as a way for users to dramatically improve their Excel skills by focusing on Pivot Tables, with the promise of making them more proficient in data analysis and business decision-making [1].
Master Excel Pivot Tables, Excel Slicers and Interactive Excel Dashboards – FULL COURSE!
The Original Text
– Good day guys and girls, I’m John Michaloudis here from MyExcelOnline.com and I want to welcome you in this free Excel Pivot Table course. Now this is a massive course, it is over 10 hours long, and it has 230 short and precise tutorials. So you can learn Excel and Pivot Tables straight away. So you can become more efficient, more skillful, gain confidence, you can get the promotions and pay rises that you deserve. Now, I used to sell this course here, this individual course here, over at my website for $300. And I’ve decide to put these on the issue platform for free. Because I know a lot of people need to learn Excel and there are not that many good tutorials or courses on YouTube. So this is free, this is for you because a lot of people don’t have money to spend on online courses and I understand that. So this course is for you and it’s going to make you much, much better at Excel. Now in this course you’re gonna learn about Pivot Tables. And with Pivot Table, you can analyze thousands of rows of data, we drag and drop ease. So when you drag and drop into your Pivot Table, it’s gonna create a report that analyzes your data. And with that data, you can make some awesome reports and you can make some great business decisions and also it’s gonna give you the power that you need to take your Excel skills to the next level. In this course, we’re going to talk about the following topics. First of all, we’re gonna talk about how to customize your Pivot Table in a different layout. Then we’re gonna go into the summarize values by, and also show values as calculations. Next, we’re gonna go into grouping your data, then into sorting, then we’re gonna go into filtering. After that we’re gonna show you slicers which is an awesome feature that was introduced in Excel 2010. Then we’ll go into calculated fields and items. Then we’ll dive into Pivot Charts, we’re gonna show you a bit of conditional formatting, with Pivot Tables. Also, we’re gonna show you the GETPIVOTDATA formula. Then we’re gonna into Pivot Tables and Macros. Also, there are a bonus videos with some awesome tutorials there, and we’re gonna go through the new Excel Pivot Table features that were introduced in Excel 2013, in Excel 2016 and Excel 2019 and Office 365. So 230 short and precise free guide tutorials, 10 hours of Excel training free for you. Now, this course is for any Excel level. Whether your a beginner, intermediate, or you think you’re an advanced Excel user, this course is for you. Whether you’re young, you’re old, you’re unemployed, this course is for you. Once you learn Excel Pivot Tables, your Excel skills are going to skyrocket. Now in the description area below, there’s a link to download all 230 tutorials that I’ll through in this course. And each tutorial is in a start and finish format. So you need to download it so you can practice along with me as I show you. Now, the more you practice, the better we get. That’s with anything in life. So click on the button below, download the workbooks and let’s get straight into it. Now, finally, if you have any doubts any questions about any video tutorials, use the comments area below. Put in your question and I will be manning the comments and replying back to you with an answer. Also give this video a thumbs up. The more thumbs up we get, the more videos that we’re gonna create for you in YouTube. So go ahead and do that right now. And finally, after you watch this course, then we invite you to join our MyExcelOnline Academy Online course. This course here has over 1000 Excel video tutorials and covers, formulas, macros, PBI, Pivot Tables, Power BI, power query, power pivot, charts, axes. Also, we go into Microsoft Word, Microsoft PowerPoint, and also Outlook. And we also cover dashboards and heaps of more videos. Over 1000 video tutorials and if you really wanna elevate your Excel skills to the next level, we invite you to join the MyExcelOnline Academy the details are in the card that will pop up now or in the description below, click on that, but first of all, let’s get into this Pivot Table course. I want you to get better at Excel and Pivot Tables, jump onto different Pivot Table areas. We’ve listed every tutorial in the description. So you can click the button and go directly to an area that you want to learn. And once you learn Pivot Tables, you don’t have to finish all the course. You can just do different sections. Then you are gonna be ready to take your skills to the next level. And we are here, I am here, my team is here to support you in your journey to become better at Excel so you can stand out from the crowd and get the promotions and pay rises that you deserve. Now let’s get into it. (upbeat music) Before you begin with a Pivot Table, you gotta arrange your data set. Now there are three rules to follow. Number one, is to have your data in a tabular format. Number two, is to make sure that there are no gaps in the data. And number three is formatting. Now here, I’ll talk about tabular format. So what type of format means is that for a Pivot Table to work, you must make sure that your data is organized as a list with labeled columns, also known as tabular format. So you should have your column names on the top showing a distinct category of information. As you can see here, we have customer and going down the rows, we have the different customers. And we have products and we have information on the products. Salesperson, we have the different salespeople. And then as you can see, we go all the way up. We have order dates and so on. So let’s try to do a Pivot Table by pressing Control + A to select all of our data and then going into insert and Pivot Table. And then let’s put into a new worksheet and press okay. Wow, we’ll get an error message. It says here, “The Pivot Table field name is not valid. To create a Pivot Table report, you must use data that is organized as a list with labeled columns.” So the key data is labeled columns. Let’s cancel out of here. As you can see here, our years and our quarters don’t have any names. So therefore it’s not gonna work. Now let’s put in the names in there. Sales year and sales quarter. And once again, Control + A to select everything. Now, when you press Control + A makes sure that it gets everything. Insert, Pivot Table, new worksheet press okay. Here we go. We can start building our Pivot Table. (upbeat music) The second rule to arrange your data set is having no gaps. What that means is having no blank columns and no blank rows. The reason is that that section of your data might not get picked up when you create a Pivot Table. Now, as you can see here, we have column F that is blank and rows 11 and 22. Now let’s create a Pivot Table from in here and see what happens. Let’s select all, we’re going to the right and all the way down. Okay, so we’ll pick up everything here and go to insert and Pivot Table and press okay. We’ll get an error message. It says “The Pivot Table field name is not valid. To create a Pivot Table report, you must use data that is organized as a list with the labeled columns.” Press okay, and cancel to get out of there. Well, in here it’s picked up that we don’t have a labeled column so obviously we gotta delete it. Right-click and delete. Now we have to delete row 11 and 22. Now imagine you had lots of rows in your data set that were blank. And I’ll show you a quick way where you can delete all those blank rows. Now, once again, let’s highlight all your data set and then go to the home tab and find and select, go to special. In here, we go to special dialog box and choose blanks and press okay. So what it does is within your selection, it chooses all the blanks. So another way in here, what we can do is, we can hover over here and right-click and delete from there. Or instead of doing that, let’s press a shortcut, which is Control + the minus key. Now in here, we want to delete the entire row. So all the row gets shifted up. Okay. And then press okay, there you go. Now, what we gonna is press Control + A, make sure that it picks up everything. OKGo to insert, Pivot Table and press okay. (upbeat music) To have a great data set, you got to make sure that your formatting is in order. This is because it avoids inaccurate reports when creating a Pivot Table. So it is essential to format each column that contains numbers and each column that contains dates. Now, for example, our sales column here, we’ve got to make sure that it’s in a number format. To do this, we just click on the column F and then from the number group, the dropdown box, we can choose a number from in here and press okay. We’re gonna also put in a comma and then give it to the decimal places. Now in our older date here, we have numbers as well. Now Excel treats dates as a sequential number with the first of the first 1900 being day one. Now does this in order to perform arithmetic operations. Now this information here doesn’t make much sense to us. So we need to convert that to date. To do this, let’s click on column eight and once again, from the dropdown arrow, let’s choose short date. That’s much better. So now with all of our information in the right format, we can begin with our Pivot Table. (upbeat music) Excel Tables are a new feature from Excel 2007 and onwards and they’re great. You should always use them. The best feature is that it has a structured referencing. This means that as your data expands with more rows or columns being added, then the table automatically gets updated, as Excel refers to the table as a whole. Now when creating a Pivot Table and your data changes in a table, to update your Pivot Table, you only need to refresh and avoid having to update your data source. Now to insert a table, first, you got to click anywhere in your data source and press insert and table, and Excel is smart enough to detect your data source. Now you could scroll down to make sure that it’s inserted. Another way is to press Control + T which is a shortcut. Now this dialog box says create table. Where is the data for your table? So it’s taken out data and in here check it if your table has headers. And then press okay. So now it creates an Excel table. Now, if we step out of it and then step back in, once you’re in the table, you get the table tools option. And in here you have the design and different styles. So you have all these different styles that you can choose. Now also you can put in a total row, which means that from in here, you go different ways to summarize your data. So you can put in some in there. Now, if wanna expand your data, what you got to do is just grab the edge and just drag it down, or you can just go anywhere in here, right-click and insert. Let’s go back up again. Now you can also change the name of the table from in here. It’s called table1 but we can change it to my table. And once you’re in here, you can summarize the Pivot Table or you can export to SharePoint list. Another thing is you have your different filters in here. Now Excel tables are a great feature, and whenever you’re having data, you should always use them. Because they’re gonna save you lots of time in the end. (upbeat music) We have some data information here, and we’re gonna put it into a Pivot Table to see what the results are gonna give us. Go to insert and Pivot Table and let’s put it here next to our data. Now let’s get our data and put in our row labels and then our data into our values to get the count. So what it says is we have three separate values for IN123C104Z, but that can’t be right. This here should be equal to three. Now there’s a problem in here, and it usually happens when you’re importing data from external data sources. Now, what happens is you may get some leading or trailing spaces. Now let’s have a look in there and press F2. We can see that’s fine. Let’s go down and press F2, here we have a trailing space. And going there and press F2 here we have two trailing spaces. So the Pivot Table treats these values as separate so therefore it gives us a different count. Now I’ll show you a way to clean up your data before you create a Pivot Table. One ways to use the trim function. So press trim and click in there, and then it trims all your values like that and you can just double-click and you have the trim values there. Now another way is to use an add-in from Abelbeats.com, which is called the Trim Spaces. And that’s one that I use all the time. All you gotta do is just press a button and then it trims all your selected area. Now I’ll show you another way where you can trim your data, highlight your data like this, the whole column, go to data and text to columns, and then choose delimited and press next. And in here, make sure that the space is checked and then press finish. So let’s go back in here and have a look at second value. Press F2, you see that it’s cleaned the space and in there again it’s cleaned that. So let’s go in our Pivot Table, right-click and refresh, And now you see our data is being cleaned up and it counts it correctly. (upbeat music) So we have our data set here and it’s in a table, and we know that because when we click in our data source, we get the table tools option in there. And we can see there that the table name is table 13. Now to insert the Pivot Table, on the table tools tab, design, we can choose, summarize with Pivot Table, or we can actually go to insert tab and then Pivot Table like this. Now Excel is smart enough and it selects the table 13, so you’ll see that it’s all selected all the way to the bottom and we scroll to the right. Now, it tells us, “Where do we want to put a Pivot Table?” Now we can choose a new worksheet or we can actually put it into an existing worksheet. So we can actually go somewhere in here and put our Pivot Table there. Now, because we don’t have that much space, we’ll put it into a new worksheet and press okay. So you see it’s created a new worksheet called sheet one, and the Pivot Table is in here. Now if we step out of the Pivot Table, We don’t see the Pivot Table tools tab. We got to actually step into it to activate the Pivot Table tools and in here we can choose options and design. Now in the options we have the Pivot Table name, which is Pivot Table number one, and we can change that to customize that and call it my Pivot Table. And you can see here, it changes the name as well. On the right-hand side we have our Pivot Table field list, and we have all the column names here that were in our data source. Now if you can’t see this, then under Pivot Table tools tab options, you can activate it and disactivate it by the field list button there. Now our Pivot Table is gonna look similar to this design here. Now let’s go over to the right-hand side and I’ll show you how to create a Pivot Table. So the Pivot Table is gonna look similar to the design that we’ve just brought over here. The fields that get dropped into the report filter, will be shown on the top left-hand corner of the Pivot Table, fields that get dropped into the column area, will be shown on the horizontal area of the Pivot Table. Fields that get dropped into the row labels area will be shown on the left-hand side of the Pivot Table, fields that get dropped into the values area will be shown into the middle part of the Pivot Table. Now let’s go in and drop some fields into the respective areas. Now we can just hover over the name, grab it and drop it in there, just like that. And you can see on the left-hand side, the Pivot Table is gonna be built. Let’s get salespersons into the row labels. Let’s get sales here in the column labels and finally, let’s get the sales into the values area. And you can see we’ll get the live preview of our Pivot Table and then we have it, we’ve created a quick Pivot Table with just a few clicks. And as you can see, the design is similar to the one that we saw before. (upbeat music) When you click into a Pivot Table, just like I’ve done now, and now you’re onto the field list, ’cause there’s a couple of ways where you can bring it up. One of them is to go to the Pivot Table tools tab, under options choose the field list button. Here you can show it or hide it. And let’s get out of this. Another way is to click anywhere into the Pivot Table, right-click, and the last option is show field list. Now we can do a couple of things in here. We can actually move or resize the field list, from the drop-down arrow we can choose move and then with the mouse, just click and drag to the left and we can move it out here. Let’s move it back in. With your mouse again, move it all the way as if you’re throwing it out of the screen and the locks back in. The other option is to resize so you can resize it from in here. And then we can close it. Let’s bring it up again. Now from this dropdown box, we have five different views that we can see the field listing. The first is that field session and areas sessions stacked which is a default, then we have the field section and area section side-by-side. Then we have the field section only, then the area section only two by two, and then area section only one by four. Now I personally like the default view. (upbeat music) In our Pivot Table field list, on the top half, we have our fields or column headings. If we go back to our data, you can see these column headings from customer products, salesperson, sales region, order date, sales, sales year, sales month and sales quarter. They’re all inputted into this area here. Now in the bottom half of our Pivot Table field list, we have our four different areas where we can drag and drop our fields or column headings into. Now let’s talk about these. First we have out row labels. In here, you can show the unique values from the fields chosen on the left-hand side of the pivot. So here you should look to add fields that you’re looking to group, for example, products, company names, locations and business units. For example, we’ll take our products. Now you can actually click in the box and it will dropdown in to the row labels. And sometimes by clicking, it doesn’t actually drop into the area that you want. So the best way to do it is to drag and drop. So you can drag that all the way down and you can drop it in here. When you see the little blue line underneath row labels and just let go of your mouse and you see products has been dropped into the row labels. Now on the left hand side, our Pivot Table is automatically updated, you get a live preview. So here we have our unique values that are within the products column. We have bottles, ice cubes, soft drinks and tonic. Now we can check that if we go back to our data set and go to our products and in the dropdown box, we can see that we have our four unique values in there. So these are transferred into the left-hand side of the Pivot Table. And next we have our column labels. In the column labels, you’re mainly looking to show the trend of your data. For example, periods, phases, time, months, and years. So in that case, we can go and grab our sales here and drag it all the way into the column label area. And as you can see on the top side of the pivot, we have our unique years, which are 2012, 2013 and 2014. Now in the values area, this is where you put fields that you want to calculate or quantify. The different type of calculations that you can use to summarize your data include sum for sales, count for number of units, average for prices, and maximum or minimum for your values. So in here, we’re gonna grab our sales and drag and drop in there. As you can see on the left-hand side, we have our sum of sales in there. So what it says in here is that in 2012, we sold $2,754,838 worth of bottles. So what it’s done here, it summarized, so it summarized all the sales and put into a neat little table here where you can quickly analyze it within a few seconds. Now, finally, we have a report filter. This is an optional filter. Here you can put fields that you want to drill down on and focus on. For example, regions, periods, business units and staff. So for example, we’re gonna grab our salesperson and drop them in there and sales region and drop them in there. As you can see on the left-hand side, we have our report filters with a dropdown box and we can see the unique values that make up the salesperson, and sales region, we can also see the unique fellows in there. Now, Pivot Table is very powerful because you can chop and change until you get the outcome that you like, that your boss like, or that your business is looking for. Now, if we want to move products from row labels to columns, you can do that. You just grab, drag and drop in the columns and then grab the slaes year and put into the row labels. As you can see on the left-hand side now we have our years and on the top column cycle, we have our products. So you can analyze the information in a different way. It depends on what you’re looking for. If you don’t get it right the first time, don’t worry, with trial and error, you will end up with your desired look. Now Pivot Tables are that easy. (upbeat music) So that you’re analyzing your data and you come across a strange value, and it doesn’t add up. For example, in 2012, for bottles, we have $2.7 million, but it should be more like 3 million. Or you can actually drill down and order that. To do that, you just click in the sale and then double-click and you get a snapshot of your data set, only for bottles and 2012. So in here you can go into the sales and see where the error was. Now, if you wanna make any changes, this is not the place to do it. You need to do it in your data set. Now let’s get out of here, Control + Z to get out and press the delete. So any changes you got to make has to be in your data set. And then you got to make sure that when you’re in your Pivot Table, that you refresh it by right-clicking and refresh to update any changes that you made in your data set. And you can also drill down into the grand totals, so say you wanna look at 2013, you just double-click and it gives you all the values for 2013. And you can begin with checking your information there. And once again, Control + Z to get out and delete. (upbeat music) We have our field list here, but it’s not in alphabetical order. And sometimes it can get confusing if you wanna try and find out some fields, especially if you have more than 20 different fields. Now to put this into alphabetical order, all we’re gotta do is go to the options and then options, and under display at the bottom, you have the field list and you can sort from A to Z. Press that, we’ll press okay, and you can see this will change to an alphabetical order. (upbeat music) You can click on any row or column label items, so we can show more fields. For example, in the row labels here, we double-click, you wanna get these show detail dialog box. And in here it says, “Choose a field containing the detail you want to show.” So it has all the fields not already chosen in the row labels, so we can choose for example, sales quarter and press okay, and then we have the sales quarters in there, and as you can see the areas in here sales quarter, has also been added. And the same thing we can do in the column labels. Double click, and we can add anything from in here, Let’s use sales month and press okay. (upbeat music) If you have many rows in your data source, and when you create a Pivot Table, it takes time to generate the live preview and the results, and all you can do is click here on the bottom left-hand corner, the, defer layout update. What this does is, when you drop in your information like sales in your values area, you see you don’t get a live preview here. So you can just drop into whatever you like in here and then sales years up there, so you can drop whatever you like and then when you’re happy with it, just press update, and then it updates it. So what it does is, it defers the layout update until you press the update button. (upbeat music) When your data changes either by your data source having its values updated or more rows or columns having been added to your data source then you need to refresh your Pivot Table. The reason is that when you created your first Pivot Table, a snapshot of your data was stored in a pivot cache. A pivot cache is a snapshot of your data set and this is where your Pivot Table is created from. You don’t see this pivot cache, but it runs in the background system. This attributed a copy of your data set allows for your Pivot Table to run faster when you’re making changes to it. So when changes are made to your original data set then you need to refresh, so you can update the pivot cache and ultimately update your Pivot Table. (upbeat music) When your data gets updated, you need to refresh the Pivot Table to reflect the changes made in your data set. For example, if we going through our data and we go into bottles and we change the sales here to $10 million, then if we go back to our Pivot Table we see that no change has been made here. What you need to do is refresh the Pivot Table to update the values. There are two ways to do that. One of it is to click in the Pivot Table and go to the Pivot Table tools tab under options, and in the data group, press refresh. As you can see here, our bottles for 2012 have changed. Now let’s press Control + Z to go back. The second way to do this is just to right-click anywhere in your Pivot Table and you have the refresh option there, you just click on there and your values get updated. (upbeat music) In this example, we have two separate Pivot Tables that were created from two distinct data sets. So our Pivot Table on their left-hand side was created from data1, which is over here. Pivot number two on the right-hand side was created from data2 in here. So now what we’re gonna do is go in and change the information in each of the datasets. And we’re gonna use the refresh all button to refresh both Pivot Tables simultaneously. So what we’re gonna do is we’re gonna update bottles in 2012, so we’ve highlighted that in red so we can see the change. And on our second Pivot Table, we’re gonna change the value in soft drinks for 2012. So let’s go to data1 first and I’ve highlighted this in red so we can make the change. So I wanna add in 10 million in there. And data2, let’s scroll up, it’s for soft drinks. So I wanna add 10 million in there as well. So let’s go back to our pivots. Now, if we have multiple Pivot Tables, we can actually use the refresh all option, which is from the dropdown box and you have their refresh all and what that does, it updates both Pivot Tables simultaneously. So let’s press a button and we’ll see the numbers change. There you go. So as you’ve seen on the left here, the numbers to change the 12 million and soft drinks it’s changed to 11 million. So if you have more than one Pivot Table in your workbook, then you can use a refresh all button to update those Pivot Tables simultaneously. (upbeat music) Say that in your company, you have a workbook that you and your colleagues are sharing and updating on a hourly basis or a daily basis. And you want to create a Pivot Table on your personal desktop, well, you can do that. Now in here, we have our shared data set and let’s imagine that this is sitting in your company’s server and everyone has access to it and they update it accordingly. So let’s have a look at that. So this is a dataset that we’ve been using in our previous lessons. So we can get out of here and let’s go into our workbook, which sits in your desktop. So we can click on that. And now here we can create our Pivot Table. So to do that, we have to go to insert and Pivot Table, and we choose the use an external data source option, and then we have to choose a connection on the bottom left-hand corner, we click on browse for more, and then we have to search for our data set, which is sitting on our imaginary server and press open. And we have a pop-up box that comes up and we’ll have to select the data, which is the first one. The check pop here says that the first row of data contains column headers, and that’s correct, and we can press okay. Let’s create a Pivot Table. Let’s put in our products on the row labels, our sales in the values and our sales here in the column labels. So there you go. You have created your Pivot Table from an external data source. Now let’s save this and we can get out of it. And let’s go back into our shared dataset, which is my server, and let’s start making some changes. So imagine that your colleagues are making changes to this data set on a daily basis. So let’s put in some fictitious numbers in there. And we can just… Okay, so we have that, so we can get out of that. And imagine the next morning you come into work and you’re having this workbook and it hasn’t been refreshed. Well, there’s a couple of ways that you can update it. You can right-click and refresh, and that will get updated as you’ve seen there. Let’s Control + Z to get out of that. Now, another way that you can a refresh it is by clicking on the connection properties under refresh, and we get this connection properties pop-up box, and we have a few options here. We can actually refresh every X number of minutes. So let’s just refresh after one minute and press okay and we’ll see what happens. Well, there you go. The updates have been made. So this is a great feature to have. So every X number of minutes, depending on the default that you put on there, your personal Pivot Table will get updated. So we can go back in here and change that again, under condition properties, we can uncheck that. And let’s check the refresh data when opening the file. So what that means is when you open your personal workbook, that the Pivot Table will get refreshed automatically you don’t need to do anything. So let’s save this and we’ll get out of there. And let’s go back to our shared data set which sits in our imaginary company server. And let’s imagine that more changes have been made during the day. Well, these changes have been cleared out and we can save there, and we can get out. And let’s go back into your personal workbook and look at this. It has refreshed automatically, the information from your external data source without you having to do anything. This is a great feature because sometimes you can come into your work in the morning, open your Pivot Table without refreshing, it happens. And this is a great feature to have so it’s automatically it gets updated without you having to do anything. Now, the last refresh control in here is enabled background refresh. Now you’re only gonna use that when you’re running a query in the background, which will enable you to use Excel while the query runs. Refresh every X number of minutes and refresh data when opening the file, they’re the options that you’d be using when you linking your workbook to an external data source. (upbeat music) So if you’re working with an access database, you can certainly export that information into an Excel workbook and pivot that information accordingly. Now we have our database here, which is a similar data set that we have been using in our previous lessons. So I have saved this database number one in my desktop. Now let’s get out of here and we can open up our new workbook. So to import the access database information into a Pivot Table, we need to go into data and choose from access, and then go to my desktop where I saved my database1, double-click on that and we’ll get our dialog box that says, “How do you want to view the data in our workbook?” We can view it as a table, as a Pivot Table report, as a pivot chart and Pivot Table report, so now we will choose the second option, Pivot Table report, and we’ll put into our existing worksheet in here and press okay, and we’ll get our Pivot Table. So now we can create our Pivot Table here, we can put our products in the row labels, sales year in the column and the sales in there. So we have our Pivot Table here. Now we can also refresh our Pivot Table, so when the exit that access database gets updated accordingly, well, we can actually refresh it automatically by pressing refresh, or we can put in a time refresh every X number of minutes, or we can also refresh the data when we opening our Excel workbook. And this was covered in lesson 1.34. So there you have it, you can insert information coming from an access database and use it inside a Pivot Table to analyze the data. (upbeat music) So we have our Pivot Table here which is referenced to a data range. We can save that by going into options, change data source, and we can see here that the range is all the way here. Now when our range gets changed by having more rows or columns into there, then we need to go into our change data source and capture the new range. So let’s go in there and add in some extra information. So press Control + Down to go to the end of our data set. And let’s enter some fictitious data in here. Let’s press Control + D to fill there. Let’s go back to the Pivot Table and go to options and change data source. As you can see here, it only goes all the way to row 577. So our extra rows that we’ve added in there haven’t been captured in our range. So what we need to do is include all that in our Pivot Table data source. So to do that press Control + Shift + Across + Down and press okay. So as you can see here, the information now has been refreshed and included in our Pivot Table. Now that is a long way to do it. When you’re using a Pivot Table, you should always have an Excel table as your referenced information. So what we’re gonna do now, going to our table here and select that. So as you can see here, it’s looking at it as a table as a whole. So every time we add in the extra rows or columns, then we don’t need to go back into the change data source button and include the extra lines, because it’s already gonna include it into these table 13. So let’s have a look here, our amount is 3.2 million, and let’s add in here a few extra rows or you can actually drag down. And then once again, we can copy down, we’re pressing Control + D. So all we need to do now is just go back into our Pivot Table and refresh. Click at options and refresh. As you can see there, the new information has been updated. Now because Excel tables use structured referencing, then we don’t need to go in and change the data source because it looks at a table as one whole data set. So you should always use Excel tables when you’re doing Pivot Tables and even if you’re not doing Pivot Tables, they’re great for data analysis. (upbeat music) If you have many fields of reports in here, as you can see, and you wanna clear them quickly, one a quick way to clear them is to go into options and into your actions group, and under clear, choose clear filters. Now if you’ve been working with your Pivot Table and you don’t like the look of it, and you wanna start again, but you don’t want to delete your pivot cache, then you can easily do that by going into the options and clear and clear all. So what it does is it clears all your field list items in your areas, and you can start fresh again. So now you can put in your new fields into your areas, just like that, and you can do your new analysis. (upbeat music) To format a section of a Pivot Table, such as sub-totals, columns, or unique row entries, then you need to go into your Pivot Table tools tab on the options in the actions group, under select, you gonna make sure that enable selection, let’s tick. As you can see there, we have the orange border around that. So now what you can do here is actually go into the sub-totals as you can see the black arrow is pointing and click there. So now we can go into our home tab and format so we can put that into red. Now, if we move our products from row labels to the column labels, then the bottles product is still gonna be formatted in red, as you can see there. So let’s put that back. Now we can also format the individual row items, for example, Q1. So by right-clicking in there, we can highlight that in a different color. And once again, if we move sales quarters from row labels to column labels, then that formatting stays there. So let’s move that back. Now, if we go on top of the row label and click there, then we can actually make some adjustments in there. Right-click to make those in italics. Now we can also go into our column headings and do the same thing. Let’s go to the grand total, right-click, and we can put in there a border, the same thing we can do for the grand total down here, right-click and put in there a border. Now, if you’re not highlight the whole Pivot Table, then all you got to go to is on the top left-hand corner, where it says sum of sales and you have the black arrow, you click on there, that’s one way. The other way is going into the options tab, select an entire Pivot Table. So when you’re in here, you can put in a different color if you like, or you can press delete and delete Pivot Table let’s press Control + Z and get out of there. So there are many things that you can do with the options and select item to make your Pivot Table look a little bit funkier. (upbeat music) Now, if you want to move out Pivot Table to another location, we can certainly do that. All we’re gotta do is click anywhere in the Pivot Table, go into the options tab and the move Pivot Table button, click on that, and now we get two options. We can either move it to a new worksheet or to an existing worksheet. So let’s move it to an existing worksheet and let’s choose up here, press okay. As you can see, it’s moved there. Now let’s move into a new worksheet and press okay. As you can see a new worksheet has been created called sheet1, and our Pivot Table has been moved from the pivot sheet into sheet1. (upbeat music) The default Pivot Table style is pretty dull looking. But luckily you have different styles where you can use and apply and make the Pivot Table look bright and beautiful. Now to activate that you need to go under your Pivot Table tools tab on the design, and you have your Pivot Table styles here. Now in the dropdown box, there are 85 different styles from light as you can see here, you get a live preview as you’re scrolling through each style. And there are some nice colors and some not so nice colors. You have your medium. As you can see there, you have a few nice styles, and then you have your dark styles. And depending on what you like or what your boss is looking for, you can choose one of these 85 different styles. I personally like this style here. You can make further changes under the Pivot Table style options over here. You can put in banded rows, and also banded columns. Now you can also uncheck the row headers and also the column headers. And once again, it’s up to you, whatever style you like, you choose and use that style for your Pivot Tables. So now let’s customize our row headers or make it into italics. And now if say we wanna change the style to a dark style, and if you right-click in there, you’ve got the first option apply and clear formatting. What that means is it’s gonna apply the new style and clear the italic formatting or any other formatting that you would have had in your Pivot Table. So let’s click that and you can see the italics have gone. Now again, if I change this to italics and then I choose another style and right-click apply and maintain formatting, then I will maintain the italics in the row headers. Now, the other option that we have there is a duplicate. So we can actually duplicate this Pivot Table style and change the styling in there. And we’re gonna talk about that in another chapter. Now, the other option is set as default. So you can set this as default and when you’re creating another Pivot Table within your current workbook, then you’re gonna get the same style. So let’s create a quick Pivot Table here and we’ll put into a new worksheet and press okay. And let’s put in some values in there. As you can see, it creates the Pivot Table based on your style that you have chosen, but it’s only gonna work in your current workbook. And the third option that we have when we right-click in there is add gallery to quick access toolbar. So we click on that quick access toolbar is down here, so we have the other option in there. (upbeat music) Now in the Pivot Table styles, you can create your own style. All you need to do is go under your Pivot Table styles, and then you have the option at the bottom here called new Pivot Table style, click in there, and you get this dialog box where you can name your new Pivot Table style, and you have your table elements. And here is where you can format your Pivot Table and you get a preview here. And you got 25 different elements in here, so you can start from scratch and create your own style. Now, if you’d like a style from one of these 85 predetermined styles, if you like one of them, you can just right-click and press duplicate. So what it does is it duplicates the Pivot Table and you can make changes from there. So we can rename this, I can rename it to John’s Pivot Table Pivot Table1 and you have your different table elements here. Now we’re gonna go to first column and we can format the first column and that relates to here. So let’s put her into a great color. Now what we need to do is we don’t get a live update, we actually gotta go back into the Pivot Table style and then choose our custom, which is the second one here and we got to activate it like that, to see the changes. So watch out for that. Now we can go back in and right-click in there and press on modify, so we can keep on making our modifications in there. So in our header row, we can format that into a different color, you see it’s blue now, but we can make it into a different field effect. You can choose one of this. We have the color white and the color blue and the shading styles, the variance here, we can choose any one of these just to spice it up a bit. Let’s choose that. Press okay. And you see, that’s changed there. And let’s go back again, modify, and let’s go to the grand total row and format that. And we can make the grand total row black with a bold white color. As you can see there, the change has been made there in the preview. Okay. And also here now we have made that change. We can go back in there, we can see grand total row and the element formatting, what we’ve chosen, bold background one and where we have shaded. So now if we don’t like this, we can clear up and go back to the start. Press Control + Z. Now, as I said, within modify, we have the table elements. We have 25 different elements now to change every single one of it I think is a bit over the top. So what I’ve done here is I’ve created a table of what the different elements are and where the changes will occur. So just the important ones, you don’t have to go through all 25, but the main ones I’ve highlighted here for you to go in and play around with them when you have some spare time and you’re bored. So you’ve got all these different styles that you can change and they’re all highlighted here. (upbeat music) when you customize a Pivot Table style, you can only use it in that workbook that you created it in. Now this is a new workbook and we created a new Pivot Table. As you can see, the customized style that we created previously is not in here. Let’s say you wanna bring it over in here. Well, we can do that. All we have to do is go back into our customized style that we created in our previous lesson 1.52, go to options and select the entire Pivot Table, press Control + C to copy the Pivot Table. Now let’s go through our new workbook and paste the Pivot Table here, right-click and paste it in there. And what happens now is that the customized style has been embedded into this new workbook. So what we can do is click on our Pivot Table, apply the custom style, and then we can go and delete the Pivot Table that you brought over by selecting all and pressing the delete button in our keyboard. So now we have our customized style brought over into our new workbook. (upbeat music) Under Pivot Table design tab, you have more options on the left-hand side, under the layout group. And the first one is sub-totals. So in here you got the three options. The first one is, do not show sub-totals. So the sub-totals disappear. Second option is show all sub-totals at bottom of group. So you can see sub-total is at the bottom of each item. And the third option is to show all sub-totals at the top of the group up here. So depending on what you like, you can choose to have those three different options. (upbeat music) So the other option under design and layout group is the grand totals. So in here we’ve got four different options. The first one is off for rows and columns. So the rows and columns grand totals disappear. The second option is on for rows or columns, as you can see there, they come back on again. The third option is on for rows only, so we just have the row grand total here. And the last option is on for columns only. So you can see the grand total is down here. So you have four different options, but I like using on for rows and columns. (upbeat music) Under the report layout, you have three different ways that you can show your Pivot Table. The first one is new in Excel 2010, and it’s called show in compact form. Now in this form, you can see multiple fields in one column. As you can see here, we have our products and our salesperson in our row labels. So they’re all compact into column A. So if I grab sales region and bring it there, as you can see, it would just drop it into column A. So it’s all compacted into one singular column. Let’s take our sales region from here. And the second form is called the outline form. As you can see there, this layout separates the row fields into separate columns as you can see, we have products, we have that have salesperson and it’ll drop in the sales region, it’ll go into column C. So you see sales region is in column C. And the third form that we have is show in tabular form. Now this is the legacy form, and this was created many years ago. As you can see, you can still use it here, but the new compact form for me works the best. Now another feature that we have here is the repeat all item labels. And now let’s go into show in outline form first, and if we choose a repeat all item labels, what it will do is it’ll fill in all the gaps that we have here. And this is fantastic if you want to copy and paste this information to analyze it into another sheet. So let’s put it in there. And as you can see, we have the information here and we can analyze it. We can just delete the gaps there. And we have it into a tabular format that we can use to make our analysis. And in here, I have the different layouts for you to have a look at. We’ll have the compact, the outline, tabula and the outline but with repeat all item label. So you can have a look and see which format you like. (upbeat music) Now under design and the layout group we have the blank rows button here. And what it does is insert a blank line after each item, or you’re gonna remove a blank line after each item. So let’s insert a blank line. And as you can see there, we have a little bit more space in our Pivot Table, and it just looks a little bit more presentable. But if you don’t like that, you can always switch it up. (upbeat music) For all of the old schoolers out there, that still like the 2003 version of the Pivot Tables, well you can actually bring that to life in Excel 2010 and 2013. Now to do this, you got to click anywhere in your Pivot Table, right-click and go to Pivot Table options, in display, choose the classic Pivot Table layout. Now this will enable dragging our fields into the grid just like in 2003 and press okay. Now finally, just to make it look like the 2003 version, just choose a none in the design layout. So we get this view here. So there you have it. We have our sales year there, so we can drag it and move it out there. We can get our sales month and then drag it all the way and drop it into there and it take you back to 2003. (upbeat music) The expand or collapse option allows you to drill down on specific rows or columns, or you can summarize at a higher level. Now this is located next to the item names and you can see there by the minus sign. So if we press on that, we can collapse the individualized item and click again, you can expand it. There’s no way to do that, just right-click anywhere in the item name and go to expand collapse and press collapse or expand. Now, if you want to collapse all the fields all you have to do is go up to the ribbon, which is under the options tab, and you have the green plus sign there, which says expand entire field or the red minus sign, which says collapse entire field. So if you click on there, you collapse or the fields, and then the plus sign, you expand them. Another way is to go into one over the field items, right-click in there and go expand collapse and go to expand entire field or collapse entire field options. Now the salesperson don’t have the minus sign next to them because of they’re last and the hierarchy, as you can see there, But what you can actually do, you can actually expand them. So you can bring in more fields to analyze. So we can do that by right-clicking anywhere in the salesperson and go to expand collapse and choose expand entire field. Now you get a dialog box. So in here you have all the fields that are not part of the row labels. So you’ve get everything except products and salesperson. So you can bring fields into analyze. So let’s bring in our sales quarter and press okay. So as you can see there, each sales quarter has been added into the individual salesperson within the products. So you get all that down there, so you can do some in-depth analysis if you like, and you can bring in more as well. Let’s right-click and bring in the months, expand entire field and choose sales month and you get that in there. Now as you can see on the row labels, the sales quarter and the sales month have been added in there automatically. Now expanding and collapsing is not only for row labels, you can also do it in the column labels. So let’s put in our quarters in there, as you can see our months are collapsed so we can click on the box there and expand them, or you can just do it individually into each year. And also you can right-click on the last field, which is sales quarter and expand the entire field. We’ll get a pop up box and we’d get add in there our sales month. So you see sales month its gonna move from row labels to column label when we press okay. Now we can collapse everything, so click in the quarters and press a collapse, click on the years collapse, let’s click in the row labels products and collapse. So you have the high level view that you can analyze where you can take a screenshot and send it to your boss, or if you want, you can actually click on individual items and drill down from there. Now on the right-hand side here, under options under the show group, you have the plus or minus buttons. You can uncheck that, but that doesn’t mean that you cannot drill down. Well, you can. It just means that the plus or minus signs are not evidence. So you can use the expand and collapse buttons even though you don’t see the plus or minus signs. (upbeat music) There are a few ways to move items within a Pivot Table. One way is to click in one of your items. Right-click, and you’ve got the move option there, and you’re gonna move the ice cubes down. So move down one, or you can move that to the end. And it goes all the way to the end. Now we can bring that back to the top, just by highlighting where the box is, and you get the four point the arrows and then bring it all way to the top, that’s another way. Or another way we can bring tonic from the bottom to the top is actually type it in with a keyboard. TO, then it gives us the options, tonic, and then press enter. And that gets moved up there. And the same thing we can do with our salespeople, we can type in the names. We can put in John, press enter, and John gets moved up to the top of the list, or we can bring in Homer to second place. And as you can see, he’s moved in each item there as well. Now we can do the same thing in the columns. We can bring in 2014 at the start, and then 2013 and then 2012 goes to the end. So there’s a couple of ways we can do that. Now we can also move the fields around. One way is to right-click, and then it says, move. And you’ve got the products to the right to the end, or you can actually move the products to the columns area. So let’s move it to the end. So as you can see, salespeople have gone up and then the products have gone down a level, but the best way to do it is from the areas here. So you can move this around here, or if you wanna move it from row labels to the column labels, this is the best way and the quickest way to do it. Now we can also remove just by grabbing it and dragging it all the way back up there. Control + Z. Another way is from a dropdown box, you’ve got the remove field. And then finally we can actually right-click anywhere in there, and it says the options to remove products. So there’s a few ways where you can move and remove fields and items to make your Pivot Table to your liking. (upbeat music) There’s a couple of ways to show your field list. If you click in your Pivot Table, go to options and under show, choose field list, that’s one way. Let’s uncheck it. Another way is in your Pivot Table, right-click, and the last option is show field list. (upbeat music) If you wanna get rid of your field headers, for example, here row labels and column labels, all you gotta do is go into options, and then the last option on the right-hand side under show is called the field headers. Just uncheck that and you’ll see that they go away. (upbeat music) Let’s create a Pivot Table. Let’s grab our products and put into our row labels, salesperson into our row labels. Our sales here into the column labels and our sales in to our values area. Whoa, what’s happened here? We’ll get a count of sales. I’m sure you’ve come across is at least once. Let’s have a look in our data table. Look at this, in F2 we have a blank cell. Now Excel treats blank entries as texts, and therefore chooses to count rather than sum. So what we’ll need to do is go back to our Pivot Table field list and make some changes. In our values area, count of sales, there’s a down box, click on there and choose value field settings. Now in summarize value field by, we choose sum rather than count and press okay. And there you have it. Our Pivot Table is being analyzed by sum of sales. (upbeat music) There are a couple of ways to format the numbers when creating a Pivot Table. As a default format is in general format. Now the first way is to go into your values area and in the dropdown arrow, choose value field settings. And on the bottom left-hand corner, you have number format. You click on there, and you can make your changes from in there. Just cancel out of there. Another way, you can go into your options tab, and in the field settings, click on there, and then the dialog box comes up again and you can go into the number format and make your changes from there. Now I like the third option, which is click anywhere in the Pivot Table, right-click and choose value field settings. And you get the same dialog box, choose number format. And then in here you can make your changes to a number or currency and you have accounting and time. Depends on what values you’re showing. Now we’re showing sales. So let’s go into currency and we’ll put in some dollar signs there, and we’ll put negative four minus signs in red and zero decimal places. Press okay, and okay. And then you have it. Your numbers have been formatted and it looks much better than it was previously. (upbeat music) One to nine feature in Pivot Tables is that the values field are named sum of sales or count of sales to distinguish them. Now, if you want to just name them total sales or sales, then you can make these changes a couple of ways. In the dropdown arrow here, choose a value field settings. And then in the custom name, you have sum of sales. You can make the changes in there. So we can actually put in there, total sales, and press okay. As you can see, the name has changed there, but in your data table, it hasn’t changed there. It remained as sales. So what’s happening is that it’s only changed in your pivot cache and therefore you can only see it in here. Let’s say that we wanna amend that name instead of total sales we wanna put in sales. Press okay. We’ll get an error message. It says here, “Pivot Table field name already exists.” Well, that’s true because sales is already here. So a work around, is the click just after the S, and press the space bar. Now Excel recognizes that as a character, and then we can actually use that as a different name and then press okay and you have sales there. Another way we can change the field names is to click in the Pivot Table and go to options. And because we’re in now sales, we get the active field as sales. We can click in there and we can see that the change that we made. And let’s click on the column labels sales here. Okay, so we’ll have sales here in there and we can actually go in there and change it, and let’s call it financial year. And press the Enter key. And they’re changed to financial year. And also, as you can see, the column label name changes into financial year as well. So there’s a couple of ways to change the field names, just to make it to your liking. (upbeat music) We have our months and our sales in our Pivot Table. And we want to format to include a comma and no decimal places in our values. So what we need to do is just right-click anywhere in the values and choose number format, and then decimal place is zero, and you use the 1000 separator. So now let’s grab our sales again and drop it into our values just so we can analyze something again. And look at this, our formatting doesn’t maintain. Now, we’ll show you how you work around to fix for this. Let’s press Control + Z and go back to where we were before. And what we need to do is go into our Pivot Table tools and under options in the actions group, select entire Pivot Table, then select values. Press Control + 1 to bring in the format cells dialog box. And from in here, you can make your formatting changes. And now when we drop in our sales again, to make some further analysis, you can see the formatting has been kept. (upbeat music) We have our sales region in our row labels and our sum of sales in our values area. Let’s grab our sales again and drop it into our values area. Now let’s see what happens as soon as we drop it in there. We’ll get the sum of values field in the column labels. Now that happens because we have more than one metric in our values area. Now let’s change this to the count. And all we’re gonna do now is grab the values field and move it into the row labels. And you see, we have a different view of our metrics there. And we can also grab it and move it to the top. And then we see this different view once again. (upbeat music) In this Pivot Table, our report layout is compact form. Now if I wanna change it into an outline form, but without choosing that option. And there is a way. Let’s go back to show in compact form. Now under options and on the left-hand side options in layout and format tab, you have here when in compact form indent row labels. So let’s move it to the right by 10 characters and press okay. And as you can see, our salespeople have moved to the right, but they’re maintained in column A. So we get to feel as if we’re using an outline form, but we’re actually using a compact form. (upbeat music) We can also make changes to the layout of the report filter. Let’s go into options, and options. And we have here display field and report filter area. Our default is down then over. And in the dropdown box, we can choose over then down. Press okay. And as you can see, our report filter is in one row. Now let’s go back and choose down then over, and press okay. Now the second option here is report filter fields per column. The default is zero. You can actually change it to whatever amount you like Now let’s choose two per column, and press okay. So as you can see here we have two report filters per column, and we have the third one in another column. So say we’re dropping sales region into our report filter, you’ll go into the second column and let’s drop in customers into our report filter and it goes into our third column. So there’s a couple of ways where you can play around with your report filter. (upbeat music) Sometimes you may get error values in your Pivot Table like the one shown here. With a DIV on the name. Now we’re gonna fix that, click in that Pivot Table, go to options and options. Now under format, there’s an option that says, for error values show. Let’s tick that. And we can put anything in there. We can leave it as a blank and press okay. Or we can put in there a zero and press okay. Or we can write in there, error. So let’s do that and press okay. As you can see, the changes have been made. (upbeat music) Sometimes you comes across a Pivot Table with empty cells as you can see here. We can actually change that. We can go to options and options and we’ll get under format the, for empty cells show. So we tick that box and in there we can put in there a zero amount, and that feels in our Pivot Table blank cells with zero. Now, if you’re an accountant or an orderor and you go to your data table, you can see that in April, we had no transactions. So something’s gone wrong in there. So instead of having zero, which in accounting terms can be a credit in a debit summed up, so you can have minus 10 and plus 10, and that equals zero. But in this case, we have actually no transactions. So we can go back in there and click under options and options, or we can actually change that. Instead of saying, for empty cells show zero, we can say no transactions and press okay. And then you can make sure that there are no transactions in there rather than the zero values. (upbeat music) One of the thing that really annoys me within Pivot Tables, is that when you refresh your Pivot Table, the column widths go back to where they were previously. Now there’s a way around this. Under the options tab and options, there’s an option at the bottom here that says, auto-fit column widths on update. So upon a refresh it auto-fits that back to where it was previously. Well, let’s uncheck this and press okay. And now all we can do, we can move our columns and how we like them. And let’s refresh again. Right-click, refresh and I pressed them, they haven’t moved. (upbeat music) Now say that you have a shared workbook that you and your colleague keep updated. Now say you open these shared workbook and you need to refresh the data, you would go into the options and refresh. And sometimes you may forget. Now that is usually the case when you’re sharing with a colleague. So to avoid the mistake of working with Pivot Tables that haven’t been refreshed, a quick tip is to go into the options tab, and under options choose the data tab and then select the refresh data when opening the file. So next time you open this file, your Pivot Table will be automatically refreshed. (upbeat music) Now to print a Pivot Table, click anywhere in the Pivot Table, and go to options, select the entire Pivot Table. And then in the page layout, print area, set print area. And go to the file, and then under print, you can see that it’s in there. Now let’s go back. Say that you wanted to put in a page break in here, in between the years, we just click in there and choose breaks and insert page break. We’ll do the same thing for 2014. Now let’s go back to print. You can see our view here. If you press right, you can see the different pages. But we don’t get the titles of the Pivot Table. Now let’s go back again and go into our Pivot Table, go to options, and under options and printing, there’s a set print titles option there. Choose that, press okay. We’ll go back to our print preview, where you can see that in each page we have the Pivot Table titles. (upbeat music) We have our sales result from 2012 to 2014 showing for each month. And in our report filters, we have our products and salesperson. And all we can do is show each salesperson’s values into separate tabs. And to do this, you go on to the options tab and then under options dropdown box, choose show report filter pages. In here, you get a dialog box to choose which of the two report filters you want to show. Let’s choose the salesperson and press okay. And then when we press that, you’ll see at the bottom of the tab here that we get the different salespeople’s names in there. See that we get Homer Simpson, Ian Wright, John Michaloudis and Michael Jackson, that show their values for each of the years. We’re in Michael Jackson here hold on the Shift key and go all the way to Homer Simpson, and we grouped our sheets there. We know that because on top of the page, it shows the group in there. What we can do now, while they’re grouped, we can actually make a change into one, and then every one of them will get amended as well. So you can see there, we’ve got these there. So you got to format each one of them. Okay, now they’re still grouped, we can go to file and print. And in here, because they’re grouped, we have the four different salespeople in there and we can print to PDF. So let’s choose your PDF and then press print. We can call it individual salesperson reports. Press save, and it brings it up in here. We’ll go to the second page, third page and fourth page. Okay, let’s get out of here. And then make sure when you’re back in your Pivot Table, you right-click at ungroup the sheets. So that’s a quick way where you can see our report filters items on separate sheets with the filtered results. (upbeat music) Now there are different types of sub-totals that you can include in your Pivot Table. You’re not only limited to a sum. For example, you can include a count of transactions, the average sales, the maximum amount, the minimum amount, the product and so on. Let me show you. What you need to do is go into your Pivot Table field list and into your row labels area, let’s choose products, and then dropdown and go into field settings. And now we get our sub-totals and filters tab. Now it’s set at automatic, and we can change that. Choose custom. We can click on sum, we can also click on count, on average, maximum, minimum, and we have a few other sub-totals that we can include there, but let’s just include these and press okay. And look what happens. Each field item has its own sub-total for 2012, 2013 and 2014. Let’s scroll down. You can see for bottles here we have the sum, count, average, maximum, minimum. The same thing for ice cubes. Same thing for soft drinks and the same thing for tonic. Now, a Pivot Table has all that information summarized in a few rows. And it’s fantastic to have, if you wanna do some quick reports, quick metrics, all in one page. (upbeat music) We can summarize value fields by different calculations. Now to do that, we need to go into our Pivot Table field list and under sales, grab it, drag it and drop it again into the values area and let go. So we get a second calculation called sum of sales with the same results, but we can change that. Instead of sum, we can change that into a count. So click on the dropdown box and the value field settings, and we have a summarize the values by tab. And in here, we can summarize the value field by different types of calculations as you can see in here. Now, there are 11 different types of calculations. And now I’m gonna talk about the count calculation. So let’s double-click in there. And as you can see here in our values area, we get count of sales, and in our Pivot Table report we have our count of sales. So what the count of sales does is it counts all the cells that include text numbers and error cells. Now, one thing that it doesn’t include are blank cells. So watch out for that. If you have any blank cells, then it’s not going to count that in there. Now let’s check our numbers. So we have 48 transactions in bottles for 2012. So let’s go in here. Now I’ve filtered it by bottles and 2012. So if we go down here and we do our count, we’ll get 48. Now let’s manually count this, go all the way up. And as you can see here, count, we get 48. Now, if we have any blank cells in there, then it’s not going to include that in its total. So let’s check that. Let’s highlight a few cells and press delete to clear it. So we’ve cleared four cells in there. Now we’ll go back to our Pivot Table and right-click and refresh to update what we’ve done now. And you see there, 44. Now the count calculation will include all the cells except the blank ones. (upbeat music) The average calculation is like your normal average function. What it does, it takes the totals of all the values and it divides it by the number of values. Now we use the average calculation for sales for days to complete a project, for overdue days or for accounts payable or accounts receivable days. Now to include the average calculation, we have to grab our sales and drop it into our values area. And in the dropdown triangle, choose develop your settings, and then choose the third option, average and press okay. Now, as you can see here, we have our average values. To customize the numbers, we need to go into our average of sales field and in the value field settings. And on the left hand corner, we choose a number formats, and then we can choose the number, putting zero decimal places, and we can use a separator for the thousands and press okay, and okay. So as you can see our results much neater. So we have our average sales for each of our products and each are our salesperson for their respective years, as well as their grand total down here. And then we have the average sales from 2012 to 2014, which is $55,667. Now we can also get the average for the order dates. So in our data table, we have our order date in here. So what we can do is grab the order date, drop into the values area, and then choose the average from in there, okay? So now what we’ll need to do is to change the format in to a date, and then press okay and okay again. Let’s make this a little bit bigger. So we have the average date that the order gets placed. For bottles, for ice cubes, soft drinks, tonic and so forth. So you can do many things with the average calculation. And it’s a great metric to use when you’re doing your analysis. (upbeat music) The maximum calculation gives us the largest value from the values area. The things that you can calculate the maximum function with are sales, quantity unit sold, salary and cash position. Now to get our maximum sales, we need to get the sales and drop it into the values area. Now from the drop-down arrow, choose the value field settings, and then choose a maximum and press okay. So as you can see here, for each product and each salesperson, we have the maximum sales transaction that I made for that year. So Homer had his largest sale as being a 96,209, Ian Wright had 99,220, John Michaloudis had a 98,116, being his largest sale out of all his sales in 2012 for bottles. And Michael Jackson had 95,527 as being his largest sale. So obviously in here we have Ian Wright having the largest sale. So bottles will have the same amount as Ian Wright. So we can see here that from 2012 to 2014, that the largest sale amount was 99,878. So you can also go in and analyze and see which of the salesperson had the highest sale for each particular year and give them a bonus. So the maximum calculation highlight the extreme amounts from your data set, and you can do some pretty meaningful analysis from it. (upbeat music) The minimum calculation gives us the lowest value from the values area. The things that you can use to calculate a minimum sales, quantity unit sold, salary and cash position. Now to get the minimum sales amount, what we need to do is grab the sales and drop it into our values area. And from the dropdown box, choose the value field settings and then choose minimum and press okay. In 2012, for bottles, Homer’s smallest sale was 10,780. Ian Wright’s was 20,650, John Michaloudis’ was a 48,378, and Michael Jackson’s smallest sale was $17,030 out of all his sales in 2012 for the bottles product. So from this information you can see who made the smallest sale throughout the three years, and then go to that person and ask them why the sales were low for that period, and find out ways you can improve your product. (upbeat music) The product function multiplies all the numbers given as arguments and returns a product. For example, let’s type in the product function and choose our first number which is 25 and a comma. So it multiplies 25 by 0.4, press comma. And then it multiplies that result by the number two. Close brackets, and we’ll get out number 20. So in our analysis here, we want to know which month has products that were sold with no defects. So I wanna know which month had a flawless defect rate. So we wanna show a defective product with the number one, and a non-defensive product with a blank. Now we got a little example here that I can show you. So the first of the first 2012, that day later, and on the third of the first 2012, we had a defect, we had a defect and we had a defect. Now down here, we have another example with a defect, another defect, another defect. And the third example, we have three days with no defects. So let’s do our product function here. And let’s choose these cells. So if we have three defects and then we’ll get the number one. So obviously our result will be a defect. Now let’s copy this formula, right-click and paste that in here. So if we had at least one defect and the rest are all non defects, then we are gonna get a result of a one. So that means that we have at least one defect during that period or that month. So that month wasn’t a good month. We wanna have zero defects. So let’s copy this formula into our next example and we’ll get a zero. So what it says here is that during our three days, we had no defects and we get our return of zero, which means a great result for our company. Now let’s go to our data table. And what I’ve done is I’ve included another column here with blanks and ones. So, as it goes all the way down here, these random numbers, and we have defects and non defects. So what we’re gonna do is get our defects from our Pivot Table and drop it into our values. Now we don’t wanna count of defects. What we wanna do is choose value field settings and go to our product and press okay. So what this has done, it has multiplied all the defects with all the non defects during that particular month. So if we give a zero that means we had our flawless month and that’s a great result for our company. Now we can check this. Let’s go to one of the zeros in here and double-click. And as you can see on the defects column here, it’s all blank. So we had no defects. This is fantastic for product managers and quality managers, because zero defects means a great product and a happy customer. (upbeat music) In our example here, we want to find out what percentage of our total transactions are overdue. Now let’s go over to our data table. And in here we have our overdue days column, which shows us the number of days elapsed from the customer payment date. So we have here lots of overdue days. And also we have a comment here, which says paid when our customer has paid. So we have a few paid customers and lots of unpaid customers. Okay, so let’s go up and go to our Pivot Table. So let’s click in our Pivot Table here. On the left we have our financial year, and let’s grab our sales and drop into our values area. And from our dropdown, we choose value field settings and choose count. So it’s gonna count all of our transactions, and we have 576. Now let’s rename this, instead of count of sales, we rename it to total transactions. Okay, so let’s get out our overdue days and drop it in here and choose count numbers. Any customer with a number means that they’re overdue. So this is gonna return us to the number of overdue customers. Let’s press okay. Okay, so we have our numbers here and we can change this to overdue, overdue and press okay. So let’s go through our formula. Let’s choose our number of overdue transactions, which are in here and divide it by the total transactions in there and we’ll get 55%. So 55% of our total transactions are past due, which is a bad result. (upbeat music) In statistics, standard deviation shows how much variation from the average exists. So in our graph here on the top, we have our X-axis, which shows the values and our Y-axis, which shows the number of data points. So in our graph here, we have a normal distribution. And our average is right up in the middle. So this indicates a low standard deviation, which means the data points tend to be very close to the average, and we get this bell shaped curve, which is steep. An example of this may be the daily high temperature for a coastal city will be less than that of an inland city. Now higher standard deviation will indicate the data points are spread out over a large range of values which shows volatility. An example may be money. So a standard deviation may mean the risk that a price will go up or down. Here the bell curve is relatively flat. So what we’re gonna get is something like a straight line here. Now in Excel, we can also do a standard deviation graph. As you can see the bottom here, we can represent it by a column graph, and then we can get a shape similar to the one shown here if we have a normal distribution. Now let’s create a graph using our data table. We’ll go into our data table and insert pivot, and we’ll go into our existing worksheet and let’s put it into here and press okay. So what we’re gonna do now is find out the units sold and group them, and then find out by using the count of unit sold, how our data would be distributed. Let’s get our units sold and we’ll put into our row label. Now let’s group of this. Now in the next chapters, we’re gonna talk about grouping. Okay, let’s right-click. And click on group. And we have our dialog box that comes up, and we could start at a pretty determined minimum level, which gives us as being 1,011 and ends up 79,902. But we can start at any point. Let’s say zero and end at 80,000. An increments of 10,000 we’ll keep that and press okay. So we have our groupings in there. Now the next step is to get our unit sold again and drop it into our values area. And we get our count of units sold, which is what we wanted. Now, from in here, we can create a graph to see whether we’re gonna get a normal distribution or whether it’s gonna be volatile and have a flat graph. Now, our Pivot Table here says that 17 transactions lay between zero and 10,000 units sold. 79 transactions are between the 10,000 and 20,000 units sold mark and so on as you can see here. So let’s insert the graph, go to pivot chart, and let’s insert an area graph ’cause this will show a much better, and press okay. Let’s customize a couple of things. Let’s take out all the field buttons on chart, and we can just click on that and get rid of the chat name. So let’s just put up here, and we can click on the axis name and get rid of that. So as you can see, we have a pretty flat distribution. We’ll then get the bell curve as per our chat on the left here. And the reason is our standard deviation’s high. Now let’s check that. Let’s create another Pivot Table. Go to insert, pivot, and go to an existing worksheet. And we just put it down here. Okay, so now we’re gonna do is we’re gonna grab our units sold and we’ll get our average of all the total units sold. Now to get that, we just click on the dropdown box, value field settings and get the average. Now, the next thing we can do is, again, drop in the unit sold. Now, instead of choosing the dropdown box, we can actually go on field settings in here. And from there we can make our selection. So let’s get our standard deviation. Now the P signifies that we’re using a whole population. So that’s true because we’re using all of our data, we’re not just sampling 10 transactions. We’re using everything. Now for sampling a few transactions, we use a standard dev. So for our purpose, we use a standard dev. P, which means population, and press okay. Okay, so now we can get our values here and from column, we can move it over to the labels. So we can see it in much better. And then we can format these as well. One thing we haven’t done is the average. The average, okay, there you go. Okay, so what it says here, our average is 44,500, which is around here in the middle point. Okay. And our standard deviation is 20,689. So what it means is you can go either way to the left or right of 44,000 by about 20,000. So we have a high volatility there. So therefore, as you can see, our graph is pretty flat. Now, if our standard deviation was somewhere between zero and 5,000, then we would have got a graph similar to this. Let me get my squiggly line. And it would have been something like this. Okay. Would have been about there, and then like that, and then over there, would get a normal distribution. Okay. Maybe I can format the shape in red so you can see it better. So we would have had this if we had a low standard deviation, but as we have a highest end deviation, we get this graph, which is pretty much flat as you can see there. And which means that our unit sold can vary. It can be from zero to 10,000, or it can be from 70,000 to 80,000. So we have a high volatility there. So by using the standard deviation, you can see the variation that you get from your average and determine whether your product is volatile or not. So it’s a pretty great tool to have when you’re analyzing your products. (upbeat music) The variance calculation, measures how far a set of numbers are spread out. A small variance indicates the data points tend to be very close to the average. A high variance indicate that the data points are very spread out from the average and from each other. So let’s create a Pivot Table to highlight the variance. So let’s insert Pivot Table and go through our existing worksheet. And we’re gonna put it in there and press okay. So in our rows labels, we’re gonna put in our products and our sales month, and in our values, we’re gonna drop our units sold. And from the dropdown box, we’re gonna get our average units sold. So we can see where our average is at for each product. Now let’s right-click in there so we can format the numbers. And let’s use a number with no decimal points. Now next is we’ll get our standard deviation. So let’s put in the unit sold again, and then let’s choose a standard deviation population and press okay. So we have a one here and again, we can format our fields in there. And finally just get our unit sold for the third time. So we can get our variance. From the dropdown box, value field settings. Let’s use the variance P. Variance P means variance population. It means that our data set is a whole data set, and we using all of our population before using a few rows of transactions within all of our population and we use the variance sample which is indicated by Var. So let’s format the fields here again. Okay. So as you can see here, we have a very high variance and also a very high standard deviation. Now, if you note in here, we get one value here, which is very low. So if we drew down to here, then we’ll see that our units sold for ice cubes in June was pretty close to the average. So to test that, let’s get our unit sold here and let’s see what our average is. Now, our average is about 34,739. So let’s see our transactions. Our transactions pretty much around that 34, 40,000 mark. So our variances is very, very low. So that’s the only value here that we have pretty low variance or standard deviation. We can actually use this table to highlight months where we’ve had a low variance. And that means that our product sold a consistent amount of units. (upbeat music) In chapter 2.1, we created multiple sub-total, by going into our row labels and then choosing products, field settings, and then under sub-totals and custom, selecting the sum and average. And as you can see in our Pivot Table, each shown here under bottles, ice cubes, soft drinks, and tonic. Now another way we can do this is right-click in there and press field settings, and we can change it from in there. Okay, so now the grand total doesn’t show us an average or a maximum, it only shows us the sum. So what wanna do is put in there some extra grand total. Now there’s a way around this. First of all, we need to go to our data table and in our table, just add another column field named grand total, press enter. And because we’re using a table it’s added automatically. So that’s all we need to do. We don’t need to add any details in there. We just need the field header. Let’s go back to our pivot, right-click anywhere in here and press refresh. Now on the right-hand side, you’ll see that the grand total has been added in there. Now let’s grab the grand total and drop it on top of the row labels. Okay. So it’s shown up here. So the next thing we need to do is press the space bar and press enter. So we get rid of the name. In our new blank field, we right-click, and then choose the field settings. And in here we can use a custom and we can have sum, we can have account, average, maximum, minimum and press okay. Now, as you can see, it has gone all the way down here. It’s added that information at the bottom of the group. So what we need to do now is get rid of the grand total, click in the grand total name, right-click and choose remove grand total. Now we have our different grand totals summarized by sum, count, average, maximum, or minimum for the whole data set. (upbeat music) Now, there are a few ways to access the field settings and value field settings. Now let’s talk about the field settings first. In our Pivot Table field list on the row labels, under your first field and the dropdown box, choose fill settings, and then custom, and choose from in their sample count. Now, for this to work, you need to have at least two fields in your row label or in your column label. Now, the other way is to choose anywhere in our Pivot Table and make sure we select one of our items, so our ice cubes. Right-click, and then choose field settings and we can add in there. So let’s add the average in there. And the third way is to go into our Pivot Table tools tab under options and field settings. And now we’re under products and choose that, and we can put in there maximum and minimum. Now let’s talk about the value field settings. In our Pivot Table field list, under the values area down box, choose the value field settings. And we have it as sum, but we can change that to count, and press okay. The other way is to right-click anywhere in the Pivot Table and choose summarize values by, and we can change it from there. Let’s choose average. The other option is again, is to right-click anywhere in the Pivot Table and choose value field settings, and then we can choose a maximum. Another way is to go into where our Pivot Table tools tab in the ribbon, under options field settings, choose there. And we can change from there, let’s put minimum. And again, in the options tab under calculations, we have summarize values by, we can click that and we get our different options. Now we can click on more options and we can count the numbers. So it depends on what you’re more comfortable with. You decide what’s best for you. (upbeat music) In our example here, we want to find out what bonus you pay per zone and per year based on the channel sales made. As to do that, first let’s go to our data table. And what we’ve done is we’ve included zone numbers. Zone one, two and three. And also we’ve added a new column called channel sales. So we have our channel sales that pertain to each particular zone for each transaction. So let’s create a Pivot Table. Go to insert and the Pivot Table and existing worksheet, and let’s choose A1, press okay. So in our Pivot Table, field list, in our row labels, we’re gonna put in our months and our zones. In the column labels, we’re gonna drop in our financial year. In our values area, we’ll grab our channel sales and our report filter we’ll have our sales quarters. Now, one thing is let’s get rid of the grand total, click in there, right-click and then remove grand total. And we want to choose only Q1 for our example. So we need to do a formula that shows us the sales for 2012, zone one, two and three, and multiply by the bonus to be paid in the respective years. Now to do that, we’ll need to have each zone one, two and three in each month. Now we don’t have that because in February zone three, there weren’t any sales. And in March in zone two, there weren’t any sales. And we need to bring that up, regardless of any sales being made. Now to do this, we need to click anywhere in the row labels, and right-click and choose the field settings. Then under layout and print, choose the show item with no data So if we check that box and press okay, we’re gonna show the items that don’t have any channel sales. And now we can make our formula. Let’s put in sum product and choose 2012 January, press comma, and then choose the respective bonus to be paid here, which is 2012 for zone one, two, and three, close brackets. Now we’re gonna move this formula down and we need to make sure that the rows in here are an absolute reference. So number six, and number eight should have a dollar sign in front of them. Now, a quick tip is to click anywhere in there and press F4 twice, and that makes the row six, an absolute reference, or we can just put in our dollar sign and press enter. So we can move this across. So now let’s grab this formula and drag it down so we can feel in the February month. Double-click, and let’s just drag this all the way down. And as you can see, because of the absolute reference, the cell reference doesn’t move in there. Okay, and press enter. And then we can move that across and double-click 2014 with 2014, perfect. Now let’s finish off by dragging down to March. Double click and grab that and then go all the way across. Okay, so we have our three months, now let’s put in our sub-total. Now to do that, we just need to highlight at the bottom there and press the AutoSum and it will automatically sum it up. So let’s make this bold. And in here we can just put a comma and get rid of decimal places. So now, finally let’s put in our months where you just press the plus or equal sign and then reference it into cell A5 and press enter, and the same thing for February, and the same thing for March. Now let’s put in our total name in there. Plus we can actually reference the filter and then put in the end and then reference the name that bonus to be paid and press enter. So we’ll have Q1 bonus to be paid. Now let’s fix this up a bit and put in a space. Now to do that, we can actually put in an end in there and then in brackets and have a space. So that will give us the space. So we have our Q1 bonus to be paid, right-click and make that bold. So now we can make this interactive. Let’s choose Q2. So everything changes. The months, the title and our bonus to be paid. Now one thing we need to make sure is go into options and then get rid of this auto-fit column widths on update. So every time we make an update, these column widths stay the same. So the update being about making a change into our filter selection, Q3. As you can see there, it stayed put. So Q3 gets updated automatically and then Q4 as well. So here we have an interactive channel bonus to be paid per quarter. (upbeat music) We want to show here the unique occurrences between the channel partners and the products. To show our unique count between our products and our channel partners, it’s impossible to do with a Pivot Table. But what we need to do is insert a sum product formula in there and then pivot that information to give us our results. So our formula here says to look up in column B and in column C. And by using the sum product formula, it’ll give us trues and falses. So if we get one true in here and another true in here, then a true and a true equals one. Anything else is a zero. So if we get two matches of the same channel partner and product, then that’s gonna be two trues. So obviously that’s more than one. So if it’s more than one, then we’ll wanna show it by a zero. So that means it’s not unique. If there’s only one unique combination then we’ll want to show it as a number one, which means a unique combination. Now let’s escape here. And as you can see here, the first couple of values, because it’s the first time they’re being purchased, they’re all unique. So they’re all depicted by the number one. If we go all the way down here, and you’ll see Acme purchased soft drinks. Well, Acme purchased soft drinks up here. So it’s shown as a number one here ’cause it was the first time it was purchased. And then down here, number zero, because it was a second time it was purchased. So it goes all the way down here and it does the same thing for each row. Now let’s go to our Pivot Table. And what we’ve done here is we’ve put in our sum of unique combinations in here. And then we dropped in our products on the left-hand side. So in here we can see that there are 54 unique customers that purchase bottles. There are 63 unique customers that purchase ice cubes. There are 63 unique customers that purchase soft drinks, and there are 57 unique customers that purchase tonic. Now to see which customers are part of this number, then we can just grab the channel partners here and drop it into the row labels, and we’ll get our list here. So we get all our list of unique customers that purchased the products. (upbeat music) The percentage of grand total calculation displays values as a percentage of the grand total of all the values or data points in the reports. So what it means is that each individual data point here will show as a percentage of this grand total here highlighted in a red border of $32 million. So to include the percentage of grand total, we need to activate our field list, right-click and show our field list. Now let’s grab our sales and drop it into our values area. And in their dropdown arrow, value field settings, and choose show values as, and in the dropdown box, choose percentage of grand total. Now let’s change our name to percentage of grand total, and press enter. So we have our percentages in here as you can see. So each individual percentage will sum up to 100%. So our field items here sum to 8.26% which is up there. And if we sum across the columns, that’s 7.32, and that’s confirmed in there. So if we sum the 26%, 25, 25 and 23, that equals to 100%. And if we’ll go down here and sum base rate amounts, then that equals to 100% as well. So each value item is divided by the grand total to give us the percentage of grand total calculation. (upbeat music) The percentage of column total calculation displays all the values in each column as a percentage of the total for that column. So we have our years in our columns here, 2012 to 2014. And in the bottom, we have our grand totals and they’re highlighted in a red border. So each individual field item here will be a percentage of its grand total. So what we’re gonna get is our proportion of sales for each sales rep in each quarter in 2012, in 2013 and in 2014 respective to the totals. So to include the percentage of column total calculation, we click in our Pivot Table and grab our sales, then drop it into our values area. From the dropdown arrow, we choose the value field settings. And then we show values as, and from the dropdown we choose percentage of column total. And then we rename this to percentage of column total and press enter. So as you can see here, we have the percentages that make up each column total. So we can check this. If we highlight the 2012 Homer Simpson’s sales, they add up to 25.49%, which is up here. So if we hold down our Control key, and then with our mouse button, choose each sub-total, they should equal to 100%. As you can see here at 100% and we have here 100%. And the same thing is done for our 2013 and 2014 numbers. (upbeat music) The percentage of row total displays the value in each row as a percentage of the total for the row. So everything here highlighted in red will be 100%, and we are gonna get the percentages over the three years for each sales rep and each quarter. Now to include our percentage of row total, we click in our Pivot Table, and in our Pivot Table field list we’ll grab our sales and drop it into our values area. From the dropdown arrow, we choose the value field settings and in show values as, we select the dropdown box and choose percentage of row total. And we can change the name here to percentage of row total, and press okay. We can see that we have 100% in each of our rows and in our individual rows, if we hold down the Control key and choose 2012, 2013 and 2014 for Homer Simpson, we get 100%, which is correct here. So we can see that the proportion of sales that have occurred over three years. And the same thing can be broken down into Q1 for each respective sales rep. So let’s get Homer Simpson again and press down the Control key and choose 2013 and choose 2014. And again, we get 100%. (upbeat music) The percentage of calculation displays the value of one item, which is also called the base field, as a percentage of another item, also called the base item. Now to put this into an example, we want to find out the change of sales from year on year. So we wanna see the change in 2003 versus 2012, and also the change in 2014 versus 2013. So to do that, we click in our Pivot Table and we grab our sales and drop it into our values area, and from the dropdown arrow, choose value field settings, you show values as. In the dropdown box, we choose the percentage of calculation. And in the base item we choose previous, in the base field we choose financial year. So the way to read this is we’re showing values as the percentage of the previous financial year. So percentage of previous financial year and press okay. And now we have our percentages. Now, obviously 2012 doesn’t have a previous year, so it will always be 100%. And if we look at 2013 here for Homer Simpson’s sub-total, we can see that in 2013 it was 112% of the 2012 value. And in 2014, it was 90% of with the 2013 value. So from 2.9 million it went down to 2.7 million and that’s correct. And you can also see here in the grand totals, in 2013, we had an increase of 6.06%. So from 10.3 million to 11 million. And in 2014, our sales reduced to 96.73% from 2013, or you could say it was a drop of 3.3%. So one minus 96.73%. Now let’s do another example. And here, we have our sales regions over the three years, and we’ll wanna compare our sales to the African sales. So we’re comparing the American sales to the African sales, and then we wanna compare the Asian sales to the African sales and also the European sales to the African sales. Now to do that, we click in our Pivot Table and we’ll grab our sales and drop it into our values area. From the drop-down arrow, go to value field settings, show value as percentage of, and what we’re gonna do now is we have our sales region as our base field. And we want to put our base item as Africa. So we’re gonna show the percentage of African sales. Press okay. So obviously African sales will be 100% always. So what it says here in 2012 is that Americas is 94% of the African sales and Asia is 96% of the African sales, and Europe is slightly higher than the African sales for 2012. We have the same calculations for 2013 and then 2014. And we can also put in here products and we can compare that to one particular product being your best product and see how the other products relate to it. (upbeat music) The percentage of parent row total is a new calculation in Excel 2010. It shows us an item’s percentage based on his parents sub-total. So the calculation is the value for the row item divided by the value for the parent total row item. So in here for Homer Simpson in Q1, what it will give us is the percentage of 776 into $2.6 million. And then we’ll do the same for Q2, Q3 and Q4. So it’ll give us a percentage of 100% here, which is the total of its parent total, which is $2.6 million. Now to do this, we grab our sales and drop it into our values area. From the dropdown arrow, choose the value field settings and under show values as tab, from the drop-down, we choose the percentage of parent row total. And press okay. So as you can see here, Q1 is 29% of the total. Q2 for Homer Simpson is 24% of the total. Q3 is 22% of its total. And Q4 is 23.6% of its parent total. So if we sum all this up, you can see it’s 100%. And also the Homer Simpson’s sub-total and the subsequent salespersons sub-total, if we sum those up by holding down the Control key, they too will equal into parent total, which is the grand total, which is 100%. As you can see here, 100%. The same calculation is done in 2013 and 2014. Now let’s go on to another example. So all we have now is our years and our months in our row labels. Let’s scroll down to see that. And we want to get the percentage of each month into the parent total which is 2012. And in here or it will be 2013 and 2014. So once again, let’s grab our sales, drop into our values area and let’s choose our percentage of parent row total and press okay. So here we have our percentages. So once again, the January sales are 7% of the whole 2012 sales. February sales are 8% of the whole 2012 sales and so forth. Now let’s check by highlighting all of 2012, and that should equal to 100% of the parent row total, which is 10.3 million. And this is a great feature. And once again, it’s new in Excel 2010, and you should give it a try. (upbeat music) The percentage of parent column total shows an item’s percentage based on parent’s sub-total. So the calculation is valued for the column item divided by the value for the parent total column item. So in our Pivot Table, we have our sales reps and in our columns we have let’s show our field list. So in our columns we have our sales quarter on the top, and then the bottom, we have our sales years. And we have our sub-total for each quarter. So the percentage of parents total column will give us the percentage for Homer Simpson in Q1, based on its parent column total, which is 2.3 million and so on for Ian Wright, Johnny Michaloudis and Michael Jackson. And this will happen in Q1. We will also have it in Q2 in Q3 and then in Q4. So let’s put in our parent column total. So to do that, we grab our sales and drop it into our values area. From the dropdown arrow, which has a value field settings. And it show values as, we select the percentage of parent column total and press okay. So as you can see, we have Homer Simpson, 33%. Let’s hold down the Control key and press the 2013 amount, and then also the 2014, and that equals to 100%. So we have 100% of Homer’s parent column total, which is 2.3 Million. And the same thing happens for the rest of the sales reps. And if we go on to Q2. Let’s grab Homer’s again for 2012, 2013 and 2014, and that’s 100% of it’s parent column sub-total for Q2. And then for Q3 and Q4 the same calculation. (upbeat music) The percentage parent total calculation shows the sales percentage based on it’s chosen parent base field item total. So the calculation is value for the item divided by the value for the parent item of the selected base field. Now to include the calculation of percentage of parent total, we click in the Pivot Table and right-click, so we can show our field list. And in here, we can see that in the row labels, we have products, salesperson and sales quarter. So our selected based field will be the products. Now let’s grab our sales and drop it into our values. And from the dropdown arrow, choose the value field settings. And in show values as, in the dropdown arrow, we choose a percentage of parent total. Now, from in here, we have to choose our base field. Now our parent based field is products ’cause it’s right on top. So we have to choose products. And before we do anything, let’s rename this to percentage of, we can call it parent total, or we can call it product total. You can say parent and put here product, so we can distinguish them. And press okay. So here we have the values. So if we highlight Homer Simpson’s values for bottles, they equal to 25.19%, which is a sum here. So all of these, we’ll hold down the Control key. Now all these different sales reps totals will equal to 100%. As you can see that 100%, which is the 2.7 million. And the same thing happened for ice cubes. So let’s highlight that. And that will equal to 100%. So it’s 100% of 2.4 million. And for the soft drinks, we have 100% of 2.6 million. And finally for tonic, again, that should be 100% of the $2.5 million of sales. And the same thing, let’s scroll up. And the same thing happens for 2013 and 2014. (upbeat music) The difference from calculation calculates the difference of one item from another item. So what we’re gonna do here is get our months and see the difference between one month and its previous month. And we’re gonna also do another calculation where we see the difference between one month and the corresponding month from the previous year. So let’s click on our Pivot Table and we’ll go into our sales and drop it into our values area. And from the drop-down arrow, we choose the value field settings and show values as. In the dropdown arrow we choose difference from. Our base field will be the sales month because we’re comparing sales months. And the base item will be previous. So the previous month. So the way to read this is the difference from the previous sales month. Now let’s change the name here to call it diff. from previous month, and press okay. In our Pivot Table, we have the difference from the previous month. So the 96,000 is the difference between January and February. And then you see from February to March, we have a drop of 83,000. Now let’s format the numbers here. So we can right-click and number format. We can go to number. No decimal places, 1000 Separator, and then we’ll put in the red for any negative values. It just stands out better. We can see there that’s much better. Now let’s do another calculation. We’ll put in our sales and we’ll get the difference from the previous year. So again, the dropdown arrow, show value as, choose difference from, and now we’re gonna get the financial year and previous. So we’ll get the difference from the previous financial year. And press okay. And in here again, we can format the numbers. So what it says here, it’s comparing January, 2013 with January, 2012. So difference is 100,000 increment. And then it’s getting February, 2013 and comparing it to February, 2012, and that’s a $42,000 increment. As you can see in a 2012, it’s all blank because there’s no sales in 2011. So it starts in 2013. And then in 2014, it compares the January, 2014 amount to the January 2013 amount. Okay, let’s go into our second Pivot Table example. And now we have our salesperson on our row labels and our years. So what we wanna do now is compare our sales to one salesperson. So we’re gonna compare Homer Simpson. So to do that, we’ll grab the sales, drop it into our values area, dropdown box, value field settings, show values as, then choose different from. And we choose salesperson, and our salesperson will be Homer Simpson. So we’re gonna see the difference that each salesperson has on Homer Simpson. And press okay. And let’s format the numbers, and press okay. So what it says here is that Ian Wright in 2012 had $26,000 more sales than Homer Simpson. Johnny Michaloudis had 80,000 less sales than Homer Simpson. And Michael Jackson had 148,000 less sales than Homer Simpson. So the same thing in 2013, it’s comparing Ian Wight 2013 to Homer Simpson, 2013. John Michaloudis 2013 to Homer Simpson, 2013 and Michael Jackson, 2013 to Homer Simpson, 2013. And the same thing for 2014. (upbeat music) Just like in chapter 3.8, where we had the dollar difference from calculation. Now we have the percentage difference from calculation. So what this is, it calculates the percentage difference of one item from another item. So in our example, we’re gonna get the percentage difference of one month to previous month. And then we’re gonna get the percentage difference from one year’s a month to its corresponding previous year’s month. Now let’s click in our Pivot Table. And in our sales, we grab the sales and drop it into where the values area. From the dropdown arrow we choose value field settings and under show values as tab, we choose the percentage difference from calculation. And then we have the base field as sales month and then the base item as a previous. So this reads as percentage difference from the previous sales month. And let’s change the name to percentage diff. from previous month. And press okay. And we can format the numbers. We’re going on to custom and choosing in here. And then we’ll just put in a percentage writing of this. So if it’s a positive number, it’ll be in black, if it’s a negative number, it’ll be in red. And press okay. So we have the percentage of difference from the previous month which means that there was a 12% increment from January, 2012 to February, 2012. And then in March, we had a 10% drop from its previous month. Now let’s put in our new calculation. Dropping our sales value there, and then we can choose the percentage difference from. And now we’re gonna calculate the percentage difference from the previous year. So we choose the financial year in our base field and our base item is previous. So this reads as percentage difference from the previous financial year. And then press okay. And in here we can conform at the numbers and because we had our formatting done before, we can go to our last option and choose that. So this says that in January, 2013, we had a 30% increase from January, 2012. And then in February, 2013, we had a 5% increase from February, 2012, and then so on. Okay, let’s go on to our next example. So we want to calculate the percentage difference from the sales of Homer Simpson. So to do that, we’ll grab our sales and drop it into our values area, which is value field settings, show values as, and then percentage difference from, and we have salesperson as our base field, and then we’re comparing our sales just to Homer Simpson. And then press okay. And then we can format the numbers again. We go to custom, all the way through the end, we have a previous selection. So what it says here is that Ian Wright has 1% more sales than Homer Simpson in 2012, John Michaloudis had 3% drop in sales compared to Homer Simpson and Michael Jackson had 6% drop in sales compared to Homer Simpson. And the same thing is analyzed in 2013 and also in 2014. (upbeat music) The running total in calculation displays the value for successive items in the base field as a running total. So what that means is it will show you your year to date values. So what it’ll do is it’ll sum January and February and put it in here. Then it’ll sum the February year to date total with March and then put it into the next column. And then it’ll sum the March year to date amount with April and then all the way to the bottom where it’ll end up having the total amount of four 2012, which is $10.3 million. So let’s go and put in this calculation. We grab our sales and drop it into our values area. In the dropdown arrow we choose a value field settings, and under show values as, we choose the running total in. Now we have to choose the base field. And because we are doing the running total in for the months, we keep it selected as sales month. And then we can change the name here to the year to date and then press okay. So in our Pivot Table, we have January as 771,000. Now February is a 1.6 million, which is the sum of Jan and Feb. As you can see there, 1.63 million. In March, we have 2.4 million, which is the sum of January, February, and March, 2.4 million, and so on and so on. And so in December, we’re gonna end up with 10.3 million, which is our total for 2012. Now in 2013, again, it starts from January. It adds February, and then it adds March and April and so on to end up at 11 million, which is a total for 2013. And the same thing happens in 2014. Now this calculation is fantastic to have, because it shows you your annual sales on any given month. (upbeat music) In chapter 3.10, we had the running total in, and now we have the percentage of running total in. We each calculate the values as a percentage for successive items in their base field that are displayed as a running total. So here, we’re gonna get our year to date percentage. So we’re gonna see here the proportion of the January sales to 2012. And then we’re gonna move on to see the February year to date sales as a proportion to its total 2012 sales. Then we’re gonna see the March year to date sales as a proportion to its total of 2012. And then so on until we reach 100% in December. Now to add this calculation, we click on our Pivot Table and then we’ll grab our sales and drop it into our values area. From the dropdown arrow, we choose value field settings. And then under show values as, we choose the running total in percentage. And in the base field, we have to choose the sales month because it’s the field where we going to get our running total in from. Then we’ll press okay. So all we’ve got now is January, which is 7% of the 2012 sales of 10.3 million. And in February it shows here the February year to date sales as a portion of 2012. And in March, we have the March year to date proportion of 2012 sales. And then it goes on and it increments each month until we reach 100%. So we can see here that in June, for the first six months, we had achieved a 48% of our total 2012 sales. Now if move on to 2013, the same thing happens. So we have our January proportion on 2013, then it adds the February sales to give us the February year to date sales proportion on 2013. And then so on until we reach 100%. So we can see that in June for the first six months, we achieved about 51% of the total 2013 sales. And in 2014, the same thing happened. And as you can see, you can do a lot of great analysis to see how your sales are tracking on a year to date basis. And you can also compare it to the previous year’s running total in percentages. (upbeat music) The rank smallest to largest circulation displays a rank of selected values in a specific field, listing the smallest item in the field as one, and each larger value with a higher rank value. Now in our Pivot Table, we have our salespeople in our row labels and our dates on the column labels. So what we’re gonna get is a number one value for the lowest sales in 2012, and a number four value for the highest sales in 2012. And the same thing will happen in 2013 and 2014. So to do this, let’s grab our sales and drop it into our values area. From the dropdown arrow, we choose the value field settings and show values as. In the dropdown arrow, we’ve go all the way down and choose the rank smallest to largest option. And in the base field, we’re gonna choose which field we want to rank. Now we want to rank the salespeople. So we choose the salesperson and in the custom name, we can change the name to rank small to large. And then press okay. So we get here number one being the lowest value of 2.4 million. And then the second lowest is 2.5 million. The third lowest is 2.6 million and the largest is 2.67 million for Ian wright. Now if we go to 2013, we have a ranking for that year as well. And 2014, we have a ranking just for that particular year as well. So you can quickly see here that Michael Jackson in each of the three years has the lowest rank. And you can go and find out why his sales are the lowest amongst his peers. (upbeat music) The rank largest to smallest displays a rank of selected values in a specific field, listing the largest item in the field as one and each smaller value with a higher rank value. In our Pivot Table, we have our months in our row labels and our years in our column labels. And what we’re gonna get is a ranking value in each of the years, and where one will be the highest sales and 12 being the lowest sales because we have 12 months. So to do this, let’s go into our Pivot Table field list. We’ll grab our sales and drop it into our values area. From the dropdown arrow, we choose value field settings, and then under show values as, we go to the dropdown arrow and all the way down and choose rank largest to smallest. And in our base field, we have to choose which field we’re going to rank. Now we have our months in a row labels, so we’re gonna rank our sales month. And finally let’s change the custom name to rank large to small, and press okay. Okay, so we have in 2012, number one being July with $1.05 million and the lowest being January at $771,000. So you can quickly see which items are ranked highest and which items are ranked lowest. Now in 2013, we can see that December had the highest sales and November had lowest sales. And in 2014, we can see that January had the largest sales and August had the lowest sales. You can see there’s a big variance in each of the years. There’s no consistency in the value. So you can make some quick analysis with these numbers of rank largest to smallest. (upbeat music) The index calculation shows us the relative importance of a cell within a column. So in our example, we have our products on the row labels and our regions on the column labels. So the index will show us how important a product is to its region. The higher the number, the more important that product is to that region. And to show you an example, let’s grab our Pivot Table by clicking in the top left-hand corner and pressing Control + C in your keyboard. And then in here we can right-click and paste everything in there. So let’s go on to our Pivot Table field list and grab our sales and drop it into our values area. From the dropdown arrow, we go to value field settings and choose show values as, and in the dropdown box, we’ll go all the way to the end and choose index. And we can change the name to index. And press okay. So now let’s get rid of the sum of sales, just so we can have the index values. Now, finally, right-click and format the numbers, and we can put in two decimal places and press okay. So now we have our index on the bottom and our sales on the top. So for the bottles, you can see that Americas has the highest amount compared to its other regions. So what that means is that if there was a price change in the bottles product, then Americas will have the biggest effect because they have the higher index amount. Now we can see this, that Americas has the largest sales all across the region and also in its column grand total. So it calculates it based on the row total and grand total. And also the grand total which we have in the bottom right-hand corner. Now let’s calculate this. We have the calculation here of how the index is calculated. So let’s grab the value in cells. In our example, we choose the American’s bottles. So press plus, and we can reference Americas and then multiply by the grand total of grand total. So 32 million there. And press enter. So we have our amount there. Now next let’s grab our grand row total. Grand total here and multiply it by the grand column total, which is the 7.9 and press okay. And finally, L4 divided by L6, which gives us 1.1 which you can see there. And the same calculation happens for each of the other values within the regions and products, and they’re all depicted here. And the grand total will obviously be one. So we’ll see for ice cubes that the African region has the most important value. In soft drinks, we have Europe and in tonic we have Asia. So any price change in those products, then the biggest effect will be in the regions which have the higher index. (upbeat music) Now there are a few ways to get the show values as dialog box. In our tutorials we’ve using the Pivot Table field list and going from the dropdown box and value field settings and show values as. And the other way is once you’re in the Pivot Table. So anywhere in here, you can just right-click and choose show values as, and you can choose one of the calculations here. Now let’s choose percentage of grand total. And you can see that changes for all the cells. Now, the other way is to go on to the options tab in the ribbon, and the calculations you have the show value as, so you can change it from there. And finally, you can go out into the field settings and you can make your change from in here. So you got a few different ways where you can show values as option in the Pivot Tables. (upbeat music) In chapter 8, we created a P&L where we used calculated items to see the difference between the revenue and the COGS, which gave us the gross profit. And then also the difference between the gross profit and the expenses that gave us the calculated items called operating profit. Now, what we want to do is use the P&L types to determine what percentage of revenue they have. Now under row labels dropdown box, we have the P&L type chosen there, and we have our different P&L types. And we want to see what percentage of revenue is associated with COGS, gross profit, expenses and operating profit. Now let’s cancel to get out of that. To do this, we click in our Pivot Table. And then from the sum of actual dollars, we choose the value field settings. And then under show value as, from the dropdown arrow here, we choose the percentage of option there. Now for the base field, we’ll need the P&L type, and the base item will be revenue because we’re gonna show you the values as a percentage of the revenue for each P&L type. Now let’s press okay. Now the items within the P&L type will not get a percentage allocation, and that’s fine. We can see that in each of the totals, we have our percentages in there. Now to get rid of this, all we gotta do is just click on the minus button and we’ll get rid of that, and we can do the same thing for the revenue. So now we have a quick snapshot that shows that for example, in 2012, COGS is 2.5% of revenue. Gross profit is 97.4% of revenue. And if we add gross profit and COGS that will give us 100%, as you can see in our total there, which is correct. And then expenses accounts for nearly 36% of the revenue and the operating profit is that 61.58% in 2012. Now you can see the same calculations are done for 2013 and 2014. So this is a quick way to show your margins for your P&L. (upbeat music) In our data set, we’ve added another column called status. And in here we have the actual and the plan status stages. So for each transaction, we have an actual, and also a plan. Now we have the order dates here, and these can be also transaction dates, or they can be sale dates. But what we’re interested in is the actual and plan. So what we’re gonna do now is create a Pivot Table where we show the actual versus the plan for our products. And then we’re gonna create a variance report to see whether we have met our plan or not. Now to do this, let’s go to our pivot here. And in the Pivot Table here, we’re gonna add in the following items. On the row labels, we’re gonna add in the financial year and the products. In the column label, we’re gonna put in the status. So as you can see, we have the actual and plan status. And in the values area, we’re gonna put in there our sales. So grab that and drop it in there. Now let’s go in here and just change the number format, we’re going to value field settings and then choosing a number format, and we’ll do a number with 1000 Separator and no decimal points, and then press okay twice. And in here we can just center it like this. So we have the actual and plan for our products for 2012, 2013 and 2014. And now we’re gonna add in another column here to get the variance between the actual and plan. And also we’re gonna add another column that’s gonna show you the percentage variance. So to do this, let’s click back into our Pivot Table and grab the sales and drop it again to the values area. From the dropdown box, we choose the value field settings. And under show values as, we choose the difference from. Now, the base field is gonna be the status because we wanna see the difference between actual and plan. And the base item is gonna be the plan. So it’s gonna be the difference from the plan. Now let’s press okay. And as you can see, we have the difference here. And let’s make a few cosmetic changes. Let’s go back in here in our value field settings. We can change the name. Instead of instead of sum of sales 2, we can change it to dollar variance. And then number format, we can go into number separator, no decimals and we choose the red font for the negatives and press okay, and then okay. And next thing is let’s drop in our sales so we can get the percentage difference from the actual versus the plan. Drop it in there, dropdown box value field settings, show values as, dropdown box percentage difference from. The base field again will be status and the difference from with the plan. So we get the percentage difference from the plan. Let’s change the name in here and call it percentage variance. And the number format we can go to custom. And then let’s choose in here one of these. Now to make it a percentage, just go next to the semi colon and press the percentage. And then for the red, it’ll be at the end a percentage. So if it’s red, it’ll be negative percentage, and if it’s positive, it’ll show in a black color. Press okay, and then okay. Now finally, we have the plan here. Now the difference between the plan and the plan is zero. So that’s why we get no value there, but we can get rid of that. We just click in there, right-click and hide. The same thing for column G right-click and hide. Now, finally, we can go back into our Pivot Table and then right-click and show the field list. Now we can also drop in some more fields in here. Let’s drop in our sales region into the row labels. And as they’re been included in there, we can see that our calculation has also picked up the sales region. So you can add in as many fields as you want. The calculations are gonna extend to those fields as well. So there you have a quick report where you can see the difference between the actual versus the plan on a dollar basis and also on a percentage basis. (upbeat music) In our example here, we have our order date in our row labels. And then as you can see in our Pivot Table, if we scroll down all the way, we have lots of order dates ranging from 2012, 2013 and 2014. So it goes all the way up to row 259. Now say we want to group these dates, we can do that. What we gotta you do is just click anywhere in the Pivot Table. Right-click and choose group. And we’ll get the grouping dialog box. Now in the first part, in the top part, we have the starting at date, which is automatically added in, which is our first date. The 3rd of the 1st, 2012. And ending date is the 1st of the 1st, 2015. In here by we can group by seconds, minutes, hours, days, months, quarters, and years. What we’re gonna choose is days. Now let’s click on month so we can uncheck that. So we have checked on days and now we get the number of days here. We get the scroll box and we can choose the number of days to group by, and let’s group it by seven days or a week, and then we’ll press okay. So we can see here that our information is grouped by seven days, starting from the 3rd of the 1st and going all the way down to the 1st of the 1st, 2015. We can drop in our sales and then what it will do is group those sales that fall between each of the ranges. So for example, the 3rd of January, 2012 to the 9th of January, 2012, it will sum up the sales that fall between those ranges. And we’ll do the same thing for all the grouped dates. So let’s grab our sales and drop it into our values area. You can see there, we have our sales. And a scroll all the way down. And you can see that as well. Okay. Now we can also drop in the sales again. Instead of using sum of sales, by choosing the dropdown box, we’ll go to value field settings, and we can count. So we can see the number of transactions that happen between those group dates, and press okay. So we have a different transaction as well. So now you can go in and do some meaningful analysis with your data. (upbeat music) We have our order date in our row labels and now we want to group by months. To do that, we click anywhere in our Pivot Table. Right-click and choose the group option. And we’ll get our grouping dialog box. Now we need to choose months, which is already selected. We know that because it’s in blue. If we click again, that means it’s deselected. So let’s click on that again to activate it and press okay. So we have the 12 different months where our orders have been grouped into, and we can analyze the sales that relate to those different months. Now we grab our sales and drop it into our values area. Now we can see the different sales that pertain to each month. And just make a note that our data ranges from 2012 to 2014. So that’s three years of data that’s grouped into each month. So if you send this report to your boss, then you’re gonna make a note that this information includes three years of data. So it’s not just one year. And to avoid confusion, you can just right-click again, in the row labels, you choose group. Now we can also select the year. So you just click on the years. Now it’s in blue. So now months, and the years are selected and press okay. So we can see here, that’s broken down into each year. So when you send this report to your boss, then you won’t have any issues. (upbeat music) Now, say that I want to group our order dates by quarters and years, well, we can easily do that. In our row labels, we have our order date. So let’s go on into our Pivot Table. And right-click and choose group. We get our grouping dialog box. Now in the starting and ending dates, we can leave that as selected automatically. In by, we’re gonna choose quarters and years. So quarters, years. And unselect months. So let’s press okay. And we can see that the quarters and years are depicted in our Pivot Table. Now on the right-hand side, make a note that the years has been added in our Pivot Table field list in here. So what that means is that a new field has been created in the pivot cache called years. Now this has not been added into the original data set. So if we go in here, we’re not gonna see a field list called years. Now, all we can do is grab the sales and drop it into our values area to analyze the different sales that occurred in the order dates that have been grouped by quarters and years. Now, the years field that has been newly created. We can actually move that if you want, from row labels to the column labels, just by dragging and dropping. And you can see, we can analyze the information in a different view. (upbeat music) We actually group by sales ranges, and then get our sum of sales that belong in our ranges that we depict. And also we can get the number of transactions that belong to the ranges that we choose. So to do this, we’ll grab our sales and we’ll drop it into our row labels. So we have all of our sales that have occurred starting from $10,014, all the way to our maximum sales, which was 99,878. So it can go up again. Now in our Pivot Table, we right-click and choose group, and we’ll get to the grouping dialog box. The starting being the minimum value that we see there, and the ending being the maximum value that we saw before. The by means the amount that we’ll want to group our sales by. Now, we have 10,000 in there. So let’s leave it as that and press okay. As you can see, our sales are grouped by $10,000 increments all the way down there. And now we can do some further analysis. So let’s grab our sales and drop it into our values area. And from in here, we can choose the sum of and press okay. So what it says here is that between the sales ranges of 10,014 and 20,013, we had total sales of $1,011,401. Now we can check that we can just double-click in there and we can see our sales ranges in there are between what we’ve chosen before, from 10,000 all the way to the 20,000 amount. Now press Control + Z, and go back. Further, we can see the number of transactions that occurred between each of the sales groups. We can grab the sales again and drop it into our values area and we get the count of sales. So we had 67 transactions between our sales ranges, 10,014 to 20,013. And then 57 transactions between our sales ranges, 20,014 and 30,013 and so on. So we have our total number of transactions being 576 and our total sales being $32,064,332. Now, if you want to round these groups, then you can just right-click again, go to grouping. Now, instead of starting at 10,014, we can start at 10,000 and we can end at, we can say 100,000 and the increments we can leave at 10,000 and press okay. So we have our groupings, which look a little bit better. And then our sales and our number of transactions. (upbeat music) We can actually group our data by text fields. In our row labels we have our sales region. So say that we want to create some new regions. Now we want West, including Americas, East, including Asia and Central, including Africa and Europe. So let’s start off with the central. Let’s click on Africa and then hold the Control button in our keyboard and choose Europe. So we have Africa and Europe and want to name that into our new group or into our new region called central. Right-click and choose group. So Africa and Europe are included into group one. Now we can actually change the name of group one. We can call that Central. Americas, we can call that West. And Asia, we can rename that to East. So we have renamed our regions. Now on the right-hand side you’ll notice that we have a new field list that’s been created called sales regions 2. This is created in our pivot cache and not in our original data set. Now let’s rename this by clicking on the dropdown arrow, go to field settings and so sales region two, we can call it new regions. And press okay. So let’s change there. Now, all we can do, we can also drag this newly created group from the row labels to the column labels, just like this. And on the top here, we can see that our newly created region called central includes Africa and Europe, which is correct. Our West region includes Americas and our East region includes Asia. (upbeat music) So we’ve added a new column in our data source called time of sale, where it shows us the time that each sale was made throughout the day. And the format is in a 24-hour time format. And what we want to do is find out at which time of the day we have the most sales and which time of the day we have the least sales. So let’s go on to our Pivot Table. And what we need to do is grab the time of sale field and drop it into our row labels. So you can see, we have our different times that the sales were made. And now what we need to do is right-click in our Pivot Table, choose group, and we get the grouping dialog box with the starting time and the ending time being the minimum and the maximum time in our data source. And what we need to do is choose hours, we select months and press okay. So now we have different hours grouped, and finally we’ll need to grab our sales and drop it into our values area. So we can see now that the sales that we’ve made throughout the different times of the day. So during 1:00 a.m. we had 1.1 million of sales. During the 9:00 a.m. we had 4.1 million of sales and so on. So you can see here that at 11:00 p.m. that’s where we have our most of sales. (upbeat music) Now with grouping, there are a couple of things to note. If you have numerical data fields, then all you need to do is just click on one of the cells and then right-click and press group. And you can group like that. Another way is to go to the options tab and you have under group, your group and ungroup options. So you can group from there. And the third way is to go to the group field. And what this says is you can only group numerical data fields, and you can also group from there. Now, if we put in there text for example, sales month, and we try to click and right-click in one cell and try to group, we’ll get an error message. It cannot group that selection, press okay. You need at least two items selected to activate the grouping in the text field. So now we can group and we can start grouping from there. Now to ungroup we go to select the group heading, right-click, and then ungroup. Or we could do the same thing from the options tab and the ungroup selection there. So let’s group again, right-click group. And by pressing Control + Z on your keyboard, you can ungroup. (upbeat music) So we want to analyze our sales on a bi-annual basis. So we want to see the first half of the year, and also the second half of the year. Now we can group this. We can click in our Pivot Table and select January to June, right-click and group. So we have group one and we can call that first half. And then to group the second half, we just click on our items there July to December, right-click and group again, and we have the name group two. We can change that to call it second half. So now we’ve created our two groups. And on the right here, we have our new field called sales month two, and that’s in the pivot cache. So we can get rid of the sales month in there. So we’ve got the first half and second half. Now let’s put it in our sales, in our values area to see our results. And also what we can do now is put in our financial year and drop it into our row labels. And we can see our results from 2012 to 2014 on a bi-annual basis. And you can also do this if you had customer names. For example, if you had over 100 customers and you wanted to group them into groups of customer names, starting from A to K and also from L to Z. So you can have two groups of customers and you can analyze their sales over the years. (upbeat music) When you’re grouping dates, it automatically takes the first date in your source data and groups starting from that date. Now that date could be a Tuesday, it could be a Sunday, could be anything. But say you want to actually start on a Monday. Now we can change that when we go into our grouping selection. So first of all, we need to find out what our first date is. So we have the 3rd of the 1st which is our first date. Now by doing the formula weekday, weekday, it returns a number from one to seven, which identifies the day of the week of a date. Now, number one is Sunday. So let’s choose the 3rd or the 1st. So if one’s a Sunday, two is a Monday, three is a Tuesday. So we can find out that three is a Tuesday. Another way to find out what day fell on the 3rd of January, we can actually go into our calendar and click in 2012. And in there we can choose the 3rd of January, 2012, which was a Tuesday. So we wanna group on a date starting on Monday. So let’s do this, which is the second. Right-click anywhere in your Pivot Table and choose group. Now in the grouping dialog box, we have this automatically starting at the first date, but we’re gonna change this, we’re gonna override it. I want to pull up the second. Okay. So we want the grouping to start on a Monday. And we want to group it by days, and then we can put in there seven. So a whole week. And press, okay. So that’s a good tip to know if you want to group dates starting on a Monday. (upbeat music) If you want to isolate dates and do further analysis for your reporting or ordering purposes, then you can certainly group those dates depending on which ranges you’re looking for. So let’s do an example, click in that Pivot Table, right-click and group. So we have our data from 2012 all the way to the end of 2014, and say we want to just look at the first six months of 2013. So we’re gonna have the starting day being the 1st of the 1st, 2013 and the end date being the 30th of June, 2013. And then we want to group by months and then press okay. So you can see now that anything before the 1st of the 1st, 2013 is grouped into one amount here, the 10.3 million, and anything after the 30th of June, 2013 is grouped into another amount here, which shows 16.6 million. And then you have the six months isolated in there. You can do your analysis by double-clicking and seeing your transactions from there, and then press Control + Z and delete to go back. Or you can, if you want, click on any transaction after 30th of June, 2013, double-click on that, and that will give you all the transactions that occurred after that date. Control + Z to go back. So you have a lot of flexibility when you’re grouping dates in a Pivot Table. (upbeat music) In Excel, you can group by a calendar year and also a calendar quarter. Say you want to group by a fiscal year or a fiscal quarter. Now that’s a little bit difficult and you need to create some formulas and put that in your data source. And from there, you can create your grouping in your Pivot Table. Now a fiscal year, for example, in Australia, starts in July and ends in June. In other countries, it starts in October and it ends in September. And you can also have situations where the fiscal year start in April and ends in March. So I have an example here of some dates from July all the way to June. So this is a typical fiscal year that starts July ends in June. So what we need to do is do a formula where it gives us the year, and then it adds onto that a one if the month is equal to, or more than seven. And if it’s less than seven, then it will return a zero amount for the month. So this is one example to show this. In here, we can do an equal or a plus sign, and let’s put in year and let’s get our example date there and put in brackets. So that’ll give us the amount 2012. And then let’s add in here, in brackets, month and choose the same date and what we’re saying here, if it’s bigger than or equal to seven, close brackets, then it’ll give us a amount of one. So 2012 plus one is 2013. So any dates from July to December will give us a 2013 value, which means that the fiscal year 2013. If we drag all the way down, it’ll be 2013 all the way to December. So our fiscal year is 2013. Now, once we drag into January, well, our fiscal year is at 2013 because it’s counting 2013 and then it’s adding a zero because it doesn’t meet the criteria of equal to, or bigger than seven. And let’s drag all the way down here. So we have our fiscal year of 2013, which is correct for a July to June calendar. And now we can do the same thing for the fiscal quarter. So in here, we’re gonna use the choose and month functions. So what we’re gonna say here is it’s gonna return us a value for the month. So for 1st of July, 2012, it will return us a value of seventh. So what it means here to choose from these amounts here, that we’ve depicted, it’ll choose the seventh value. So one, two, three, four, five, six, seven. So the seventh value is a one. So that means quarter one. So that’s how this formula works. So let’s put it in here. So choose the month number from our date here, close brackets, and then return the value from our predetermined values that we’ve entered here for a July to June fiscal year. So this number three means that January is three, February three, March three, April is four, May is four, June is four, July is one, August is one, September is one, October is two, November is two, December is two. So the seventh value in here is a one. So it’ll return us a one. And then we can just drag all the way down and we can see we have our fiscal quarters in here. So now we can grab this formula here. Control + Copy, and then go into our data table where we’ve put in another column called the fiscal year, and we can paste it in here and then we can move the cell references to the order date and then press enter. And it automatically fills all the way down, and it gives us our fiscal years. Okay, let’s go back and grab our formula in here instead of writing it again. And let’s go to our data table, our fiscal quarter. Control + V and then let’s reference it to the order and again, enter. So it returns our values. So let’s update our Pivot Table and then do the grouping based on the fiscal years and quarters. So first we need to refresh the Pivot Table. So I can right-click in there and press refresh, which will give us our fiscal years and fiscal quarters. Let’s grab them and put them in a row labels and let’s put them in our row labels again, our fiscal quarters. And our sales we can drop into the values area. So now you can see, we have our fiscal years and our fiscal quarter. So a new year was created 2015 and the Q1 and Q2 quarters there. So this is a great tool to use if you’re not using a calendar year for your accounting or company purposes. Now, what I’ve done also is I’ve created a fiscal index where I’ve got the July to June fiscal years and October to September fiscal years, with the different formulas in there for you to copy and paste into your workbooks. And also for April, I’ve done the same thing. I’ve changed the formulas. So you had the different formulas to use, depending on what fiscal year your company is using. (upbeat music) Let’s try and group our order dates by right-clicking and pressing group. Well, we get an error message, cannot group that selection. Now, when you get that it means that your data source has some error values in your dates. So you may have some N/A values in there when you imported the data, or you may have some dates that are not entered in correctly. For example, you may have month 13, or you may have a date number 31 in February. Now we can check that. We can go into our data table and into our order there, we can select everything and press Control + G, which is the go-to special. Or you can go under find and select and go to special from there. Now let’s select constant, and we want to find out any errors or any text. So if we have any dates with 31st of February, then that reads it as a text. Let’s only select texts and errors and press okay. So it’s made the selection, we cannot see it, but it’s in there. Well, now we’re scrolling down, we can see that. Now let’s highlight that in yellow. And it’s also highlighted our text up there. That’s fine. Now let’s filter by color. So it gives us all our texts or our error values. So in here we can make our changes highlight all these. Again, we can go to Control + G, Go To Special and visible cells only. And if they all have the same order dates, we can just put in 28th of the 2nd, 2012, hold down the Control key and press enter and that will fill in the selection. And then for the N/A, if that’s an error, then you can delete everything or you can go back and find out what the date is. And now let’s for our purpose, we can just put in 14th of the 2nd, 2012. Now we can go back to our Pivot Table. What we need to do first is unselect the order date. And then what we need to do is refresh our Pivot Table So right-click, refresh, so that updates the pivot cache with the new information. And then we need to put back in the order date into our row labels and from in there, right-click and group. So now we can do our grouping. (upbeat music) Okay, I want you to group our order dates into weeks, and then I want to do another Pivot Table and then group to months and quarters. So let’s group these into weeks of seven and press okay. So we have that. So what I wanna do now is go to options and select entire Pivot Table, Control + Copy, and in here, Control + V to paste it. And now what I wanna do is group these into months and quarters and press okay. Well, see what happens. The first Pivot Table gets updated as well. That’s because we’re using one pivot cache. Now that’s annoying, but there is a work around. Let’s press Control + Z to go back. What I need to do is grab this and then cut it and paste it into another workbook. Let’s go to file, new, blank workbook and then in here, right-click and paste. So in here, I can group it into the way that I want, into months and quarters, and then I can select everything, Control + X. I can go down into my book where I was previously and in here, right-click and paste. So now we have two separate pivot caches, and we can group each individual Pivot Table independently. So if I go in here and say group again by months, quarters and years, well, that happens. And then this doesn’t change. Now there’s another way we can do this. We can go to our data table. And what we need to do is bring up the old pivot wizard by pressing Alt + D + P. And then what we need to do is press next. And then that chooses our whole table. And then that’s fine, press next. And then we want to put this Pivot Table into our new worksheet and press finish. So in here, let’s put our order date and then let’s put our sales and then we can group this by days and put seven and press okay. Now let’s go back to our data source and then press ALT + D + P again to create another pivot cache, and then press next. It selects that, press next. Okay, now we get a message here says your new report will use less memory if you’re base on your existing report, which was created from the same source data. Do you want your new report to be based on the same data as our existing report? Well, no, we don’t want that, we want a new pivot cache. So we press no here. If you click, no, the two reports will be separate. That’s what we want. Press no and go into our existing worksheet, sheet one, and we can place it there and press finish. So in here we can do the order date and sales, and then we can group, right-click in there and group by months, quarters and years. So there you have it. Two ways where you can group Pivot Tables coming from the same data source independently. (upbeat music) We’ve grouped our sales here by increments of 10,000. And we have our sum of sales here that show the results per group. And so we take this out and then we’ll wanna drop in the sales again. What happens, we get a count of sales. That’s because once you group sales, then it reads that as text. So it automatically shows us a count of sales. To fix this, all we gotta do is right-click and ungroup the sales. And then if we drop in our sales, once again, then we get sum of sales. (upbeat music) We’ve got our order dates here, and we want to group these. So right-click anywhere in there and press group, and then we choose days of seven and press okay. So this is a grouped by seven days, but as you can see here, there’s data missing. So from the 16th of the 1st, it goes to the 24th of the 1st. Now we wanna show all the group dates. Even if they don’t have any transactions in there. So let’s right-click in there once again and go to field settings, and under layout and print, let’s choose the show items with no data and press okay. So it shows all the weeks here, even if they don’t have any information in there. Now, finally, we can go to the options tab, and option and for empty cells show zero, and press okay. (upbeat music) We have our P&L report that we created in chapter eight using calculated items. What we do there is get the difference between the revenue and COGS to give us the calculated item called the gross profit, and then get the difference between the gross profit and the expenses to give us the calculated item called operating profit. And here we have the P&Ls for 2012, 2013 and 2014 with our sales here. And now what we’re gonna do here is drop in our months into our column labels and then group those to get the quarterly reports. And from there, get the difference between the previous quarter. And let’s get our month and drop it into our column labels. And let’s escape from there. So we have our months on there. Now to group these into quarters, just go to highlight the months that you want to put into quarters and then right-click and group. And where it says here, group one, we can actually change that and call that quarter one. The same thing for April, May and June. Right-click, group, and call it quarter two, and depending on which part of the world you are, each quarter will be different. Now in Australia, the Q1 starts in July, but in America, it starts in January. Now, let’s continue here, right-click, group and call it quarter three. And then finally let’s put in there quarter four. Okay, so we’ve done that. Now, let’s right-click and show the field list. As you can see here, though, we have the month two field that’s been added here, which shows our grouped months. Now let’s click in the dropdown arrow and choose field setting, and let’s change the name here. Instead of month two, let’s call it quarter and press okay. So you see that’s changed there, and also in our Pivot Table field list. Okay, now we want to drop in our actual sales into the values area and do a calculation to get the difference from the previous quarter. Now, if we grab the sales and drop into the values area, we’ll get this warning here. It says multiple data fields of the same field are not supported when a Pivot Table report has calculated items. So because we have calculated items here for gross profit and operating profit, we cannot drag it in there, but there’s a workaround for that. Let’s get out of these. Now, what we can do is click in here, go to the options and select entire Pivot Table, press Control + Copy in your keyboard. And down here, let’s paste it by pressing Control + V. Okay, so what we can do now is just group these into the quarter, just by clicking into the minus box there. Or a quick way is to go to the options once we have selected that area, and go to the minimize entire field. Now let’s highlight this and double-click between the columns just to center them, and then we can press the center twice there. Okay. So we have our quarters. Now, what we can do is right-click to show our field list again. And now from in here, from the dropdown box, we’ll go to value field settings and show values as, we choose the difference from. Now, the base field will be the newly created quarter field, and the base item will be previous. So we’re showing the difference from the previous quarter. And finally, in the custom name, let’s call it variance per quarter, and then the number format we can just keep it as currency and the negative font there and press okay. And now, okay. And we can see here that we have the variance. Obviously Q1 will be zero because there’s no previous quarter to compare it to. And we have the Q2 difference from Q1, the Q3 difference from Q2 and the Q4 difference from Q3. Now we can just go up here and we can see and compare this to the top chart there. Okay. Let’s click in there, options and we can just minimize that. Okay, so we have the actual values there, and we also have the differences at the bottom. So that’s a quick work around when you have calculated items within your Pivot Table. Now I’ll show you another example where we’re gonna include the variances between the quarters. So to do that, we’re gonna use a Pivot Table that doesn’t have a calculated item in there. So let’s go to our new workbook here. Okay, so in here we don’t have the calculated item for the gross profit and operating profit. So to drop in the variance, just get the actual dollars again and then dropdown box, we can go again and choose the difference from. The base field will be the quarter and base item will be previous. So the difference from the previous quarter, and then in here, we’re gonna pull variance per quarter and then number format, we can just put in the currency and we’ll use the dollar and the negative red font, and press okay, and then, okay. Let’s close down the field list and we can see in here, let us make this a little bit better to see. Okay, so the first variable we don’t have. So we can get rid of that. Click on the column C, right-click and hide. And here you can see the variance per each quarter, the Q2 versus the Q1 variance, the Q3 versus Q2 variance, and the Q4 versus the Q3 variance. So with a grouping and show values as calculation, you can do some pretty good analytical Pivot Tables for your clients. (upbeat music) We have our data set here with the bank balance date and the actual bank balance for each date. Now we have each transactional date up until all the way, 2014, 31st of the 12th. So let’s go back up again here. And what I want to do is we want to show the minimum and maximum bank balances for each month in each particular year. Now let’s go to our Pivot Table here and click in our Pivot Table. Now to do this, we have to grab our bank balance date and put it into our row labels. And from in here, just choose any of the items in there and right-click and press group. And we’re gonna group it into the months and years. Now, the starting and ending point is automatically entered in there, and we’ll just leave it like that, and we’ll press okay. So we have our months and years for 2012, 2013 and 2014. As you can see here in the row labels, the years field has been created now that we’ve grouped our bank balance dates. And also it’s been created here in the field list. The next step is to drop the bank balance into the values and then get the minimum and the maximum amounts. So grab the bank balance, drop in to the values. From the dropdown arrow, choose the value field settings, and in the summarize of the values by, we choose the minimum. And in here, we can just leave out minimum or bank balance. That’s fine. And the number format we can go to number, and we can just put a separator and use the negative red font there, and press okay. Now, we’ll do the same thing for the maximum. So we go to the bank balance, drop into the values area, dropdown arrow, value field settings, and do the maximum this time round. And number format, again, you choose the same, and press okay, and then okay. So you can see that for January, 2012, the minimum bank balance for the whole month was at minus 8,306, and the maximum bank balance during the month of January reached to 9,662. Now let’s go to our data table just to confirm that. And from the dropdown arrow, we can choose in here only 2012, January and press okay. And from in here, we can see that the minimum amount was 8,306, and the maximum amount was 9,662. Go back in here, that matches. Now let’s get out of the Pivot Table field list. Another thing that we can do is put in a graph in there. So we can see how the movements have tracked over the months and the years. Now, click in the Pivot Table, go to options and choose pivot chart, and just put in a line chart in here and press okay. Now let’s expand this to make it a little bit bigger. Now from in here, right-click and let’s hide all field buttons on chart because we don’t want that ’cause it clogs up the screen. And in here we can change the color. Right-click, and then choose the red in here, and from in here, right-click and we can choose a dark blue color there. So we can see here that the minimum bank balance for each of the months throughout 2012 and 2014 is around 10,000. So it doesn’t go below the 10,000 mark and also the maximum bank balance for the years was trending about the 10,000 mark. So in this situation, your bank overdraft limit will be around the 10,000 mark. But as you can see, there’s not much margin that you can play with. So by grouping the transactional dates and also showing the values by a minimum and maximum, you can do some in-depth reporting and graphical representation of your data. And it’s not only limited to bank balances, this can also be a situation where you have sales, unit sold, or the amount of time you took to repair a product. (upbeat music) There are few ways where you can sort your Pivot Table. One of them is to go to your Pivot Table field list. And in here you just click and you get the dropdown arrow and you have your sort options there. The other way is to go to the options tab in the ribbon and under sort and filter, you have your sorting from there, A to Z or smallest to largest, Z to A, largest to smallest. And then you have your more sort options in there. And another way is within the Pivot Table, right-click, and you have your sorting there, and you can also go into your values and then you could sort from there as well. So let’s use the ribbon to sort. So we want to sort by largest to smallest. Now, when you create a Pivot Table, it automatically puts the row labels into alphabetical order. So say you want to put it from Z to A, we can click there and all the values change. And A to Z, it goes back to where it was. Now, if we want to change the values, you gotta click in the values area there, and then say, we want smallest to largest, we click A to Z. If we wanna see largest to smallest, we’re click Z to A, and their respective items change as well. Now, if we want to do the same thing for the sub-totals, we gotta click in the sub-totals area and we can show smallest to largest and then largest to smallest. (upbeat music) If want to sort an item row from left to right then we can certainly do this. For example, tonic, we have our values from 2012 to 2014, and we want to show the highest value first, and then on the right, going on to the lowest value. Now to do this, you’ve gotta click into the values area, right-click and sort. Choose more sort options, and we want to show the largest to smallest. And then in the sort direction it’s left, right. So in our summary, it says sought financial year by sum of sales in descending order using values in this row, tonic and press okay. And we’ll see that tonic has the highest value. And then on the right, it goes all the way to the lowest value and the years change there as well. And also the other items have been moved accordingly based on the 2013 totals. So you can certainly sort from left to right, as well as top to bottom. (upbeat music) There are a few ways where you can sort manually in a Pivot Table. One of them is to click in your row labels and on the border, you get your four pointy arrow and then click your mouse and you can move it up or down and you can see the bar there, so you can move it all the way up like that. So that’s one way. Another way is that you can actually write in the items that are in the row label. So say we wanna move tonic from the bottom to the top, well, we just write in TO and then it knows that we’re gonna type in tonic, and then press enter. So it moves tonic to the top and the same thing in here. Anywhere, we can put in there, you’re gonna say okay soft, SOF and then it automatically puts it in there. Now, if you make a mistake in there instead of putting ice and then press and delete and enter, it’ll override the item that you had there. So just to make sure you don’t make that mistake. Press Control + Z to go back, and finally we can right-click in the item, and then we have the move option there. So we’re gonna move bottles. The beginning up down, or to the end. And it just depends on which position it’s at. We can make those moves. So if we go to bottles, right-click. Obviously we can only move that down or to the end. So there’s a few different ways where you can manually move around your Pivot Table item list. (upbeat music) We have our data on the left here sorted alphabetically, but sometimes you want to create a custom list. You want to have, for example, Americas first and second, you may want to put in Europe, Asia then Africa. So you wanna set up a list where every time you refresh your Pivot Table, then Americas is first all the time. Now we can do this. First, let’s create our list, the way we want to see it every time we update our Pivot Table. So we want Americas, let’s copy and put it in here. And then let’s put in Europe and Asia and then Africa. Okay, so that’s the format where we’ll want to see our Pivot Table each time we sort it from A to Z. Now what we need to do to activate this is we need to go to file in our ribbon, go under options on the left hand side, in the Excel options dialog box on the left-hand side, we choose advanced, and then we scroll all the way down and under general, there’s an edit custom lists option there. Now we’ll click on that and now we can create our new list. Let’s go into this box here so we can choose our newly created custom list. Highlight, and then press import. So you can see there, it’s been imported. Now you can also see that the custom list that have been created by Excel for the dates and the months and we have our custom list here. All we need to do now is press okay. And then again, okay twice. To activate this, we’ll need to refresh our Pivot Table. So right-click anywhere in our Pivot Table, refresh, and you can see that Americas is first, Europe is second, Asia is third and Africa is last. Now, if this wasn’t in that order, you can right-click sort A to Z, and it’ll put into that order. If your custom list doesn’t work, when you refresh and then sort from A to Z, then you need to go to another place to activate it. Right-click, sort and more sort options. And then on the bottom left-hand corner, click again, more sort options. And if your custom list didn’t work, that means that this was unchecked. Okay. So make sure that the autosort is always checked. So next time you refresh, then your custom list will be as per the way that you created it. So you can create many lists for regions, for products, for salespeople, whatever you like. It’s a great way to get a custom list on your Pivot Tables, where you can analyze as per your company’s preferences. (upbeat music) We’ve added a new column here called managers where we’ll put in a manager’s name. For example, Jan, April, Adam and Scott. Now, Jan is a name as well as a month. It’s short for Janine, for example, April as well is a woman’s name and a nice one at it. So when we put this in our Pivot Table, it’ll sort by month names because it will think that the managers’ names are months because of Jan and April. Now let’s have a look at this. Let’s go to out Pivot Table. We can right-click and refresh. Now the managers, or we can drop it into a row labels. As you can see there, Jan is first, then April, then you get Adam and then you get Scott. So it’s not in the correct order. Now to override this, you gotta right-click in the Pivot Table, go on to Pivot Table options and under totals and filters, the last option under sorting says use custom lists when sorting. And then we’re gotta uncheck that. And then press okay. And as you can see, that has been updated. So we’ll have Adam first, April second, Jan third, and Scott last on our list. (upbeat music) Now we have our regions and months in our row labels and our sales in our values area. Now, what we want you to do is sort the sales from highest to lowest, and then sort the regions alphabetically. And to do this, we click in our sales, right-click and sort largest to smallest. And we have the largest to smallest values for Africa, for Asia, for Europe and for Americas. And now we want to put the regions into alphabetical order. We can right-click. So there’s two ways to do this. We can actually sort from A to Z, or we can go through the more sort options, and under the ascending, A to Z, we can choose the sales region and press okay. So we have Africa, Americas, Asia, and Europe in alphabetical order. And we also have these sales in descending order as well. (upbeat music) We have our products on our row labels here, and I want to add in a new product and then refresh it and see where it goes on our Pivot Table. So let’s go in to our data source and add a new product called cider. So we need to add this in our table. Let’s go to the end of the table by pressing Control + Down. And let’s click out of here. And from the corner we can just drag and add a row. Let’s highlight the row and press Control + D to copy down or whatever is directly above. Now, what we need to do is we’ll keep the same information. The only thing we’re gonna change is the product. So from tonic to cider and press enter. Let’s go to our Pivot Table, right-click and refresh. And as you can see, cider has been added to the end of the list. Now we need to update that. To do this, all we’re gotta do is just right-click anywhere in the Pivot Table, sort and sort A to Z, and then we have our product list sorted alphabetically. (upbeat music) Now we know that our Pivot Table is sorted by the arrow pointing down in our filter here. Now we can clear that by going to more sort options, and then selecting the manual option there. You will press, okay. Then the sort has been cleared. (upbeat music) We have our months going across the column in the Pivot Table. And we can actually sort by the grand total. If we click in there and right-click, choose sort and then sort largest to smallest. (upbeat music) In that Pivot Table on the left in the row labels, we have the order dates and we have our sum of sales in the values area. Now the order dates range from 2012, all the way down to 2014. So we have three years of data in there. Now we can filter that data. If we go to the row labels dropdown arrow, and then under date filters, we have all these filters here. Now the date filters option is only available when your row labels or column labels have dates in there. So let’s have a look at the different filters that we have. We have the equals, before, after and between. So let’s choose between. And we get this a dialog box and we can choose a date here by clicking the calendar and today being the 5th of March. So we can go back and say December 1st to today, and then press okay. So you can see everything has been filtered there for our selection. Now we can clear that or click in there and press the clear filter from order day. Now the next day filter, we’ve got some virtual filters here, for example, tomorrow, today, yesterday, next week, this week, last week, next month, this month, last month, next quarter, this quarter, last quarter, next year, this year and last year. And these are virtual filters. So what that means is when you put that filter and you open your Excel workbook next week, then those filters will get updated based on the date that you open your Excel workbook. So it’s a live filter. And let’s click on tomorrow and have a look if there’s anything in there. Well, there’s nothing in there in our selection, and that’s okay. That’s normal, it can happen. Now if I open this file in a month’s time, and then I choose tomorrow, then I may have an order date there. So let’s go back there and we can choose today, well, I have no order date values for today. And today is the 5th of March and we can go to date filters for yesterday. And again, I don’t have any order dates there as well. Now, if I come back there in a couple of week’s time and I’ll do the same filter, then I may have an order date in there. So don’t freak out if you don’t have any values in there. Okay, let’s have a look at next week. So next week I have to order dates there. And another thing that you can do here is instead of order dates, there can actually be due dates when the customer is going to pay you. So you can have this filter, and then every week you can just open this workbook and the Pivot Table will get refreshed and then it’ll have the next week’s dates. So that’s a great advantage of having these virtual filters. Another thing we need to point out is that the weeks in Excel start from Sunday to Saturday. So if we ever look here in our calendar. So next week, we’ll start on Sunday, the 9th of March and finish the 15th of March. So as we can see here, the 9th of March, that’s correct. So it includes the 09th of March into the next week’s order dates. Let’s go to another one. Let’s go have a look at this week. We just have one order date there. Let’s go to last week and we have a few order dates there. If you go to next month, this month, last month, next quarter, this quarter, and last quarter. Now, obviously this quarter ending in March while last quarter will start in October and in December. And let’s go to some more date filters. We can have a look at this year. So it has all our values there for 2014. And then also go back to last year and have a look, all the order dates for last year. So I have all these order dates down there. Now another option here he is year to date. So only show us the order dates that have started from the 1st of January, 2014, up until today’s date, which is the 5th of March, 2014. So you can see there, this is the year to date. And then another option we have here is all dates in this period. So what this means is it’ll get all the order dates, they were like Q1 for our data set. Our data set being 2012, 2013 and 2014. You can see that, all Q1 for 2012, for 2013 and 2014. Now the same thing for the other quarters can also go for the months. So the take or the February order dates for 2012, 2013 and 2014. Now we have the custom filter. So if we click on there from this drop box, we can choose the different filters equals, does not equal, is before, is before equal to, is after, is after equal to, is between, or is not between. So you can choose one of these in here and you can apply different filter that you like and press okay. And it will come up there. So you have an array of filters which you can use, and you do your analysis. And the great features about these is that we do a virtual day filter that the next time you open your workbook, then your data gets updated automatically based on that given date. And you can do some great reporting like due dates for payments, and also for project management, you can have your due date for your different milestones. So go ahead and spend some time on this, it’s a great feature. (upbeat music) In our Pivot Table, we have our sales months in our row labels and our sales in our values area. Now we filter by labels, which means that we can filter by any text. So as our sales months, our texts then we can filter by that. Now you can also drop in there products if you like, you can also put in there countries. And also if you had employee names, you can also apply labels to filter. So to filter by labels, we click in our row labels and we get the label filters there. And we have these 14 different filters. So let’s choose equals. Now, if you’re gonna choose this option, then you gotta make sure that the full name is entered in there. You can’t just put in there a letter or a couple of letters. So we’re gonna put in there, it equals January and press okay, and we’ll get that filter there. And you can clear by pressing this clear filter there. Let’s go to the next one, does not equal. We can put it does not equal July and get the rest of the items there. Now we can go to begins with. Now in here, you can put in a single letter so we can say it begins with A, and press okay. And we’ll get the months of April and August. Or we can say it does not begin with A, and we get the rest of the months there. We can say, ends with and put in their year and press okay. And it’ll give us the months that end in the year. So we’ve got September, October, November and December. Now, once again, we can go, it does not end with, and put the year and we get the other months. And finally we can say contains. Now in here, you can put in there letters as well. So let’s enter the letter E and press okay. And it’ll give us all the months that contain the letter E. And does not contain E, and then will give us the rest of the months. So there’s plenty of combinations there that you can do. And it’s not only limited to months, you can apply to products, countries, employee names, whatever text fields. (upbeat music) If you have a column field that has a numerical and alphabetical sequence, then you can filter that by labels. Now, in our example, we have item numbers with three numbers followed by three letters. Now that can actually be a product number or a serial number, or it can even be number plates. Now let’s go to our pivot and we have our item number here on our row labels and our sales in our values area. Now let’s press on our dropdown arrow and then choose label filters. And we can filter by greater than. And in here we can put in an amount, for example, 110AAA, and we’ll get all the values that are bigger than that. And you see there. You go all the way down. Okay. Now we can clear the filter by pressing clear filter from item numbers. Now we can activate the label filter once again, by going greater than or equal to. And in here we can put 110ZZZ and press okay. So it includes the 110ZZZ. You can say, less than 110AAA and press okay. And we have all those there. And when we say less than or equal to 110ZZZ and press okay. So it includes the 110ZZZ item in there. And finally we have the, between, and not between filters. So we’ll say between, let’s say 104 and 107 without putting any letters. So if it’s 104, it include 104A, AA, ABC all the way to the top. And then if we say 107, well, it’s only gonna include 107, it’s not gonna include 107A or anything above that. So let’s put in there 104 to 107 and you’ll see that. And there you have the results. And not between, well, let’s put in there once again, 104 and 107 and we get the rest of the results. So there were certainly a few filters there that you can use when you have fields that include numerical and alphabetical sequences. (upbeat music) In our Pivot Table, we have our order dates on the row labels going all the way down. And then we have our sum of sales in our values area. Now we can filter by values simply by going into the row labels dropdown arrow, and then choosing value filters. And in here you have nine different ways that you can filter your values by. We have equals, does not equal, greater than, greater than or equal to, less than, less than or equal to, between, not between and top 10. Now let’s choose the equals. In this filter, you will use it when you want to drill down and find only one value. Putting 24,640, you can press okay. So here we get two different order dates that have the sum of sales that equals to 24,640. Now let’s go on to another value filter. We can simply go in here and choose it does not equal to. Now you will wanna use this when you don’t want to include a certain sale amount. So you can put in here does not equal to 24,640 and press okay. So to give you all the other amounts that do not include 24,640. So again, let’s go to our next value filter. And we have the greater than. Now in here, you wanna focus on a certain sales level. For example, if you want to look at the sum of sales that are more than $500,000. So let’s put in there $500,000 and press okay. And what you’re gonna get now are all the sum of sales that are above the $500,000 mark. Now, if you want to drill down and see what makes up these sales, then you can just double-click in there and you get a whole list of the amounts that make up those sales. Now to go back and press Control + Z and delete, and you go back to your Pivot Table. Now you can also do the greater than or equal to if you want to make it equal to that amount, and also be greater than. Now, we can go on to our next filter which is less than, and click on that. And again, in here, you wanna focus on a certain sales level. So once again, let’s put in our example, of less than $500,000 and press okay. And we’ll get all these different sales values that are less than 500,000. Now go back to our values filter, and we can also put in that less than or equal to amount. The other filter that we have there is the between filter. Now in here, you want to drill down on a certain range of values. So you can say between 100,000 and 200,000 and press okay. And you have all these sum of sales that are between that range. Let’s go back and choose not between. And again, let’s put in 100,000 and 200,000. So it’s not gonna include any values from 100 to 200,000. So what it will include is anything less than 100,000 and anything more than 200,000, and press okay. And you have all these values there. So there are a few filters there that you can use where you can drill down and analyze your financial data in a quick and easy way. (upbeat music) In our data set, we’ve added a new column called the channel partners. And in here we have 125 different channel partners. Now, all we’ve done is we’ve gone to our Pivot Table and refreshed, and we’re putting the channel partners in our row labels and our sales in our values area. And what we wanna do now is get our top 10 channel partners so we can see, which are the best performing. Now, what we have to do is click on the dropdown in the row labels and choose the value filters. And the last option is top 10, click on that. And you get this dialog box that comes up that says, show top. Now you can also choose the bottom if you would like, but we’ll do the top 10 for now. And then in here, it gives us a default number which is 10, but you can go down or you can go up and you can also manually put in there the numbers, but we’ll choose top 10 for now. And then we’re gonna analyze by items now, and we’re gonna use the sum of sales. So that’s how we’re gonna choose and then press okay. And now we get the top 10 channel partners from our data set, and finally, we can also sort this from highest to lowest, just click anywhere in the sum of sales, right-click, sort largest to smallest, choose that. And we see that ABC Telecom is the best performing channel partner. And then you have the other nine best performing channel partners from 2012 to 2014. Now you can also do this analysis if you had lots of customers or if you had lots of salespeople or lots of products. So it’s not only limited to channel partners. There’s many different ways where you can use the top or bottom items filter. (upbeat music) The top or bottom percent filter gives us a list based on a percentage of items that make up the sum of sales. Now, in our Pivot Table, we have our channel partners here, which is a new column that we’ve added in. And we have our sum of sales. Now, what we need to do is go to the row labels dropdown arrow and choose our value filters. And then the last option, top 10, we choose the top 10%. Okay. And then instead of 10, we’re gonna choose the 25%. We can scroll up or down, or we can write in there manually. Let’s press okay. And we get our list here all the way down there. And we get our grand total as well. Now we can sort these from highest to lowest, click anywhere in the values, right-click and then sort largest to smallest, and we have that there. So now we have our list of channel partners that make up at least the top 25% of sales. We can go and see the bottom 25% list by going on to the value filters, top 10, and show bottom. And then press okay. And we have our lists there. And we can sort from smallest to highest. Sort from smallest to highest. And with this analysis, you can drill down to your best and the worst performing channel partners, customers, salespeople, or products, and take the appropriate action needed. (upbeat music) The top or bottom sum filter will give us the channel partners that make up a certain amount of sales. For example, we’re gonna get the top channel partners that account for $2 million of sales. Now to do this, we click in our row labels filter and choose the value filters and top 10. And in here instead of 10, we put in 2 million and then from the drop-down box, we choose sum. and press okay. So now we have the top channel partners that account for 2 million of sales. And we can also sort these from highest to lowest. Right-click, choose sort, and sort largest to smallest. Conversely, we can choose the bottom channel partners that account for 2 million of sales. Let’s go in the filter and choose top 10 again. And from show, we use bottom, and press okay. And here we have our list of the bottom performing channel partners that account for 2 million of sales. And we can sort these from smallest to highest. Right-click in the values, sort smallest to largest. This analysis can also be done on customers, on products, on item numbers, on any given metrics that your company is looking for. (upbeat music) A report filter is used if you want to show a high level summary with multiple combinations of fields. In our Pivot Table, we have put in the salesperson, customer products, sales region, and sales month in our report filter. And you can see in our Pivot Table, it’s shown up here on the top left-hand corner. Now we can choose the dropdown and select one item and press okay. You can see here, Homer Simpson is selected, or we can go to another one, to products and select another item, soft drinks. And we can go to sales month and select January. Now our results are shown in here with our grand total. So it’s a good way where you can drill down and analyze certain specific items within your data. Now to go back, you just click in there and choose all, or you can press Control + Z if you like, and it’ll go back to where it was before. Now, we can also select multiple items and click down on our filter there and choose from the bottom left hand corner, select multiple items. So that activates it. And now we can select and deselect the all button, okay. When it’s not selected or we can choose individual items like this and press okay. Let’s go to products and again, select multiple items and you can also deselect and keep the active items selected that way. Now press okay. And in sales month, we can also choose a couple of months. Say Jan, Feb and March, and then press okay. And we have our results down here. Now let’s press Control + Z to go back. Okay. Another thing that we can do is the use the search box up here, but first of all, to activate the search box for all the items here, we gotta select multiple items. So say, we wanna put in O. So any items that have the letter O will be selected. And let’s add this in there. Okay. Next one, to the products, select multiple fields, and then we can choose in there. IC, you see IC as ice cubes in tonic the letters IC. And let’s choose that. And then sales month, again, activate the multiple items, or we can choose ER, and it gives us the month that end in ER, and then press okay. Now to add more items in our selection, what we can do is go into a report filter and start typing here, J, and you get the option here to add current selection to the filter that’s already there before with the ER. So what we can do is if we choose this box, then January, June, and July will be added to the month that ends with ER, and press okay. And then let’s check that. And we can see Jan, June, July has been added with September, October, November, December. And this is a very powerful feature to have. (upbeat music) Excel is smart enough, and if 90% of your fields are texts, then you’ll get a label filter. If 90% of your fields are dates, then you get a date filter. And if 90% of your fields are values, then you get a value filter. Now, a quick way to activate the filter is to go to your Pivot Table field list. And then you get these hover box. And from the dropdown arrow, you can choose your label filters, value filters, and search box filters. And then you can click on your channel partners, and also from in here, you can choose your filters. Another way is to go into your Pivot Table, right-click anywhere in there, and then go to filter. And then you’ve got your three filters there. You got your top 10 filter, you’ve got your label filters and value filters. Now, if you click in your label filters, you’ll get the 14 different options there. And then if you click on the value filters, then you’ll get your eight different options there. And the third way is to choose your row labels. And from in there, you can choose your label filters and value filters. Now, if your row labels don’t have the drop-down error, then you can go to options and the field headers would be off so you just need to select that. So there are a few ways of you can activate your filters. (upbeat music) We can quickly select items by going on to the Pivot Table and then with a mouse highlighting your selection, right-clicking and in the filter option, choose, keep only selected items. So January to June has been selected and we can see this by going into the row labels filter that January to June are selected there. Now we can press Control + Z to go back. And now we can also hide our selection by going onto the filter and choosing hide selected items. So now only July to December are shown as you can see there as well. So it’s a quick way where you can select and keep or hide your items. (upbeat music) A text wildcard allows us to filter by many different combinations. An asterisk will return any series of characters before or after the asterisk. A question mark or return text that contains only one variable. Let’s go to our Pivot Table filter in our row label and choose a label filters, choose begin to with. So in here, what we’re gonna do is put begins with GLO and asterisk. So anything that comes after the letters, GLO, will be included in our filter. So let’s press okay. And you can see we get Globex Corporation, we get Globo Gym American Corporation and we’ll get Globo-Chem. And you can see here in blue, we have the GLO and the asterisk includes all these letters after GLO. Let’s go back and clear this filter. And we can go in and use another label filter that contains, so anything before the word tech will be included because we’ve put an asterisk before the word tech. Press okay. And we’ll get Initech and Primatech. And you can see here from our example. Now let’s go to another label filter. And we can put equals, and now we’re gonna put asterisks Inc and asterisk. So that means that it includes the word Inc. Asterisks Inc asterisks, and press okay. And we get here our results. And you can see there, everything in blue has been included. So any items that have the word Inc are shown in here. Now, let’s go on to another filter and put in contains. And now we’re gonna use the question mark filter. So in here, we’re gonna say, A?C. So the question mark means any one variable. So it could be ABC, it could be ARC, or it could be A, a space and C. So the question mark means any variable, and press okay. And we’ll get out results here. In blue here we get ABC Telecom. We get Monarch Playing Card Company because it has the ARC, R being the variable, and Sombra Corporation, the space between A and C is the question mark, and Spade and Archer, well, the R is there question mark. So you get different combinations based on the question mark. Now, also you can do this in the search box. So in the search box, for example, let’s type in INC. And in here we have the results that include the word INC, and put an asterisk. Then anything that begins with INC will be included. So you can also do this in the search box. (upbeat music) We can also filter by a multiple fields. In our row labels, we have the sales region and products. So if we go to our Pivot Table from the filter dropdown, we have the select field option there, and we can choose sales region or products. Now let’s choose sales region. And then in the value filters, we’re gonna put in there greater than 8.1 million. So cool, okay. And then press okay. So we only have Asia and Africa because the sub-totals are greater than 8.1 million. Now we can do another filter for our products and say values that are greater than 2,100,000. And we get the items that are bigger than 2.1 million. So you can do two filters when you have multiple fields in your row labels. (upbeat music) Now we’re gonna apply multiple filters in our Pivot Table. Let’s choose the filter button here. And for sales month, we’re going to use a label filter, anything that contains ER, and press okay. So we’ll get all the months that end in ER. Now, what we’re gonna do is apply a values filter for any values that are bigger than 800,000. We choose the sales month and let’s choose value filters greater than 800,000. Okay, so we get anything that’s greater than 800,000, but we also want the sales month filter to include ER, and if we hover over our row labels, we see that our filter is only for sum of sales, which are greater than 800,000. And you have a multiple filter, we need to go into our options. So right-click anywhere in the Pivot Table and choose Pivot Table options. And under the totals and filters tab and the filters, we have the allow multiple filters per field. Now we need to check that and press okay. Now let’s go back to our filter, sales months, label filter, contains ER, and press okay. So now we get all the months that contain ER and are greater than 800,000. And if we hover over there, we can see that we have the multiple filters, sales months contains ER, and value filters, sales months, sum of sales is greater than 800,000. So if you ever gonna do multiple filters, then make sure that you activate that option under the Pivot Table options. (upbeat music) How about sum of sales in our Pivot Table here? Now, so that we want to include the average of sales. Now we can do that, and we can also filter by that. Now let’s our sales and drop it into our values area and from the dropdown arrow, which choose value field settings. And then we choose the average. And we can rename this to average and press okay. So we have the average of sales here. Now we can actually go and filter that. We can go to value filters then for example, we can choose greater than. Now in the show items, we have sum of sales, but in the dropdown box, we can actually choose the average so we can filter by the average, and in there we can put is greater than, and let’s put in an amount, greater than 55,000 and press okay. And we have our average filtered by that amount. (upbeat music) How could I manually filter here for the products? And I wanna show you how to add new items into this manual filter. Now let’s have a look at what’s in here. What I have are the bottles and tonic selected and the rest is not selected. So if we go to our data table, what I’m gonna do here is just copy and add a new item in there. And I wanna change the products from tonic, I wanna change it to cider. Okay. Now we’ll go back and refresh the Pivot Table and then we’ll see what happens. It doesn’t get updated in our Pivot Table, but what it does is cider is included in our filter list. So what we actually do is activate a setting within the Pivot Table, right-click and go to field settings. And in the filter, it says in here include new items in a manual filter. So we actually tick that box and press okay. So let’s go back and let’s add in another product and see what happens. Control + Copy, and paste in there. Instead of cider, let’s put in a new product called beer. And we’ll go back and refresh the Pivot Table. And we’ll see what happens now. Right-click, refresh, beer has been included in there. And also if you have a look at the filter list, beer has been ticked there. So now we’ve added a new item in a manual filter. Now last one we’re gonna do is bring beer on top, right-click and we can sort from A to Z and we have it there. So it’s a good trick to know if you want to include new items in a manual filter. (upbeat music) When you have multiple filters like we have here, it’s hard work to clear them all. For example, we’ve got a lot of filters in there, okay. Say we want to clear them with a click or a couple of clicks. Well, we’ll have to go in there and press all, okay., and then go in there and press all, okay. So it takes time. That’s not the quickest way. Let’s press Control + Z to go back. Now one way is to click in our Pivot Table and go to the options tab and choose clear, and press clear filters. But there’s another way, a quicker way. Let’s press Control + Z to go back to where we were with all of our filters. Now in our quick access toolbar, we can add in a clear filter button. Now let’s right-click in there and choose customize quick access toolbar. And in here we have the commands. Now the default setting is popular commands. Let’s click on the dropdown button and we can go all the way down to the data tab, choose that. And then we have the clear filter button there. We click on that and we’ll press add, and then we’ll press okay. And you can see it’s been added here. So now with a simple click, we clear all of our filters. Now this also works in your Excel tables. So a great tool to have when you’re working with filters in a Pivot Table or in an Excel table. (upbeat music) In the column labels over here, there’s no way that you could put in there a filter. Now if you highlight here, press filter, it doesn’t give you the option, but I’ll show you a quick work around. Click outside the Pivot Table and then press Control + Shift + L, and then automatically it adds in there a filter. So you can filter individual column items by going in there. Number filters, then you’ve got all these different options. (upbeat music) We have our P&L here shown in a Pivot Table, that shows the revenue, COGS and expenses for 2012, 2013 and 2014. And we want to show the top five expenses for each of the years. To do that, in the row labels dropdown box, we choose the P&L type and let’s choose only expenses. So unselect all and choose expenses. Now press okay. So we have the three years of expenses there. And the next thing I want to do is see the top five values for each of these items. So once again, click back into our filter. From the select field the dropdown box, we choose the item and from value filters, we choose top 10. And instead of top 10, let’s change that to the top five. And we’ll keep these to the items and actual, and press okay. So we have our top five items there. Now, finally, we click in our Pivot Table. So it gets sorted from highest to lowest, right-click, choose sort, and sort largest to smallest. So they’re all sorted there from largest to smallest. Now, finally, we can go to our Pivot Table tools tab and see a different layout. And just to show you how it looks, let’s choose the outline form. So in the outline form, we’ll have the year in one column, the P&L type in a second column, the item in a third column and the actual dollars in a fourth column. And in here, you can see the actual filters. So you can choose that. And you can see that we’ve chosen the expenses. And in the item, you can see that we have the value filter in the top five items. And also you can see that it’s sorted from Z to A. Now, another good thing with the outline form is that you can see each of the field headings in here. So the layout depends on your personal preferences. And as you can see from this analysis by filtering and sorting, we can get the top five expenses in your P&L. (upbeat music) We have all of our channel partners and we have 123 records. Now, we want to find out the top 25% of channel partners and list them. So what it is is 25% on 123 gives us around 30 records. We wanna find out the top channel partners. Let’s go down to our records by going Control + Down, and we’ll see our grand total is around 33 million and let’s go up. Now, the first thing we need to do here is right-click and then sort from large to smallest. Let’s go to a row labels, value filters and top 10, and let’s go to 25% of sum of the values and press okay. So let’s see how many records we get here. We get 21, which we put here earlier, and it doesn’t equate to the 30 or so mark we have there. So what this does is it gives us a top channel partners, that makeup 25% of sales. So we saw before that 33 million of total sales. So 25% of that is about 8.3 mil. Next one is let’s go in there and value filters top 10, instead of percent, let’s got to items. So in here let’s count that. So what it does is it gives us the 25. So it gives us 25 items and it doesn’t equate to the 30 mark that we’re looking for. Now, I’ll show you a workaround to get to our problem here. Okay, let’s go in here and clear the filter. Now, a work around to this is to press Control + Shift + L while you’re next to the Pivot Table. And then be tricks the Pivot Table to include a filter as you can see there. Let’s press Control + Z to go back, and then another way is to go to data and press filter. Okay, so from in here, we can go to number of filters, top 10, change it to 25, from the dropdown box, choose percent and press okay. So now if we count all this, we can see that we have 30 transactions. So we get our top 25% of channel partners. (upbeat music) In our Pivot Table, we have our sales month and products in our report filter. And we have some multiple items in there selected January, February, and March, but we don’t know which months are selected if we look at it from this view here. It says multiple items, but it could be any item from January to December. Now this was a real problem in earlier versions of Excel, but in Excel 2010, there’s a new feature. It’s called course license slicers. Slicers are large buttons that shows you what has been selected in your filters, or you can call them visual filters. Now let’s insert a slicer. We have to click inside our Pivot Table and go into the Pivot Table’s tools tab under the options, and in here, we click on insert slicer, and insert slicer. And we’ll get the insert slicers dialog box. And in here, we have all the field lists. So whatever’s in here is also included in the slicers. And we can select any one of them. Now we can select the ones that already active here, or the ones that are not, and let’s let the ones that are active. Sales month and products. And then we can also select sales region and financial year. And finally we’ll select the one that’s not part of our Pivot Table, and that is salesperson, and press okay. And now we have our five different slicers. So let’s arrange these slicers in our workbook. Just grab them and put them anywhere in there with your mouse. Okay. And the products we can bring down here. What we can do is just from the bottom, we can bring it all the way up and like this. Okay. So now we can see that the sales months in May have the three months selected as we said before, and also in our slicer sales month, we can see which months are selected. So now we get a view that’s not available in the report filter. So it gets rid of the multiple items problem there. And we can see that we have Jan, Feb, March, they’re highlighted in blue, and whatever is not highlighted that means it’s not selected. What we can do is from the top right hand corner, we can clear the filter and the Pivot Table updates automatically, and as well as the report filter. And now we can choose a slicer that’s not part of the Pivot Table filter. And then we can choose Homer Simpson and that gets updated automatically. Let’s choose each one of them. Now in here, it means that in the sales region that they’re available in Americas. And because they’re grayed out here, Europe, Asia, and Africa, there are no values. We can check Americas. And let’s clear the filters from here and go back. Now we can select multiple items by holding the Control key. For example, if we want Americas, we chose Americas, hold down the Control key, you choose Europe and choose Asia, and we get our Pivot Table updated accordingly. Now let’s clear that. If we want to choose six months, then hold on January, press the shift key, and then go all the way down to June. And that highlights there automatically as well. Slicers are a great feature, they’re new in Excel 2010, and they give us some visual filters that we never had before. (upbeat music) And let’s resize our slicers. We can actually click in the slicer and we can resize it with our mouse up or down. Now, if we go too much up, then we get the scroll bar. So we just make sure that that’s not there. And we’ll do the same thing here. And also for salesperson, we can also scroll inwards if we like, Control + Z to go back. And now we can move this up here just to make that a bit neater and this one up here. And then finally we can get them months all the way to the bottom there. Okay. So we can just resize as we see fit there. Let’s click out of the slicers. Now, when we click in any one of the slicers, we’ll get this slicer tools option. And we get this option here, which is similar to the one in the Pivot Table. So once you click in the Pivot Table, you get the Pivot Table tools tab. So you get the options and design. And if we click in a slicer, we’ll get the slicer tools option. And we have all these different options here. Now we have one that’s called slicer styles. Click down there, we have our light version and our dark version. Now we don’t have as many styles as we have in the Pivot Tables, but that’s okay these are good enough. And another thing that we don’t see is a live preview when we scroll over this. So we have to actually scroll and choose and then go back in, which is a bit annoying. Now, another thing you can do is if you hold on the Control key and choose all your slicers like that, you can actually go back to your slicer styles and choose one, and they all get updated automatically. You can choose from in there as well, Go to the light, and then you can choose from in there. There’s a few different options where you can work with. (upbeat music) There are 14 different styles under the slicer styles, and you may not like one of them. That’s okay. You can create a new slice of style by choosing the new slice of style option. And then you get a dialog box in here. Now you can rename this to John’s Wicked Slicer. then you have the different slicer elements in here, which relate to the different parts of the slicer. Now, once you make a change, you get up preview in here. And to make a change, you just gotta click in that format area. So for the whole slicer, we’re gonna put in a field of this color. You can also put in a border, if you like, or a font. Now the font will apply for the item in here. So we just press okay. And you see you get a live preview. Next, we’ll go to header and then format. For the header, we’ll fill it in with this color here. And the font will make it into a white font and press okay. Selected item with no data. Here we want to create a slicer where the effect is of a button. So to do this, we go to fill and then fill effects and let’s choose a gray color here. And then on the shading styles, we can use the star here, but you have the different star, so you can choose your sample here. Okay, so if we choose that we get the sample, but we wanna choose this pattern here and press okay. And then press okay. And you can see it over there. Selected item with no data. Here we just want to make it flat gray, and press okay. Unselected item with data, the same thing here. Unselected item with no data, here we’re just gonna make it into a white color and press okay. And the hover is whenever you hover over the slicers, you get the different color. So we can choose the same hover color for each of these four different hover options. So we go to the format. We can go to fill effects and then in here, we can choose this red and we can choose that hover effect. And we’ll go and do the same thing for the rest. You can see here on the right-hand side of the preview, we get the hover options and now we can press okay. Now, if we want to make this a default slicer, then we can click that. So any new slicer that we insert, then this is gonna be our default style. Now press okay. Now to activate it, you just gotta click inside that styles and then you go to custom style. So you’ve gotta click on that. So here we have our slicer with our button looking effect. And if we hover over there, you can see the red hover color. If we choose an item, then everything else that has a value is in gray. And if we anything that doesn’t have a value, is in white. So there it is there. Some pretty cool effects that you can do. And if you wanna go back and modify it, you can just right-click and modify, and you can make the changes there. You can also right-click and duplicate, and you can make some further changes and keep the changes that you make and you can rename it to whatever you like. Another thing you can do is you can delete it. You can also set as default. You can also add it to the gallery, to your quick access toolbar. And another thing that we can do is that if we have a current slicer that we like, and we just wanna make some slight modifications, then all we do is right-click in there and choose duplicate. And in here we can rename it to whatever we like. Name it John’s Wicked Slicer 2. And then here we can make the changes. For example, we can put in a whole slicer, format, we can put in a dark background if we like here. And then our border, we can make it into a dash and apply it there. And the fonts, we can change the fonts here. Instead of black, we can make them gray and bold. We can also make it a little bit bigger, 12 and press okay and okay. And to apply it, we just click from in here, or you can open it and click there. So now we have an extension of one of our current styles. Now, finally, if we go to a slicer elements in here, I’ve explained briefly what the different elements are. And if you make changes, then what part of the slicer will change? So this is a great tool to have when you’re formatting your slicers. (upbeat music) Now we can copy a newly created style that we’ve made previously into a new workbook. Now this is our new workbook here. And if you click on there, you can see the options. We have no custom styles. Now let’s go back to our workbook from chapter 7.3. And in there, we have our custom slicer which we’re created. Now we can move that onto the new workbook and then apply that custom slicer into all the slicers within that workbook. And to do that, we’ll just select the custom slicer Control + Copy, and let’s go back to a new workbook. And in here we just press Control + V. So now you can see that the custom slicer is in this workbook. All we’re gonna do now is click on the old slicer and press Control + A to select all and then choose the custom style. Escape, and now we have the custom style here. Now, this slicer doesn’t work in here. So what we’re gonna do is just highlight it and delete it with our keyboard. So now we can use the new custom style into a new workbook. (upbeat music) Apart from slicer styles, slicers also have settings. Now to activate it, you can go into your slicer tools option once you click in one of the slicers, and then on the far left-hand side, you can choose slicer settings. And in here you have your slicer settings dialog box. That’s one way, just cancel over there. The second way is just right-click in one of the slicers and the last option is slicer settings. So in here we have a few settings. First, we have the source names. That’s the sales region that comes from the data tables. That’s the field name. The second one is name to use in formula. And here you can use this slicer when you’re using cube formulas. And the name you can name this to whatever you like, something different, just something to distinguish one of your slicers. So I could rename this to John’s Slicer 1, and if I press okay, and then I click in the slicer, you’ll see here on the far left that John’s Slicer 1 is activated. So it’s in the name box there. Now right-click, again, go back to slicer settings. And then here we have the header. So the header is over here, where you see sales region, financial year and so forth. And you can uncheck that. If you uncheck that, then the header goes. Right-click to go back in. Now you can display the header, but you can also rename it to whatever you like. Instead of sales region, we can name it sales continent, and press okay. You can see that’s changed. Now, your data table has not changed. It’s only the slicer that has changed. So if you click in here where you had sales region up here, now that is not gonna change. It’s only the slicer. So it’s only for cosmetic purposes. Let’s right-click and bring it up again. Now we can also sort it here. We can sort it from ascending from A to Z or Z to A. If you click there and press okay, you can see that sorted. Now, right-click in there. You can also sort it from in here. Sort of A to Z or Z to A. Let’s go back to our settings. We have the option here to use a custom list when sorting. Now we’ve used the customer list in previous chapters and we can activate this. Let’s press okay and A to Z. And we’ll see, the custom list has been sorted accordingly. And we created a custom list to have Americas first, Europe second, Asia third and Africa fourth. We can check that to go into our files and options. And in advanced, we go all the way down and then edit custom lists. And here we previously created a custom list. So when we’re sort it A to Z, then Americas will be first, Europe second, Asia third, Africa fourth. If it’s Z to A well, it will be the other way around. Press okay to exit. And then we also have a few other check boxes here that you can check or uncheck. Now this says here, visual indicate items with no data, show items with no data last, and show items deleted from the data source. So you can leave them on. Depends on what you like. I usually leave them on. And let’s press okay. Let’s click in one of the slicers and press Control + A to activate all the slicers. Now, if we right-click and bring the settings again, look, we can’t change the names, which is fair enough. But we can get rid of the header for every one of them in one go, or we can rename the caption to call it cool slicers. And if you do that, then every slicer heading will be the same. Now, right-click to go back in there. We can also sort them all from A to Z in one go, just like that. And then also you have the custom list and also the check boxes if you want to show or not show some datas. So there’s a few settings there that you can play with and you have the flexibility to chop and change your slicers. (upbeat music) With a slicer, you can resize it to make it to your own liking. Now, mainly you can click in a slicer, and then from in here you can re-size it whichever way you like, as big or as small as you’d like. Another way is to right-click and choose size and properties. And in here you get your dialog box size and properties, and you have here the size option first, and you’ve got the height. So you can make your changes there and you get the live update and the width and the scale, you have the height as well and the width. You can lock the aspect ratio. So if you change one thing, all of them change accordingly. Now we can also see the position and the layout. So we can move left or right. Up or down. You can disable it as well from there. Another thing you can do is add columns. Now you got one column here going all the way down, but you can increase that to two, three or four, as many as you like. Imagine you had a lot of information there, putting it in two, three or four columns is much better visually and aesthetically. This works well when you’re using months. Now the button height as well can be adjusted, and the width as well. In properties, you can move and size with cells. You can move, but don’t size with cells or you don’t move or size with cells. You can also print object and lock it in there, and press okay. Now, another way that you resize is once you click in the slicer, under slicer tools and options, on the right-hand side you have the size, and the width, and you have the height. And also from here, you got the columns you can change. By pressing Control + A, you can select all of the slicers, and then escape to unselect. In here we have two columns, now let’s bring it back to one. So they can all be the same. Now by pressing Control + A, we can increase the columns for all of the slicers. Press escape. Now let’s press Control + A again. And in here we can align the slicers. We can align it to the middle just so they can be in order. Another thing you can do is when you got Control + A, you can move it around to wherever you like. (upbeat music) We have our Pivot Table, which has the financial year and sales region in the row labels, and the sum of sales in the values area. And now we’re gonna insert a slicer. So we’ll go to the Pivot Table tools, options tab, and choose insert slicer. And here we are gonna choose the quarters. So sales quarter, and press okay. And we can resize it and then move it up here. So we have our slicer for our Pivot Table, number one here. So we’re going to insert our new Pivot Table now. So let’s go to our data set, and in there we can choose insert Pivot Table and let’s put it into our existing worksheet and we can move it in here and press okay. And now we’re gonna put the salesperson in the row labels, the sales month in the row labels, and the sales in the values area. And we’ll get out of that. And we have our second Pivot Table. Now this Pivot Table is called Pivot Table number four. And this Pivot Table here is called Pivot Table1. And we can change this to Pivot Table number two, just for our example. Now let’s insert a slicer for our Pivot Table number two. And let’s choose the products. Slicer, and put it in there. So let’s resize it. Okay, so if we choose that, then our Pivot Table number two on the right-hand side gets updated accordingly. Now, what we want to do is connect these two slicers. So Pivot Table number one and Pivot Table number two, change accordingly. So if I choose Q1, then both Pivot Tables change based on that selection. And to do that, we need to select Pivot Table connections. One way is to go into the options tab and choose Pivot Table connections here on the left. Or another way, you just right-click in there, and halfway down, we have Pivot Table connections and we get this pop-up box that comes up. So what we’re saying is that this slicer that we created is connected to Pivot Table number two on the right hand side, and that’s right. Now we want to activate it and connect it to Pivot Table number one on the left, and press okay. Now we wanna do the same thing for the first Pivot Table slicer. It’s connected to Pivot Table number one on the left, and we want to check it so it can be connected to the Pivot Table number two on the right, and press okay. So now if we choose Q1, then both Pivot Tables change accordingly. Q2, Q3, if we choose the products, then as you see both Pivot Tables change accordingly. So you can have multiple Pivot Tables, multiple slicers, and you can connect them all together and with the press of a button that will all be in sync and talking to each other. (upbeat music) I will show you a few different ways on how you can filter a slicer. The first way is to mouse click on individual entries. Like this. The second way is to hold down your Control key. Once you select one item, hold down the Control key, so you can select multiple items. The third way is to select one item and then hold down the Shift key and then choose the last item you want to select and let go of the Shift key. Now this comes handy when you have a list of items that are over 50 and you just want to select half of them. And my favorite is click on the first filter, and while the mouse is still being pressed, scroll down all the way, just like that. And again, scroll all the way up, all the way down. And then by holding the Control key, you can de-select items. So there’s a few different ways on how you can filter a slicer. (upbeat music) I’m gonna show you how you can use one slicer to control two Pivot Tables without having to activate the Pivot Table connections. First of all, we need to click in our first Pivot Table on the left and select it by going select entire Pivot Table, then Control + Copy from the keyboard. And in here we’ll press Control + V. So we paste it in here. Now what we’re gonna do is we’re gonna take out the financial year and the sales region and include in here the salesperson and the sales quarter, just like this. Now that we have the two Pivot Tables, they’re using the same pivot cache. So if I click January, then they both change automatically. And if I select the first quarter, then the second quarter, and the third quarter and the fourth quarter. Now we can see this by going onto our Pivot Table connections, and the slicer is connected to both Pivot Tables. So there’s a quick way where you can use one slicer for two Pivot Tables without having to activate the Pivot Table connections. (upbeat music) If you wanna send this report out to someone else and you don’t want them to touch the Pivot Table, but allow them to use slicers, then you can do that. To do this we need to select one slicer, press Control + A to select all of them, right-click and choose size and properties. Under properties you’ve gotta uncheck the locked box and press close. And now we need to go into the review tab in the ribbon and choose protect sheet. Now in here, we’re gonna select the first option. The select unlock cells. We have to keep them activated, so we can be able to select the unlocked slicers. And then one more box that you tick is the use Pivot Table reports. And we can put in here a password to protect that, or we can just press okay, and it’ll protect without a password. Just like this. Now let’s escape so we can unselect all the slicers. Now I’m clicking in the Pivot Table and nothing’s happening. So the whole workbook is protected. But if I go on the slicers, I can actually select them just like this. (upbeat music) I’m gonna show you a cool way where you can use slicers and then have interactive employee photos show up. So here’s our slicer. If we choose Homer Simpson, comes up. Ian Wright, myself here, and Michael Jackson. Now there’s a few steps that want need to go through. And once you go through those steps, it’s pretty easy to understand. Now, what I’ve used here is the camera tool to take a photo. And I’ve linked that photo by putting a named range called show employee picture. Now that name range has an offset function in it. Now the offset function is here. It says, start here. Now I’ve named that range. And I’ve also named the range number of cells down. So for an offset function, we have the first part which is where do we start? So it has a starting point and I’ve called a start here. So I’ve called it here on the left cell, A15, start here. The second part of the offset function is how many rows down do we go? And I’ve named this number of cells down. Now I have named cell A9 number of cells down. So as I change this, then it goes down accordingly. So one row down, three rows down, four rows down, two rows down. So by linking this picture with the offset function, we can go grab these pictures that we have here. And it brings them back into this photograph that was snapped by the camera tool. So let’s go to the how to, and I’ll explain how it’s done. First of all, we need to create our table. So let’s get our table here with our number and employee names. So we can Control + Copy and Control + Paste in there. So we have our table here and double-click there. So the first thing we can do is insert a Pivot Table. So let’s grab our data and go to insert and Pivot Table, and we’ll put it into our existing worksheet. Now let’s pull that all the way down here, because once we insert a Pivot Table, we get all these area here and it’s gonna overlap. So we’ve got a bit of space here. Now what we’ll need to do now is to drop in the number field into the row labels. And that’s all we need to do. We’re not gonna put here anything else. So we’ve created our Pivot Table. Just one thing, right-click in here and remove the grand total. Okay, so we have the Pivot Table here and we’re gonna press Control + X to move it here and press Control + V. Okay. So we have our Pivot Table here. Now, one thing we need to do now is name the range. So we’re gonna make a selection, for example, number two and number three. So this cell here, D10, we have to name it because we’re gonna use that for the number of rows to go down in our offset formula. So to name the range, we’re gonna name a number of cells down. All we need to do is go in out main box in there and name it number of cells down. And we’ll put an underscore as well because in our previous example, in the interactive employees, we used this named range. So just to distinguish that we’ll put another underscore. And press enter. So now we have that named. Next, we have to insert a slicer. To do that, we click in our Pivot Table and go to options, insert slicer, and now we choose the employees. So we have the employees’ names there. Now, if we select all we have all of them there. So let’s put our employee names up there, just for now we’ll pack it up there. So the next step is to define our name for our starting position because of the first argument in the offset function is our starting position. So we’re gonna start over here and we’re gonna call this, start here as a defined name. So in there, we’re gonna call it a start here. Now we’ll use that name in our previous example. So we just put the underscore again just to distinguish it. So we call it, start here. The next step is to make these four rows here high enough, so we can include our pictures. So I’ve made them about 100 and you can adjust it to whatever you like. And also you can make them as wide as you like as well. Step number six is to insert the pictures. Now you can insert pictures by bone going into insert, and picture, and you have them all there. Now let’s cancel out of there. What I can do is just go back and grab these pictures. So Control + Copy, and Control + V. So make sure that it sitting there. So we’ve inserted our pictures. Step number seven, we need to define the name for the formula that will drive the pictures. So now we’re gonna put in there the offset function. To do that, we’ll go to our name manager and press new. We’re gonna call that function that will get our pictures. Now you gotta make sure that there are no spaces in there for that to work. And this refers to, well, we’re gonna put in there, our offset function. So we’re gonna say offset, where is their starting point? And we’ve made our starting point, start here. So we’re gonna put in there start here with the underscore, then comma, how many rows down? We’ve named that in there, number of cells down. So number of cells down and underscore. And the next argument is how many columns do we go right or left? And we’ll put in zero and then a comma and then close brackets and press okay. So we’ve defined our range there. Step number eight is to take a camera shot of a blank space, large enough to fit our picture. So we can take a shot over here, but I’ll show you an example. What you can do is just take a shot of a picture like this or space like this with your camera tool. Now, if you don’t have the camera activated, you can activate it by going into file, options and quick access toolbar. Now from the dropdown box, we choose all commands, click in there and press C to go down to the camera tool. And then you can just press add in that. Now I’ve already added it. Press okay, and it gets added in here. So we’ve selected a range here and let’s take a photo. So we’ve taken the photo and now we need to put it somewhere. Where do we wanna put it? We can put it in there. Just click anywhere. So we have our background. Now, finally, we need to reference this blank photograph to our offset named range. So we’ve defined our offset function with a name. So if you put out fun, it would give you the name tag there and then press tab and press enter. So now what it does is the offset function starts here, it goes a number of cells down, one. So it goes down here and it returns back whatever’s in there into our camera shot. Now, if we choose Ian Wright, it goes down three rows from our starting position. So it goes one, two, three. So in here, it’s looking in there and just taking whatever’s in there and returning it back in there. So that’s how it works. Now, finally, we need to format the photo here and just put in a background like this. And now you can use your slicer to bring up all the different pictures and you’ll be as happy as Homer. (upbeat music) In chapter 8.15, we created a P&L by using calculated items. And now we’re gonna add in some slicers where we can control the years and the months for the P&L. After that, we’re gonna drop in the plan numbers and then do a comparison between the actual and the plan numbers. Now let’s click in our Pivot Table and go to the options and choose insert slicer. And we’re gonna insert the months and the year fields. So we have the year field here and let’s just reduce the size here so we can drop it into the top left-hand corner, just like that. Now, instead of having a column heading called year, that’s right-click and get rid of that, let’s choose a slicer settings. And from in here under header, uncheck the display header option and press okay. Now let’s just drag it up a bit and it can fit in there. Okay, perfectly. Next thing is to get the months. And again, right-click, slicer settings, uncheck display header, and from in here on the top options tab, we’re gonna add in some columns. So we’re gonna have four different columns, and then let’s just resize this a bit so we can see it and we can just drop it in here as well. Okay, now the next thing that we can do is highlight the first slicer, press Control and with a mouse, select the other slicer, and then we can just change in the colors from in here. So now we have the different slicers, let’s check 2012, and you see the number changes there, and then we can choose Jan, Feb, March, whatever month that we want, and then the P&L gets updated accordingly. We can do the same thing for 2013 and 2014. Now the next step is to add in another Pivot Table with the plan numbers. So let’s highlight the Pivot Table, press Control + Copy. And in here we can just press Control + V and we’ve copied the same Pivot Table, but we can actually change this around. So instead of sum of actuals, we can get rid of that and let’s drop into the plan numbers in there, just like that. Now, instead of having the field and items listed here, we can just highlight this column and right-click, and press hide. So we can hide that. And we can just reduce this a little bit further in there. So now we can use the slicers to change the actual and the plan, just like that. Finally, we wanna add in a variance. So we wanna see the difference between the actual and the plan. In here, let’s just reduce this. And from in there, we’re gonna choose the revenue. Now we GETPIVOTDATA, so let’s escape out of that. Now to fix this, just click anywhere in the Pivot Table, go to options, and from the options dropdown box unselect the GETPIVOTDATA. Okay, now let’s go back in there and let’s do equals or plus. The revenue of the actual minus the revenue of the plan, and press okay. We can get this and drag it all the way down. And we have our values there. And in here we can put in the header called variance. We can click in there and from the format painter use the same formatting in there and the same thing in here. So now as we choose the different months, we get the actual, the plan and the variance. (upbeat music) We have our actual numbers for the year, 2014, and we want to create three different scenarios for 2015, 16, and 17. We wanna create a base case, a best case and a worst case. Now in our data set here, we have our actual values for 2014 hollered here in purple. Now all we’ve done is we’ve copied these values here and we’ve put in this scenario for base, best and worst case. So we’ll just copy and paste the values, and our idea is just to change the name from actual to base, to best, and then to worst, because when we do our Pivot Table and we create the three different scenarios, we want the 2014 actual numbers to be shown all the time, because we’re gonna do a variance analysis. Now, what I’ve done here at the bottom here is putting their 2015 values. So I’ve done the three different scenarios, worst, base and best. So I put in different colors just to distinguish them. And then in here, what I’ve actually done is I’ve put in a formula that relates back to the 2014 actual values. And I said, the worst case will be 95% of that. So I’ve done the same thing for each of the months. Now for the base case, I said that it’s gonna be about 5% increase on 2014. And then for the best case, I say, it’s gonna be about 20% increase 2014. Now I’ve done the same thing for 2016, but I’ve changed the values there. I said that 2016, the worst case will be the same as 2014. It’s base case will be about a 10% increase. And then its best case will be about 25% increase. And the same thing for 2017, I said, it’s gonna be a 5% increase on 2014 for its worst case, 25% increase for its base case, and then a 50% increase for its best case. Okay, so what I’ve done is I’ve gone through the Pivot Table in here, and what we have to do now is drop in our sales month into the row labels, our financial year into the column labels, and then our sales into the values area. And let’s just double-click in there to make it even, and then just put it into the center and we can just make it a little bit bigger like that. So we have our values here, but what we can do now is drop in our slicer. So go to options, insert slicer, and let’s drop in this scenario slicer and press okay. Now let’s make a couple changes to this. Let’s make it into four columns and then we’ll just drag it across like that. And then right-click in there in slicer settings, and let’s get rid of the display header, ’cause we don’t have to save that. And let’s just put it like that and put it on the top there. So we’ll have all our scenarios here. So if you choose the actual case, then we just see the actual numbers for 2014. If you choose the base case, because we had the numbers in 2014 in base case as well, we can see that 2014 shown there and we’ve got it 2015 to 2017 base case scenarios, but we’ve got the best case and the worst case. So finally what we’re gonna do is it drop in a value calculation to see the difference between the 2014 numbers. So click in the Pivot Table, grab the sales, drop into the values area. From the dropdown box, choose value field settings, show values as. From the dropdown box, we choose the percentage difference from. Now, the base field is gonna be the financial year and the base item will be 2014. So we’re gonna show the values as the percentage difference from 2014 financial year. Now the custom name we’ll just change that to percentage variance from 2014, and then in the number format, the custom, let’s just choose this format here and before the semicolon, let’s put in that percentage and the same thing there, let’s put in a percentage and press okay, and then okay there. Let’s just reduce this a bit. So we see the worst case scenario for 2015 is minus 5% on 2014, 2016 will be even, and 2017 is a 5% increase. If we go to our best case scenario, we see the different increases there. We have our best case scenario as well, and we can see the changes. (upbeat music) A calculated field is a newly created data field. This is created when you make a calculation with your existing fields. So in essence, you’re adding a virtual column to your data set. And to create a calculated field, you gotta click anywhere in your Pivot Table, and then in the options tab under the calculations group in the field items and set dropdown, choose a calculated field, which is the first option and you get your insert calculated field dialog box. Now in the fields here, you have all the fields that are in your Pivot Table field list. Okay, so you can choose any one of them. Now on the top, we have the name and this we can change to customize it. So what we’re gonna do now is get our cost of goods sold, which is our costs divided by our sales. Now the name, let’s change it to cost of goods sold or short, COGS. Now in the formula, you have the zero there. You gotta get rid of this. Backspace to delete it. Now the next step is, choose your field. So to do that, we’ll get our costs. We can click once and press insert field. And now we can use the mathematical science that we use in Excel. So we can use divide. We can also use the minus, plus, multiplication, we can use percentage, we can use to the power of, and also smaller than, bigger than or equals to. We’re gonna use the divide. So cost divided by sales. Now let’s find our sales and we can press insert field or double-click, and then we can add this. So it’s added in our list here. And then we can press okay. Now when we press okay, you’ll see that a new column will be created, and also COGS will be added into our field list as a virtual field. Press okay. So COG is added there and also in here in our values. Now, one thing we need to do is to right-click in here and number format, because it’s a percentage, we need to use the percentage and press okay. So just to format it there. So we have our cost of goods sold there. And finally, we can go into our values area and change the name from sum of COGS to COGS. Now click on the dropdown box and choose the value field settings. And in here we can get rid of this. Now, what I usually do is put in an asterisks there, just so I can distinguish that it’s a calculated field. Because if not, then you may confuse, you may think that this COGS might be a summarized value, or it could be a calculation. So it’s always good to put some kind of sign beforehand just to distinguish it and to show us that it is a calculated field. Now let’s press okay. And we have our COGS in there. So what we can do now is when we filter our Pivot Table, then this calculated field changes as well. Now if I put in there the sales month in there, then that will change as well. If I take out the financial year in there, we have our COGS which gets recalculated. So it’s embedded into the pivot cache just like a grand total. (upbeat music) Now, we can use any existing calculated field to create a new calculation within our calculated field. And to do this, let’s click in our Pivot Table and go to the options tab and under calculations, choose fields, items and sets, and choose calculated fields. What we want to do is calculate our sales margins. So the calculation will be one minus COGS. First of all, let’s change the name and call it sales margin. And in our formula, you get rid of the zero. And then put in one minus. And here we’re gonna choose COGS. So our previously created calculated field is included in our field as well as our field list. So let’s double-click on COGS and press okay. So we get our sales margin there. Now we need to format the numbers into percentages, right-click and press number format, and percentage, and this put one decimal place just to activate the percentage symbol. Finally, we’re gonna put in an asterisk to our sales margin just to distinguish it, so that we know it is a calculated field. To do that, click in our dropdown arrow, and choose a value field settings. In here, we get rid of sum of sales. And if we get rid of these and press okay, we’ll get an error message, that the Pivot Table field name already exists. Well, that’s correct because it exists in our field list down here because we created that in our calculated field. So we’ll need to distinguish it. Now we can put in a space and that will work because Excel recognizes space as a character, but instead of a space, because we want to distinguish this calculation as being a calculated field, then we’ll just put in an asterisk, and then press okay. So we have the sales margin there, which is our calculated field, just like our COGS. And up here, we have our sales margin and our COGS. So we’ve used one calculated field to create a second calculated field. (upbeat music) If you made a mistake whilst creating a calculated field, then you can go back and edit it. Now to do that, you click anywhere in the Pivot Table and choose the options tab and then fields, items, and sets, and calculated field. In here from the dropdown box, you’ve got your calculated fields. Now we can choose our sales margin. We can modify or delete. If we press delete, then it’ll delete the calculated field from our Pivot Table and our pivot cache. Now let’s press that just to see, and press okay. So you can see that it’s gone from there. And sales margin is also gone from our pivot cache and our values area there. If you press Control + Z, you can’t go back. You have to go and recreate it. So once you delete it, make sure that you’re certain that you don’t want the calculated field before you proceed. Now to modify our calculated field, we can choose calculated fields. And from our dropdown box, we choose our COGS. Now, first of all, we gotta press the modified button. So in here we can make our changes. So instead of costs divided by sales, we can say, for example, one minus costs, and then we can press okay. And you can see here the change we’ve made. Now one minus cost, it means nothing. I’m just showing an example that you can go there and modify it. We can go back and change that. Once again, COGS, modify, instead of one minus cost, it’ll be cost divided by sales, and then press okay. So the changes we made and the calculated field also remains in our Pivot Table field list and our values area. (upbeat music) Within a calculated field, we can use any Excel functions like a sum, an if, an or, an and, or an average as an example, as long as I don’t reference external cells. Now, let’s create an if statement. Click in our Pivot Table and go to options and fields, items, and sets, and calculated field. Now in the name, we’re gonna change that. So what we want to see are rebate given if our sales are more than 700,000. So if our sales are more than 700,000, then we’re gonna give a rebate of 3%. So the name will be rebates given, and the formula, we’ll get rid of zero. Now we’ll start typing in the if statement just like we would in our Excel workbook. Now, one thing in here is when you’re typing in an Excel function, you don’t get the help bar. So you gotta make sure that you know what each step within a function needs to be. So within and if function, the first step is our argument. So if sales are more than 700,000, then we have to give a 3% rebate on sales. So would be sales times 3%. If not, then zero rebate, close brackets and press okay. So you can see there, we have our rebates given for anything over 700,000, anything less than 700,000 is zero. Now just make a note that we have to get rid of the sub-totals and the grand totals, because what’s happening is that when you’re doing a calculated field, that it’s also calculating each sub-total. Now this is not correct because our sub-total for January is 23,000. That’s the only a rebate that was given. But here we’re getting 81,000. So make a note of that when you’re doing calculated fields, and you have an if statement where you can get zero values, then you gotta make sure that the sub-total is turned off. So to do that, we just go into the design, sub-totals, do not show sub-totals, and then grand totals, off for rows and columns. One final step is to rename our rebates given in our values area. From the dropdown arrow, choose value field settings, because we want to distinguish our calculated field. Then we just put in there an asterisk, and then press okay. So we have our rebates given. So it’s that easy to create a calculated field with an Excel function. (upbeat music) A calculated item is a virtual data item, created by using a row label or a column label item. So calculations will be based on the items in the months row label and also the years in the columns labels. Now, when we did the calculated fields, we used the field list to do our calculations, but now we’re gonna use the items within the field list. So let’s click anywhere in our row label or column label. So if we go to options and field items and sets and choose calculated item, we’ll get our dialog box. Now on the left-hand side, we have the fields and on the right-hand side, we have the items. So we’re gonna use our items to create a formula. Now, what we wanna do is create a bonus scheme. So for the first half of the year, we give a 10% bonus. And for the second half of the year, we give another 10% bonus. So our formula name will be H1 Bonus, and then in the formula, we get rid of zero. And we’re gonna put 10% times, now in here we can put any mathematical equations that we will normally use in an Excel function. So we can use the times, the plus, the minus, the power, less than, more than and equals to. In our example, we’ll use the times. So 10% times and a bracket. So the first half of the year, it’ll be January plus February, plus March, plus April, plus May, plus June, close bracket and we’re gonna add this. Okay, so we’ve added this in there. Now we again create a new formula for H2. So we can override this, that’s fine. So let’s get rid of the content in there. And what we need to do is choose a sales month. Now it gets added in here. Now that’s fine. If you press okay, it’s not gonna work because we’re in calculated items and it can only take in the items. So let’s get rid of this. And let’s put in our months from July to December. July plus August, plus September, plus October, plus November, plus December. Now, you’ll see here that our calculation that we did before is included in items within the sales month. And we can use this later on to make further calculated items. Okay, so we’ve done our H2 Bonus and press add. And we’ll see, it’s gonna get added in the bottom of our row labels. Press add, and then okay. So we have our H1 Bonus which is 10% of the first half of the year. Now we see our sum there is 5,011,116. So our bonus will be 10% of that, which is here, which is correct. And from July to December, our sum is 5.3 million and we have the same there. And it calculates it for each year as well. Now the grand total, make sure that that’s where you stop, because if you add this, it adds the H1 Bonus and the H2 Bonus as well into the grand total. Now that’s a shortcoming of calculated items. So we have to delete the grand totals or any sub-totals that you may have. So to do that, go to the design, grand totals off for rows and columns. (upbeat music) We can use an existing calculated item within their new calculation. What we wanna do is get our H1 Bonus and see what our average is for the six months. So it’ll be the H1 Bonus divided by six and the same thing for the H2 Bonus. And to do this, let’s click in our row labels, and go to the options tab and choose the field items and sets and the calculated items. In the name, we’re gonna call it average H1 Bonus. In the formula, get rid of zero. And we’re gonna choose the H1 Bonus. I wanna divide it by six, and then we can add this. Next we’re gonna get the average H2 Bonus. We can just override these and get rid of this first, and let’s go back to our sales month. Okay. And get rid of sales month. Now, let’s choose our H2 Bonus, insert item, and divide it by six. And we can add this and press okay. So we have our average H1 Bonus, which is 101,000, and our H2 Bonus 89,000 for 2012. For 2013, we have the same values. And 2014, we have the calculated item as well. So you can use previously created calculated items and you use them in your new calculations. (upbeat music) There’s a couple of ways to edit a calculated item. First of all, let’s click in our row label, go to options and fields, items, and sets, and calculated items. From the dropdown box, let’s choose the average H2 Bonus. Now in here, if you want to delete it, you can just press delete, and press okay. Now that’s gone forever. So before you deleted anything, make sure that you really wanna delete it. If not, you gotta go back and recreate the calculated item. Now, if you wanna modify a calculated item, just go back and choose calculated item. And for a dropdown arrow, choose average H1 Bonus. Now we can make changes to the formula here. That’s fine. We change that to eight or 12 or anything like that, but if we make any changes to the name, for example, we change that one letter, then it adds whatever we’re making here as a new calculated item. So if you wanna modify a calculated item, you can only modify whatever is in the formula. So let’s go back and press S. So for example, instead of six, we can change it to 12 and modify and press okay. And you see that changes there. Okay. Another way where we can make a change is within the Pivot Table. Now the formula is there. So we can go back and change that to six and press enter. What happens is it only changes that for that here. So you need to go and change it for each subsequent year. You can also change the name there. Instead of average H1 Bonus, you can call that average bonus. And if we go back to our calculated item, then you see the name there has changed as well. It can be a little bit tricky when you’re modifying within a calculated item. So just make sure you take care before you make any changes. (upbeat music) We can also use Excel functions to calculate an item, as long as they don’t reference any external cells. So you can use functions like sum, if, or, and, average, and so on. Now to do this, let’s click in our row label and go to options and fields, items, and sets and calculated items. So what we’re gonna do here is get the average for 2012, 2013 and 2014. So in our name, we can call that average. And in our formula, we can go back, get rid of the zero. Let’s put in average, open brackets and put in January, February, March, April, May, June, July, August, September, October, November, and December and close bracket, and we can just press okay. And then we’ll get out average here for each year. And we can check this. Let’s highlight Jan to December and in our status bar. We have our metric set. Now, if you don’t wanna have this just right-click, and you can choose here, the average count, numerical count, minimum, maximum or sum to activate it. Now we can see that our average is 865. If we go back 865. So the same thing for 2013 and 2014. (upbeat music) We’ve been using calculated items in our row labels. We’re gonna also use them in our column labels. So to activate that we can just click anywhere in the column labels and then go to the options tab, fields, items, and sets and choose calculated item. Now in here, we can create our formula. So what we’re gonna do is get the variance between 2014 and 2013, and also 2014 and 2012. So put in here variance 14 versus 13, and get rid of the zero and the formula will be 2014 minus 2013. And let’s add this. And let’s do another one. Variance 14 versus 12, and in here financial year. Okay, let’s get rid of financial year and choose 2014 minus 2012, add, okay. So we have our newly created calculated items that give the difference in our column items. And it’s important that we name them as we have, because if we filter, for example, take away 2012, then we’ll know what these values relate to. And to go back, we just filter off. Another thing is that we can put him in there sales regions, add it in there, and that calculated item will also calculate in there. Also we can go to design and add in our sub-totals to the top of the group. And again, we have our calculated item working there as well. And you can also do this if you had actuals versus budget instead of years. It just depends on what items you have within your data source. And if you put them across the column labels, well, you can do your calculated items there. (upbeat music) There are a couple of limitations when you’re using calculated items. One of them is if you have the average sales here, like we do average of sales in our values. And also you can see there, we’ve summarized it by the average. And say, we want to go into our row label and calculated item from there, we get a warning that averages, standard deviations, and variances are not supported when a pivot has calculated items. So that’s one short form. Now, if you had a calculated item in here already, and you wanted to add in there an average standard deviation or a variance, then that also could not be done. Let’s go to our next shortcoming here, grouped items. We have group sales here. Now let’s go into our field items and try to put in that calculated item. Again, we get a warning that we cannot do this because the Pivot Table report field is grouped. Now, if we had a calculated item in there and wanted to group anything with a calculated item in there, then that also will not work. And finally, when you’re in a row label here, you can only do a calculated item for the actual row label that you’re in. So with selected products, you can go and create a calculated item for salesperson. To do that, you gotta click in salesperson and then go to fields, items and sets, and choose calculated item. If you wanna do it for products, you gotta click in products and then choose calculated items. (upbeat music) I’m gonna create a couple of calculated items. One for the row labels and one for the column labels. And then I’ll show you how we can use the solve order to get around a problem that can occur. So, first of all, let’s do a calculated item for our month, click in the row labels there and go to the options and fields, items, and sets and choose calculated item. And our formula will be the December sales divided by all of the year sales. So we want to see the weight of sales in December. So let’s change the name to December portion percentage. And in here, we’re gonna choose December and then we’re gonna divide it by the sum of all the months. Close bracket and press okay. Just to make this a bit bigger and in here. So we’ll go to the home tab and then we just put in a percentage in there and we just put a decimal place if you like. And next let’s sum the different regions that we have. So we’re gonna sum Americas and Africa into West and Europe and Asia into East. So to do this, we just gotta click anywhere in that column label and go to the options in the fields, items and sets, choose calculated item. In here, we’re gonna call first formula East, and we’re gonna choose Europe plus Asia and we’ll add. The next formula will be West. And in here we’re gonna choose Americas and Africa, and press okay. So finally we change this around. So Americas and Africa we can put together along with West. And then Europe and Asia are together and the totals are there. Okay. Now let’s just format this a bit. Okay. And in here, we’ll put up a bold for the West and for the East, we can bold that. So we can see our results. And we have a problem here in that the calculation that’s down here is not the same calculation that’s in the December portion. It’s giving us a wrong calculation. So we can fix this. Let’s go to the options and fields, items and sets, and choose solve order. Now in here in solve order, it says, if the value in a pivot cell is affected by two or more calculated items, the value is determined by the last formula in the solve order. So we have two different calculated items. So what it’s doing, it’s taking the last calculation that we did, the West, which is Americas plus Africa. So it’s taking that calculation to calculate this. We don’t want that. Now we’re gonna move the December portion percentage to the end so it can take effect. Now to do that we click and then move it down and press close. And you can see the percentages have changed. And also you can see there that the formula has changed, and in there the formula has changed. So can you use a solve order if you have two or more calculated items that are clashing. (upbeat music) Every time you create a calculated item or a calculated field, then the formulas that you use are listed. Now to check that you just click anywhere in the Pivot Table, go to options, fields, items, and sets, and then choose a list of formulas. Now, in here, we have our calculated item formula for the December portion. Also we have the calculated item for our column labels. Choose that, and we can see the East formula equals Europe plus Asia. The West formula equals Americas plus Africa, and with December portion percentage, equals December divided by the sum of the whole year. And they’re listed in order of one, two, three. So the December portion is the last solve order, and that takes precedence when there are two or more calculated items that clash. So this is a good way to see what calculated items or calculated fields that you have in your Pivot Table. (upbeat music) Now we have our calculated field here, which we created earlier. Now we’re gonna remove this temporarily. If you can see here on the right-hand side, on the Pivot Table field list, we have the COGS calculated field in our pivot cache. We can just uncheck and remove it. And then we can go and do some adjustments to our Pivot Table. For example, we can take out the sales region and sales month, and we just bring in here the quarters. And if you want to bring in the COGS again, then we can bring it in there. And the only thing is that we just need to format this into a percentage, press percent and press okay. So you can temporarily check or uncheck the calculated fields. (upbeat music) The order of operations is a rule used to clarify which procedures should be performed first in a given mathematical expression. The calculations in a Pivot Table also follow this order of operations. For example, we have our table here called order operations listed by first to last. So the bracket take precedent, then the percentages, then be exponents, then we have the division and multiplication come next. Now these are equal in precedence. Addition and subtraction, come next. Now these are also equal in precedence. And then we have the comparisons. Now let’s go to our example up here, just to have a look. We have the formula two plus four times five. So in here, the multiplication has a higher order than the addition. So this will get calculated first. So the four times five will get calculated first. So four times five is 20, and then it’ll add the two because the addition comes after the multiplication in the order so we have 22. Now in the next example, we have put brackets between two and four. So obviously the bracket which come first in the order of operations take precedent. So it will calculate two plus four first, which is six, and then it’ll multiply it. So six times five is 30. Now this order of operations is also used in Excel and in your calculated items and fields. So when you’re making your formula, just make sure that to look at this order of operations, if your formula is not working properly. (upbeat music) In this chapter, we’re gonna create a P&L where we show the revenue, COGS, gross profit, expenses, and then get our operating profit over the 12 months. And what we’re gonna do is add in a trend line by inserting some sparklines. And also we’re gonna put in a slicer to see the different years. Now, we’re gonna include calculated items in here and the calculated items are the gross profit. So the calculation will be revenue minus COGS. And then the second calculated item is down here, which is operating profit, which will be the calculation, gross profit minus expenses. So we have our data in here and we have our months going down on the month column. We have our different years. We have a different P&L types, separated COGS, expenses and revenues, and they’re separated into the different items. So we have different expense items as you can see in a normal business. And we have the actual values and the plan of values. So this is a typical P&L that you find in most businesses. Now, from in here, we can create a Pivot Table. We go to insert, and Pivot Table, and we’ll put it into a new worksheet, and press okay. In the row labels, we’re gonna drop in the P&L type. And then we can drop in the item at the bottom. And then on the column labels, we’ll put in our months and in the values area, we’ll put in the actual dollars. And let’s close this, and we have our P&L taking shape. Now let’s just make a few design changes. We can choose this Pivot Table design, got to view and get rid of the gridlines, but we can reduce this to about 80%. Now, the grand total, we don’t want that. We can just click on grand total, right-click, and then remove grand total. So we have our P&L here. Now let’s click on COGS, and we can actually bring the revenue up there by just typing in revenue. REV, and you’ll see, it gives us the revenue option. And then press tab. So it automatically moves the revenue from the bottom to the top. We have our COGS, and that’s fine. Let’s minimize that. And now we have the expenses there. So what we’re gonna do now is get the gross profit. So let’s click anywhere in our P&L type item. And then go to the options and fields, items, and sets. And in here, which choose calculated item. Now, the calculated item we’re gonna do is gonna be called gross profit. So it’s gonna be revenue minus COGS. So the name is gross profit. And the formula we just click there, and backspace to get rid of zero. And then we’re gonna get the revenue. Double-click, the minus sign, and then double-click on COGS. And then we’ll press okay. And you can see it’s added it down here, gross profit. Now it’s calculated all the different items that belong in the revenue, but we don’t want that. So what we’re gonna do is the actual values are within the sub-totals in here. So let’s minimize gross profit and let’s go up here. And then we can just click on there and grab it and just put in there. Now let’s right-click and to show the field list. And from the values area, let’s just format the numbers. So we’re gonna put in there a comma. Let’s choose number format, and then number, no decimal places and use 1000 Separator, and put a negative red font there. Okay. So we’ve entered our first calculated item called gross profit. Now we’re gonna add in our second calculated item, and it’s gonna be called operating profit, and it’s gonna be the calculation of gross profit minus expenses. So firstly, we have to put in our cursor in one of the items within the field name called P&L type. And anyway, here in the blue area, we can choose and then go to options, fields, items and sets, calculated item. Now from the dropdown you see we have the gross profit, but we wanna create a new calculated item, and we wanna call it operating profit. In the formula, let’s get rid of the zero. And then in the items we have the gross profit, which was the calculated item that we created earlier. Let’s double-click there and then press minus and then double-click in expenses and then press okay. And we’ll see we have the different calculations for the operating profit. Now in our operating profit here, we have the expenses being deducted from the gross profit amount. Okay. So now let’s go to the operating profit. Now we don’t actually need these numbers here, they don’t mean anything to us. We just need the sub-total there. So let’s minimize operating profit and then go all the way up. Now, one thing is, let’s get rid of the grand total there. We can go to design, grand totals, and off for rows and columns. Now let’s select the months there and right-click, and column width let’s choose 12. Now in here, we’re gonna put in our trend. So we’re gonna put in some sparklines to see our months and how they’re trending. So let’s type in trend and we can click there and format the painter and just bring it in there. Now, one thing let’s make this centered, okay. And we can just the format there. Okay, so now let’s go into insert and sparklines. Let’s choose a column for our sub-totals. The data range will be January to December. Press enter and then press okay. So we have the sparkline, let’s make this a little bit bigger. Now let’s choose a different color. We just get a light color there and then under the marker color for the high points, we want a red. So we wanna see the highest points. So we’ll see here that our fifth month was the highest point. Now what we’re gonna do is press Control + Copy and highlight this area, hold on the Control key, and then highlight the operating profit. Let go of the Control key, and now I just press Control + V. And it fills in these sparklines for the respective sub-totals. Now we’re gonna put in a sparkline in here. So go into insert and let’s put a line, data range, January to December, press enter, and then okay. Now once again, the color will go blue color and that high point we’ll make it red. And again, Control + Copy, select that, hold the Control key down, and then select the other range. Let go of everything, now Control + V. So we have entered the different sparklines there. Now, last thing we wanna do is put in a slicer on the top here so we can tell the years. So click in your Pivot Table, options, insert slicer, let’s choose a year and press okay. So from in here, we can actually now this is selected, choose the columns two, three, and then we can just make it a little bit bigger, and right-click in there and then slicer settings, let’s get rid of the display header, and press okay, and we can bring that like that. And let’s put it in the corner there. Okay. So we have our P&L here with our sparkline trends, and our years here. So if we choose a year, 2012, the numbers change, the sparklines change, the same for 2013 and the same for 2014. And I think your boss would be pretty proud of the final product. (upbeat music) in this chapter we wanna create a variance report for our sales regions and products over the different quarters. And we’ve used the calculated fields to calculate our variance. So what we’ve done is we’ve got the actual versus the plan. Now you can see that in yellow for each of the quarters and also for the total on the right-hand side there. And with our slicer, we can actually change the months and our calculated fields change as well. So let’s go over to our data table and I’ll explain to you how we can set this variance report up. So here we are in our data source. We have the customer, product, salesperson, sales region, as we had before, the order date. And here we have the actual and plan. And we’re gonna use these fields here to calculate our calculated field. So we’re gonna say the actual minus the plan will give us our new calculated field. Now let’s click anywhere in our data table and go to insert, and Pivot Table. And we’ll go in and put it into a new worksheet and press okay. Now the row labels, we’re gonna put in the sales region and the products. On the sales value, we’re gonna put in the actual and the plan. And in their column labels, we’re gonna put in there the sales month. And let’s grab the sales month and just bring it on top there on the column labels. Now this view, we’re gonna see the actual and then the plan. And then we’re gonna add the calculated field in the next column down here. It’s gonna say actual minus the plan and will give us the variance. So let’s close in here and do some cosmetic changes. And we’ll go to view and get rid of the gridlines. And then let’s minimize this. Let’s go all the way down like that, like that just for now. And we can make the changes later. ‘Cause another way in our Pivot Table, we can just go to the options and choose the fields, items and sets, and then choose a calculated field. Now in here the name, we’re gonna call it actual versus plan. Actual versus plan. And the formula, let’s get rid of the zero, and we’re gonna double-click on the actual field, put the minus, and then double-click on the plan field, and then press okay. So you can see there the sum of actual versus plan has been added for each of the months. We go right across there. You can see that’s been added there. Now, let’s click and right-click on show field list. Now the sum of actual versus plan, the calculated field has been added into the values area. Now let’s choose a drop-down, go to value field settings. And instead of calling it sum of actual versus plan, let’s get rid of this, and let’s put in an asterisk. Just to distinguish this that it is a calculated field. And then just press okay. Let’s go to select entire Pivot Table, then select values and then press Control + 1 to bring up the format cells dialog box. And in the number, let’s just format the number, get rid of decimal places, the 1000 Separator and the red and negative font and press okay. You see that changes that for everything there. Now what we wanna do is a group our months into quarters. So let’s highlight the months that we wanna group, right-click click in there and press group. Now the default name, Group 1 is shown, but we can overwrite that by just typing in Q1 in the keyboard and pressing enter. Let’s do the same thing for the other months. Let’s grab April, May and June, right-click, group and call it Q2. Let’s grab July, August, September, and call it Q3. And then finally, October, November and December, right-click, group and call it Q4. Let’s just make this a little bit neater. Okay. Let’s pull up on the left. ‘Cause now that everything’s grouped, we can just right-click and expand, collapse, collapse entire field. So it collapses all the fields and we have the actual versus plan for each of the quarters now. So let’s highlight the actual versus plan. Hold the Control key down on your keyboard, and let’s just select the calculated field. And then in the font, let’s put in a yellow font, just to distinguish it. Now, if we hover over the actual versus plan calculated field, we get a little black arrow. Now press the mouse, and then it highlights everything. Press Control + 1, and we get the format cells in the border. We can put in there a black or a blue border. Let’s put in a blue one. And then on the right-hand side, just to show where the actual versus plan finishes. In the column labels and the row labels, we don’t want that. Let’s go to options and then get rid of the field headers. Now, finally, we’re gonna put in a slicer just to see the different years. So let’s make a bit of space in there to put in the slicer. Click around in the Pivot Table, go to options, insert slicer, and let’s grab the sales here. So financial year and press okay. Now right-click in there, go to slicer settings and click in there just to get rid of the display header and press okay. Now let’s move this like that. And then the columns let’s make it into three columns there. And we can move it over there and we can change. So with a slicer, we choose 2012, the actual versus plan calculated field which changes for each quarter and the total. 2013, the same thing, and 2014 as well. So here’s a quick variance report created by using a calculated field for your actual versus plan fields. (upbeat music) With chats, you can roughly see your data and can easily see trends within your data. Now in a Pivot Table, you can insert pivot charts. Now this is a visual way to see your Pivot Table data. A pivot chart is an extension of your Pivot Table. So as you’re making changes to your Pivot Table, then your chart also gets changed. So let’s insert a pivot chart. Well, first, we have to click in our Pivot Table and in our Pivot Table tools tab in our ribbon, we choose the options and on the right-hand side under tools, we have pivot chart, and then we choose there. Now we get our insert chart dialog box, and we have the different charts on the left. Now let’s insert a simple column chart and press okay. So now we have our chart in here. And note that the X-axis has the sales region. So all the row labels are always in the X-axis. And then the Y-axis, we have our years and our values. If we wanna move around the sales regions to the column labels, we can do that. And you’ll see that our pivot chart will also change. So let’s get the sales region from the row labels to the column labels, and then the financial year down to the row labels. As you can see now in the X-axis we have our financial year and on the Y-axis, we are showing our regions. And you’ll also note that when you’re using a pivot chart, that the names change here. So instead of row labels, we have axis fields categories. Instead of column labels, we have legend fields series. Another thing that you will notice is that we’ll get our filters in our pivot chart. So we can actually filter from in here. So we can choose the financial year just to include two years, and that gets changed, as well as our Pivot Table. So they’re both connected. So this pivot chart is an extension of the Pivot Table. Now we can make the changes from the Pivot Table and select all and pivot chart gets changed as well. Sales regions, we can just choose one. This gets amended and so does our Pivot Table. We can also go back to our Pivot Table and make the changes there. Now these report filters within our chart takes up space. So I like to get rid of them. Now, just hover over one of the report filters and right-click, and then choose the hide all field buttons on chart. So now we’re just showing our chart. Now we can use our Pivot Table to control the filters, and change our chart. Go back and select all. So this pivot charts give us the power to visually see our data within our Pivot Table. (upbeat music) Now, if we click on our pivot chart, we get the pivot chart tools on the ribbon here. And we have the design layout format and analyze. Now let’s click on analyze. In here, we can see the field buttons. So we can show all or we can hide it all. We’ll choose whichever ones we want to see. We also have the option to see our field list. We can clear all or clear all filters from here. We can also refresh the data and we can insert slicers. Let’s insert a slicer. Now we’ve covered slicers in our chapter seven. Okay, so let’s go and choose our sales quarter. And then we can also choose our salesperson and press okay. So we’ll have our two slicers added in here. And we can just move them a bit like this. Okay, so as we choose the salesperson, then the chart and the Pivot Table gets filtered automatically. Now the same thing for the sales quarters. If you wanna say Q1, Q2, Q3, and Q4. So the slicers, it gives us a visual way to see our filters that we have selected. Now we can also click in our Pivot Table and choose options and insert slicer, and from in here we can choose our financial year and press okay. And now we can choose 2011, 2013 and 2014. So there’s a couple ways to insert a slicer. And once you insert them, they’re all connected to the pivot chart and also the Pivot Table. (upbeat music) If you click in your pivot chart, you get the pivot chart tools tab. And we have analyze, format, layout and design. Now under the design, we have a few different options here. First of all, we can change the chart type if we click in there. We can choose any of these charts to change them to. Let’s choose pie chart and press okay. And you see that that has changed. We can go back, choose aligned and then that changes as well. Let’s choose an XY scatter and press okay. Now we’ll get a warning that we can not use an XY scatter, bubble or stock chart type with a pivot chart. So that’s the only limitation to choosing a chart, is there’ll be cannot choose these three charts. Press okay, and we can go back. Now we have some more information here. We can switch the row column. So we have the years in our row labels and our regions in our column labels. And we can switch that around by pressing this. As you can see, the Pivot Table has changed and so has our pivot chart. You can press it again to switch it back. Now in the chart layout, in the dropdown arrow here, you have 11 different layouts to choose. We either have the chart title, the axes titles, the legend, and the data labels in different formats. So you can pick and choose from in here, whichever format you like. And some of them work well, others not, but you have the option to have a look. Okay. Another thing that we can do is choose a different chart style. So from these dropdown arrow, you have the multi-colors here. You have the gray look and let’s move that over here. So you can look at the different colors. Now we can choose in there. This is a 3D look. Of course you can choose these different styles here, just depending on what you’re trying to achieve. Personal preferences. I like this style here. And finally, on the far right-hand side, we have the location where we can move the chart. So if you click on there, you have the option to move the chart, it says, choose where you want the chart to be placed. We can create a new sheet or we can move it within one of our existing sheets. Now the pivot is a pivot sheet here, and our data table is a data table sheet there. We can move it to a new sheet and then press okay, and then move it into a brand new sheet and it’s called the chart 5. Now in here, if you go to your Pivot Table and you choose a quarter and you go back, then this changes accordingly. Now we can go back to our analyze and put in our field buttons from in here, and then we can choose our filters from in here. If you wanna move it back, go back to the design, move chart, object in, pivot where we were before. And it moves it back in here. (upbeat music) When we click in our pivot chart under pivot chart tools, we have our layout tab. Now in here, we have the different labels, axes, background, and analysis that we can choose. Now for labels, we have the chart title. The first option is none. Then we can center it overlay title, and then we can center above chat. Under the axes titles, first of all, the primary horizontal axis, which is on the bottom of the chart, we can choose the title below the axis, or we can have none. We can also choose more options in here where we can format it. Go to the vertical axis on the left-hand side of the pivot chart, we can have the rotated view, the vertical title and the horizontal title. And once again, we can go to more primary options down here. In the legend, we can turn it off. We can show it on the right, at the top, left, at the bottom of the chart, we can overlay it at the right, or we can overlay it at the left. And we can choose more options here where we can format it. And the data labels, we can switch them off. We can center them, inside end, inside base and outside end. And more options there. On the data table, we can show data table at the bottom, we can show with legend keys. More options down there. On the axes, the horizontal axis, which is the bottom. We can have none, we can show left to right, we can show axis without labeling, we can show right to left axis. The vertical axis, we can have none, we can show default axis, show axis in thousands. We can show axis in millions, in billions, and show axis with log scale. Let’s go back here and set it up to the left to right and default axis. In the gridlines, we can have minor gridlines, major and minor gridlines. In the vertical gridlines, we can have major gridlines, minor gridlines, and major and minor gridlines. In the plot area, we can have none, we can show the plot area. We also have more plot area options. Now, in here in the analysis, you can choose a trend line. So we can put in that linear trend line, and it gives us the option of which region to choose. Let’s choose Americas, and we’ll get the trend line there. You can have an exponential trend line, a linear forecast trend line, and the two period moving average. In error bars, we can have error bars with standard error. We can have error bars with percentage, and also with a standard deviation. And finally, the chart name, chart 1, we can change it to our own name, regional chart and press okay. You can see this changes here and also in the name box, our chart changes there. So there’s a few different ways where you can lay out your chart to make it look more appealing and have information stand out so people can analyze it quickly. (upbeat music) Now to format our pivot chart, we just have to click in there and we’ll get the pivot chart tools tab. And then we choose the format tab. On the far left hand side, we have the dropdown box where we can choose which part of the pivot chart we can format. So first of all is a chart area. And in here we can use a different shape fils. Okay. So what I’ll do now is I just put in a slight background, like this. We can also change the text from in here. What I’ll use is a slight gray. So you have all these options here where you can change a text in the shape fill and also the different colors from in there. Borders and fields from in there. Okay. It’s got a different variety of options that you can choose. Now, let’s go through our next area, which is the horizontal category axis. Now you see the border there. Okay. So I wanna put in there a text fill to make it a little bit darker. Okay. Now the legend we can put in there a shape fill, like that. The plot area. Now we’re gonna use the same shape fill as we have for our whole area. Okay. Next is our vertical value axis. And in there we can change the way that the text font is. We can put up a little bit darker, just like that. Now the vertical value axis major gridlines, now these are the gridlines here, I usually like to have them a little bit lighter in the background. So you don’t see them that much, and you can choose these different styles. You’re gonna have them as dark as you want so they can stand out. But I like to have them a little bit lighter. So the shape outline, with a white background, which will look nice as well, or you can put a slight gray background. Next is the series Americas. Now in here, you can actually change the color of the column chart. On the shape fill, we can choose different colors, or we can go to the gradient and choose different gradient variations. And you see the live preview. Okay. If you go to more gradients, then you can choose from in here some different options. Now you can do the same thing for Europe, Asia, and Africa. Okay. Now let’s go back to the chat area and the show outline. Let’s put a border in there. So I wanna put a dark border like this. Go back in, the weight, I wanna make it a little bit thicker. I did like that. We’ll go back in. You can actually put dashes if you like. Now, back in here under more lines, under the cap type, I wanna make that round and then round here. So we’ll get the round edges okay. And then around corners. And you can make it thicker like this. Usually have it about five points, and then close. Now there’s another way where you can change your graph. If you click in there and press Control + 1 from your keyboard, it actually brings up the dialog box. So you can change it from in here. You can put in the fills, border color, border style, shadows, glow and soft edges, 3-D format, the size properties and alternative texts. So you’ve got a few options there. Now you can actually click where you want to change, and this dialog box changes as well. So the solid line, we can go back and put in a white line and we change. Let’s click on our axis. So it brings in the format axis. So in here we have the minimum and maximum points that we can choose. Now, if we keep it an order, you’ll be at auto, but say, we want to change it to 1.6 million. So the maximum amount, let’s fix that. Instead of 1.8, let’s put a 1.6 and we’ll see what happens. You see that, it changes like that. So you can have it fixed, or you can have it auto. If it’s a one off graph, you can fix it. But if it gets updated automatically, then keep it at auto. You can also have the major tick marks outside, inside, across. Now these are the marks in here. The same thing, if you click on the bottom there, you’ve got the cross you can choose, or you got the minor tick mark you can have inside. No, you can’t see it there. But if we go in here in the minor tick mark, let’s make it inside, you can see that there. Now if we want to go to our graph, we can choose there and make our color changes, or we can go here on our axis or our labels there. So you can use this option as well. So it’s important to spend some time to make your graph more attractive, because you never know who’s gonna end up looking at it. The more appealing it is, the better it looks on you. (upbeat music) Pivot charts have come a long way since Excel 2007. In 2010, we have more formatting options. First of all, you don’t lose the formatting when the Pivot Table changes. And then you also have the option to your slicers as your filters. Now, one thing you can do is insert XY scatter, Bubble chart, or stock charts from your Pivot Table. But there’s a work around. What we can do is reference the cells outside the Pivot Table. Now to do this, make sure that when you’re in the Pivot Table under options and the dropdown box, if you generate GETPIVOTDATA, it’s switched off just like this. There’s no tick option there. So let’s go outside our Pivot Table and press plus or equals, and then choose row labels. Then grab the row labels, drag all the way down, and then drag it all the way across. So we have that in there. Now, what you can do is Control + Copy if you like, and then right-click and paste the values so you can hard copy them, or you can just keep it like this. So it can be linked to the Pivot Table. Now, what we can do is go to insert and scatter chart from in there. So now we have our scatter chart inserted. So if we go to our Pivot Table here and we filter by regions, our pivot chart changes accordingly and as well, we just want to choose a few other months we can, and that changes as well. We can go back and select all. So this workaround works well if you want to use XY scatter charts, bubble charts, or stock charts. (upbeat music) In chapter 9.5, we created this chart here. And now I wanna save this chart as a template and then use it next time I create a pivot chart. So I don’t have to go again through all the steps to create this beautiful looking chart. So, first of all what we need to do is save this chart as a template, click on the chat and go to design. On the far left hand side, there’s a button here called save as template. Now it brings us to this extension, which is Microsoft, templates, charts. We have to save it in there so we can access it later on. Now, let’s name this to cool column chart. You can name this to whatever you like and press save. Okay, so now let’s go back to our Pivot Table and insert a pivot chart. Like this you can put a column and press okay, and move it up here. Now, what we need to do is go to the change chart type and under templates we have our previously saved template. If you hover over there, you see the name cool column chart and press okay. And it changes it accordingly. It also works on other charts. So instead of a column chart, we can change it to a pie chart and press okay. And it keeps those formats in there as well. (upbeat music) If you click on your pivot chart and then right-click, you have these options here that you can choose. So you can refresh the data. You can cut and paste the pivot chart somewhere else. Now you can use the font there. You can also change the chart type from in here. So you can choose different charts. Another thing you can do is select the data. So it brings up the data here and you can switch the row and column from in there. So you have the Americas now on the x-axis and if you go again, you have the years on the x-axis. Press okay. Right-click again, you can move the chart to a new sheet or to a existing workbook. You can also format the chat area from in here. So you have the option there and you can click anywhere in your chart to change the area that you can format. And another way to format, is to go to format and format selection, and from there you can make changes or you can choose which part you want to make the changes. (upbeat music) We’re gonna set a chart title, and then link that to one of our Pivot Table cells. So every time we filter, then the chart changes as well. So let’s go to our layout and then choose chart title and put it above the chart. And we’ll have the chart title there. We can actually go into a formula box and press equals and then choose the filter there and press okay. So now when we go and choose Q1, it changes to Q1. When we choose Q2, it changes to Q2. Now the only thing is if we have multiple values, it’ll show us multiple items. And then if we put it all, it’ll show as all. But it works well if you wanna show each quarter and take a snapshot and send it over to your manager to have a look at it. (upbeat music) There’s a couple of ways that you can copy a chart. You can click in the current chart and press Control + Copy from your keyboard and then click anywhere else in your workbook and press Control + V. Control + Z to go back. Another way is to click in your pivot chart and then hold the Control key while your mouse is selecting the chart and then move across like this and let go of your mouse. And now let go of your Control key. And you’ve copied the chart in there. And now you can go to design and change the chart type. You can change it to a pie chart. (upbeat music) There’s a quick way to insert a pivot chart. All you need to do is click in your Pivot Table and press the F11 key on your keyboard. And it puts the pivot chart into a new worksheet called chart number three. And from in here, you can make your changes and also your Pivot Table changes accordingly. And if you go back, you can see the changes that you’ve made there. We’re back in our Pivot Table. If we click in here and we’ll press ALT + F1, create a pivot chart on the same page. (upbeat music) we can create a pivot chart directly from our data source. All we need to do is click in our data source and go to the insert tab. Now from the Pivot Table, dropdown arrow, we choose pivot chart, and then we select to put into a new worksheet and press okay. Now we have our empty Pivot Table and pivot chart. And now we can start creating our pivot chart. Now let’s drop in our X-axis into our row labels. Let’s drop in our Y-axis into our column labels, and let’s drop in our values into our values area. So now we’ve created our Pivot Table, as well as our pivot chart all in one go. (upbeat music) You can take the pivot chart and email it to one of your colleagues or your managers and click in your pivot chart and press Control + Copy from your keyboard. And then go to your email Outlook, or whatever email that you’re using, and in your new message, you can just press Control + V and then it gets inserted in there and you can send it to your boss for review. Now let’s get rid of that. Another way you can do it is go into your insert and screenshot and then go to screen clippings. Now, if you choose that, it’ll go to your previous screen. So we’re using the Excel screen, and then from in here, you can actually take a snapshot like this. And then that gets embedded into your email body. And from in here, you can format it whichever way you like, and then email it from there. (upbeat music) We can copy and paste a pivot chart into a PowerPoint presentation, and then make the changes back into the Excel sheet. And then from there, update the PowerPoint. Now to do this, just click in your pivot chart, press Control + Copy. Let’s go into our open presentation and right-click, and then choose the keep source formatting and the link data, and press okay. So in here you can make different changes if you like, let’s go back and close our file here. So what we can also do is edit the data. So next time you open this and you wanna edit the data, you can just press that and it opens up the information. So from in here, we can actually make our choices instead of Q3, we can choose Q2. You can see that changes automatically and also we can change the way this looks, so we can move financial year there and sales regions there. And that changes as well. Or we can put in there some products, and then our pivot chart changes in our PowerPoint presentation. So we can save this, get rid of it. And then we have the updated chart in there for our presentation. (upbeat music) There are a couple of ways to print your pivot chart. If you have your Pivot Table and your pivot chart in one worksheet, and then you can just click in your pivot chart and go to the file, and print. Now in here, you can see that we have the view on our right-hand side. Under settings, we have pre selected chart. So it’ll print this little chart and we can press print and you can print it to your printer or if you choose PDF, you can actually save it as a PDF format. Let’s press print and I’ll show you here, and press okay. And we just reduce this and we’ll see it’s printed into PDF, and you can save it or email it to whoever you like as a PDF format. And let’s get out of this. Now, another way you can print the pivot chart is if you click in your Pivot Table and press F11, then it creates a another chart or to a separate worksheet. Now, when you’re in here, you can go to file, and press print, and you can see here now, the view is a little bit bigger than before. So it’s much better using this format. And from in here, you can print to your printer or to a PDF document. (upbeat music) Sparklines are new in Excel 2010, and what they are are small graphical representations of each data row. So to insert, we’re got to click outside our Pivot Table and go to insert, and sparklines. Now it asks us here our data range. So let’s select the first row and then location is F5. That’s fine, press okay. And now we can just drag it all the way down. So you see there, our graphical presentation for each of our rows, we have our troughs and peaks. If we click in there, we can actually change it to a column chart, and then we can do a light color there. And if we go to the market color, we can actually highlight the high point. You just make it a little bit darker so you can see the high points there. Now we can also, if we choose Q1, you’ll only collect Q1 data. Q2, and then choose all. We’ll see all the data. So it gives us a quick snapshot of our troughs and peaks without having to insert a pivot chart. (upbeat music) There are few tips to follow when making a nice pivot chart. First of all, make sure that you have a title here. What I’ve done is I’ve linked the title to a reference cell here. And what I’ve said is it equals the report filter. So 2012 and I put the and sign and then in brackets I put my title in there. Okay, so if we change these to 2013, then that gets changed automatically. So always have a title in there. And another thing we need to do is sort the pivot chart in descending order or ascending order. But I like descending order because you see the best performer first. So to do that, you go onto your pivot chart, right-click, sort largest to smallest. So we have the largest to smallest there. And the next one is to make sure that we start at zero. Now, this starting point is at 2.4 million. Now we can see here that it seems that Europe is twice the size of Americas, but that’s not the case because the value for Europe is 2.6 million and the value for Americas is 2.5 million. And to fix this problem, you click in your axis, press Control + 1, and then the minimum amount, the fixed change to zero and press okay. And finally, instead of having numbers in your axis, we can actually get rid of them and put some and data labels. Now to insert data labels, just click in your graphs and make sure they’re all selected, right-click and add data labels. So we’ve added the amounts there. Now let’s click on the data labels. We can actually click twice to edit one, or we can click out and go back into edit all of them. Press Control + 1, and in here we can move them wherever we want but let’s put them on the top there. We can also choose the category name there, and we’ll leave it as is. Now we can click on access there and press delete to get rid of that, and also in our gridlines, click and delete. And now we can just sort, right-click and sort largest to smallest. And then when you click in here and we can just make that a little bit darker. And always try to keep your graphs simple. And this is more when you’re working with pivot charts. (upbeat music) What we’ve done here is created three different charts. And with our slicers, we can actually choose which chart to show based on a named range formula that we used here. And then we can also use slicers to change the data if you need the chart chosen. Now, this is a similar concept to chapter 7.11 for the interactive employee photos with slicers. Now, instead of inserting photos, we’re gonna insert pivot charts. So I can go on to the how to section and explain how it’s done. Now first of all, we have to create a table. So number one, two, three, and the charts we’ve named regional sales, orders received, and top five channels. So second is to create a Pivot Table. So we can highlight this and insert a Pivot Table. And let’s put into our existing worksheet just out here on the right. And we’ll want to put in there the row labels tow equal numbers. So grab the numbers of the row labels. And let’s get rid of the grand total just to here. So now that we’re moved to the pivot table here, we need to name the first row, number of cells down. So this will tell us if it’s selection number one, it’ll move one row down. If it’s selection, number two, it’ll move two rows down. If it’s selection number three, it will move three rows down. So to name the range, we need to go into our name box in there and call it number of cells down. And let’s put an underscore and press enter. So if click out of there and click back in, we’ll see that named range. So the next step now is to insert a slicer. To do this we’ll go to the options, insert slicer, and choose a chart. So we have the three different charts. So what we need to do next is to define a name for the starting position. So this is our starting position, and we’re gonna name it, start here. Let’s call it, start here and underscore, okay. The next step is to make this row 230 high. So let’s grab the three different rows that we’re gonna put in our charts, make the height at 230. So the pivot charts can fit in there perfectly. Number six is to insert the pivot charts. So all we can do is go back in here and grab the charts that we made before. So all these charts are from in here. Okay. So we can grab these and copy them instead of redoing it again. Control + Copy, and then we’ll can put them in here. Control + V and escape. Okay, so they’re in here and we just gotta make sure that they are within the boundaries. Okay. So we may need to just adjust that a little bit. Move this there and this in here. Okay. So this should be fine now. So step number seven is define the name for the formula that would drive the pictures. So in here, we need to go to the formula and name manager, and we’re gonna name this, show chart. So let’s go to the new and call it show_chart and underscore. And this will refer to this function here. It will be offset. And then we’re saying, where’s our starting position? Well, we named the range previously and we said it was start here. So we can put in the start here. The next argument is how many cells down? But we’ve named the range, number of cells down, and that was named in the Pivot Table. So we can type that in. Number of cells down, and then the next argument is how many columns to the left or right? Well, we’ll put in the zero and then comma and close bracket and press okay. And then close. So we’ve named that. Next is to copy the pivot chart and paste the picture link. So to copy the pivot chart all we need to do is just go in our cell here and press Control + Copy. Go up here, click in there. We’ve made this a 230 as well. So as big as those, so we’ll go right-click and choose here, link the picture. So we’ll paste in the linked picture from our first chart. Now we could grab any of the three, but we just use the first chart for now. Now the next step is to reference the picture to our offset and named the range. So all we need to do is grab this show chart define name, which we did before and link it to the chart here. So we can actually link named ranges to pictures. So we can say, show, and when we put that, we’ll get the options here so we can double-click the second one and press enter. Okay, so now it’s working, and let’s go to number 10. Is to insert the slicer from the pivot chart and connect them. Okay, so first of all, let’s grab our first slicer and we can move it up here, and we just to make sure it’s working properly. Orders received, regional sales, top five channels. So that’s correct. Now we can go in and grab the slicers that we have here. So these slicers were created from in here, by going on to options and insert slicers. Sales region, the financial year and the salesperson, and press okay. Hold down the Control key, Control + X, and we’ll go in here and press Control + V, escape, okay. So we have our slicers in here. So grab the slicers and put them in there. And all we need to do is connect these slicers to our Pivot Tables. Okay, we can put it like this for now. So right-click Pivot Table connections, and they’re all connected. Just to make sure they’re all connected, right-click, Pivot Table connections all connected. Okay, they’re all connected. So these slicers here are connected to the pivot charts and this slicer here changes the chat type. So if we click 2012, that gets changed. If we click by salesperson that gets changed, and by sales region. Based on what we’ve learned in this chapter, if we put everything together, we can make some interactive charts here, which are very powerful and you can start making some dashboards. So have some fun and let me know what you think about them. (upbeat music) With a pivot chart, you can not create an XY scatter, but I will show you how to do this via a workaround using the index function to get our sum of sales and our sum of costs. And then with a slicer create an option where we select sum of sales and then the graph changes to that. And then also select the cost. And then the graph changes to reflect the cost information. Now first of all what you do is create another Pivot Table, which includes sales and cost in there. And I’ve numbered them one and two. So let’s select that and go to insert, and Pivot Table. Then we’ll go to an existing worksheet, and we can put it in here and press okay. So I’m gonna drop into the field in the row labels and the number in the values area. And what I’ll do now is from in here, go to insert slicer and insert the field name in there. So we have created our slicer, and let’s bring it up here. Now, all we need to do is create an index function where we get the sales information or the cost information, depending on the column that we choose. So if it’s column one, it’ll be sales, if it’s column two, it’ll be cost. So let’s press index, and then the array is gonna be in here, the sales and costs. Press F4 to lock the area in there. The row number, we’re not gonna have a row number. Now the column number will be the link to the Pivot Table selection. So let’s move this up here and press F4 to lock it in there and enter. So now if I double-click in here and press costs, that means it’s looking at column two and returning all the values in column two, from our array. If I press sales, it’s returning all the values from column one in our array. So now we can create a scatter graph from here, just go to the insert and scatter, like this. And we just put in there. So we choose the cost. We have the cost scatter graph, which is a sales. We’ll get our sales scatter graph. (upbeat music) In this chapter we’re gonna create a P&L Pivot Table report with graphs. Now we have our Pivot Table here that we created in our chapter eight. And what we’ve done here was eventually put in some pivot charts for the expenses at the bottom here. And you can see the month going across from left to right. And then we have another pivot chart for the revenues going from left to right. And then we’ve added in our slicers in there for the years. So each time we change the slicer, so let’s choose to 2013, the pivot chart will change and so will the Pivot Table here. So press that, you see that changes, and then 2014, that changes as well. So it gives us a nice graphical representation of where our revenues and expenses are for each of the months. Now let’s go to our data source and I’ll show you how you can create these. So this is our Pivot Table that we created in chapter eight. And now all you need to do is just right-click in there and go to expand collapse, and collapse entire field, because we don’t wanna see every little detail there. Okay. Now let’s go to the well data source. And in here we have our accounting P&L report. Let’s create a Pivot Table, go to insert, and Pivot Table, and let’s go to a new worksheet. Now in the row labels, we’re gonna add in the month. In the column labels, we’re gonna add in the P&L type and the item and the values we’re gonna add in the actual in there. And in the column labels, we only wanna see the expenses. So from the dropdown arrow, let’s choose only the expenses and press okay. Let’s minimize this a little bit. So now that we have our Pivot Table, we can create our pivot chart because we know that anything in the row labels will be in the X-axis. So we’ll go on the bottom, and anything on the column label will be on the Y-axis. So let’s go to options and pivot chart. And in here, we’re gonna create a stack column chart and press okay. So here is our chart here and now let’s right-click in the filters there and go to hide all field buttons on chart. And then let’s just make this a little bit bigger. So we have our chart in there. Now let’s format this a little bit better. Click on the Y-axis and from the home button, we can just make that gray, press Control + 1, and the number, let’s just put in there a separator with zero decimal places. Now click in the gridlines there and press Control + 1 again. And the line color will be a solid line, but you’ll just go a light gray. Now in the months here and choose from in here, just a blue color there. Now let’s go on the pivot chart tools and let’s choose in here the layout and gridlines, primary vertical gridlines. And let’s put in some major gridlines there, so we can separate the months. So in other words, click on our pivot chart, press Control + X, and let’s go back to our pivot. And down here let’s press Control + V. Okay, so we’re gonna add that in. So we have our expense graph there and we’ll just align and so we can put in the months next to the months in there, just like that. And we’ll just probably move it into the left there. Finally, let’s get rid of the border. Press Control + 1, border color, no line. So it just is like this. Okay, so we’re happy with this. We’re gonna use the same format for the revenue pivot chart. Now click in the graph, go to design and save the template as. Now in here, it automatically takes you to the Microsoft templates and charts directory. You can name this chart to whatever you like. Let’s call it P&L Pivot Table, and press enter. Now the next step is to create a second Pivot Table for the revenue, and from there, we’re gonna create a pivot chart. So go to insert, and Pivot Table, a new worksheet. And we’re gonna drop in the months in a row labels, the P&L type and the column labels and the item in the column labels and the actuals in the value area. Just like we had before. Now, the only anything here, we want to show the revenue annually. So let’s just take the revenue like that. Let’s go to pivot chart, and let’s put in a clustered column and press okay. Now, whilst we’re in here, we can go to change chart type. And then from the templates folder, we can hover across to our P&L Pivot Table that we just saved previously, click on that and press okay. And you can see that changes accordingly. So now we can grab that, press Control + X, go to our pivot, scroll all the way up and then Control + V to include it in there. Now, one thing we gotta do is click in the months and press delete ’cause we’ve done our saving. And then we can just double-click in the home tab, just to minimize that. We can actually minimize it from in here. Just so we can have a bit more room. Now let’s move this across. So we’re gonna align that into the months. Just like we have before that. Now in here, we can add in right-click and insert, and press F4, again, just to have a bit more space. Let’s click in our Pivot Table, go to options and then insert slicer. And let’s insert that year slicer and press okay. And we can right-click slicer settings, get rid of the header. From the options tab, let’s move the columns up to three. Now from here, we can just move it like this, and we can change the color if we like. And we can put it up here, and we can just delete this so we can move it up a bit. And from in here, we can bring this up, just so it can fit into the same page. And finally, let’s right-click in our slicer and go to Pivot Table connections. And what we’ll need to do is connect the different Pivot Tables to the slicer. So by clicking the Pivot Table number two in sheet one, and also the Pivot Table number three in sheet two, we’re connecting the Pivot Tables and respective pivot charts to the slicer. Press okay. So now when we press 2012, the slicer changes, the pivot charts change and the Pivot Table changes accordingly. 2013 the same, and 2014 the same. Finally, we’ll forgot just to make this a different chart. Right-click, change the chart type, and let’s go to the first type there. It just looks a little bit neater that way. Do another test, and there we go. So here’s a great example of how you can use a Pivot Table with different pivot charts and put it all into one page and then control that with a slicer to get some graphical analytics with your Pivot Table. (upbeat music) In this chapter, we’ve created a pivot chart dashboard. We’ve done this by creating three different Pivot Tables, one for our top five channel partner, which you can see in here. And then we’ve created the pivot chart and cut and pasted it into here. The same thing for the number of sales group. We create another Pivot Table and a pivot chart. We’ve cut it out in, put it in here. And the same thing for the sales and cost per month. We have a separate Pivot Table. We create a pivot chart and then we’ve placed it in here. What we’ve also done is inserted four different slicers and we’ve connected them. And by choosing the different years the pivot charts change automatically. So you get a nice looking dashboard with lots of metrics. You can choose for different months. You can also choose the different regions. Let’s highlight everything again. And you can also choose the different sales ranges. With this dashboard, you are sure to wow your boss and get noticed. And I’ll show you how to do this in a few steps. So this is our data set that we’ve been using. And we have all the different information here for the channel partners, sales, the different regions, salesperson, and so forth. So now we’re gonna create three different Pivot Tables. And from there we’re gonna create three separate pivot charts. One for the top five channel partners, the next one for the different sales groups, and the last one for sales and costs. So let’s go to the first one and create the top five channel partners. Let’s go to insert Pivot Table and put into a new worksheet. So let’s get our channel partners and put into that row labels. We’ll get our sales and drop it into the values. So we have our Pivot Table here. Now, first of all, let’s right-click in the values and we’re sort it from largest to smallest. And from the dropdown box, let’s go to value filters and we’ll do the top five. So let’s put in there top five items and press okay. So we have our top five channel partners. Now let’s go to options and pivot chart and insert a pivot chart from in here. Let’s choose a bar and press okay. Now let’s just make some changes. Let’s get rid of these buttons by right-clicking in there. And let’s get rid of the total there. Click and press delete. Let’s got a design and let’s choose this here with a white outline. And click on the gridlines and press delete. Now let’s click on the Y-axis and go to home. And let’s put in a dark font in there. And from in here, we can delete that because we don’t want it. Now, let’s click once in your graph and right-click, and let’s add some data labels in there. Now we can color them in. Let’s put it into a blue there and then press Control + 1. In the number, let’s put in 1000 Separator and zero decimal places. In the title, just double-click in there and change the name to top five channels partners. And double-click to highlight, and let’s put it into a dark gray there. Okay, now let’s right-click in the chart and then let’s fill it in with a light gray background. And again, within the graph click on there, right-click, and once again, let’s fill it with a light gray. Now let’s click on the edges and then press Control + 1. And the border color, we do a solid line and we’ll have a white border, and the border style, we can just put in number five and let’s make round corners in here, and press okay. So you see our chart is taking shape. Now we’re gonna use this similar format for the other chart. So all we can do now to save this template, and then when we create the next pivot chart, we can apply these different formats. So we don’t have to go through all the steps again. So once you click on your chart, go to design and save template as. You get this directory here, and it goes to Microsoft templates and charts. And in here, you can save your chart. And let’s call it dashboard chart, and press save. Okay, so we’ve created our first chat. While it’s clicked, press Control + X, go to our dashboard, click anywhere in there, press Control + V. So this is our first chart in there. And let’s just put it just like that, and we can reorganize that later. So let’s go to our data table and create a second Pivot Table and pivot chart, which is gonna be sales groups. So go to insert, Pivot Table, new worksheet. In here, we’re gonna put in the sales in our row labels, and from in here right-click and press group. And once you group that into ranges starting from 10,000, all the way to 100,000. And then the increase will be 10,000, we’ll leave like that and press okay. Now again, go to sales and drop it into the values area. And because we’ve grouped the sales, we automatically get a count of sales. And leave it like that, that’s fine because that’s what we want to use. Now let’s include in there a pivot chart. Click the pivot chart, and we’re gonna use a column, and press okay. Now let’s go to the change chart type. And in our templates, let’s get the previous template that we created, which is this one here called the dashboard chart, click on that and press okay. So you see, we get the format as we had before, but now we can just right-click in there and again, change the chart type. And let’s put it into a column and press okay. So we’ve got the same format, but we have the different chart type. Now in here, just double-click and rename this to the number of sales per group. You can name it whatever you like. And let’s put that in gray. Okay let’s click on the chat, press Control + X, and go to a dashboard. Click anywhere in here, press Control + V. Or we can just bring it all the way up here. And you see now we have our second pivot chart in there. Let’s go back to our data table. And we’re gonna create a final pivot chart for sales and costs. Go to insert Pivot Table and press okay. In our row labels, we’re gonna put the financial year. and the sales month. In the values we’re gonna put in there, the sales. And then the costs. Now, we get a count of sales because before we grouped our sales. So once we group our sales, the next time we create a Pivot Table, we get a count of sales. But that’s okay. From the dropdown arrow, go to value field settings and choose sum. Let’s go to our pivot chart and insert a column chart and press okay. And let’s get rid of the buttons there. Now we’re not gonna use the same chart style as before ’cause if we do that, it’ll mess things up and it won’t look good. So let’s go back and I’ll show you how it’s done, and press okay. It just doesn’t make it nice. So let’s press Control + Z and get out of that. And now we’re just gonna manually make some updates here. So we have our sales and our cost here. I want to put our costs as a line on the secondary axis here on the right-hand side. So to do that, we’ve clicked in our sum of costs, which are in red, press Control + 1, and then plot series on, put secondary axis, and press close and then change chart type. And let’s put a line and press okay. So now we have the two charts on different axes. So the sales are in blue and they’re depicted on the left hand side axis here. So let’s put it in our blue color there to distinguish that. Like this, press Control + 1, number, and we can just put like that. Now on the right-hand side, we have our sum of costs. So let’s click on that. And then let’s put in a red color there, and again, Control + 1 and let’s format the number. Now let’s click on layout and go to axis titles. The primary vertical access, let’s choose that and we can put in there sales. We can right-click in there and just put it in a blue color. Now let’s go again to the axis titles and go to the secondary vertical axis titles just like that. And put in that cost just to distinguish them. And let’s put that in red, let’s click in there and press Control + 1, and we can move the legend to the bottom and press close. Now let’s click on our grids there, and we can delete that. Let’s click in our axis there and put in a blue color. And now let’s click in our line and press Control + 1. And the line color, let’s put it into this light red color, and then the marker options, let’s put in a circle, and the marker fill, again we just put it in like that into a light red color and press okay. Let’s go on to our layout and put in a chart title, choose above chart, then double-click in there. We’re gonna call that sales and costs per month. And double-click in there and we’ll put into a gray color. Okay, now let’s put in a gray background for our graph. Just click anywhere there, right-click and then in here, we choose a slight gray, then click within the graph. And you can press F4 and it’ll repeat the last action. So we have our graph there and now one last step is to put in a border. Click on the border, press Control + 1, the border color, we put in a white color, and the border style, we’ll make it a bit thick, number five and then put round corners and press close. So we have our chart there. And now all we gotta do is just click on that, press Control + X and Control + V, and we have it there. Or we can just resize that just to make it a little bit bigger. Okay, so now let’s just double-click in the home tab just to get a bit more space so we can see that. So we have our three different pivot charts there that we created from the three separate Pivot Tables. Now, the final thing we need to do is insert slicers and then connect them. So every time we change the slicer, the three pivot charts are in sync. So to do that, we can simply click on any chart. So let’s go analyze and insert slicer, and let’s put in there the financial year sales month, the sales region, and a sales and press okay. So we have our four different slicers there. So let’s grab the financial year and bring it up here and we’ll just make some adjustments to one. Let’s put it into three different columns and then let’s make it dark. And we can move it like this, right-click and slicer settings. Get rid of the display header. Let’s make the height for the buttons a little bit bigger. So we have one slicer there. Let’s grab our sales month. And again, right-click just to get rid of the header. And from the options, let’s put into three different columns, that shows the different quarters. And then we can just resize it in here, just like that. And then once again, let’s make the buttons a little bit bigger, and then we can put in there a dark color. The next one are the sales regions. Let’s grab it in here, get rid of the display header. And then let’s put into two different columns and resize that accordingly. And then again, let’s make this a little bit, the buttons little bit bigger. We can put in a different color if we like, just to distinguish that. And finally we have there sales. Now they’re grouped into the different ranges. Let’s get rid of the header. And then let’s put it into separate groups. Then again, resize this and we can make it a little bit bigger again. Let’s drop it into two columns just so we can see that better. Okay, all the way to the bottom there. And then the color, let’s choose that. Let’s click on the different slicers, press Control key and click on all of them just to align them. Go to align, we can say align center. Now we need to connect the slicers because if we choose one slicer, it’s only gonna change the bottom one, because that’s a chart that we chose to insert the slicer. So let’s right-click in each of the slicers and go to Pivot Table connections. And let’s pick on the empty boxes. So what we’re doing here is we’re saying that Pivot Table number three in sheet two, and Pivot Table number two in sheet one have been connected. Press okay. The same thing for all of them. If we choose 2012, the chart changes 2013, and 2014. The same thing for the months. Let’s highlight all of them like that. And then we have the different regions, and also we have the different sales ranges there as well. Now you can see there, we’ve got the different sales ranges. If we go to our first sheet, we can see the different size ranges. So as we choose that, it changes the Pivot Table accordingly. So this gets updated even though you don’t see it, it’s another sheet, it gets filtered accordingly. Let’s go back in here. Now let’s highlight everything just by holding down the mouse and selecting it all. So you can see we’ve created a pretty impressive dashboard in just a few steps. And I’m sure that with this dashboard you’re gonna get noticed. By the next round of promotions, I’m sure that your name will be mentioned. (upbeat music) In Excel 2010, you can add the new conditional formatting options like data bars, color scales and icon sets. The conditional formatting is embedded within the Pivot Table structure. So if you update and refresh the Pivot Table, then so does the conditional formatting. And to insert conditional format, you gotta click anywhere in your Pivot Table, and then from the home tab, choose conditional formatting. And let’s choose highlight cells rules. And we choose greater than. So in here we get a dialog box, and we can choose the amount to put in there. So let’s say anything greater than 600,000. Now we can format it with a light red fill, with dark red text, and you got a few other options there. Now we can also custom format. Now we get the format cells dialog box and under fill, we can choose the different color to format with. And let’s choose a red and press okay. And then, okay. Now we get this dropdown box here that says apply formatting rule to. We can choose selected cells. We can choose all cells showing sum of values. If you choose that, it’ll also highlight the sub-totals and grand totals. And the third option is all sales showing sum of sales, and also the values that are in the row and column labels. Now this third option is not gonna highlight any sub-totals or grand totals. So most of the time you’re gonna choose the third option. So we have the values there that are more than 600,000, and to make changes to this conditional format, we’ll go back into their conditional formatting option and we can choose clear rules, and from in here, clear rules from this Pivot Table, we can just click on that. Or we can go to manage rules so we can edit the rule. We can change the dollar value from in here. We can also change where the rule applies to. Let’s cancel out of there. We can create a new rule or we can actually delete a rule from in here. Let’s press okay, just to get out of there. So with conditional formatting, it gives you the option to highlight the values that will make your analysis much easier to do. (upbeat music) To highlight cell rules based on their values, you would actually click in the values within your Pivot Table. You need to go to the home tab and under conditional formatting, choose highlight cells rules. And from in here, you got four different options. Greater than, less than, between and equal to. Now, let’s choose the between option there. And we want to format the cells that are between 400,000 and 500,000. And we’ll use a light red fill with dark red text, that’s available to us. And then we press okay. Now from the dropdown arrow here, we choose the last option. So it shows us the two values that are between that range. So let’s go to conditional formatting and manage rules. So in here it says, applies to. So it applies to the sum of sales that are within the sales quarter and financial year. So if we take out the sales quarter and the financial year, then this conditional format will not work. So if you change your column and row label fields, then you’ve got to reapply the conditional formatting for those new fields. (upbeat music) We can actually highlight cell rules based on text labels. Now we have our quarters here in our row labels. Now let’s highlight all of them and then go to the home tab and conditional formatting. Highlight cell rules. Now because we’ve highlighted the text, then it gives us the option to highlight text that contains. So in here let’s highlight texts that contains Q1 and press okay. And then we can go back and highlight again, texts that contains Q3, press okay, and go back there. So it gives us the option to highlight row label or column label text items. (upbeat music) We can also highlight cell rules based on date labels. Now in our Pivot Table, we have our order date dates. We can have any dates that could relate to when a payment is due or when an invoice from a customer is meant to be paid. So we can put a conditional format that’ll show us which dates relate to a particular month. Now to do that, we need to highlight in our Pivot Table and select all the dates. Now we’ll go to the home tab and conditional formatting, highlight cells rules, and then choose a date occurring. And in here you have the option to choose the different dates. Now I wanna choose this month and we’ll want to put it in here in a green fill with dark green text, and press okay. Scroll down all the way. You can see that these three dates are due to be paid this month. And so that you open this Pivot Table on a future date, then this conditional format will be refreshed automatically and apply to your current date’s values. (upbeat music) We can use conditional formatting to highlight the top and bottom cell values. Now let’s click in our Pivot Table anywhere in our values and go to the home tab and conditional formatting, and choose a top bottom rules. And let’s start by choosing the top 10 items. And instead of the 10 items, you can actually choose a top five and then we’ll keep it with this formatting and press okay. Now from this formatting options, we select the last option. So we can see all the values except for the sub-totals and grand totals. So we have our top five items there for our three years of financial data. Let’s go back and use conditional formatting, and we can clear the rule by choosing the last option, clear rules from this Pivot Table. And then we’ll go back here and let’s create another rule. Let’s choose the top 10%. So in here we can choose a different percentage. For example, the top 25% of sales for the three years, press okay. And then from this dropdown formatting rule, let’s choose the last option. So it shows us here the top 25% values from our three years. To go back, and we can go and clear the rule. Let’s apply another rule here, above average. So in here, based on our selection, it’s gonna give us the values which are above the average and press okay. And then let’s apply it to everywhere. So what it’s done, it just calculated the average over the three years and the values which are above that average will be highlighted in red. Okay, let’s go back and clear the rules. Now we’ve got the option to do the bottom 10 items or the bottom 10% or the below average. And let’s go to the more rules in here, and let’s choose the apply rule to, let’s choose the third option once again. And in here, let’s choose the top. We can actually choose the bottom as well. So let’s choose the bottom one item for each column group. And we’re gonna format that in red and press okay. So this is a one column group and it’s highlighting the last value. That’s another column group. So is that, and so is this. So it’s showing us the bottom value based on that. Let’s go back and clear this rule. And then finally let’s go the more rules. And in here, let’s choose the third option and bottom one and each row group, and let’s format it in red and press okay and okay. So what it shows us here is that the bottom value from this group, the bottom value from the selection and the bottom value from this selection. And it does the same thing for each of the different regions. So the top and bottom rules in conditional formatting are a great feature and your data really does stand out. (upbeat music) Data bars, color scales and icon sets are new in Excel 2010. And they’re a quality feature on the conditional formatting. Now let’s highlight our Pivot Table values here and go to the home tab and conditional formatting, and then choose data bars. And here you got the option of a gradient fill in different colors. And as I’m scrolling, you can see the live preview in the Pivot Table. And you’ve got the solid fill as well. So here it highlights the values automatically by highest to lowest. Let’s go to the more rules option, and let’s apply this rule to the last option, which means all the values except the sub-totals and grand totals. And here will be only show the bar. If we press okay, you can see that the values go and we can see the bar only. And let’s go back to your conditional formatting, manage rules and choose Pivot Table two, and double-click in there just so we can make some changes. Now in here, we can actually choose lowest value. We can actually put in a number, a percent, a formula, a percentile or automatic. Now let’s put in a percent in there. And let’s put 50% and the maximum we’ll put 50%. And then we can have a solid fill or a gradient fill, we can choose a color in here. And let’s put in there a light blue. We can have a border or a solid border, and then the color of the border as well. And in the bar direction we can go left to right, and you’ll see the preview here for right to left. Just keep it in context. And you’ve got some options for negative values and axes. Let’s untick the show bar only ’cause we wanna see the values and then press okay and okay. So we’ll see the values there. So in here it’s highlighted the top 50% in a blue color and the bottom 50% in a blank background. Now let’s press Control + Z to go back. And let’s go back to conditional formatting and choose color scales. So in here you have the different scales here. And you can see automatically that the lowest value is 599 and that’s highlighted in red. Choose the second option. The lowest amount is in green and the highest is in red. And you’ve got these different options there. Now this is good for resourcing or popularity scores. And let’s go to the more rules here, and the format style can use a two color scale or a three color scale. And then once again, we can choose the lowest value. The mid point and the maximum there. And we can leave it like this. The lowest value will be in red. The mid point, the 50 percentile mark, will be in yellow and the highest value will be in green. And press okay. And we can see that there. We can press Control + Z to go back. And then finally we have the icon sets. Now these are good for when you have some budget values and you wanna see whether you’ve achieved your values or you have certain scores, or if you have a project and to indicate whether you have any risks or opportunities. So you got a lot of different ways where you can highlight the numbers. Now, under more rules we can actually choose to show the icons only instead of the numbers. And in here, we’ve got the option of the different icons to choose as we saw previously. Now under here, we can actually change our values. So it says here, give us a green when the value is more than or equal to 67%. Now we can change this percent to a number, to a formula or to a percentile. Now it says here, when it’s between 67 and 33%, then it’ll give us an orange. And when it’s less than 33%, give us a red color. So we can change the as well and even change the numbers. So we can say 50, and then when it’s 50, give us a red. So anything that’s below 50 will be a red, and let’s press okay, and you see that. And we can go back and just do one more thing under manage rules. Double-click in there and say that we had a budget. So anything that’s over 800,000, we get a green mark because our budget was 800,000 per month. So anything above 800,000 is green, anything less is red. Now we can do this by saying, first of all, number bigger than or equals to 800,000 is green. If it’s less than 800,000 and zero number, then it’s a red. So let’s press okay and okay. And quickly here, we can see that all the values that did not meet our budget are in red, and the ones that do are in green. So these data bars, color scales and icon sets are fantastic if you want to quickly show and highlight relevant numbers. And your numbers really do stand out, which makes your analysis that much easier to do. (upbeat music) In our Pivot Table we have all our sales people and we have their sales per year and per quarter. And we’ll want to give them a bonus if they’ve earned more than $700,000 of sales in one quarter. Now, first of all, we need to highlight those people so we can identify them and then we can give them a bonus. Now to do this, we can insert a conditional format, which is format cells that contain. Now from the home tab, we’ll go to conditional formatting and we can highlight cells rules that are greater than. But instead of going there, we’re gonna go to create a new rule. Now we want to apply the last rule, which means all the cells apart from the sub-totals and grand totals, and then we’ll choose the format only cells that contain. And in here, we’ll keep it at cell value and then we’ll choose here greater than or equal to. And in here, we’re gonna reference a cell. We put in here at 700,000, press enter and then format. We can choose a green color and press okay, and then okay. So automatically it shows us the salespeople that have earned more than $700,000 of sale in one quarter. I said that this metric changes, we can say anything that’s bigger than 750,000. Then the conditional formatting in the Pivot Table gets updated automatically. (upbeat music) In Pivot Table, we have our list of channel partners. And there are sales from 2012, all the way to 2014. And what we wanna do is give our top three channel partners per year an award so they can go away on a trip. Now to do this, we can actually click in our Pivot Table and go to conditional formatting. And on the top bottom rows, we can choose the top items there. But we can also go under new rules and then the apply rule to will be the last option. So show all values except grand totals or sub-totals. And then choose the format only top or bottom red values. Now we’re gonna choose the top three, and therefore all values we’re gonna change that, we wanna choose each row group. So what that means that it’ll show us a top three in each year. And it will highlight it in the color that we choose. Let’s just choose a green, and press okay, and then okay. As you can see there, we have the colored. You go all the way down. You can quickly highlight and see which were the top three channel partners for each year. And we can send them a trip and thank them for their contribution over the last few years. (upbeat music) We have our salespeople and their respective sales per year. And what we wanna do is see the salespeople that have been performing above average for three consecutive years. Now those salespeople will get a promotion. To do this, we click in our conditional formatting. We can actually choose the top bottom rules and choose above average from here, but let’s go to the new rule. Let’s choose the last option. So we can highlight the values except the grand totals and sub-totals. And from in here, we choose the format only values that are above or below average. Now let’s choose above average and then we’re gonna select the all values there. So what it’s gonna do is get the average for all the values, and then whoever is above that average will get highlighted in the color that we select. Now let’s choose a green, and press okay, and then okay here. So let’s have a look here. If we highlight everything, and on the bottom status bar, we’ll say the average is 2.672. If you don’t have this just right-click, and can you can click it into action there. So 2.672. So any sales that are more than 2.672, will get highlighted in green, and that’s evident there. So the only sales manager that has exceeded expectations has been Ian Wright. So he’ll receive a big promotion in 2015. (upbeat music) What we’re having our Pivot Table are our 2013 sales and our 2014 sales. And we’ll want to compare to say where that our 2014 sales were bigger than the previous year per month. So to do this, we’ve gotta highlight the 2014 column. Go to conditional formatting and then choose new rules. For the apply rule to, we’ll keep it to selected cells because we just want to conditionally format the selected cells. And then the rule type we choose the use a formula to determine which cells to format. Now in our formula here, we’re gonna put 2014 is bigger than 2013. If that’s true, then it’ll highlight in green. And then the next rule would be is 2014 for February, bigger than 2013 for February? If that’s true, highlight in green, if not, do not highlight. So first of all, let’s click in C3 and then we’re gonna press the F4 button three times, just so we can get rid of the absolute reference. Because we need to apply the argument for each row. So if we had the dollar signs or the absolute reference, then it’ll only have this argument here for row three and not the rest. Okay, so let’s do bigger than, and then B3 and again, press the F4 sign to get rid of the absolute reference. And then we press that and press format in green and press okay and then press okay. So can see here quickly that we have January, July, September, October and November months, that were bigger than the previous year’s totals. And let’s go back to conditional formatting to manage rules. And we can see this under the Pivot Table that the rule applies to the selection, which is correct. And the formula is C3 is bigger than B3. Because we don’t have the dollar signs or absolute reference it means that you have to go into each row and calculate that argument and return back the true or false into the 2014 column. If it’s true, it’ll give us green. If not, it’ll be blank. (upbeat music) In chapter 10.10, we selected the 2014 values there. And we said is 2014 bigger than 2013? If yes, then highlight in green, if not, then don’t highlight. Now can see this by going in to the conditional formatting under manage rules. And we can see if we double-click there, that we’ve applied the rules to these selected cells. And this is the formula, C3 is bigger than B3, and press okay. Now what we can do is actually take out the sales month and drop in some other fields. And this conditional formatting will still apply to that. Let’s have a look. Take that out and bring in customer. You see that works fine there. Send people products, salesperson, sales region, we’re can can see the sales quarters. And then the channel partners. We’ll scroll all the way down. We’ll see whether it was highlighted green, means that 2014 was bigger than 2013. So when you apply the rules to the selected cells, then you can expand the conditional formatting rule to other fields. So what it does is it gives you more flexibility to analyze with more fields. (upbeat music) We had the 2013 and 2014 sales, and the months here as well. And we want to highlight the top five sales for this. And then what we want to do is take out the sales month and drop in the customers and keep the conditional formatting alive. Now to do this, we highlight in our values there, and go to conditional formatting, new rule, and we selected the second option. All cells showing sum of sales values. So what we’re gonna do is keep the conditional formatting for the values live every time that we chop and change our fields. Now let’s put in our rule and we’re just gonna say our top five and then format. And let’s put it in that color there and press okay. So we have our sales month there and let’s take that out and put in our customer, you see that’s highlighted. Our products, our salesperson, our sales region, our sales quarter, and then our channel partners. So we scroll down, we get our top five channel partners. So if you choose the all cells showing values, then you can apply that conditional formatting to one field. You can chop and change that field. And the conditional formatting will still apply to the new field. (upbeat music) We’re gonna create a conditional format for our values here that says, highlight the top X% of values. And we’re gonna control that conditional format with some slicers. So as we choose their percentage on the slicers that the conditional format will get changed as well. Now let’s create a Pivot Table. Let’s highlight the percentage list that we created here. Go to insert, and Pivot Table and existing location, and we can just put it there for now. Now what we’re gonna do is throw in their percentage into our row labels. Our grand total, we can get rid of that. And what we’re gonna do is reference this first cell here to our conditional formatting rule. Now, what we’re gonna do is insert some slicers, go to insert, and percentage slicers, and then we can just add some more columns. And we can just put it there. Change the color. Let’s click in our Pivot Table here and go to conditional formatting, new rule. And let’s choose all sales, showing sum of sales value, and then the format our cells based on values. And let’s choose the three color scale. The loss value we’ll have a 0%. The mid point will be a 50 percentile, and the maximum will be a percentage. And we’re gonna reference this to the first cell here, and press enter. Now the color, we’re gonna change to green and red, and press okay. So now as we press 50, our sale reference is here. So it’ll show the 50% on our maximum value. And then we would go 55, 60, 65, 70, 75, 80, 85, 90, and then 95. So what is showing here is the top 95% of the values as you can see here. So the top two or three values there are highlighted in red. The midpoint is in yellow and the low point is in green. So you can do some pretty funky stuff with slicers and conditional formatting just by referencing the selection chosen by the slicer back to your conditional formatting rules. (upbeat music) We can show texts in the values area of the Pivot Table with a bit of conditional formatting magic. Now, to do this, we need to set up a couple of rules. Now, what I’ve done here is I’ve added in a new column called region code, and I’ve coded the regions as Africa being number one, Americas being number two, Asia being number three and Europe being number four. Now also each row of data relates to a unique date. So I’ve got one for every two days. I’ve got a unique transaction. Okay, what I’ve done now is I’ve gone to our Pivot Table here and I’ve included our order dates on the left-hand side and our products on the top. And then what I’ve done is I put in the max of region. So what that means is for each region I get the maximum value. So obviously the maximum value will be a one for Africa, a two for Americas, a three for Asia, and a number four for Europe. So that’s the second step there. Now third step is to do a conditional format where we’re saying that if the value equals a number one, then show me Africa. If the value equals number two, show me Americas and so on. Now to do this, we’re gonna click in our Pivot Table. So let’s start on the top left-hand corner of our values being cell B5. Go to conditional formatting, put a new rule and then select the third option here. So we can see all our values. Now let’s use a formula to determine which cells to format. So all we’re saying is B5 and let’s press F4 three times. So it’s not an absolute reference. And then we’ll say if B5 equals one, then let’s go to the format area and under number, let’s choose the custom. And in here, under type let’s get rid of that. And let’s put in brackets, if it equals one, close brackets, then Africa, and then let’s put in their general. Okay, so that’s a trick there, and press okay. Now we can also format this in terms of a fill color. So let’s put in a fill of a light color like this, and then press okay. So now you see all the number ones have changed to Africa. Let’s do the next rule, new rule. We’ll say again the same thing. If it equals to two this time, and let’s format. And in here I’ll paste what I had before and I just changed the values. So number two equals Americas. And then we’ll put in a color like this and press okay, and okay. You see that’s changed there. Let’s do the next one. So equals three will be Asia. Asia, and then we’ll fill with this. Like that. You see that, and then finally, we’ll do the rule for number four. Equals four, let’s format and let’s paste the rule. This changes number four and it’ll be Europe. And then we could fill it in with this color there. And okay, so there you go. Now we can see for each transaction that in which region it belong to for all the different products. (upbeat music) So we have our order date in our row labels and our products going across, and I’m gonna drop in our sales in there and want to get the count of the sales. So press okay there. Okay, so we have different counts there in our products and our order dates. And now we want to highlight the blank cells. So if there’s any blank cells, put that in red so they can stand out. So just click anywhere in our values. Go to conditional formatting, new rule, and then apply a rule to the all cells showing count of sales values for order dates and products. So that means it’s gonna show it for all the values except the sub-totals. Now choose the format only cells that contain. Now from the dropdown box, choose blanks, and under format, choose any color you want. Let’s choose a red here and press okay, and then press okay. So it highlights everything that doesn’t contain a value. So you can quickly see which dates don’t include any values. (upbeat music) In this chapter, we’re gonna create an accounts receivable ageing report, and it’s gonna show us when the receivables were meant to be paid and how long they’ve be an outstanding. We have our receivables due date in here, and we have the actual receivable date in the next column here. And we can create a matrix report. Simply click in the data source, go to insert Pivot Table and go to new. On the row labels, we’re gonna include the receivables due dates, on the column labels we’re gonna put in there receivable actual date. We click in the date and we’re gonna group that into the months and the year. So right-click, press group, and then choose months and years and press okay. Now, as soon as we’ve done that, a new field has been created called years. Now we do the same thing for the receivable actual date. Let’s go on out column area, right-click and group. Let’s choose years and press okay. And you can see there years 2 has been included into our field list and also in the column labels area. And let’s get out of here. And in the row labels, we have the original due date and we just wanna see 2012. So let’s just choose 2012. And in the grand total, click in there, right-click and remove grand total. And let’s get rid of the gridlines. Let’s right-click and show field list. And in the values area, we’re gonna drop in our sales. So grab the sales and drop it in there. So we have our matrix-looking the Pivot Table. So in the values area, let’s right-click and show values as percentage of row total. Let’s just the center this like that. Okay, now we have zero in there, but I wanna get rid of it. so we can get rid of those zeros by using conditional format. So let’s go to conditional formatting and go to new rules, and let’s choose the third option there. And this will apply the conditional format to the values and not the sub-totals. And then in here we choose format cells that contain, cell value equal to and in here let’s put a zero. Now, the format we’re gonna put in there a color of the white, because the background is white, it’s gonna get rid of the zeros, and press okay. So one thing is just to get rid of the grand total down here, go to design, grand totals off for rows and columns. So what it says here is that receivables that were due to be paid in February, 2012, 20% of them were paid during that month. 31% were paid in March, 4% in April, 35% in May and 8% in June. And if you select all these, you see the sum is at 100%. So we can see that the ageing is pretty bad because it lacks about three to four months. And we’ll get the same trend here all the way down for the different months. Now, what we’re gonna do is highlight the percentages that were received in the particular month. So for February receivables that were received in February, we’re gonna highlight in green. For March receivables that were actually paid in March, we’re gonna highlight in green and so forth. So we’re gonna highlight the receivables that were received on the actual due date. Now for that we’ll need to do some conditional formatting. Let’s go to the home tab and press conditional format, and create a new rule. Let’s choose the third option there, just so we can highlight all the values except the sub-totals. And then let’s use that formula to determine which cells to format. And in here, we’re gonna put a if formula. So we’re gonna say if February equals February, then highlight the values in green. So to do this let’s type in the if formula, and we’re gonna say if A7, click on there and press F4 twice, just so we can lock in column A. So if A7 equals B5, now press F4 once, just so we can lock in the row number five. So if A7 equals B5, then true or else false. Now let’s put in here on equal sign, we forgot that. Now, if this is true, then the format we’re gonna fill it in a green color there. Press okay there, and you can see all the receivables that are due within the actual due date are highlighted in green. Everything else is overdue and what’s paid on a later date. Now we’re gonna do another Pivot Table report and we wanna see the distribution of the accounts receivable over the months. So let’s highlight the Pivot Table, press Control + Copy and we’ll just go down here and press Control + V. So we have the same Pivot Table here. And let’s click anywhere in the values. Right-click and show values as. Let’s change that to include percentage of grand total. And now we’re gonna go to design, grand totals and we’ll have it on for rows and columns. And what it says here is if we highlight everything, we’re gonna get a hundred just like that. So it shows us the distribution of the age receivables over the months and years. Now finally, we can put in that heat map down here, and we can just highlight there, go to condition format, and a color scale, and let’s put in there this one here. So this is 100%. Now it says that out of the age receivables that were due in 2012, about 15% were actually received in January, 2013. And the rest, you can see the distribution in May. So conditional formatting allows you to highlight problem data, and you can take some action to improve your business. (upbeat music) We have our sales results for our regions and our products going across the years, and we have the grand total here as well. And we’re gonna put in there some conditional formatting just to show the highest and lowest sales values throughout the years and throughout the regions. So, first of all, we’re gonna put a data bar just within the values here. So to do that, just click anywhere in the values and go to conditional format and go to data bars. Let’s choose a gradient fill like that. Now we’re getting this dropdown box here, and then we’re gonna apply the formatting rule two. And this third option here means that it will only conditional format the values and not be sub-total. So click there and then we can go back to conditional formatting, manage rules and double-click just so we can change that color here. So the color, we can leave it like this and the border, let’s put in a blank border and we can get a preview here and then press okay and apply, and then press okay there. Next we’re gonna put in a three color scale on our grand total. So let’s highlight the grand total there, press Control key from the keyboard and then with the mouse, highlight the rest of the grand totals, but exclude the sub-totals. Go to conditional formatting and go to color scales, and we’ll include this second one in here. And finally, we’re gonna insert some slicers in there. So in your Pivot Table, go to options and select slicer. We’re gonna put in the financial year, the sales quarter and the sales month, and press okay. And let’s grab the financial year, right-click slicer settings and get rid of the display header. And we can just put it like this, and move it in the corner there. Now what we’re gonna do is just double-click in here, just so we can have a bit more space and then highlight the rows and insert just like that. So we have the years there. Let’s put in our sales quarter, let’s get rid of the display name again, and then we can just bring this up and then put it there. And then the sales mark, again, let’s get rid of the display header. And then from the options, we’re gonna choose the three different columns. So that means each quarter is separated. And we can just put it like this, just make it a little bit bigger. Okay. And move it up there, and there we have it. So we can click on the slicer, hold the Control key, click on all of them, go to options. And then we can choose whichever color that we want. Okay, so now that we have the slicers, if we choose one here, the conditional format applies to that year as well and it changes accordingly. If we go to Q1, the same thing. Now we can hold the mouse key and scroll down to highlight everything again. We can choose each month individually, and quickly see which are how high and low sales values. So with conditional formatting, you can put some visuals on your Pivot Table and your data does stand out. (upbeat music) GETPIVOTDATA is a formula that uses the Pivot Table to create customized reports that give the user more flexibility. It uses the Pivot Table as its engine to spit out numbers based on the user’s needs. There are certain advantages of using a GETPIVOTDATA formula. You can produce a report to your liking. So you’re not limited to the Pivot Table format. When the pivot data source changes, then all you got to do is refresh the Pivot Table and your report will update as well. You can also format your report and upon refreshing your Pivot Table, you will never lose its formatting. And finally, you can add extra columns for business metrics that are unable within a Pivot Table. There are lots of people that don’t use the GETPIVOTDATA formula. It’s because they don’t know the power that it can have. The reason is that most people will actually go outside the Pivot Table and try to do a quick sum formula, for example, 2013 plus 2014 like this. And when they try to scroll down, then they get the same number. And then they look at this formula and they’re saying, well, it’s a GETPIVOTDATA, I don’t like it, I don’t understand it, so I’m not gonna use it, which is fair enough. But I’ll show you ways where you can use GETPIVOTDATA, to enhance your reports. Let’s press Control + Z to get out of there. Now to activate the GETPIVOTDATA, you got to click in your Pivot Table, go to options and the options from the dropdown arrow choose generate GETPIVOTDATA. That’s ticked, it means it’s on. If you uncheck and you click anywhere inside your pivot data, then you get a cell reference. If you want to use, GETPIVOTDATA, make sure that it’s selected. So let’s get a number from within our Pivot Table and press enter. Now let’s go to our function in here. Just click anywhere in there and we can move this around here. Move up here. Now, if you wanna get the explanation of GETPIVOTDATA, just click on there and you get the Excel help, or you get the details about the function and what it does. Now, the data fields, these are the values that you want to return. For example, sum of sales, count or average. Now in here, it gets the sales, which is the sum of sales here. So it’s the sum of sales that we are showing. And the second argument is the actual Pivot Table. So in here you can click anywhere in the Pivot Table, but we usually click on the top left hand corner. Now the third argument, this is the field name. So we’re looking at salesperson. We have here salesperson, and we also have the quarters. So the field name is first of all the salesperson, and then within salesperson, we have the item, which is in, right. So we’ve selected cell D12, so it’s in right as a salesperson, the over to the second field, which is the financial year, which is up here. And the item within the financial year, it’s 2014 because we’ve checked in there. Finally, the third field is a sales quarters. So we have the sales quarters in the row labels then item three, we have the actual sell that we’ve chosen, relates to Q4. So it puts it in that order and you get put up to 126 different combinations there. Let’s press enter and we’ll get our value out there. And the power that comes with a good pivot data formula is with the item numbers, we can actually reference them to a cell. So instead of say, 2014, we can change it to 2013 and see what happens. The value changes to 670. This change Q4 to Q3, it changes to 624. And finally, instead of the Ian Wright, let’s put in John Michaloudis. So you get, John Michaloudis’s 2013 sales. So based on this, you can see how you can create a report in here where you can reference your items with your own custom format, your own metrics, and every time the Pivot Table gets updated, all you got to do is refresh and then your data gets updated here. And I’ll show you how to do this in the next chapters. (upbeat music) Now, we’re gonna create a custom report with the GETPIVOTDATA formula and what we’re gonna do is reference our formula to the items that we have here. So the years, 2013, 2014, the quarters, and then the regions that we have there, and we’re gonna paste the formulas into the empty boxes. We’re gonna get our totals and then our variance or Delta amounts. And then we’re gonna use this combo box to change the variance based on our selection. So, first of all, let’s go and choose Americas Q1 2013. So the equal sign or the plus to activate our formula, Americas Q1 2013, let’s click in there. Now let’s go into our Pivot Table. So it’s taking out sales data and the Pivot Table is A1, which is correct. Field number one is sales region, correct. Item number one is Americas. Now instead of having Americas there, we can actually reference it to here. Now let’s press F4 three times so we can fix the columns. Now let’s go to the next field, financial year 2013, same thing, get rid of that and let’s get the reference in there. Now let’s press F4 twice so we can fix the rows. And then we have field three and item three, get rid of that again, reference it in there and press F4 twice so we can fix the rows. Now, press enter and we get the amount of 652,159 which is there. What we’re gonna do now is drag this down to get our values and you can see that match in here. Now sometimes your formula may not work and it happens sometime when for example, you have some leading or trailing spaces. So let’s double-click in there and put in that space and press enter, we’ll get a reference. So sometimes when you’re copying and pasting texts or values, make sure that there are no leading spaces. Control + Z to go back. Okay, so let’s drag this across in there and we’ll have our values there, Control + Copy, and then Control + V. So we have our totals there and we have our variances there. So quick and simple, in a matter of minutes, we’ve created our own custom report with our own metrics and we can extend this to add some more metrics at the bottom if you like. If you had data changes, then this will get updated as well. So let’s go back to our data source and change Americas Q1 2013. Let’s change the value so we can see if it gets picked up here. So data table or in Americas, 2013 Q1. And let’s put it in our (indistinct) like a million dollars. We’ll go back. So this will get updated here when we refresh the Pivot Table, right click, refresh, and see if that automatically get updated. Now, finally, we’ve put in here our metric, which is the variance. And what I’ve done here is I’ve put in a form control from the developer tab. So I went in there and pressed insert, form control, I chose the combo box. And I place it in there. And then if I right click in there, under format control, the input range is this here. So it’s Q1, Q2, Q3, Q4, total. The cell link is there, so when Q1 is chosen, it’ll be number one, when Q2 is chosen, it’ll be number two and so forth. And then dropdown lines I’ve chosen five and 3D shading. So as we changed this, our values change. So what I’ve done is a formula in here, which is an index formula with array formula. So what I’ve said is area 2014, so the array is in here in blue, 2014, the column selection, I’ve named it range, which is number two. So in this array is choosing the second column two, because I said Q2 equals two. So you’re choosing the second column and then using the same thing in 2013, it’s choosing the second column. So the second column in 2014, minus the second column in 2013, we get our value. Now to make this work all the way down, what I did is I highlighted all the rows here and then I press Control + Shift + Enter to turn it into an array formula. You can see that we get the live results as we’re changing our selection. So call it a trick there that you can do outside the Pivot Table. And another great reason why GETPIVOTDATA is fantastic because you can add things that you normally wouldn’t be able to in your Pivot Table. (upbeat music) We can also reference dates with the GETPIVOTDATA formula. In our Pivot Table here, I’ve got our order date in our row labels, and our sum of sales in our values area. And what I’ve done is I’ve taken the date of the first order, so the order is sorted from the earliest date all the way down to the last date. So I’ve taken the first date and typed it in here, and now what we can do is type in out GETPIVOTDATA formula, and cell reference, this cell in here. So let’s press equals or plus and write in a GETPIVOT and then press the Tab key. Now the data field is gonna be sales so we need to, with the brackets, type in sales, close brackets, the Pivot Table, we can click anywhere in here. We usually click on the top left hand corner and press F4 just so we can fix that value in there. And comma. Next is the field name. So we have the order dates, as we said before. So let’s type it in order date and make sure the spelling is the same and the item number one, well, that’ll be this reference here. So all we’ve got to do is just click in there, close the parenthesis and presenter. And you see you get the value there. Now, the data that you put in here, you gotta make sure that it actually exists within your data source. If it doesn’t exist, then you’re not gonna get a value. For example, we had the third of the first and then our next transaction is on the 12th of the first. So all we can do is for example, let’s put in the 4th of January in there, you get a reference because the data doesn’t exist. Instead of giving us a zero, it gives us a reference there. So let’s put in the second valid transaction. There you go. We have the value there. Another way that you can do this is actually putting the date formula. So I’ll press Control + D just to copy what’s up there. So it’s the same formula there. Now, instead of referencing the cell here, which we did before, what we’re gonna do is put in there the date formula. so let’s type in date, D-A-T-E, press the Tab and the year, or we can type in 2012, the month is January, and put in one, and the day is 12. And then we’ll go out here and we can close the parentheses and press Enter. And we’ll get our value. So there’s a couple of ways that you can reference dates with GETPIVOTDATA, just make a note that your dates do have values, if not, then you get a REF error. (upbeat music) We can use data validation to make out GETPIVOTDATA formula interactive. What we’re gonna do is create two dropdown lists, one for the months and another one for the regions. And then incorporate those into the GETPIVOTDATA formula by way of self referencing. And then once we change the months and regions from our dropdown list, then our GETPIVOTDATA results will also change. So let’s grab our months from in here, Control + Copy, right click and paste the values. Next we’ll choose our regions. So click in Americas hold on Control key, choose Europe, and then again, hold onto Control key, choose Asia, and then hold on the Control key and choose Africa, press Control + Copy, come up here, right click and paste the values. Now what we’re gonna do is create our data validation. So in our data tab, we choose data validation. And then in the dropdown box, we choose the list and our source will be our months in here and press okay. And we’ve created our list of all the months in there. Now let’s do the same thing for region. So again, data validation, choose the list, our source is in here, press okay. And we have our list for the regions created. Now let’s create our GETPIVOTDATA formula just by referencing it in our Pivot Table. So we have our value there. So now what we’re gonna do is instead of using the January argument in our formula, we’re gonna get rid of it, and we’re gonna reference it in our data validation list. Now for Americas in the regions, we’ll do the same thing, backspace to get rid of that. Choose our data validation list and press Enter. So we have our value there. So now as we changed our months, the formula gets updated. And also as we change our regions, our formula gets updated as well. So I call it a trick that you use when you’re creating customer reports with GETPIVOTDATA formula. (upbeat music) Then GETPIVOTDATA, it does have a shortfall. And let’s reference our pivot data for Q1 Americas, and press enter. And we have our formula in here and say that we’ll want to add in the sales month of January into this formula, and let’s see what happens. So in there we’ll put in sales month, and then January, and press Enter. We get an error message. So if the sales month is not part of our Pivot Table in here, anywhere in our column labels or row labels, if it’s not part of our Pivot Table, then it’s not gonna give us a result. So what we’re gonna make sure is to grab the sales month and drop it in there, and then we have our GETPIVOTDATA updated. Now in here, we have our sales quarter Q1 as part of our formula. So we grab our sales quarter and we’ll take it out. The reference is valid because our data is not part of our Pivot Table. So to fix this, we just got to make sure that you get rid of that argument there and press Enter. So if you wanna make sure that your GETPIVOTDATA formulas is working, then make sure that you drop in all the fields into your areas here so they can work properly. Another thing to note is that if you drop in fields into the report filter, then your GETPIVOTDATA is not gonna pick that information up. (upbeat music) We have our Pivot Table here with our regions on our row labels and our months on our column labels. And it goes all the way to the right-hand side there. So what I wanna do is bring these grand totals to the left-hand side of the Pivot Table. Now to do that press equals or plus, and we’ll go all the way to the grand total and enter that in there. So we’ll get our grand total there. Now, if we drag it down, then it’s fixed to the Americas grand total. So what we need to do in here, instead of having this Americas name, we just got to reference the cell C3, and then we can just drag it all the way down there. Now for the grand total to work, we just got to do the same clicking the grand total. If you see in the argument here for our grand total, it only has the data field, which is grabbing the sales values and the Pivot Table location, which is C1. Now this works well if you keep the format like this. But say that you want to add in some more fields, like the quarter in there, well, this doesn’t work. First what we wanted to do is say that if the first cell here C3 equals to grand total, then we need to put in this formula here. If not, then put in that formula there. So let’s grab this formula from in here, Control + Copy, and then in here we’ll say, if C3 equals bracket grand total, then let’s press Control + V and put in our formula that we took from the bottom. So if you C3 equals to grand total, then it would give us the grand total amount there. If not, then it would give us all the regional totals. And then close parentheses and press Enter. And now we can just drag all the way down here. You can drag all the way down if you want, if you’re gonna add some more items in there. So that fixes that problem. And we’ll have another problem with the REF error. Now to fix that, all if to put in there is an if error formula, if you’re using Excel 2010 or beyond. If you’re using Excel 2007, then you’ve got to pull if is error, I’m using a 2010, so I’ll put in if error, go to the end, comma value if error, well, the brackets, so it means blank. Then I’m here, double-click, and then you can see we have our grand totals for our regions and also our grand total in there. So call it dope workaround if you wanna see your grand totals on the left-hand side of your Pivot Table. (upbeat music) We’ve got our Pivot Table on the top here, and we’ve included our sales regions and our sales months and years on the top and the actual and plan in the values area. If we go right across you can see it all the months there all the way across. Now it’s pretty ugly looking. Now, we’re gonna do a better report here at the bottom, by using the GETPIVOTDATA formula. Now let’s close the field list there, and what we’ve done here is we’re putting the months from January all the way through December here, we just type that in. And we’re gonna use that later in our formula. And in there, we’ve actually put in the months and if I press Control + One, you can see that we’ve entered the mmm and that puts in Jan, if we press in another M, you put in the whole month, it doesn’t really matter, we just wanna show that it is January, even though the actual date is the first of the first, 2014, and then the first of the second, 2014 and so on. Then we’re gonna us these to determine whether our action month is an actual or a planned month based on the end of month date. So let’s go into the end of month date and in there, we’re gonna put in a formula called end of month and today. So press Plus, end of month. And then the start date, we’ll just put in there another formula which says today and close brackets, comma, and the month we don’t want any full months, we just want today’s month. So today’s end of month is the 31st of the fifth, 2014. We know that because if we go here, today’s the 21st of the fifth, 2014, so the end of the month is gonna be the 31st of May. Now in here, we’re gonna put actual or plan based on what today’s date is. So if today is less than the end of month date, then it’s actual, if it’s not, then it’s planned. So in here, we have to put in a if formula. So let’s put in if, and let’s click in there. So if B15, so if January, now let’s press F4 twice so we can lock it in because we’re gonna drag the formula down. So if January is less than or equal to the end of month date, let’s press a F4 once to lock it in. So if that is true, then return us an actual in text, if false, we return us to the plan. So anything from May onwards will be plan, and before that, it will be actual, as you can see there. So now we can create our GETPIVOTDATA, and based on this actual or plan detail, you’ll return us the values from within the Pivot Table, press plus or equals and click in the Pivot Table there next to Americas, it doesn’t matter that it’s 2012, we can change those values later and we’ll get the GETPIVOTDATA. Now what we’re gonna do is we have the actual there in brackets, so the first argument is the data field. So it’s taking the data from the actual, but we want it to reference this cell here, if we press Enter, we get a reference. Now I’ll show you a trick that will give us the actual data. So it’s B16, let’s press the and sign and then the two parentheses and that means that it’ll lock it in as text. You see that it works. So we’ve got the actual there, now let’s go into B16 and press F4 twice so we get lock in the row 16. The second argument is the Pivot Table and the defaults to A2, it could be anywhere in the Pivot Table, let’s leave that, that’s fine. The sales region is Americas, but let’s get rid of this, and then let’s reference it in here, and then press F4 three times to lock in the column A. And the financial year, 2012, but we can actually put an actual formula there and we can say, year and then we can go in there. So we’re getting the year from January, which is 2014 and let’s press F4 twice to lock in the row 15 and then close bracket. And then sales month, we’ve got January, let’s get rid of this and then let’s reference it to January. So, as I told you before, we’re gonna use the months names in there in our formula and here it’s where it’s gonna help us out. And then press F4 twice to lock in the row and then press Enter. So we get 260,257. So we can see that Jan, 2014 actual is 260,257 so that’s correct. So our formula works, all we’re gotta do is just drag it across there and then drag it all the way down. And I’ve what I’ve got here are some sub-totals. Now we’re gonna put in some conditional formatting here, so if it’s planned, then it’s gonna be grayed out, if not, it’ll be blank. So let’s highlight all the cells there and go to conditional formatting, new rule, and then use a formula to determine which cells to format. Let’s put in there an if function, so if B16 and press F4, so if B16 equals plan then true or else false. So we’re saying, yes, if it’s plan, then we’re gonna format it in gray. If not, it’ll be blank. Let’s format and let us put in a gray color there, press okay and okay there. So you see that it’s grayed out. Now let’s test to see if this works. So let’s say we’ll go into our next month and let’s put in the 30th of the 6th, 2014, and you’re gonna see that this plan here changes from plan to actual and the values change from the plan, it gives us the actual values for 2014 in June and the conditional format as well, gets blanked out. So let’s put in there one month ahead and press Enter. And you see, we get the live change there. Again, let’s go two months ahead, and that changes accordingly. So you’re gonna have a situation where your actual data gets inputted in here, and all you need to do is just go to the Pivot Table, right click, refresh and then the information will get updated accordingly. And when you go into the end of month date, then the values change. So the GETPIVOTDATA formula gives you some awesome power to create some live reports, and you can print this out and send it off to your boss on a monthly basis and you don’t have to recreate this each month. (upbeat music) We have listed our channel partners on the left-hand side here, and we’ve got the different months, and we’ve got the individual values for the years. And on the right-hand side here is we have a report that we’ll want to compare each channel partner and based on the base year that we choose, and the comparative year, we wanna see the variance. Now we’ll need to do this by inserting a GETPIVOTDATA formula, and I’ll show you how to do this in a second. Now, the first thing we need to do is create some Pivot Tables. Now we’ve got the month base year and comparative year. Now let’s highlight the month and go to insert and Pivot Table, and let’s go to an existing worksheet and we’ll just put it in there just for the moment. And it’s drop in the month in the row labels, so we have our Pivot Table there. And then we can go to move Pivot Table and let’s put there. Now we’ll do the same thing for the base year, insert Pivot Table, and let’s put it in here and I press okay. And then the base year into the row labels, and then let’s move it in May, and then finally, we go to comparative year and let’s do the same for that. Because that’s the first step done, let’s go to our report up here. Now, the base year, so let’s reference that to the months Pivot Table. So the first entry there and press Enter and the same thing for the comparative year. Now we’re gonna use slicers later on to control the months. And I’ll show you how to do that in a second. Now in the base year we’re gonna reference that to the base year Pivot Table, so the first entry there, and then the comparative year, and we’ll do the same thing and then enter there. The next step is to put in there a GETPIVOTDATA formula. So let’s press Enter and then choose anywhere in there. So we’re taking the sales as being the data field, which is correct ’cause we are using the sales information there. Our Pivot Table is in A3 and the financial year instead of having 2012, let’s get rid of that to here and then press F4 twice to lock in the row number. Your sales month is January, but then let’s get rid of that and then we can reference that to I12 and press F4 twice to lock that in. And the channel partners, we can get rid of that. We can go in there and just go up one and press the F4 three times to lock in the column and then press Enter. So we can check this now that ABC telecom in January, 2012 was 103,000, so 2012 January, ABC Telecom 103,501. So let’s put in there an if error, because if we get an error then we’ll get a zero because some channel partners that don’t have any values and press Enter. Drag this across press Control + Copy, and then highlight this area right-click and then put the FX in there. So we’ll have our values there. The final step is to insert a slicer. So let’s click on our Pivot Table and go to options and insert slicer, and then press the month. So we have a month there, now let’s put that into three groups and we can move it up there. Let’s do the same thing for the base year, insert slicer and then move that up here as well. And then finally, put the comparative year, click in there and insert the slicer. And let’s move that in there. So then we’ll have our slicers if we choose the base year, you can see that this changes and so there is the GETPIVOTDATA for the base year column, 2013 and 2014. Comparative year the same thing happens, the information changes, and you can see that because it takes the first entry in the Pivot Table. And we’ve referenced that in here. Now let’s choose a value there. And now we can also do the months as well. So then do the analysis based on the different months. And we get the variance dollar and the variance percentage. So by using the GETPIVOTDATA in some slicers, we can do some comparative reports on channel partners, and it’s not only limited to them, you can do comparative analysis on products, on employees, on whatever metrics that you like. (upbeat music) Macros enable you to record steps that you do in Excel, and then run those steps automatically with the press of a button. You can create these macros for your clients or colleagues to give them the analysis power that they wouldn’t normally have. And to create a macro, assumingly to go into your ribbon and then choose view. And on the far right hand side, you have the macros button and you can press the record macro button from in here. Another way you can do it is through the developer tab. Now you may not have this activated, but I’ll show you how to do this. First, you go to file, then options, then under the customize ribbon option, on the right-hand side, you have the developer box there. Now it may be unchecked if that’s the case, just check it and press okay. And that will activate it. Now, another thing you’ve got to take into consideration is the trust center. So click in there, then under trust center settings, click that and choose macro settings. Now in here, you have different macro settings. If you choose the disable macros, then once you open your workbook, the macros will be disabled and then you’ll need to enable them. Now, if you want to enable them with a notification, that means that on top of the formula bar, you’re gonna get a yellow strip that says enable. Well, choose this option. If you wanna disable all macros without notification, choose the first option. And if you wanna disabled all macros except digital sign macros, then choose this option. Now, the last option is not recommended. That means enable whole macros when you open the workbook. Now you may have some dangerous code in there, so never choose this. So let’s choose the second option and press okay, and okay. And now in your developer tab, you have the code here, the macros that you’ve recorded, you have also the addings here that come from your computer, and also you can insert some form controls in here. So there’s a few options in here under the developer tab. The main thing is the record macro, or bringing up the macros that you’ve already recorded. (upbeat music) We’re gonna record a simple macro to refresh a Pivot Table. Now to do this, first of all, we need to go to the record macro button under the developer tab, or you can go to view macros and record macro. So let’s do it from here. And then it brings up the dialog box, and in here you need to name your macro. So let’s call it RefreshPivot, make sure there are no spaces. You can put in here a short key just to activate that macro, next time you want to run it. And also you’ve got the option here to store the macro in. If you choose this workbook, then that means that you can share this macro with other people. So if you email these documents to someone else like a client or a colleague, then choose this option. If you want to keep it just for yourself, then choose the personal macro workbook. And if you want to store it into a new workbook, then choose that, we’ll choose this workbook, so that can be embedded into this workbook. And description, you can just write in a short description, if you like, we’re not gonna have to write anything we’ll press okay. So now the macro is running and we know that because on the bottom here, we have the blue box that says, “A macro is currently recording.” And we can press that to stop it. Now, if we go to the developer tab, then you can see that it’s running there and it says “stop recording.” So we’ll know that it’s recording. Now, all we’re gonna do is click in our Pivot Table, right click and press refresh and stop recording. That’s our macro. Now to see our macro, let’s go to macros in there and you can see the refresh pivot there. Next in all open workbooks, or we can choose this workbook and it comes there. So now we can run it from in here and we can edit it or delete it from in there. Let’s cancel out of there. What we’re gonna do is insert a shape and then we’re gonna attach that macro in there. So let’s put in a shape, and then from the shape styles, let’s choose a button like this. And then in here we (indistinct) to right-click and choose assign a macro, and then from in here, we can just choose our RefreshPivot macro and press okay. Now, once we’ve done that, you can see that the hand has been activated. So if you click that, you will refresh the Pivot Table. Now let’s right-click and then type in there refresh Pivot Table. Press Control + A and let’s format this a bit, let’s make it a little bit bigger and then put this in the yellow and then step out of it. So if we go to our data table, and then we change the amount here to say 10 Million, and then we need to refresh our Pivot Table, we just go in there and press refresh. It’s very simple to create a quick macro. Now, another thing you’ve got to make sure is if you go to file and save, it’ll give you a dialog box that says, because this is a macro you need to save it as a macro enabled workbook. So to continue saving as a macro free workbook click yes, that’s not advisable let’s click no and then it brings up our save as dialog box and then from in here, all we’re gonna do is just from the dropdown box, choose Excel Macro-Enabled Workbook, and then press save. And let’s get out of this. And now we’re in here and you can see that this Macro-Enabled Workbook, the type is macro-enabled workbook and also you get the exclamation mark. So let’s double-click to get back in. And now we have the enable content. So for the macro to work again, if you click here, it’s not gonna work, we would click enable content and then refresh Pivot Table and you’ll refresh again. (upbeat music) Now we wanna create a macro where we filter our dates, so we can see this month’s values. This quarter’s values, and also the year to date values. Now we have the order dates here in our row labels, but it can be invoices due or customer payments to be received. It could be any dates. Now to create this macro, first of all we’re got to clear whatever is in the filter and then run the macro for each of the three filters. So let’s choose any day filter. Now let’s go into our developer tab and press record macro. So the first macro will be called this month, and we’ll keep it into this workbook and press okay. So the macro is recording, the first action would be to clear the Pivot Table. So let’s go in there and press clear filter, and then let’s create the date filter for this month. So click on the dropdown box, date filter, and choose this month and then stop recording. That’s our first macro. Let’s create our second macro, press record macro and call it this quarter and press okay. First step is to clear the filter, the second step is to choose this quarter filter and then press stop recording, record macro for year to date, record macro, call it year to date, press okay. Let’s clear the filter, let’s go back in, date filter, year to date and stop recording. So now we’ve created our three macros and now we’re gonna put in some buttons and assign those macros to each of the buttons. And let’s insert shape and we can insert this color shape there, and let’s choose the start here. So what we can do now is just click on the shape with our left mouse button, press Control + Shift. And then this moves that across, let go of the mouse button, you’re still holding on Control + Shift and then click and drag across. So we’ve created our three shapes. Now in here, we’re gonna call this month, in there we’ll call it this quarter and the next shape we’ll call it year to date. We can click on one shape, press the Control key and select the three shapes. And then we can edit it from in here. We can center it, we can make it bigger, we can make it into a yellow color, and then we’ll go to the format and text effects we can put in their a shadow if you like. And then we may just drop it down a bit like that. So we have about three different buttons. And now let’s assign the macros. right-click, assign macro, this month, okay. Right-click, assign macro, this quarter, assign macro, year to date. So let’s see if this works. This month, if I click this quarter or the step was to clear the filter and run the date filter for this quarter. So it does that and then year to date. Now today’s date is the 20th of March, so obviously we’re in the 1st quarter. So year to date and this quarter will be the same. So here you have a quick macro, to see your date filters, so when you open this workbook next month, or in a few months down the track, then Excel is smart enough to know your current date and then recalculate the date filters based on your current date. (upbeat music) We’re gonna record a macro, where we’re gonna get different Pivot Table views depending on the button that we’re going to press. Now, the trick to this is that the first step of the macro is to clear the Pivot Table and the the second step is to create the Pivot Table. So let’s go and record our first macro and call it region by quarter and press okay. So the first step is go to the options tab, clear, clear all, the second step is to create the regions by quarter. So grab the quarter’s, region and then we’ll grab the sales twice into the values area. Now from the dropdown arrow, we choose value field settings, and then we put in here average and press okay. Finally, select everything, go to the home tab and just put in a comma and go to the decimal places and then we can go to developer and stop recording. That’s our first macro done. The second macro is gonna be called year to date sales by month, so press record and call it year to date sales by month and press okay. The first step, once again, go to options, clear, clear all, the second step is to create the Pivot Table, let’s grab the years in the row labels, sales month as well, and drop in the sales twice into the values area. And from the dropdown arrow, the value field settings and show values as, and from the dropdown box here, we’re gonna show values as a running total in and the base field will be sales month. And the custom name we’ll change it to year to date and press okay. And then once again, click in here, go to the home tab and customize it a bit like this, develop a tab, stop recording, the third macro is gonna be called top 10 channels, press okay, options, clear all, grab our regions and our channel partners on the left and our sales in our values area, and then from the channel partners, we can filter it by value filters and top 10 and just press okay. And then finally, in the Pivot Table, we just right-click and so largest to smallest and go to develop a tab and stop recording. So we’ve done our three macros. Now all that’s left to do is to insert the shapes and assign the macros to the shapes. So let’s do this insert shape in there, and we’ll get one set in there. Now let’s choose the color of the shape like this, now hold down with your mouse key and then press Control + Shift and drag down with your mouse, let go of the mouse key then while the Control + Shift key is still being pressed, click the mouse, drag down and then let go of the mouse key. So we’ve created three similar shapes. Now let’s name them. Region by quarter, sales and average, year to date sales by month, and then top 10 channels. Now let’s click in one here. Press Control + A, so we can format the shapes. And then in here we can just choose a color. I like color like this and press Bold and we can just make it a little bit bigger like that. Escape. So now let’s assign the macros. Right-click, assign macro and in here we wanna choose region by quarter, and then the second one is year to date sales, and the third one is the channel partners. Now all that’s left is to press the button, sit back and enjoy the magic of the macros. So we have the region by a quarter sales and average, the year to date sales by a month, and then the top 10 channels by region sorted from largest to smallest. (upbeat music) We’re gonna record our macro where we’re gonna see our top customers by using eight scroll bar. So as we scroll up or down, then the number of customers in our Pivot Table changes as well. So our macro will be to clear an actual filter and then create the filter of the top X customers. Now let’s go to the developer tab and record macro, and call it top customers, and press enter. And in here, make sure that you have the filtered Pivot Table. So the first step will be to clear the filter. The next step is go to our value filter, choose top 10, and then just press okay from in here. And then stop the recording. So that’s our macro done. The next step is to insert a scroll bar. So in the developer tab under insert, let’s choose this scroll bar here, and then we can just simply put it in there, just like this. Okay. So right-click in there and format control. The minimum value will be one, the maximum you can do as high as you want, just depending on your customers. We’ll put in there a maximum of 100. The incremental change will be one. So as you move the scroll bar, it moves by one. And the page change will be 10, keep it at that. Now the cell link, we have to link it into the cell, A1. And press okay. So now as we’re moving this, you see that the number changes automatically. Okay, up or down, or we just take this and move it all the way up or down like this. Okay. We’ve got 100 there, let’s make this a little bit bigger. So now let’s go into our visual basic button, and then under modules, choose this and double-click. And the value that we have here instead of one, let’s get rid of that and we’re gonna type in there ActiveSheet.Range, and then put in there the parenthesis and in brackets we gonna put in there E1. So (*E1*).Value. You can save this and close it. So what we’re saying is, as this number changes, then our macro filter will change as well. The final step is to right-click in our scroll bar and assign the macro top customers, press okay. So now we can move this up or down and you can see that our customers and the values change as well as the grand total. So a cool little trick that you can use to see your top customers. (upbeat music) In our chapter 12.3, we created three different data filters. And now what we’re gonna do is put these macros into our quick access toolbar, which is over here. And then we can access them from there rather than having buttons. So let’s go to file and options, and then quick access toolbar. Choose here from the dropdown box macros, and you have the three different macros. So we can just select the macros and add, and add, and add. And then from in here, now press modify. And in here we can choose whatever design we like. So just up to you, there’s all these different designs. Now I just use a different color like this. And for in here, I’ll use another color and a year to date, I can just use that. Press okay, and then you can see that the macros are here. If you hover over it, you’ll see what it relates to. Just press that and they change automatically. (upbeat music) One way to reduce your file size is to copy an existing Pivot Table into a different worksheet. Now we have our Pivot Table here and we can go to that file and look at our properties, and our size is 45 meg. Let’s escape out of there. So what we can do now is go to options and select entire pivot, press Control + Copy. Now we’ll go to file and new blank workbook, and press Control + V. And here we have our new Pivot Table. And if we go to file and save, we can save this as book three, that’s fine. Now, if we go back into the backend, we can see that our size has reduced to 11.8 megs. Now let’s just go out of there. Now, if we go to options and change data source, it’s linked back to our data source in our other workbook. That’s fine. Now we can also see our data set if we just go to the grand total and double-click, and it comes up in this workbook. Okay, so that’s a quick workaround where you can reduce your file size by copying it into a new workbook. (upbeat music) Another way to reduce the memory size is to delete the data source. Now because our Pivot Table is run by the pivot cache, then we can make the changes without having the data source there. But the only thing is that we cannot refresh the Pivot Table. Let’s look it out file size, which is 45 meg. And let’s go to our data source, right-click and delete and press okay. So we deleted out data source. If we go to options and refresh, well, we cannot refresh because our data source is gone. But what we can do, we can actually rearrange this Pivot Table because it’s written by the pivot cache and then put in there the regions. So we can make changes to our Pivot Table. That’s not a problem. I’m gonna press save. Let’s go and see our five size now, which is 12 meg. So it’s reduced dramatically. Let’s go back. Now if we want to see our data source again, all we’re gonna do is click in our grand total on the bottom right-hand corner, double-click, and our data source has been included in there. Now we can connect the Pivot Table, just go to options, change data source, and then select our table here, and press okay. And we have our data source connected once again. (upbeat music) A good way to reduce file memory is by saving your Excel file as an Excel Binary Workbook. Now these files store information in binary format since .XLSB files are binary that can be read from and written to much faster, making them extremely useful for very large spreadsheets. Now our file size is 45 meg. Now, if we save this as a binary format all we gotta do is choose the format from in here. And the third option is Excel Binary Workbook, click that, press okay. And now let’s go to the file tab and have a look that our size has reduced to 26 meg. (upbeat music) If you have over a million rows of data, then it’s best to use Microsoft Access to create a Pivot Table. Excel only allows you 1,048,576 rows of data that you can input. So anything above that you’ll need to put it into a database like Access. In here, we have an Access database with over 1 million rows of data. So we’ve got about 1.5 million rows of data here. And what we’re gonna do is we’re gonna import that into an Excel worksheet and create a Pivot Table. Now let’s get out of this and go to our Excel workbook. And from in here, we gonna go to insert a Pivot Table, now we’re gonna choose the use an external data source option, and then click on choose a connection. We’re gonna browse for more. And then what we’re gonna do is go on to our directory where our file is kept. So here it is here, we’re gonna double-click that, and we’re gonna create a cell A1, press okay. So from in here, we have all of our fields and we can simply drop in and create our Pivot Table just like that. So we have all our data there that you can see. Now just to make sure that everything is there, we can drop in the sales again, and then you use account just to count the number of transactions that are there. We can go all the way down, you’ll see we’ve got 1.5 million rows of transaction as we had in the Access database. So as your Access database gets amended, then all you do is press refresh to make the updates, or you can go to your connection properties, and in there choose refresh every X number of minutes, or refresh data when opening the file. Another advantage is that if we’ll save this, then our file size is small. Let’s have a look, file and we’ve got 28 Meg of data. (upbeat music) Now there are a few compatibility issues with Excel 2007 and Excel 2010. Now Excel 2010 has slicers. In Excel 2007, they’re not visible. So if you create an Excel file with slicers in Excel 2010, and you open an Excel 2007, they’re not visible. A box will appear instead stating that the slicer cannot be viewed in Excel 2007. Also Excel 2010 has six different calculations. Now if these calculations were created in Excel 2010 and opened an Excel 2007, you will see the results, but if you refresh, then these go away. And in Excel 2010 under the report and layout, the repeat all items labels option, if these calculations were created in Excel 2010 and opened in Excel 2007, then you will see the results, but if you refresh, it’ll go away. And finally, if you’ve saved an Excel file in Excel 2007 as compatibility mode, and you open that in Excel 2010, then you need to refresh the pivot in order to have the full Excel 2010 pivot features. (upbeat music) Now you can share a Pivot Table via Microsoft’s OneDrive. Now OneDrive is the same as SkyDrive, they’ve recently changed the name. And before that to have access to it, you needed to set up an account with live.com. So all you need to do is have access to OneDrive via Microsoft, and then you can upload all your files in there, which I’ve done here, and then you can share it. So if you click on this creating a custom style Excel workbook, and then go to share, we’ve got the option to invite people or get a link. Now we have three options. The person can view only, they can edit, or it can be public to everyone. Now let’s go to edit and create link. So now, what I can do is go to invite people and then I’ve got here, I can write a note that please see my Pivot Table for this year’s results. And then here you got the recipients can edit, which is the step that I chose before, but you can click there and you can do the view only, but let’s do edit. And in there recipients don’t need in a Microsoft account. So you don’t really have to have Microsoft account to access it, you can open it in a web browser. Now let’s press share, and then press close. And now I’m gonna go into my inbox. And now that I’m in my inbox, you can see that I received this email and it says here, John has a document to share with you on OneDrive. To view it, click the link below. So I’m going to click this link, and it opens up a web browser. So you don’t have to have an account there. And in here we have our Pivot Table with our slicers that work. Now in here, I can just right-click and show field list. And then I can take out the information here. So financial year goes out, and then I’ll put in there the sales quarter, and that gets updated as well. So you can make changes there with your field list. Also, if you want to save this on your computer, you can just go to file and then save as, and then save it onto your computer there. So this is a good way to send information and view your Pivot Table with the slicers over the web, and it eliminates sending emails. (upbeat music) We have our data source here and in our financial year, we have data just for the year 2014. And what we wanna do is create a sales forecast based on a 5%, 10% and 20% increase on the 2014 actual financial year. So let’s go to our Pivot Table here. And we’ve created a Pivot Table with our sales regions and our months going on top, and we have that up there. So now we can create a sales forecast simply by going into the calculated field. So now we’re in anywhere in our Pivot Table, we can go to options and then fields, items and sets, calculated field. So from in here, we can create our different calculated fields. The first one is gonna be forecast next year at 5%, forecast next year at 5%. So the formula will be the field with the actuals, and then let’s use the multiplication sign and press 1.05. So that’s a 5% increase. And we can add that. Now let’s set another one at 10%. And in here, we’re gonna bring in the sales and then 1.1 and press add. So we’ve got that there. And then we’ll do another one at 20%, and then 1.2 and press add. So we’ll have the three different scenarios there, and press okay. So we can see here in our values that there’ve been added in here. Let’s just make this a little bit bigger. So we can see that there. And let’s go in there into each one, the value field settings. And in here we can put in an asterisk just to distinguish that it’s a calculated field and press okay. We’ll do the same for the next one. And then last for that one there and press okay. So we can reduce that a bit there, and like this. Now they’ve also been added to our field list. Okay, let’s get out of here. And we can see if we just double-click in between the columns that we have the actuals, the forecast at 5% increase, the forecast at 10% increase, and the forecast at 20% increase. And it goes all the way across each month. And then we have the totals. So now, right-click and go back into the show field list. So what we can do is actually take out the sum of actual and just leave in that 5% amount. So we can see from in here if we can just double-click to reduce it, we have the 11 million. Now right-click to show the field list again. And let’s take out 5% and let’s put in there the 10% figure and see what we get, we get the 11 million grand total. Okay, we take that out and we put in the 20% and we get 12.7 million there. So with calculated fields, you can put in different scenarios based on your actual data, use a multiplication sign and then put an increase or a decrease, and you can do some different sales forecasting models. We can just highlight all that, press Control + Copy, and Control + V in there and Control + V there. Okay, so the first one we can put in the 5% scenario. In the second one, we’ll put in the 10% scenario and click on your third one and we’ll leave it at 20% there. So we have our three different scenarios and you can make your decision on which one to use based on what your business sees as feasible. (upbeat music) With a Pivot Table wizard, we can actually consolidate information into one Pivot Table. Now we have here four different salespersons data, and they’re all in a similar format. You see their salesperson one, salesperson two, salesperson three and salesperson four. Now, if they’re all in the same format, then we can consolidate. Let’s go to the consolidated report. Now the pivot chart wizard, there’s two ways to bring it up. One is to press ALT + D + P, and we can bring it up like that. Let’s cancel out of there. The other way is to go into the quick access toolbar, commands not in ribbon, and then put in Pivot Table and pivot chart wizard. Now to do that, we’ll go to file and options, quick access toolbar, and then from the dropdown box commands not in ribbon, and then click in there and put P to go all the way down. And then we can just choose the Pivot Table and pivot chart wizard and press add, press okay, and then it’s added in here. So let’s press that to start our wizard. And it gives us three options. The first option is Microsoft Excel list or database. The second option is external data source. And the third option is multiple consolidation ranges. And we choose the Pivot Table and press next. And then in here to create a page field, choose I will create the page fields, and press next. So now the first step is to select the range and let’s go in here and select that, and press enter, and then add. Go to the second sales range, select data then add it. The third salesperson’s data select that, add. And then finally the fourth sale information there, and then press add. Now how many page fields do you want? We’re put in there zero, ’cause weren’t gonna use any page fields. And then press next. And then finally it asks us, where do you wanna put the Pivot Table report? Let’s choose somewhere there and press finish. And you see the Pivot Table field list here has got a row, column and a value, because it’s defaulted to those names. Now the values we can just choose a dropdown arrow, go to value field settings, and then a number format. And go to number, no decimal places, 1000 separator, and press okay, and then okay there. So here we have our consolidated report from the four different salespeople. Now, if we go into one report here and let’s just say, there’s a change that was made. For example, let’s put in a big amount, 1 million, and press enter. Put that back here. All you need to do is right-click and refresh. And then you see the value change on the bottom right-hand corner there from 127 to about 128 million, press refresh, and see that gets updated. So each time your salesperson sends you an updated report, you can just refresh this Pivot Table and you get the consolidated data. I’ll show you another cool little trick. Say you receive your data like this in this format, and you want to put it into a tabular format. For example, in the first column, you want to show the regions, the second column you wanna have all the months, and the third column, you wanna show the values. Now to do this, we have to bring in our Pivot Table wizard and then choose multiple consolidation ranges, press next. I will create the page fields, press next. In the range just choose this, okay. Press enter, and then next. And we can just put it in there and press finish. So it brings up our consolidated range, but we already consolidated one piece of information, that’s fine. The trick to this is you’ve gotta double-click in your grand total. And then you see here that you get your tabular layout. So you have your regions in the first column, you can change this to regions instead of having a row. The column, you can change that to months, and we have the values, and go all the way down there. So it’s a good workaround when you get information that comes in on tabular formatted style, and you wanna put it back into a Pivot Table tabular format. (upbeat music) Sometimes in finance or accounting you wanna do a frequency distribution to see how your sales or costs are distributed depending on different groups. Now we have our information here without actual dollar sales and let’s create a Pivot Table, we’ll go into insert, and the Pivot Table, and let’s put it into our existing worksheet in there and press okay. Now from in here, we’re gonna put in now actual dollars in our row labels and then drop it again into the values area. And from in here, we just wanna get a count. So we’ll see how many transactions fall in between a different group. So let’s click in our row label and right-click, and only to group here our sales, and we have a automatic starting and ending point based on the minimum and maximum amounts. Now we can change that to put in 10,000, and then ending at 100,000. And increment, we can have 10, that’s fine. Press okay. So we have our sales ranges there and we can see the amount of transactions that we have in the values area there. Now we can put that in the graph by going into options and pivot chart, and we can choose a column and press okay. Now just right-click there and hide all field buttons on chart. And let’s make this a little bit bigger. And we can call this frequency distribution of sales. Okay. And then you can just get rid of that. So now we have our graph, and we can see the amount of times that we have sales between 10,000 and 19,999, you can see it there if you hover over this 22. And you’ve got the different sales groups, and we’ll have our frequency distribution in the graph. And you quickly see which sales ranges are more popular and which sales ranges are not. (upbeat music) We can do a breakeven analysis with a Pivot Table. We have a scenario and item and a value table here. So in our scenario, we have three different scenarios, slow production, normal production, and fast production. For each scenario, we have a variable cost per unit and a total fixed cost. And we see the values there. Now we can create a Pivot Table from here. Just click anywhere in there and go to insert and Pivot Table, and we just put it down here for now. In our row labels, we’ll put in there the item, and the value, will go into the values area. Now let’s drop in a slicer for the scenario and press okay. So we have our slicer there. So as we choose the different productions, the Pivot Table changes. And by this, we can go into our breakeven model and then reference the sales to the total fixed cost, and the variable cost per unit. And as we change these scenarios, then our break even model gets updated accordingly. So let’s grab our slicer, press Control + X and go into our breakeven point and Control + V to put it in there. Now we’ll have our breakeven model, which is a price per unit, which is a manual entry we’re gonna pull. The unit sold, again, a manual entry. And the total sales is the price per unit times the unit sold. The cost is the variable costs, and in here, we’re gonna put the units times the variable cost per unit that’s in our Pivot Table. So let’s choose the units and then press times and go to our scenario to get the variable cost per unit, which is in there and press enter. Now, the fixed cost would just be the fixed cost total from the Pivot Table. So go in there and grab that and press enter. So now we’re gonna put in a price. Let’s put in $10 and the unit sold, let’s put in there 2,000 and press enter. So on a slow production, we’re making a profit. On a normal production our profit reduces because our variable cost has increased and our total fixed cost has increased. And under a fast production, we have a breakeven point there. Now you can change these amounts just to play around with the numbers, but based on this analysis and using a slicer, you see how you can put in different scenarios and determine what your breakeven point is for your product or a new business model. (upbeat music) Now in here, I’ve created several different slicers that you can copy and paste into a new workbook and apply to your current slicers. Now, for that, you need to go on to chapter 7.4, copy a custom style into a new workbook, to see how you can do that. But here, I’m gonna show you the different styles that I’ve created and also shows you the flexibility that you have when creating a custom slicer. You have many options and I’ll give you some ideas to see what things you can do. Okay, so let’s click in our slicer there and go to options. And in here on the top, I’ve created eight different slicers. So the first one is to this, and you see the things that they can do. Let’s go to the second one. We got to the third one there. The next one. Then in here. So you see the different styles and fonts that you can use. And finally, we have this one here. So depending on your creativity, probably yours is much better than mine. You can create different styles. Now I’ve created this, it’s pretty quick. You have the guide here on the left to see what you need to change. And once you do one, you can actually do many more. So once again, in chapter 7.4, I teach you how to copy a custom style into a new workbook. And in chapter 7.3, how you can create a custom style. (upbeat music) With a Pivot Table and slicers, we can create a balance sheet that’s interactive. Now I’ve created the one here and what I’ve got in there are four different Pivot Tables. I’ve got two graphs that I connected and also some metrics up here. And then with a slicer, once I make the change, the metrics change, the graph changes and so do the Pivot Tables. So I can see my different status as at every month. So you can see, you can do some pretty powerful reports, and it’s not that hard. I will show you in the next couple of minutes, how to do this. So the first thing you need to do is when you’re creating a dashboard or a interactive slicer with charts, you gotta make sure that you set out your canvas and then separate it into different areas. So on the top here, we’re gonna put in there the slicers. And then second, we’re gonna put the metrics. At the bottom, we’re gonna have the graphs and down here we’re gonna have our Pivot Tables. So let’s go onto our data. And we have our data here which has the months. And we have the years for 2014 only. And we have our balance sheet items into current assets, current liabilities and non-current assets and non-current liabilities. And the type here, we have the different types of assets and liabilities as defined in a normal accounting business structure, and press okay. And we have the actual amounts there. So from in here, we can create a Pivot Table, go to insert Pivot Table and existing worksheet, and let’s put it in here and press okay. So we’re gonna drop in our balance sheet into the row labels and type into the row labels. So you can see it’s like this. Now we’ll make some space here so we can fit it in. Now, from in here, make sure that under options and options, the auto-fit column is switched off. And the design sub-totals, do not show sub-totals, and then field headers, we can get rid of them. And also the no buttons. Now, one thing we’ve gotta drop in are the actuals into the values area. Now, from in here, we can actually get rid of that and then just press a space. So it recognizes that as a character and it’s a work around to having a blank header. In the grand total, we’re gonna change that to total current assets, and press enter, and that’s fine. Let’s go back to our values and we can put in there the dollar signs into the number format. Let’s go to currency and choose dollar signs with a negative red and zero decimal places, and press okay there. Now we can filter this just for the asset. So when we go into the balance sheet, we can just select the current assets, and press okay. So it just gives us the current assets and in the design, we can choose this one in there. Now we can click and go to options and select entire Pivot Table and press Control + Copy. And in there, press Control + V. We’ve pasted the similar format in here. So we’ll have to go and redo all the formatting again. So the only thing now is instead of the balance type being assets, we can just choose current liabilities. And that changes there. Now let’s do the same for the non-current asset. So again, click there, select entire Pivot Table, Control + Copy, and down here press Control + V. And we can do the same thing there. So let’s click in the non-current assets. Let’s change that to select the non-current assets. And in here let’s select to include the non-current liabilities. Now let’s delete this space here. And then in here, we can just highlight it and put in a light gray. And the total assets, let’s do the sum, which is the current assets plus the total current assets. Now we get up and GETPIVOTDATA. So let’s escape out of there. Let’s click in a Pivot Table, go to options, and from the dropdown option, let’s get rid of GETPIVOTDATA ’cause we don’t want that. Once again, let’s click in there. And then we’ll do the same thing for the liabilities. So we have our Pivot Tables there for the assets and the liabilities. We can actually highlight all of this and choose a different font if you like. Okay, so the next step now is going to put the ratios there. So the current ratio is current assets divided by current liabilities. The quick ratio is the current assets minus the inventory divided by the current liabilities. So we’ll get that minus that inventory, and then divided by the current liabilities. Now the debt equity ratio equals the total liabilities divided by the owner’s equity. So let’s go to the total liabilities there, and divide it by the owner’s equity. And the owner’s equity is simply total assets minus total liabilities. So total assets minus total liabilities. So we have our numbers there. And in here, we can just adjust that if you like. Now, one thing I noticed here that we didn’t change the names for the grand totals here. So here it should be total on current assets. Here it should be total current liabilities. And in here total non-current liabilities. The next thing is to put in here the chart that relate to the total liabilities. So let’s highlight total liabilities and the amount there, and got to insert and bar chart, and we can include that in there. So let’s just start in here for the moment. We can get rid of the totals there. And then the gridlines, let’s make this a little bit bigger. And then highlight that and get rid of it. So there’s a click in our bar chart and press Control + 1. And then from in here, we can go to fill and pattern fill, and then choose this format there. And then we can choose the red color and then let’s click outside of the border there. And then the border color have no line as well. Now from the X-axis, click on that, press Control + 1. Maximum, you can leave it as automatic, but we can put it into maximum of 1 million. And the major unit will be 200,000. Display unit, we’re gonna put that in hundreds. And then the minor tick mark we’ll have that cross. We’ll show display units on global chat, that’s fine, and press okay. And finally, let’s make this in gray color and this as well. So we’ve created the chart or we can just make it a little bit smaller or bigger just depending on the size there. So we’re gonna do the same thing for the other chart. So instead of going through the same process, we’re gonna save this chart. So go to design, save template as, now when you do that, it goes to the Microsoft templates and charts, and we’re gonna call it in the interactive balance sheet. So let’s create the other chart. Let’s click on the total assets and go to insert and bar, and bar, and we’ll have that there. So let’s go to the change chart type from the template. Let’s hover over here and go on to our interactive balance sheet and press okay. Now we’re gonna change this to a green color, and also it’s gonna go from right to left as well. Let’s click in the chart, press Control + 1. And then the fill is gonna be a green color. And let’s click in here and the values are gonna be in reverse order, and press okay. One thing you’ll notice is that we have hundreds and let’s click in here and press Control + 1, and we change it to thousands. And the same thing for that, let’s click the X-axis and change that to thousands. So now we can put the charts in our dashboard and we can reduce it like this, just to make it fit. We can change that later on. Okay. Now the same thing for this. We can just put in there. Okay, now one thing is that background should be great. So click there and then put in the light gray background, click in the graph, press F4 to repeat, the same thing in there, press F4 to repeat. And we have our chart in there. The final thing we’ll need to do is put in that right slicer so we can control the months. So click anywhere in the Pivot Table, go to options and insert a slicer, and let’s choose month and press okay. And from in here, we can put it into six columns. We can drag it across like that. Right-click, slicer settings, get rid of the display header. Now let’s choose the custom slicer, which I created earlier called Johns Wicked Slicer. And we can read this like this, or we can just make the buttons a little bit bigger. So we’re gonna fit it in there. So that’s fine in there. Now, one thing we need to do is connect the slicer to the four different Pivot Tables. So and click on the slicer then right-click, and Pivot Table connections and just check all the boxes. So we’re connecting all the Pivot Tables to the slicer, and press okay. So now we can press January, the Pivot Tables change, the totals update and so do our metrics. So we have our live and interactive dashboard. You can see at any time how your business is doing, which is a pretty powerful tool to use, but it is pretty easy to create this once you know how to use Pivot Tables, slicers, and a couple of charts. And by going through this course, you’re gonna find out how to do all this stuff here. And it’s not that hard. It looks pretty fascinating to you use the Pivot Table principles and some common sense and then you can create a dashboard just like this. (upbeat music) Here we’re gonna create a monthly sales manager performance, where we see the sales for each sales manager on the left-hand side going all the way down the rows, and then get a percentage variance from the previous month. So we track each salesperson’s progress from one month to the next, to see whether they’ve increased the sales or decreased the sales from their previous month. And we’re gonna use some conditional formatting to show the variances visually. Now let’s create this. Click anywhere in our data set, go to insert and Pivot Table, and go to new worksheet. On the left-hand side, we’re gonna put in the financial year, then the sales month, the salesperson will go on the column labels. And we’re gonna put in there, the sales twice. Now let’s close that. Let’s reduce this a bit. Now go to design and grand totals off for rows and columns. Let’s make a few adjustments here. Let’s just make this centered. Okay, now let’s bring in our field list and we have our sum of sales there. Now let’s click in there just to format our numbers. And in here we can click and choose show values as, and we get the different from percentage difference from the previous month. So we’re gonna show values as a percentage difference from the previous sales month and press okay. We can go back in there and just format the numbers. Let’s go to custom and choose a red in there. And then before the semi-colon we’ll put in a percentage. And press okay, and okay. Let’s change the name here. Instead of sum of sales, let’s call it delta or variance, I’ll call it a delta and that changes there. And here, instead of sum of sales, let’s call it sales. That already exist. Let’s press okay, and put a space. And then we have that in there. Now we need to put in there a conditional format. So let’s click in the variance column and go to conditional formatting and choose the icon set. And let’s choose these arrows here. Now, from this dropdown box, let’s choose the last option just so we can see the conditional format only on the values and not the sub-totals. Now finally, we need to go back to conditional format, manage rules, double-click in here and let’s show icon only. So we don’t wanna see the percentages, we just wanna see the icon. And the values here, when the value is bigger than zero, a number and also zero and number. So it’s bigger than zero it will be green. If it’s zero, it’ll be orange, and if it’s less than zero it’ll be red. Now let’s press okay. Apply this, okay, it works perfect, and okay once again. We can see here that 294 is bigger than 170, 312 is bigger that 294, and then 229 is the less than 312. So it goes down. So you see the delta for Homer Simpson, the delta for Ian Wright and so forth. Now, finally, let’s go and put in a slicer. Let’s make some space up here so we can put it in there. Let’s got to options, insert slicer and got to salesperson. Now let’s make it like this. And we can go to columns, just make it a little bit bigger, and we can increase the size a bit. And we can change it to a color like that. We can put in there to make it a little bit bigger. Okay, so we have that salesperson there. So now if we choose Homer Simpson, we see his values there. We can just make this a little bit smaller so you can see it goes all the way down there. Okay. Now let’s double-click here so we can get a bit more space. Ian Wright, John Michaloudis, and if you wanna see all of them again, just clear the filter. So by using show values as, some conditional formatting and some slicers, you can do some impressive sales manager performance reports. (upbeat music) We have a list of customers here, and we have the payments column here. And in black, we have the outstanding receivables and in red, we have the payments that we have received from them. Now we can do a reconciliation by going into customers and sorting from A to Z. So we can have all the customers sorted alphabetically, and then we can go to the payments column and then manually see whether that equals to zero. And we can see down here that it does equal to zero. But imagine if we had 1,000 customers, we’ll be here all day doing this. And there’s a quick way to reconciling customer payments. Let’s go to insert Pivot Table, and we can just put it in here in existing worksheet and let’s drop the customers in the row labels, and then the payments in our values. Now, by doing this, it sums up the credits and the debits, and it gives us a zero amount if our customers have paid. And then we can see here that 123 Warehousing and ABC Telecom have paid the bills. Now Acme Corp, we have $3,467 outstanding. So they still owe us some money, but in Ajax we have a negative 2,900. So they overpaid us. And we can have a look here. If we highlight that, we can see that there were two payments of 3,200. So it shows us there that our customer has overpaid us and wanted to return the money. So by doing a Pivot Table, you can quickly analyze a bank reconciliation instead of doing it the manual way, and you’re sure to save heaps of time. (upbeat music) Now, I’m gonna show you a great add-in that will save you heaps of time when you’re working with Pivot Tables. It’s called pivot power and an add-in from contextures.com. Now, Debra Dalgleish is the one that invented these add-in and it’s absolutely fabulous. And she’s written lots of books on Excel Pivot Tables, and has been around the game for many years. And based on her experiences, she’s come up with this little gem where it’s gonna save you heaps of time. Now I’ve got a 20% discount for you. So if you stick around at the end of this video, I’ll show you the code where you can use and purchase it from her website. I’ll show you some quick benefits of this add-in. Now when we have a Pivot Table and we create it into a new worksheet or any worksheet. Say we wanna put in there some fields. For example, let’s put in our quarters in our row labels, and then let’s put in our sales month down here as well. Then the years on the column labels, and then the sales here. So we’ll have our Pivot Table here, and that’s the default Pivot Table. And it’s pretty ugly looking. You have this star here, which is not very nice. The numbers are not formatted with a comma, and you have the gridlines in the back, which looks pretty ugly. So every time you do a Pivot Table you gotta go into the design, choose your favorite design. Then you gotta go in to here, value field settings, and then change the number format from in here. So you see there, you’ve got about three, four steps to choose. And then view and get rid of the gridlines. So you got a few steps here every time you do a Pivot Table. So imagine you had a default setting. So you press one button and then it gets updated automatically. This is where that Pivot Power comes in. So the add in is in here. So once you purchase it and you download it, you’ve got all these different features here. And I’ll talk about it, just a couple of them now. So you’ve got the set default. So in here is where you see your default of how you want your Pivot Table to look. And then next time you come in there, all you gotta press the apply default, and it will apply to all your Pivot Tables. So in here we can choose the format, auto-fit column widths. We can get rid of that because every time you refresh, you don’t want it to reduce the column size. You wanna keep it the way that you want it. You’ve gotta get some grand totals as well. Now, the good thing here is the sort field list from A to Z. So you can sort it automatically. So from A to Z, imagine you had a big list and it wasn’t sorted alphabetically, then it’d be a mess to get in there and trying to find some fields. You’ve got some printing settings there. Now in the report layout, you can choose a compact tabular outline. Now I personally like the compact, but I know a lot of people like the tabular and the outline format. So you can choose whichever one you like. I’ll keep it compact. And the style, you got all the different styles there. Okay. So if you go into your design, and then hover, whichever one you like, you see you get your style number. So this is called medium two. So I can go back in there and say, I want to show the medium two. Okay. So medium two, you got everything in there. You choose whatever one you like, I’ll keep it there. Okay, so let’s go back here and just do the compact. We had the auto-fit columns off. Now in here, you got a few other things that you can do refresh data on file open. You can keep it like that. Now let’s go to the pivot field. You got a few settings in here that you can choose. And also if you’ve got a number format, here, you got the different formats. So you got number, accounting, percentage, now I like a number with a zero decimal. And I’ll apply this selected number format all the time. You also have the workbook settings here. And in here, you got the option to show gridlines or not show. I hate gridlines, so I’m not gonna show them. Let’s press save and apply. And look at that, it’s applied that to our Pivot Table. So you wanna go back to your data table and do another Pivot Table in to a new worksheet. You just drop a few things in here, it doesn’t have to be an order. Whatever you like. What you gonna do is go back to your Pivot Power add-in and say, apply default, and apply our defaults, and it updates it automatically, and you save heaps of time. So you have a Pivot Table here, and you just want to quickly put in some number formats. Or you go to Pivot Power and apply this number or format, and it does automatically and saves you heaps of steps. In this Pivot Table, I’ve got some counts of sales and average of sales. And if you look in there, right-click, you have your different values there. And so when we wanna change all of these into your sales, we’ll have to go back into one value field settings, sales and do the same thing for each one of them. Imagine you had about 10 different metrics there, and you just want the sales. Well, in Pivot Power, you go to Pivot Power and choose the sum all. And it changes it to the sum, and go to number format and it puts in the format in there. Now let’s drop in some filters in here. And then we can just quickly choose a few of the filters. And then sales region, choose a couple. Okay, so we’ll have a few of our filters there. Now say you wanna clear them, you gotta go in and press all and then go in and press all. And that’s a long way. Let’s press Control + Z and we’ll go back. And so we had, okay, a couple of more filters here, a lot of our customers, and choose a couple. Now a quick way to clear the filter is Pivot Power, and then go to clear all filters with one step. Another things is the GETPIVOTDATA is just right there. So you can turn it on or off. A great feature is on the Pivot Table. You go to Pivot Table and list or Pivot Tables, and it shows you in a separate sheet, which Pivot Table that you have active in the workbook. And it also shows you the pivot cache number in there. And also the last refresh date. We’ll go back in here, you go to cache, you go to cache list. It shows you how many caches you have. And here we have cash index number one. So if you’ve got an array of different buttons, that will save you heaps of time when you’re working with Pivot Tables. Whether you’re new to Pivot Tables or an advanced user, this was definitely a great add-in. And because you’ve purchased my Extreme Pivot Table Course, then Deborah and I will give you a 20% discount if you put in this code in here, M-E-R-X-P-T-C. And that’s short for My Excel Online Extreme Pivot Table Course. So you put in those seven letters when you go to the checkout, which is also listed now. You’ll get a 20% discount upon checkout. So if you have any queries or any issues, you can send me an email and then I’ll be glad to answer any questions that you may have. (upbeat music) So we have Excel 2013 on the top here. We have the ribbon here and we have Excel 2010 at the bottom half here. And I’m just gonna through a couple of the cosmetic changes in Excel 2013. When we click in the Pivot Table, we get that Pivot Table tools option in Excel 2010. And we have the options and design tab. In Excel 2013, options has been changed to analyze. So you can see that it’s called analyze, and 2010 it’s called options. That’s pretty much the major difference. Everything else has remained the same. It’s just a name change just to confuse us. But apart from that, nothing else has changed. So another thing you see in 2013, we have the insert timeline, which I wanna talk about shortly. That’s a new feature in Excel 2013, and also the recommended Pivot Tables over here. I’ve got a design and then go to design in Excel 2010 here, nothing much has changed. Everything else has remained the same there. So the major change is a name from options to analyze, but that is just a simple name change, and a couple of extra features that have been added into Excel 2013, which I’ll talk about. Now let’s talk about the Pivot Table field list here. So now on the left hand side, I have the 2013 Excel version. On the right-hand side, I have 2010. So we see the different pivot field lists. Nothing has changed there. Now, one thing you see here is more tables here. Now this is the data model in Excel 2013, which I will explain. That’s the more tables option here. And the other thing we can see here is, in Excel 2010, it’s called row labels. In Excel 2013 it’s called rows. In Excel 2010 it’s called column labels, in Excel 2013 it’s called columns. Now they’re just simple main changes. Apart from that, everything else seems the same. (upbeat music) We have an Excel table here, and let’s go insert a Pivot Table. Go to insert, and you can see here in Excel 2013 is recommended Pivot Tables here. Now, if you don’t know which Pivot Table layout will be best to use with your data, then I highly recommend you apply this recommended Pivot Table options. Now click on that. And it has the 10 different ways that you’re gonna summarize the Excel table that we have here. And you can scroll all the way down, and then choose with your mouse. And then you can see it over here. So it’s a count of sales by products, count of sales by sales. So it gives you a preview of the different layout that can be applied with the data that you have. Let us click down here. So this is quite good, especially if you have a lot of data and you don’t know what to stay, you think, okay. So what goes in the rows? What goes in the columns? This will definitely help you to get started, especially if you’re a newbie, even if you’re an intermediate or even advanced user, sometimes you get caught up with all the data and it’s good to sit back and see the different options that are available. And it may just you to create a Pivot Table that you never thought you would have created previously. Now you click here on a blank Pivot Table, and it gives you just a blank Pivot Table. You can start from fresh. You can also change your data source and you can get another data source if you like, but just choose one of them here. Let’s choose this, I like that, press okay. And you see that, it’s already done it for you. It’s put in the fields in the rows and in the values here. So it’s just to save you lots of dragging and clicking. And if you don’t like this, well, you can just move it around. So, great, great new feature in Excel 2013, which is gonna save you lots of time and expand your Pivot Table horizons. (upbeat music) If you want to do a distinct count using Excel 2010, you have to put in a complex sum product formula. But in Excel 2013, you can do this quickly using the data model feature. Now we have our sales table here and there’s a lot of order dates here. You go all the way down to about 3,000 rows. And as you can see, a lot of dates are duplicated. And we wanna know how many distinct or unique counts we have in the order date. To do this, we’ll need to create a Pivot Table. Go to insert, and Pivot Table, and then add this to the data model. Now let’s put in our order to date in the rows column there. And again, the order date in the values here. Now this will count it here. So it’ll show the total number of transactions. And let’s drop it in again here. And what we wanna do now in the second one, we’ll not do the distinct count. So let’s click in the drop-down, choose value field settings, and then in the summarize values by, there’s a new distinct count calculation here that’s been added, and that is just fabulous. So all we’re gonna do is we can just change this here, distinct count and press okay. So there you have it. All right, number one, just to confirm that that works properly. (upbeat music) Excel 2013 extends slicers for date fields, and these are called timelines. Now a new timeline slicer enables you to easily filter your Pivot Table by month, quarter, or year. In our Excel table here, we have order dates. So our timeline slicer will be created because you need a date to create that. So let’s go to insert and Pivot Table and put into a new worksheet and press okay. Let’s put in some sales in our values area over there, and put in the sales regions like that. Now we’re in the Pivot Table, Pivot Table tools, analyze, and here insert timeline. Let’s read these. Use a timeline to filter data interactively. Timelines make it faster and easier to select time periods in order to filter Pivot Tables, pivot charts, and cube functions. Let’s press that. Now it gives us the slicer, the only slicer that’s available is order date because that is a date. If I had more columns with dates, it’ll bring it up. Because I’ve only got one date column, it gives me only one option, click that press okay. And here we have it, how nice is this? So the order date is the field name there. And if you scroll all the way to the left there, we have Jan, 2012 all the way to December, 2014. And that is the range that we have here. So that’s the data range that we have from 2012 to 2014. And it shows it here. These are the available filters that we have based on our data. And you can expand that and make it bigger like this. And so you can scroll all the way there. And let’s choose January, February and see how it changes. And here it gives you the option to expand and include other months. So we can do the first quarter in January. And you see, when I did that, it says Q1 2012. Let’s click on April and hold down the left mouse key. And it automatically puts Q2 over there. Now let’s clear to filter here, and let’s go if we want by years. So it automatically puts it by years, and you can say 2012, 2013, 2014. Let’s put it by quarters. And let’s go Q1 two, three, four, and you can highlight all that. Let’s clear the filter, let’s go to months and it gives us the months. And then we go all the way down to days, all the way down to days. Day one from January. So this is really good filter if you wanna drill down and expand on your analysis, this is a great, great tool and a great feature in Excel 2013. Now let’s go back to quarters over here. And just sort of timeline tools. When you click on that and click out, let’s click back into it. It gives us a timeline tools option here, and we can color it like a slicer. We can use different slicers as there with different colors. And we had several other timelines, we can use this report connections to connect them. That’s creating a dashboard. And I talked about that in previous chapters. So if you have different timelines slicers, then you can connect them and create an interactive dashboard. Now you can move the height, the width, include the header title, get rid of that, the selection label, the scroll bar, and also the time label. So different stuff there. This is very, very nice, I like it. And you can also create a new timeline style, by clicking in there. And we talked about how you create slicer styles. The same thing applies here to timelines. This is a great feature. Go for it, insert it, play around with it and wow your boss. (upbeat music) An Excel data model is new in Excel 2013. And it allows you to take information from different Excel tables and create a Pivot Table from it. Now, before this, you had to use VLOOKUPs or SUMIFs, and it got a bit messy, but if there’s a relationship between each of the tables, then you can create a data model. So we have an Excel tab here, and if you click on it and go to the design, it’s called sales data. The same thing here for customer, are called the customer data and product are called the product data. So let’s go to the sales here. So this has, if we scroll all the way down, it has nearly 50,000 transactions. So these are daily transactions and it’s usually what we download from our ERP system. And it has a product key and a customer number. Now the customer number here, you can see it’s depicted by 1001 all the way to 1010. And in our customer table here, we have the customer numbers, 1001, all the way to 1010. And these are distinct values. Now, if these are distinct values, then we can create a relationship. If we had, for example, two rows with 1001 and a different customer name, then this wouldn’t work. For a data model to work, one of the table has to have distinct values. And the other one, for example the sales can have as many values as it wants. So this is the one to many relationship. So the one is this unique Excel table here with the unique customer numbers. And then the many is the sales transactional Excel table with many customer numbers. So we have customer number here and we can create a relationship within the customer here. Now we also have a product key here. So we have a product key. It goes all the way down from one to 20. And then our product table, we have these unique entries and that means that we can create a data model relationship. Now to do this, let’s click in any one of our tables. Let’s click sales and go to insert, and a Pivot Table. And we get our create Pivot Table dialog box. Now, what I can do is put in here, add this to the data model and press okay. So you can see here, it’s loading to the data model. So that loads it all the way there into the data model. And if you got it all. Now in all, we have all the different tables that are available so now for us to create a Pivot Table. So we created a data model and what we need to do now is to create a relationship. So to create a relationship under analyze and relationships, and go to new. Now, the table here is the table that has all the transactions. So that’d be the sales down here. So let’s click on the sales data and let’s create a relationship. So we said that the customer number is unique to sales and also to the customer table. Now the related table will be the customer data. And the related column will be the customer number in this Excel table there. Now the primary key is the table that has the unique identifiers. If there are any duplicates in there, it’s not gonna work. So this is the primary means of the one. The foreign means many, so it’s a one to many relationship. So if you ever get confused, always put your Excel table that doesn’t have duplicates into the primary area. And then if you have a sales data table which has many rows, then obviously we’ll have duplicates, put it into the foreign area. Now let’s press okay. So that’s created that relationship. Let’s add another one. Let’s go to new, so our sales data, we’re going into the many side, which is the foreign side, and we’re gonna put the product key now because we’re gonna relate it to the product data Excel table. And then in here, the product key was the unique identifier. So it’s one to many relationship, and press okay. Now you see when I did that here on the right-hand side, that the top of this diagram went gray. That means that there is a relationship and is now loaded to the data model. Then I wanna change this around here, just so again for our purposes. Okay, so we can see it much better, and that’s great. So now all we can do is get the sales data, the sales amounts from the sales, that will bring the values there. And we can get another field in here. We have the product key in the sales item, but we don’t know where the product name is. Because of the product key and the product key are related here, that and that are related, we can get any of these fields and drop them in here and create a Pivot Table analysis. So let’s get a product name and then let’s put it in the row labels. And you can see that it shows us the sales per product name. So it’s taken two Excel tables and created a Pivot Table from that. And without using any VLOOKUP. We can also go in here and put in a customer name if we like, because we’ve said the name is linked to the customer number. So if the customer number and the customer number here, are linked then we can take any of these fields and bring them into our analysis. Let’s put in the name in the columns area and there you have it over there. Let’s just put that over there. And that’s our analysis that we have our three Excel tables that are linked together, and we can choose which fields to use because they’re all related to each other, and it gives us the power to do all this great analysis. (upbeat music) A new feature in Excel 2016 is the ability to auto-group a date column. Now we have our table here and we have our order date there. Now let’s create a Pivot Table and I’ll show you how this works. Let’s got to insert Pivot Table and a new worksheet, and we have here our, let’s make it a little bit bigger. Okay, so you can see. Okay we have our order date there and let’s put in sales first in there. So let’s put in the order date in the rows and have a look at what’s gonna happen. It automatically grouped into years, quarters, and it’s got our order date in there as well. So in our Pivot Table, we can expand here and we say the quarters, and we’ll get them to go deeper into the months. So this is a great feature and it saves you the hassle of right-clicking in a date and then choosing the group. Now this automatically groups, and it’s super, super awesome feature they’ve added in Excel 2016. And if you don’t like this, then you can just right-lick and press ungroup, and it’ll bring everything back in there and get rid of the years and quarters. Now let’s right-click and group and put it like this as it was before. So if there are multiple years, the years will come up and then you have your quarters and you have your original data field there. So a great feature in Excel 2016. (upbeat music) In the previous versions of Excel, when you had to select a slicer and you wanted to select say a second or third slicer, you had to hold on Control key. So I’m holding the Control key and pressing Asia and Europe. You see that? Now we’ve Excel 2016, this icon here which is multi-select, so you can press that and then you can left-click with your mouse without holding the Control key, and it selects multiple items. (upbeat music) A new feature in Excel 2016 is the ability for pivot charts to expand or collapse its data. Let me show you an example. Click in our Excel table and go to insert and pivot chart, and let’s press new worksheet and just press okay. So let’s put in our sales in our values area, and let’s put in our products in our axes. And then when we put in two or more fields in one of our axes or legend series, then it gives us the ability to zoom in or zoom out. So I’ll show you that. When I put this and you see in the bottom right-hand corner, the plus and minus sign. So it’s put in the sales region in there. So you can see the collapse field, you can collapse that. And also the Pivot Table gets collapsed, or you can expand it and then see all the details in there. So this is great if you just wanna show two different scenarios, when you’re doing a presentation to your boss. (upbeat music) Now if your data has geographical fields such as addresses or postcodes, you can build a Pivot Table on a map by using the 3D maps icon on the insert tab. So we have postcodes here for US, and they’re all the way down there. So you’ve got different postcodes and what we need to do is go to insert and 3D map here. And we can put in a new tour. And you can see here, it’s put in our postcode if we zoom in there, the different postcodes that we have located. So it shows us where our values are located. So where our sales are located. Now in the height here, we can add a values field. So click on that and put in our sales. So you can see that from in here, let’s just bring this down here and we can get rid of that, and just bring it up to there. So we can see that because we chose a stack column. It shows us a stack column like this, which is a little bit weird. And then we have the bubble and then the heat map, which is a little bit better, which shows us where our most sales are. So whatever he has has a dark red there, it means that there’s a lot of sales there. So this is pretty cool in Excel 2016. If you have addresses or zip codes, it uses Bing to visualize a map. And it just gives you another way where you can visualize your data to your management team. And I think they’re gonna be very impressed. (upbeat music) So Excel introduced many wonderful new features in its update in Excel 2019. And before we get into them, I like to just let you know which Excel version that you currently have, because a lot of people get confused, every three years there’s a new update. So they get confused and they don’t know what Excel version they actually have. So I will just quickly show you how you can check which Excel version you’re currently using. Now I’m using Excel Office 365. With that, I get the new updates when they are released. So when Excel 2019 was released, then automatically I got all those features. But if you purchase the one time license for Excel 2019, then you will also have these new features. So I’m just gonna let you know how you can check which Excel version that you have. So you need to go to file and then account, and then about Excel over here on the right hand side. Now in here, it’ll say Excel for Office 365, because I’m on the subscription model. If you have Excel 2019, it’ll here Excel 2019. If you have any other version like 2016, 2013, 2010, it’ll say that up here. So being Office 365, I always get the new versions updated. So therefore being on this subscription model, I have the latest version, which means I have Excel 2019. You can also check below. I have a link that goes to my blog and explains the different Excel versions and how to check them. It just makes it easier for you to find which Excel version that you’re using. (upbeat music) Over the last few years, I’ve held many Pivot Table webinars, and I get lots of questions in the webinar chat. And one of the most common questions is how can I make my Pivot Table layout a default layout? So every time I create a Pivot Table, it shows me the layout that I want and not the default layout that Excel gives. Thankfully in Excel 2019, this feature is finally available. So you can personalize the default Pivot Table layout. Now I’ll show you what I mean. We have a data source here and I’m just gonna create a Pivot Table. So I got to insert, Pivot Table and put it into a new worksheet and press okay. And I’m just gonna create a Pivot Table here, and it’s going to create a default Pivot Table layout based on Excel’s options. So let’s put in our customer in here, and we’ll put in our products. Now let’s get our order date in the columns area. And now let’s get the sales in the values area. So this is it, this is Excel’s default Pivot Table layout which is in a compact form. And you can see that the sub-totals on the top here. Okay, so the sub-totals on the top and then the values at the bottom. And you can see the grand total is in the rows and in the columns here. But some people may not want this ’cause is confusing. Because normally when you’re adding these up, this is what the value should be, should be at the bottom, the sub-total should be at the bottom. And in previous lessons I’ve showed you how you can do that. You can just go to the design and go to the report layout and you can change it all here in compact outline tabular, repeat all items, grand totals, there you go it’s all there. And the sub-totals as well. You also got the blank rows. So this is how you can change it, but you can actually predetermine the layout. So each time you create a Pivot Table in the future, Excel knows the layout that you like and it’ll always show you that layout. So let’s get into it. To do this, you gotta go to file and then go to options. And under data, you’ve got this one here, you’ve got edit default layouts and make changes to the default layer of Pivot Tables. Click on that. The sub-totals, we want to at the bottom, we don’t want them at the top. The default is show all sub-totals at top of group. It makes no a sense for me. I wanna put it at bottom of group. Grand totals on for rows and columns, yes, I like that. Let’s keep that. Report layout showing compact form. I personally like this, but a lot of people wanna show it in a tabular format or in the outline format. Let’s put an eight tabular form. Now you have many other things you can do. You can insert blank line after each item if you like, you can repeat all item labels and you can include filter items in totals. Now there are more options here. So I’m just gonna unclick that and go to Pivot Table options. And in here, layout and format, whatever you do here, it’s gonna affect the layout. So if you have any error values, you can put in a zero or N/A. And for empty cells, you can put any number that you like. The same thing here. So totals and filters, display, printing, and data. And we go through this in the earlier lessons of this course. So any changes you make here, it’s gonna affect this here. I’m just gonna leave it the same now. Okay. So I’m just gonna press okay. And these are the changes that were made. We made two changes. Show all sub-totals at the bottom of group, and also the report layout should be in tabular form. Now let’s make a third one. Let’s insert a blank line after each item, just to make it a little bit different, and press okay, and then press okay. Now it hasn’t taken shape because you need to create a new Excel Pivot Table. So let’s go in here again. Click on there, insert Pivot Table, new worksheet. Let’s put the customer in there. The products as we had before, the order date in the columns, and now have a look at it. You see, it’s taking shape, it’s a different shape, isn’t it? Let’s put the sales in here. There you go. So as you can see here, it has made these changes. It’s put in the sub-totals at the bottom of the group. It’s made it into a tabular format as you can see here, you can compare to the earlier version, it’s in compact, so everything is in one column. Tabular means it just expands that out into two columns. And then it has a values there. And we’ve also added a blank line after each item. So it looks much better. And each time you open your Pivot Table report or any Excel workbook, and you input data, and you create a Pivot Table, this layout, the default layout that you tell Excel is going to show all the time. So this is awesome, it’s gonna save you a lot of time. (upbeat music) Another cool feature in Excel 2019 is the automatic relationship detection in your Excel tables and in your Excel Pivot Table. Now Excel knows when your analysis requires two or more tables to be linked together and it notifies you. Now with one click, it does all the work to build the relationships so you can take advantage of them straight away. Before you go any further into this tutorial, it is a must that you watch the video that I did in the new Excel 2013 features. It is for the data models. So I’ll suggest you go and watch that video now. So you can have a look at how you can do this data relationship manually. So pause this, go back into chapter 15.5 data models, have a look at it and come back when you’re finished. All right, so I hope that you got lots of value from that video tutorial. And it just goes to show you what data model is and the relationships and how to create them manually. There’s a bit of work involved, but in Excel 2019, they’ve made this a lot easier. Now we have the same data source as the tutorial that you saw previously in 15.5 called Data Models. We have the sales here, as you can see. These all are transactions, about 50,000 transactions. And this is the many transactions that we have, and these are the one or the unique. So this is the one to many, and the product is one to many. So as I said before, under the customer table, we have the unique values to do a relationship. You can have unique values here. If you had two rows with the same number 1001 and 1001, it’s not gonna work. So these values have to be unique. And this table here also has unique values for the product key. Now to do this auto relationship detection, we have to do the same thing. Go to insert Pivot Table and just click here, add this to the data model and press okay. And it automatically will add all the tables in this workbook in the data model. We know this because if you go to all here and we just hover over here, you can see the data source, the name, table, it’s customer data. That’s a good name there, and it’s model table name. So that’s in the data model. We click on there, and it’s a model as well and that’s a model. So it’s created the tables into the data model. So now instead of going to relationships and new, we can press auto-detect, but this is not the best way to do it, ’cause if we do this now, it’s not gonna know anything. So we’ll need to put in some values in here first. In the sales data, let’s put in the sales amount in here first and the product data, we said that the product key and the product key are connected. But we can put anything in here from the product data. It doesn’t have to be product key, it could be product name, product costs. So we can put in any of these items in here. So let’s put in our product name in the columns in there. And this message comes up. Relationships between tables maybe needed. Instead of creating, let’s go to auto-detect. And it says one new relationship created, perfect. Let’s go to manage relationships. And we’re gonna see that the product key and the product key are going to be in that relationship. So click on that and go to edit. As you can see here in the foreign is product key and the primary is a product key. This is the one to many relationships, and these are the tables that got the relationships from. So it detected it, it’s awesome. So you don’t have to manually go and click this table like we did previously in Excel 2013 and then put in the relational column. So you didn’t have to do that. It’s just automatically does that for you here. So that’s perfect. All of these press okay and close it. There it is there. Let’s do the same thing for the customer data table. As we said in the previous video, customer number and customer number are related, and we can put in anything in here. We can put in the address, the city, the country. Let’s put in the country, let’s put it in the rows in there. Now another message that comes up, let’s press auto-detect. It says one is created, manage relationships, Now let’s go in there again and click edit and you see the customer number that is a related column. It’s perfect, we like it. Press okay and close. And as you can see here, the data changes and it has automatically created relationships for us. We didn’t have to go create it, it’s a great new feature. You should go and give it a try and practice, and once you have data that is all related, you can create some awesome Pivot Table reports without using VLOOKUP or SUMIFs. Data model is great. It’s gonna make your life easier and you create much more enhanced reports, which is gonna make you a data wiz. (upbeat music) In Excel 2016, Microsoft introduced the automatic grouping of dates. And that was an awesome feature where you can just drag your data field into the Pivot Table, and it automatically puts it into months, quarters and years. Now, you can see that tutorial in the Excel 2016 tutorial videos that I have for you. And in Excel 2019, they have gone one step further and they’ve included automatic grouping of time. In our data set here, we have the time of order as you can see here all the way there, and we can actually group this automatically. Let’s give it a go. Let’s go to insert, and Pivot Table and press okay. And we have here our Pivot Table, and we’re gonna get our time of order and put it in our rows. Once we do that, you bring this on the left, the automatic grouping of the time. All right. So then you see there from 12:00 a.m. all the way down to 11:00 p.m., beautiful. And if you click on there, you can see that it is grouped into minutes as well. And you can see here that it automatically puts it into hours and minutes. It’s created this hours group and also the minutes group. And you can also see it here. So it’s automatically created new fields for us. And that is just awesome just with drag and drop ease. Now let’s put in our sales in here and we can see that the sales values are grouped into the time that the order was made. So you can do a lot of analysis on there. You can have a look at what is your best time where most of the orders come in and go into that data and give it to your boss and you’ve got some great and awesome insights there just with drag and drop ease. Now, if you don’t want this option to be automatic, you can switch it off. Go to file, options, and under data you’ve got here, disable automatic grouping of date or time columns and Pivot Tables. So if you click on that and press okay, the next time you drag a date or time field into your Pivot Table, it’s not gonna automatically group. But I suggest having this checked off and it’s just gonna make your Pivot Table slicker, and you’re gonna be able to analyze your data much, much quicker. So there you go. Automatic time grouping in Excel 2019 and Office 365. So give it a go and see what you can come up with.
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These texts, likely from a course on advanced pivot tables, provide a comprehensive guide to working with pivot tables in Excel. The content covers essential foundational skills, starting with creating a basic pivot table from raw data, ensuring the data is properly formatted and cleaned, and placing it into a table for easier management. It then moves into more advanced techniques, including importing data from external sources like text files and Access databases, demonstrating how to consolidate data from multiple sources, and showing various ways to group data within a pivot table by text, numbers, and dates. Finally, the texts explore formatting options for pivot tables, including applying built-in and custom styles, and handling error values and empty cells to present data clearly.
Fundamentals of Excel Pivot Tables
Based on the provided sources, here is a discussion of pivot table basics:
Pivot tables are described as the single most useful tool available in Excel for analyzing data. They are helpful for analyzing data in different ways, such as seeing the total sales accumulated by managers, sales in each category, sales by store, or sales between certain dates. The sources mention a recap of basic pivot table skills early in the course for those who haven’t used them recently or are not overly familiar with creating them from scratch.
Before creating a pivot table, it’s recommended to start with clean data. Cleaning data involves ensuring consistency and the absence of anomalies, such as blank rows, blank cells, inconsistent case, duplicates, and ensuring everything is formatted correctly. The sources also emphasize the importance of putting your data into a regular Excel table before creating a pivot table. This can be done by selecting the data and using Control + T, or by going to the Home ribbon, Styles group, and selecting “Format as Table”. When data is in a table, the “Table Design” contextual ribbon appears when clicked within the data. Another indicator is the presence of filter buttons at the top of each column. It’s also recommended to name your table for easier reading and understanding. Naming a table involves going to the Table Design ribbon, Properties group, and entering a name (without spaces, using underscores if needed), remembering to hit Enter. Putting data into a table also makes it easier to update pivot tables later when new data is added, as the table automatically expands to accommodate new rows.
To create a pivot table from scratch, make sure you are clicked within your data. You can use the “Summarize with PivotTables” option on the Table Design ribbon or go to the Insert ribbon and select the “PivotTable” button in the Tables group. Clicking either option opens a dialog box.
In this dialog box, you need to:
Choose the data you want to analyze. Excel often intuitively picks up the table name or range you are clicked within. You can also choose to use an external data source.
Choose where to place the pivot table report. It is generally suggested to keep your raw data separate from your pivot tables, so placing it on a new worksheet is recommended. You can rename the new sheet to something meaningful like “Pivot Table”.
Click OK.
Once the pivot table is created, you will see an empty pivot table report area on the left and the PivotTable Fields pane on the right. If the pane is not visible, ensure you are clicked within the pivot table report area, or go to the PivotTable Analyze ribbon, Show group, and click “Field List”.
The PivotTable Fields pane lists all the column headings from your source data. Below the list of fields are four areas: Filters, Columns, Rows, and Values.
The core basic operation of building a pivot table is dragging any of these fields into any of these four areas.
Values: Fields dragged here are typically numeric and are used for calculations like sum, count, average, etc..
Rows: Fields dragged here display their unique values as rows in the pivot table.
Columns: Fields dragged here display their unique values as columns in the pivot table.
Filters: Fields dragged here create a filter above the pivot table, allowing you to filter the entire report by selecting specific items from that field.
Building a basic pivot table often involves some trial and error depending on the information you want to extract. For example:
To see total sales broken down by manager, drag “Sales” to Values and “Manager” to Rows.
To see total sales by category, drag “Sales” to Values and “Category” to Rows.
Dragging a field like “Manager” or “Product” between Rows and Columns changes the layout and how the data is presented.
Dragging “Category” to Filters allows you to filter the sales data shown in the report by selected categories.
Combining fields in Rows and Columns (e.g., Towns in Rows, Categories in Columns, Sales in Values) creates a cross-tabulated report.
The sources also mention the Recommended Pivot Tables option on the Insert ribbon, which analyzes your data and suggests potential pivot table layouts based on what might be useful. This can be a quick way to get a starting point, pre-populating the pivot table fields in the appropriate areas. However, this option cannot be used when combining data from multiple tables; in that case, you must use the standard “PivotTable” option and select the “Add this data to the Data Model” checkbox.
You can have more than one field in each area. When multiple fields are in the Rows or Columns areas, their order determines how the data is organized (e.g., organized by country first, then product, or product first, then country).
In summary, the basics involve preparing your data by cleaning it and putting it into a named Excel table, creating the pivot table using the Insert or Table Design ribbon, choosing the data source and location, and then dragging fields from the PivotTable Fields pane into the Rows, Columns, Values, and Filters areas to analyze and summarize your data.
Importing External Data for Pivot Tables
Importing data is a fundamental step when the information you need to analyze with a pivot table is not already in your current Excel workbook. The sources discuss various methods and considerations for bringing external data into Excel so it can be used effectively in pivot tables.
The primary location within Excel for accessing data import tools is the Data ribbon, specifically within the Get & Transform Data group. While the options available might differ slightly depending on your version of Excel, this is where you’ll find utilities for importing data from numerous sources.
The sources detail importing data from two main types of external sources:
Text Files (like .txt or .csv):
One method is using the Get & Transform Data tool from the Data ribbon and selecting “From Text/CSV”. This opens a preview window where Excel attempts to detect the delimiter (the character separating columns, such as a tab, comma, or semicolon) and data types. You can change the delimiter if needed. From here, you can either “Load” the data directly or “Transform Data” using the Power Query Editor.
The Transform Data option is highlighted as a way to clean up data as part of the import process. In the Power Query Editor, you can check and correct data types (e.g., ensuring numbers are formatted as currency or dates are recognized as dates) and remove columns that are not needed for your analysis. Once satisfied, you can use “Close & Load” to import the data into an Excel table or “Close & Load To” to load it directly into a pivot table report.
Another way to import a text file is by opening it directly through the File menu. This often triggers the Text Import Wizard, which guides you through steps like defining the delimiter and setting column data formats. If you use the wizard or simply open a file, cleaning steps like correcting case, splitting columns, removing duplicates, and applying correct number formatting need to be performed after the data is in the worksheet using standard Excel tools. After cleaning, it’s recommended to put this data into a regular Excel table before creating a pivot table.
Databases (like Microsoft Access):
To import from a database, you again use the Get & Transform Data group on the Data ribbon. Click the “Get Data” drop-down, select “From Database,” and then choose the relevant database type, such as “From Microsoft Access Database”.
You browse and select the database file, and Excel will connect and display the tables contained within it. You then select the specific table you want to import.
Similar to text files, you have the option to “Load” or “Transform Data”. Using “Transform Data” opens the Power Query Editor, allowing you to refine the data before importing, such as removing columns that are not relevant to your pivot table.
After transforming, the “Close & Load To” option can be used to directly import the cleaned data into a PivotTable Report on a new worksheet.
Regardless of how the data is imported, the sources strongly emphasize the importance of starting with or creating clean data. This means ensuring consistency, formatting data correctly, and removing anomalies like blank rows, blank cells, inconsistent casing, or duplicate entries. Cleaning can be done during the import process using Power Query or afterward using various Excel functions and tools.
Furthermore, after importing data into a worksheet (if not loaded directly into a pivot table), putting the data into a regular Excel table and naming it is recommended. This makes the data easier to reference, understand, and is particularly beneficial because a table automatically expands when new rows are added, making it much easier to update pivot tables built upon that data later on using the refresh function.
A more advanced scenario discussed is consolidating data from multiple tables into a single pivot table. This is necessary when your data is spread across different sets of information that need to be linked for combined analysis.
Each set of data must first be placed into a regular Excel table and named.
The tables must share a common field (referred to as a “key” or “primary key”) that logically links the data between them, like an “Order ID” shared across customer, order, and payment information.
To create a pivot table from multiple tables, you must use the standard “PivotTable” option on the Insert ribbon and select “Add this data to the Data Model” in the creation dialog box. The “Recommended Pivot Tables” option cannot be used for this.
Once the pivot table is created, you will see fields from the initial table in the PivotTable Fields pane but can click “All” to view fields from all imported tables.
The crucial next step is to create relationships between these tables based on their common key field. This is done via the PivotTable Analyze ribbon, using the “Relationships” button. By defining these links (e.g., linking the Order ID field in one table to the Order ID field in another), you enable the pivot table to draw data from different sources correctly.
After relationships are established, you can freely drag fields from any of the linked tables into the different areas of the pivot table to perform your analysis.
In essence, importing data involves using the tools on the Data ribbon to bring external information into Excel, potentially cleaning and transforming it using Power Query, ensuring it is in a clean Excel table format, and for analyzing multiple sources, creating relationships between the tables via the Data Model.
Essential Data Cleaning for Pivot Tables
Data cleaning and preparation are highlighted as absolutely crucial steps before analyzing data, particularly with pivot tables. The primary reason for this is that if your data is not clean, you might end up with inaccurate or misleading results.
Clean data is described as data that is consistent and free from anomalies. This includes ensuring there are:
No blank rows or blank cells.
No inconsistent casing (e.g., some text is all uppercase, some proper case).
No duplicate entries.
All data is formatted correctly, such as numbers, currencies, and dates.
Cleaning can be performed at different stages. If you are importing data using the “Get & Transform Data” tools, you can utilize the Power Query Editor to clean and transform data as part of the import process. Alternatively, if you open a file directly or data is already in Excel, you can clean it afterwards using standard Excel tools.
Here are some specific techniques and tools for cleaning data mentioned in the sources:
Checking and Correcting Data Types: When importing with Get & Transform Data, Excel attempts to detect data types, but you should verify and correct them in the Power Query Editor (e.g., changing numbers to currency or dates). If opening a file directly using the Text Import Wizard, you can set some formats, but often you need to correct them after import using the Home ribbon’s Number group. For values in a pivot table, number formatting is best done via Value Field Settings > Number Format to ensure consistency across the entire pivot table. Custom number formatting can be used to control how positive, negative, and zero values appear, including adding currency symbols, colors (like red or blue for negatives), or text (like “no data” for zeros).
Handling Blank Rows and Cells: Blank rows can be efficiently removed by selecting all columns, going to Find & Select > Go To Special > Blanks, and then using the Delete Sheet Rows option. For blank cells, you can select them using the same “Go To Special > Blanks” method and then enter a value (like 0) followed by Control + Enter to fill all selected blank cells at once. Pivot table options also allow you to specify what to show for empty cells (e.g., 0 or custom text).
Ensuring Consistent Case: You can use the PROPER function in a helper column to convert text to proper case. After using the function, it’s recommended to copy the helper column and paste values over the original data to replace the formulas with the cleaned text.
Removing Duplicates: Excel has a dedicated Remove Duplicates tool on the Data ribbon in the Data Tools group. You can select the columns Excel should check for duplicate information before removing entire rows that match across the selected columns.
Correcting Text Inconsistencies: The Find and Replace feature (Home ribbon > Find & Select, or Control + H) is useful for replacing inconsistent abbreviations or spellings with a standard version (e.g., replacing “mktg” with “marketing”).
Handling Non-Printable Characters, Line Breaks, and Erroneous Spaces: Text functions like CLEAN (removes non-printable characters and manual line breaks) and TRIM (removes excess spaces) can be used. These functions can even be combined with other functions like PROPER within a single formula in a helper column to address multiple issues at once. Again, pasting values over the original data is recommended after using formulas.
Splitting Data in Columns: The Flash Fill tool (Data ribbon > Data Tools group, or Control + E) is a quick way to split combined text, like separating a full name into first and last names, by recognizing a pattern from the first few manually entered examples.
Handling Error Values: Pivot table options allow you to specify what to display for error values (e.g., custom text like “no data” or a value like 0) instead of showing the raw error (like #N/A).
After the data has been cleaned, the final and critically important step before creating a pivot table is to put the data into a regular Excel table. This can be done by selecting the data and using Control + T or by using the “Format as Table” option on the Home ribbon. Putting data into a table provides several benefits:
It automatically adds filter buttons to column headers, making sorting and filtering easier.
It creates a Table Design contextual ribbon with tools specific to tables.
It’s recommended to name your table from the Table Design ribbon > Properties group. Table names (like sales_data) are easier to read and understand than cell ranges when creating pivot tables.
Crucially for pivot tables, when you add new data (rows) to the bottom of a table, the table automatically expands to include the new data. This makes updating pivot tables built on that table much simpler, as you only need to use the Refresh function on the PivotTable Analyze ribbon to incorporate the new data. If the data wasn’t in a table, you would have to manually change the pivot table’s data source to include the new rows, which takes much longer.
In summary, thorough data cleaning and preparation are essential for accurate pivot table analysis, involving various techniques to address inconsistencies, errors, and formatting issues, and culminating in placing the cleaned data into a named Excel table for ease of use and future updates.
Creating Excel Pivot Tables from Single or Multiple Tables
Creating pivot tables is the primary goal after you have prepared and imported your data, as discussed previously. Pivot tables are considered the single most useful tool in Excel for analyzing data. This course is designed to guide you through utilizing the pivot table options to create meaningful analysis.
Before you begin creating a pivot table, it is crucial that your data is clean and, importantly, placed within a regular Excel table. As we’ve discussed, clean data is consistent and free from anomalies like blank rows, blank cells, inconsistent casing, or duplicates, and everything is formatted correctly. Putting your data into a regular table (Control + T or Home ribbon > Format as Table) is a vital final step. Naming your table (Table Design ribbon > Properties group) is also highly recommended for clarity, making the data easier to read and understand. A key benefit of using a table for pivot tables is that it automatically expands to include new data added to the bottom, making it simple to refresh your pivot table to incorporate the new information later.
There are a few different ways to initiate the process of creating a pivot table from your prepared data:
Using the Table Design Ribbon: If your data is in an Excel table and you are clicked inside it, you can use the “Summarize with PivotTable” option found on the Table Design contextual ribbon.
Using the Insert Ribbon: A more standard method is to go to the Insert ribbon and click the “PivotTable” button, located in the Tables group. This is the first option in that group.
Using Recommended PivotTables: Excel offers a “Recommended PivotTables” option on the Insert ribbon, right next to the standard “PivotTable” button. This feature analyzes your data and suggests potential pivot table layouts that might be useful, such as summing profit by country or month. Choosing one of these suggested options can create a pre-populated pivot table very quickly. However, this method cannot be used if you need to analyze data from multiple tables simultaneously.
Regardless of whether you use the Table Design or Insert ribbon’s standard “PivotTable” option, clicking it will open the “Create PivotTable” dialog box. Here, you need to make two main choices:
Choose the data that you want to analyze: If you were clicked inside a named Excel table when you opened the dialog, Excel will intuitively select that table name as the data source. You can also choose to use an external data source.
Choose where you want the PivotTable Report to be placed: The recommendation is always to place the pivot table on a new worksheet to keep your raw data separate. You can also choose an existing worksheet and specify the location.
Clicking “OK” (after specifying data and location) will create a new worksheet (or navigate you to the chosen location) containing a blank pivot table report on the left side. On the right side, you will see the PivotTable Fields pane. If this pane is not visible, ensure you are clicked within the blank pivot table report area. If it still doesn’t appear, it might have been accidentally closed; you can get it back by going to the PivotTable Analyze ribbon, clicking “Field List” in the Show group.
The PivotTable Fields pane is essential for building your pivot table. It lists all the column headings from your data source as available fields. Below the field list, there are four distinct areas:
Filters: Fields placed here allow you to filter the entire pivot table report.
Columns: Fields dragged here become the column headings in your pivot table.
Rows: Fields dragged here become the row headings in your pivot table.
Values: Fields placed here are the numbers or values you want to summarize (e.g., sum of sales, count of units). By default, Excel often sums numeric fields, but you can change the calculation type in the Value Field Settings.
Building the Pivot Table: The core process of creating a pivot table involves simply dragging fields from the list at the top of the pane into the four areas below. There’s often a bit of trial and error involved depending on the analysis you need. For example, to see the total sales by manager, you would drag the “Sales” field into the Values area and the “Manager” field into the Rows area. The pivot table report will update as you drag and drop fields. You can easily move fields between areas to change the layout and analysis. Placing multiple fields in the Rows or Columns areas will create nested levels of detail. The order of fields within an area matters for the hierarchy of the report (e.g., Country then Product, or Product then Country).
Excel provides helpful automatic grouping for date fields when you drag them into Rows or Columns, often breaking them down into Years, Quarters, and the Date itself, allowing you to easily analyze data by different time periods. You can expand or collapse these groups or customize which levels (Years, Quarters, Months, Days) are displayed via the Group Field option on the PivotTable Analyze ribbon.
A more advanced scenario is creating a pivot table from multiple tables. This is necessary when the data you need for analysis is spread across different sets of information, each in its own table. To do this:
Ensure each set of data is in a regular Excel table and named meaningfully.
The tables must share a common field (like an “Order ID”) that acts as a “key” to link the data logically between them.
When creating the pivot table, you must use the standard “PivotTable” option from the Insert ribbon. In the “Create PivotTable” dialog box, after selecting your first table and location, you must select the option “Add this data to the Data Model”.
After the pivot table is created, the PivotTable Fields pane will initially show fields from the table you were in, but clicking “All” will display fields from all imported tables that were added to the Data Model.
The critical next step is to create relationships between these tables based on their common field. This is done from the PivotTable Analyze ribbon using the “Relationships” button. In the “Manage Relationships” dialog, you click “New” and define the links, specifying which table and column relate to which other table and column (e.g., linking the “Order ID” in the ‘Order Info’ table to the “Order ID” in the ‘Payment Info’ table).
Once relationships are established, you can freely drag fields from any of the linked tables into the Filters, Columns, Rows, and Values areas to build your consolidated pivot table.
Finally, it’s a good practice to name your pivot table itself (PivotTable Analyze ribbon > Properties group) to keep everything organized and easy to reference, similar to naming tables. You can also drill down into any number in your pivot table by double-clicking it, which will open a new sheet showing the underlying data that makes up that total. For large data sets, you can use the “Defer Layout Update” option at the bottom of the PivotTable Fields pane to organize your fields before updating the pivot table, which can improve performance.
Excel Custom Formatting: Numbers and Styles
Based on the sources and our conversation, custom formatting in Excel, particularly within pivot tables, refers primarily to controlling the visual appearance of numbers and values, and also extending to the overall look and feel of the pivot table itself through custom styles.
Custom Number Formatting in Pivot Tables
Custom number formatting is a powerful tool for controlling exactly how numbers and values are displayed in your pivot table report. While you can apply basic formatting like currency or accounting format through the Value Field Settings dialog box, custom formatting allows for much greater control.
To apply custom number formatting in a pivot table, you should right-click anywhere in your numeric data within the pivot table, go down to Value Field Settings, and then select Number Format from there. This is a better approach than using the formatting options on the Home ribbon, which might lead to problems later. From the Format Cells dialog that appears, you can select the Custom category.
The key to understanding custom number formatting is remembering a simple rule: the format string is typically broken into four parts separated by semicolons. These parts define how different types of values are displayed:
Positive numbers: The format before the first semicolon.
Negative numbers: The format between the first and second semicolon.
Zero values: The format between the second and third semicolon.
Text values: The format after the third semicolon.
You don’t necessarily have to define all four parts every time.
Examples of Custom Number Formatting from the Sources:
Formatting Negative Numbers: By default, negative numbers might show in brackets. You can use custom formatting to show them with a minus sign and/or in a different color like red or blue. For example, the format #,##0.00;[Red]-#,##0.00 formats positive numbers with a thousand separator and two decimal places, while negative numbers are shown in red with a minus sign and the same number format. You can add currency symbols to these formats as well.
Formatting Zero Values: You can define how cells with a value of zero should appear. This could be simply 0 or you could display text like “no data” by putting the desired text in quote marks in the third section of the format string (e.g., Positive;Negative;”no data”).
Combining Text and Values: You can include text along with the numeric display. For example, you could add the word “loss” next to negative numbers by including “loss” in quote marks within the negative number part of the format string.
Using Placeholders (# vs. 0): Within the format parts, symbols like # (hash) and 0 (zero) are used as placeholders for digits. A # is a variable placeholder, only displaying digits if they are present, while a 0 is fixed, forcing a digit (zero if necessary) to be displayed. This is useful for maintaining consistent length for numbers, such as formatting item numbers like “1” and “100” to “0001” and “0100” using 0000 as the custom format.
It is important to remember that applying custom formatting only changes the visual appearance of the number; the underlying value in the cell remains unchanged. This means you can format a zero value to show as “no data,” but Excel still recognizes it as a zero for calculations.
If you want to revert a cell or range back to its original state after applying custom formatting, you can use the General format. Custom formats you create are saved in the Custom formatting list within the workbook for potential reuse.
Custom Pivot Table Styles
Beyond number formatting, the sources also discuss creating custom pivot table styles. This refers to defining the visual look of different elements within the pivot table itself, such as header rows, banded rows or columns, first column, grand totals, etc., including aspects like font styles (e.g., italics), fill colors, and borders.
You can access options to create a new custom style from the PivotTable Design ribbon, in the PivotTable Styles group, by clicking the “more” button and selecting New PivotTable Style. This opens a dialog where you can name your style and then select and format each individual element of the pivot table.
Unlike custom number formats, custom pivot table styles are typically saved within the workbook where they were created and are not automatically available in other workbooks. However, you can copy a custom style to another workbook by copying a worksheet containing a pivot table with that style applied into the new workbook.
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This resource provides a detailed tutorial on using Excel pivot tables for summarizing and analyzing large datasets. It begins by explaining the purpose and usefulness of pivot tables, contrasting them with static Excel tables and demonstrating their dynamic nature. The material then focuses heavily on data cleaning techniques that are essential before creating a pivot table, covering the removal of blank rows, duplicates, and unwanted formatting, as well as using formulas and features like Flash Fill to standardize text, dates, and separate combined data. Finally, the tutorial walks through creating and building a pivot table from scratch, explaining the pivot table field list, adding and manipulating fields, understanding aggregation methods (sum, count, average, etc.), grouping data, and customizing the report layout with subtotals, grand totals, and blank rows.
Mastering Excel Pivot Tables
Based on the information from the sources, here is a discussion about Pivot Tables in Excel:
What are Pivot Tables and Why are They Useful?
Effectively, pivot tables are an interactive way of quickly summarizing large amounts of data. In our data-driven world, many individuals collect data from various sources to support better business decisions. However, simply looking at large datasets in an Excel spreadsheet doesn’t clearly highlight key metrics, issues, successes, failures, or trends. Pivot tables provide a way to take this data and make sense of it.
For example, with a dataset of over 14,000 rows of sales data including region, country, item type, sales channel, order priority, order date, order ID, ship date, units sold, unit price, unit cost, total revenue, total cost, and total profit, it’s difficult to easily see things like the top 10 countries by total profit or the number of high-priority orders. Using filter drop-downs is possible but much less efficient than using a pivot table.
The key difference between a regular Excel table and a pivot table is that pivot tables are dynamic. This means you can quickly change the analysis being performed. By moving fields around, you can instantly view the data summarized in different ways, such as seeing the sum of total profit by country after initially looking at units sold. You can add other fields to break down the analysis further, like dropping ‘item type’ into columns to see sales summarized by country and item type. You can also apply filters, for instance, to show only the top five countries to make the data more manageable. Once data is in a pivot table, it can be pivoted in various ways, allowing the creation of more pivot tables and even pivot charts. This opens up opportunities for visual analysis, which is often easier for people to interpret. Ultimately, this can lead to creating interactive dashboards showing key metrics with filters.
In summary, a pivot table is a dynamic, interactive tool for summarizing large datasets. They are useful because they help analyze large datasets in a clear and effective way.
Difference Between Excel Tables and Pivot Tables
It’s important to understand the distinction between Excel Tables and Pivot Tables, as they are not the same. Excel tables are essentially static; you can sort or filter the data, but you cannot easily analyze it in many different ways. In contrast, pivot tables are much more dynamic. With a pivot table, you can move fields around and add different fields to view your data in numerous ways, making them ideal for data analysis.
The sources strongly recommend putting your data into an Excel table prior to creating a pivot table. While it might seem like an extra step, there are many advantages to using Excel tables that make working with pivot tables much easier. One of the most useful features of Excel tables is their auto-expand capabilities. If you add new data to the bottom of an Excel table, it automatically expands to include that data. This means that any pivot table or chart linked to that Excel table will automatically include the new data after a simple refresh. If your data is not in an Excel table, you would have to manually reselect the data range to include new rows.
When data is formatted as an Excel table, it automatically gets some formatting like shading and borders, plus filter and sort drop-downs in the headers. An additional ribbon called Table Design appears when you select a cell within the table. This contextual ribbon contains tools to format the table, apply options, and access table tools.
Preparing Data Before Creating a Pivot Table (Data Cleaning)
Before analyzing data with a pivot table, it is extremely important to clean the data. Data cleaning refers to processes in Excel used to tidy up datasets, make them consistent, format them correctly, and present the data in a way that a pivot table can easily analyze and produce accurate results. Skipping this step can lead to inaccurate analysis. This is particularly crucial if data is downloaded from a third party, external source, or database, as it may not import into Excel in the expected format. Issues like columns being out of place, strange formatting, blank rows, blank cells, or duplicate entries can occur.
Several techniques are discussed for cleaning data:
Removing Blank Rows: Blank rows make data harder to read and cause issues in pivot tables, appearing as a ‘blank’ entry. Manually deleting them is tedious for large datasets. Excel provides a quicker way:
Select the data range (e.g., using Ctrl+A while clicked in the data).
Go to the Home tab, in the Editing group, click Find & Select, and choose Go To Special.
Select ‘Blanks’ and click OK. This selects all blank cells/rows in the selection.
Go back to the Home tab, in the Cells group, click Delete, and select Delete Sheet Rows. Removing blank rows before creating a pivot table ensures accuracy and prevents the ‘blank’ entry from appearing.
Removing Duplicates: Duplicates can also cause problems for pivot tables. The desired removal depends on the type of duplicate; for instance, removing duplicate records where every column is identical, as opposed to repeated values in a single column like ‘Online’/’Offline’ in sales channel. Excel has a Remove Duplicates utility for this.
Click anywhere in the data.
Go to the Data tab, in the Data Tools group, click Remove Duplicates.
A dialog box appears allowing you to select which columns to consider when checking for duplicates.
Formatting Data: Applying the correct formatting is important.
Columns with text (like Region, Country, Item Type) can be formatted as Text using the Format Cells dialog box (Ctrl+1).
Dates might appear as numbers if date formatting isn’t applied. This is because Excel stores dates as numbers, counting days since January 1st, 1900. To display them correctly, select the column and apply Short Date or Long Date format from the Home tab’s Number group.
Numeric columns (like Unit Price, Total Revenue, Total Profit) should have appropriate number formatting. Currency and Accounting formats are common for monetary values. Accounting format often aligns currency symbols to the left and decimal places, which many find easier to read than Currency format where the symbol is next to the value. This can be applied via the Home tab or the Format Cells dialog box (Ctrl+1).
Tidying Up Text: Inconsistencies in text, such as different cases (uppercase, lowercase, proper case) or erroneous spaces (leading, trailing, or multiple spaces between words), can make analysis inaccurate.
Changing Case: Use Excel text formulas like UPPER(), LOWER(), or PROPER(). A recommended method is to use a “helper column” next to the column needing changes, write the formula (e.g., =PROPER(B4)) in the first cell, copy it down, then copy the results and use Paste Special > Paste Values over the original column to remove the formulas, and finally delete the helper column.
Removing Spaces: The TRIM() function removes leading, trailing, and excessive spaces within text. Even if spaces aren’t visible, applying TRIM() is a good practice. Similar to changing case, use a helper column, the TRIM() formula (e.g., =TRIM(B4)), copy/paste values, and delete the helper column.
Removing Line Breaks: The CLEAN() function removes non-printable characters, including line breaks. Again, use a helper column, the CLEAN() formula (e.g., =CLEAN(A4)), copy/paste values, and delete the helper column.
Splitting Data: Sometimes a single column contains multiple pieces of data that should be separate (e.g., Order Date and Order ID combined).
Text to Columns: This feature is useful when data is separated by a consistent delimiter (like a comma, tab, space, or other character).
Select the column(s) you want to split.
Go to the Data tab, in the Data Tools group, click Text to Columns.
In the wizard, choose ‘Delimited’ if your data has separators or ‘Fixed width’ if data is aligned in columns.
Specify the delimiter(s). The preview shows how the data will be split.
Choose the data format for each new column (optional, General often works) and importantly, the Destination cell where the split data should start appearing.
Click Finish.
Flash Fill: This feature, introduced in Excel 2013, automatically fills data based on a detected pattern. It can be used to split data (e.g., first name and last name from a full name) or combine data.
Type the desired output for the first item in a new column next to your data.
Press Ctrl+Enter to stay in the cell.
Go to the Data tab, in the Data Tools group, click Flash Fill (or use the shortcut Ctrl+E). Excel will attempt to apply the pattern to the rest of the column. You can also start typing the second item, and Flash Fill may show a grayed-out preview; hit Enter if it’s correct.
Using Formulas: Excel functions like CONCAT() (or CONCATENATE() in older versions) can combine data from multiple cells. These are useful if you need to add specific text or characters (like a hyphen and spaces) between the combined data. Formulas require referencing the cells and enclosing text within quote marks.
Replacing Data: You might need to replace specific text or values.
Find and Replace: This utility (Ctrl+H) can find specific text and replace it with something else throughout the selected range.
Substitute Formula: The SUBSTITUTE() function can replace specific text within a cell based on a formula (e.g., =SUBSTITUTE(B4,”UK”,”United Kingdom”)). Like other formulas, you’d use a helper column and Paste Special > Paste Values to apply the result.
Spell Check: Running a spell check is crucial because if something is misspelled, a pivot table will treat it as a completely separate item, leading to inaccurate analysis. The Spell Checker is on the Review tab in the Proofing group (F7 shortcut). It starts checking from the currently selected cell. You can choose to ignore, change, change all, or add words to the dictionary (useful for names or brands not in the standard dictionary).
Putting Data into an Excel Table
As mentioned, it is highly recommended to put your clean data into an Excel Table before creating a pivot table. You must be clicked somewhere within your data set to do this.
There are two main ways to format data as a table:
Go to the Home tab, in the Styles group, click the Format as Table drop-down and choose a table style.
Click anywhere in the data and press the keyboard shortcut Ctrl+T. This opens the Create Table dialog box.
Both methods will ask if your table has headers. Once applied, your data gets default formatting and the Table Design contextual ribbon appears. From the Table Design ribbon, you can customize the style, add a total row, toggle banded rows or columns, and turn the filter button on/off.
In the Properties group of the Table Design ribbon, you can see and rename the table. It’s good practice to give your table a meaningful name (like Sales_Data) instead of the default generic name (like Table1) because it makes referencing the data easier, especially in workbooks with multiple tables. Table names cannot contain spaces.
Creating a Pivot Table
Once your data is clean and in an Excel table, you are ready to create a pivot table.
Recommended Pivot Tables: Excel can analyze your data and suggest pivot table layouts.
Click anywhere in your data table.
Go to the Insert tab, in the Tables group, click Recommended PivotTables.
A window pops up showing different suggested pivot table summaries based on your data (e.g., sum of unit price by region, sum of profit by item type).
Select the one that best suits your needs and click OK. Excel creates a new worksheet with the pre-built pivot table. You can still modify this table afterward.
Creating a Blank Pivot Table from Scratch: This gives you full control over the layout.
Click anywhere in your data table.
Go to the Insert tab, in the Tables group, click PivotTable. Alternatively, from the Table Design ribbon, in the Tools group, click Summarize with PivotTable. Both methods open the Create PivotTable dialog box.
Choose the data: The dialog box should automatically detect and select your Excel table (e.g., Sales_Data). You can also choose to use an external data source from another file or database.
Choose where to place the report: The common and recommended practice is to place the pivot table on a New Worksheet to keep your raw data separate from your analysis. You can also choose an existing worksheet.
Click OK. Excel creates a new worksheet containing a blank pivot table report area and the PivotTable Fields pane on the right.
Understanding the Pivot Table Interface
When you click inside the blank pivot table report area, two additional contextual ribbons appear: PivotTable Analyze and PivotTable Design. These ribbons contain commands for managing, organizing, and changing the look of your pivot table. They disappear when you click outside the pivot table.
PivotTable Design Ribbon: Focuses on the appearance and layout.
PivotTable Styles: Similar to table styles, allows choosing a visual style. Styles are influenced by the workbook’s theme.
PivotTable Style Options: Toggles elements like row/column headers, banded rows/columns.
Layout: Controls subtotals (show/hide, position), grand totals (on/off for rows/columns), and report layout (Compact, Outline, Tabular forms). You can also insert or remove blank lines after each item.
PivotTable Name: It’s good practice to rename pivot tables from generic names (e.g., PivotTable1) to meaningful names.
Options: Accesses various pivot table settings, including layout and format options like auto-fitting column widths.
Group: Used for grouping selected items or ungrouping.
Insert Slicer / Insert Timeline: Visual filters for pivot tables (not covered in detail in this source).
Refresh: Updates the pivot table with any changes to the source data.
Show group: Toggle buttons to show/hide the Field List pane, plus/minus buttons, and headers. If the Field List disappears, check this button.
The PivotTable Fields pane (usually on the right) is crucial for building the pivot table. At the top, it lists all the column headings from your source data as fields. Below are four areas: Filters, Columns, Rows, and Values. These areas determine the layout and type of analysis.
Building and Modifying a Pivot Table
Building a pivot table involves dragging fields from the top section of the PivotTable Fields pane into one of the four areas.
Rows Area: Typically used for fields you want to appear as row labels (e.g., Region, Item Type).
Columns Area: Typically used for fields you want to appear as column labels (e.g., Sales Channel, Order Priority).
Values Area: This is where you put fields containing numerical data that you want to summarize (e.g., Total Profit, Units Sold). By default, Excel often performs a Sum on numeric fields dragged here, or a Count if the field contains text or dates.
Filters Area: Fields dragged here create report-level filters at the top of the pivot table, allowing you to filter the entire report by selections from that field (e.g., filtering by specific Countries or Order Dates).
You can easily change the layout by dragging fields between these areas. Dragging a field outside the pane removes it from the pivot table.
Aggregating Data: The default aggregation (Sum or Count) can be changed.
Right-click on any value in the column you want to change the aggregation for.
Select Value Field Settings.
In the Summarize values by list, choose a different calculation like Average, Max, Min, Product, Count Numbers, etc..
Click OK. You can also access Value Field Settings by clicking the drop-down arrow next to the field in the Values area.
You can combine different methods of aggregation by dragging the same field into the Values area multiple times. Each instance can then be summarized using a different calculation (e.g., one column showing Sum of Total Profit, another showing Average of Total Profit).
Renaming Fields/Headings: You can change the default headings in the pivot table report area (like ‘Row Labels’ or ‘Sum of Total Profit’) by double-clicking the cell and entering a new custom name. Note that renaming a heading in the pivot table report updates the name in the Values area of the fields pane, but the original field name above remains unchanged.
Number Formatting: To ensure formatting (like currency symbols and decimal places) stays with the numbers when the pivot table layout changes, apply it via the pivot table’s specific options, not just standard cell formatting from the Home tab.
Right-click on a number within the column you want to format.
Select Number Format. Alternatively, access this via Value Field Settings > Number Format.
Choose the desired format (e.g., Accounting, Currency) and settings.
Click OK. This applies the formatting to all numbers in that value field.
Handling Empty Cells: By default, pivot tables show blank cells where there is no data for a combination of criteria. This can affect charts or make the table harder to read. You can replace blanks with a value like 0:
Click inside the pivot table.
Go to the PivotTable Analyze ribbon, in the PivotTable group, click Options.
On the Layout & Format tab, under the Format group, check the box for For empty cells show: and enter the value you want to display (e.g., 0).
Click OK.
Grouping Data
Grouping allows you to combine items in your pivot table.
Automatic Grouping: Excel automatically groups dates when you drag a date field into rows or columns. It analyzes the data and creates fields for years, quarters, and months if applicable. These automatically created fields (like ‘Years’ and ‘Quarters’) appear in the PivotTable Fields pane and can be used independently. You can expand/collapse these groups using the +/- buttons in the pivot table.
Custom Grouping: You can create your own groups from non-date fields (e.g., grouping several Item Types into a ‘Food and Drink’ category).
Select the items you want to group by holding down Ctrl and clicking each item.
Go to the PivotTable Analyze ribbon, in the Group group, click Group Selection. Excel creates a new group (e.g., ‘Group1’) and a new field in the Rows/Columns area (e.g., ‘Item Type2’).
You can rename the group label in the pivot table (using F2 or double-clicking and changing the custom name in Value Field Settings) and rename the new group field in the fields pane (using Field Settings).
Ungrouping: To reverse automatic or custom grouping, select an item within the group and click Ungroup in the Group group on the PivotTable Analyze ribbon.
Inserting Blank Lines: To improve readability, especially with grouping, you can insert blank rows between groups. Go to the Design ribbon, in the Layout group, click Blank Rows, and select Insert Blank Line after Each Item. To remove them, choose Remove Blank Line after Each Grouped Item.
Layout Options
You can customize the overall appearance and structure of your pivot table report. These options are found on the PivotTable Design ribbon, in the Layout group.
Subtotals:You can choose not to show subtotals at all.
You can show them at the bottom of each group (often preferred) or at the top of each group (the default).
Grand Totals:You can turn grand totals off for both rows and columns.
You can turn them on for both rows and columns, only for rows, or only for columns. Turning them off is common when creating charts to avoid including totals.
Report Layout: This changes how the fields are displayed in the report area.
Compact Form: Optimizes for readability and uses space efficiently. It places subtotals at the top of groups and keeps related fields in the same column. This is the most compact view.
Outline Form: Moves the innermost row field to a new column, creating a hierarchical structure where each field is in its own column. Subtotals appear at the top by default, but you can change their position.
Tabular Form: Similar to Outline form, but adds grid lines within the pivot table, making it look more like a regular Excel table.
Repeat Item Labels: In Outline or Tabular forms, you can choose to repeat the labels for outer row fields on every line instead of only showing them once. This can make the table easier to read in some cases or is necessary for certain chart types like map charts. You can turn this off if desired.
These options allow you to tailor the pivot table’s appearance to best suit your analysis and presentation needs.
Cleaning Data for Excel Pivot Tables
Data cleaning is a crucial process to undertake before analyzing large datasets, particularly when planning to use tools like pivot tables in Excel. It involves tidying up data sets, making them consistent, formatting them correctly, and presenting the data in a way that allows for easy and accurate analysis. Skipping this step, especially when importing data from external sources or databases, can lead to inaccurate analysis because data doesn’t always import in the expected format, potentially including columns out of place, strange formatting, blank rows, or duplicate entries.
Here are some of the key data cleaning techniques discussed in the sources:
Removing Blank Rows Blank rows make data harder to read and can cause issues in pivot tables by being picked up as a “blank” entry. Manually deleting them row by row is tedious for large datasets. A quicker method involves selecting the data range, using “Go To Special” to select “Blanks,” and then using the “Delete Sheet Rows” command. Removing blank rows ensures the pivot table is accurate.
Removing Duplicate Entries Duplicate rows, particularly where every column’s information is exactly the same, can sometimes occur when importing data and can cause problems for pivot tables. Excel’s “Remove Duplicates” utility can easily find and remove these exact duplicates. You can specify which columns to check for duplicates, but typically, you check all columns to find completely duplicated rows.
Removing Unwanted Formatting Imported data may contain inconsistent formatting like background shading, bold text, or italics, which results in an inconsistent-looking worksheet. This formatting often isn’t desired. The “Clear Formats” option, found under the “Clear” button in the Home tab’s editing group, can quickly remove all applied formatting, including background shading, bold, italics, and number formatting, providing a clean slate. Other “Clear” options exist for different purposes, such as clearing only contents, comments/notes, or hyperlinks.
Applying Desired Formatting After clearing unwanted formatting, applying consistent and appropriate formatting is important to make your data easier to read. This is referred to as number formatting but can be applied to any column, not just those containing numbers. The “Number group” on the Home tab provides standard options like General, Number, Currency, Accounting, and Date. Dates in Excel are stored as numbers (days since January 1, 1900), so applying a Date format (like Short Date or Long Date) is necessary to display them correctly. For numeric data, you can control decimal places using dedicated buttons or the “Format Cells” dialog box (Ctrl + 1). For monetary values, Currency and Accounting formats add symbols; Accounting format is often preferred as it aligns currency symbols and decimal points, enhancing readability for lists of numbers.
Tidying Up Text Using Formulas Inconsistencies in text, such as case variations (uppercase, lowercase, proper case) or erroneous spaces (leading, trailing, multiple spaces between words), can negatively impact analysis. Excel provides text functions to standardize these:
UPPER(), LOWER(), and PROPER() functions are used to change the case of text.
TRIM() removes leading/trailing spaces and extra spaces between words.
CLEAN() removes non-printing characters, which might appear as small square boxes, and can also remove manual line breaks within cells. These functions are typically used in a “helper column” next to the original data. Multiple functions can be combined in a single formula in a helper column to perform several cleaning steps at once, saving time.
Using Paste Special to Convert Formulas to Values When cleaning data using formulas in a helper column, the formulas refer to the original data column. If the original column is simply deleted, the helper column will result in #REF! errors because the references are broken. To avoid this, the cleaned data in the helper column must be converted from formulas to static values. This is achieved by copying the helper column and then using the “Paste Special” > “Paste Values” option to paste only the resulting values over the original column (or a new location), discarding the underlying formulas. Once the values are pasted, the helper column can be safely deleted.
Splitting and Combining Data Sometimes data is combined in a single cell that needs to be separated (e.g., “Order Date Order ID”), or data in separate cells needs to be combined.
“Text to Columns” is a wizard that splits a single column of text into multiple columns based on a specified delimiter (like a comma, space, or other character) or a fixed width.
“Flash Fill” is a faster tool (available since Excel 2013) that can split or combine data by recognizing patterns based on one or two examples provided by the user. It can be accessed via a button on the Data tab or the Ctrl + E shortcut.
The CONCAT() function (or CONCATENATE() in older versions) joins text from multiple cells. Custom text or delimiters can be included in the joined result by enclosing them in quote marks within the function.
Finding and Replacing Data To standardize inconsistent text entries (e.g., replacing “Democratic Republic of the Congo” with “DRC” or “United States of America” with “USA”), you can use the “Find and Replace” dialog box (Ctrl + F, then select the Replace tab). You specify what to find and what to replace it with, choosing whether or not to match the case. The SUBSTITUTE() formula can also perform find and replace using a formula, requiring the “Paste Special” > “Paste Values” trick afterward.
Running a Spell Check Spelling errors can cause problems in pivot tables because the table will treat variations of the same word as completely separate items. Running a spell check (Review tab > Proofing group, or F7) helps ensure consistency in text entries. You can choose the dictionary language and add correctly spelled but unrecognized words to the dictionary.
Once data is cleaned, it is highly recommended to put it into an Excel Table before creating a pivot table. Excel Tables offer several advantages, including automatic formatting, built-in filter and sort buttons, and importantly, auto-expand capabilities. This means that if new data is added to the table, it is automatically included in the data source for any associated pivot tables or charts, which can then be updated by simply clicking the refresh button. Data can be converted into an Excel Table using the “Format as Table” option on the Home tab or the Ctrl + T keyboard shortcut. Tables can be given meaningful names for easier identification.
In summary, thorough data cleaning is essential for accurate and effective analysis using pivot tables, addressing issues like inconsistencies, errors, and formatting problems through various Excel tools and functions.
Excel Data Analysis with Pivot Tables
Based on the sources, data analysis is the process of summarizing large amounts of data to make sense of them. In a data-driven world where information is collected from various sources, simply looking at a large spreadsheet might not highlight key metrics, issues, successes, failures, or trends. Data analysis aims to take this data and present it in a way that allows for clearer understanding and better business decisions.
Excel provides powerful tools for data analysis, particularly Pivot Tables.
Key aspects of Data Analysis discussed in the sources:
The Role of Pivot Tables Pivot tables are described as an interactive and dynamic way to quickly summarize large amounts of data. Unlike static Excel tables where analysis is limited primarily to sorting and filtering, pivot tables allow you to pivot fields around and view data in all different ways. This dynamism makes it much more efficient to analyze data compared to manually using filters. Pivot tables help analyze large datasets in a clear and effective way. They facilitate asking questions about the data, such as finding top performers or seeing counts of high-priority orders. Pivot charts can be created from pivot table data to offer visual analysis options, as most people find it easier to analyze and interpret data visually. This can extend to creating interactive dashboards with filters for deeper analysis.
The Critical Need for Data Cleaning Before Analysis A central theme is that data cleaning is essential prior to analyzing data with a pivot table. Skipping this step, especially when importing data from external sources or databases, can lead to inaccurate analysis. Data doesn’t always import in the desired format, and inconsistencies or errors can cause problems for pivot tables. Cleaning ensures the data is tidied up, consistent, correctly formatted, and presented in a way that allows the pivot table to easily analyze it and produce accurate results. The sources highlight cleaning steps like removing blank rows, removing duplicate entries, clearing unwanted formatting, applying desired formatting, tidying text using formulas (case, spaces), splitting and combining data, finding and replacing data, and running a spell check. All these steps contribute to a “clean looking data set ready for analysis”.
Structuring Analysis with Pivot Table Fields To perform analysis with a pivot table, you use the Pivot Table Fields pane, which lists the column headings from your source data. These fields are dragged into four areas: Filters, Columns, Rows, and Values. These areas determine the layout of the pivot table and control the type of analysis being done. Placing fields in different areas changes how the data is summarized and viewed.
Aggregating Data for Analysis The Values area is typically where numeric fields are placed. By default, Excel usually performs a sum calculation for numeric values and a count for text or date fields dropped into this area. However, you can change how the data is summarized using the Value Field Settings. This allows you to choose from various aggregation methods, including Sum, Count, Average, Max, Min, Product, and more. You can even combine different aggregation methods (like sum and average) for the same data by dragging the field into the Values area multiple times and setting a different calculation for each instance. This ability to calculate averages, mins, or maxes “on the fly” expands the analysis beyond what was present in the raw source data.
Grouping Data for Deeper Analysis Grouping data is another way to analyze it. Excel automatically groups certain fields, like dates, into categories like years, quarters, and months. This allows you to see the data summarized at different levels (e.g., total profit by year, then by month within each year). You can also create your own custom groups for non-date fields to categorize data according to your analysis needs (e.g., grouping different item types into “food and drink” or “other”). Grouping allows for analyzing data in “multiple dimensions” by adding more fields to the Rows or Columns areas.
Handling Empty Cells and Layout How empty cells are displayed affects the accuracy of analysis, especially in pivot charts. Replacing blank cells with zeros in the Pivot Table Options ensures that items with no data are still represented, showing a zero value rather than being excluded from the analysis or charts. Additionally, the report layout options (compact, outline, tabular) and the choice to display or hide subtotals and grand totals affect the readability and presentation of the analyzed results.
In summary, data analysis in Excel, as presented in the sources, relies heavily on the dynamic capabilities of Pivot Tables, which allow for summarizing, slicing, dicing, and aggregating data in various ways. However, the foundation of accurate analysis is thorough data cleaning, ensuring the data is reliable and free from inconsistencies before being used in a pivot table. Using Excel Tables is also recommended as it makes managing and updating the data source for analysis more efficient.
Grouping Data in Excel Pivot Tables
Based on the sources, grouping data in Excel pivot tables is a way to summarize data by multiple fields and organize the display of that data. It allows you to analyze information at different levels or categorize data according to specific needs.
Here are key aspects of grouping data discussed in the sources:
Automatic Grouping Excel will automatically apply grouping when you summarize data by more than one field in areas like the Rows or Columns of a pivot table.
Date Grouping A common example of automatic grouping occurs when you drag a date field into an area like Rows. Excel looks at your source data and automatically groups the dates by categories such as years, quarters, and months. These levels appear as separate fields (e.g., “Years,” “Quarters,” “Order Date”) in the Pivot Table Fields pane. You can then use these fields independently to summarize data at different granularities, for instance, viewing total profit by year, and then expanding to see the breakdown by month within each year. If you don’t need a specific level, like quarters, you can simply remove that field from the Rows area. The “Group Field” option on the Pivot Table Analyze ribbon shows the date ranges and the levels (months, quarters, years) that Excel has pulled from the data.
Custom Grouping You can create your own custom groups for fields that are not dates. This allows you to categorize data based on your analytical requirements. For example, you could select several ‘item type’ categories like ‘baby food’, ‘beverages’, ‘cereal’, ‘fruits’, ‘meat’, ‘snacks’, and ‘vegetables’ and group them together under a new name like “Food and Drink”. The remaining items could be grouped under “Other”.
Creating Custom Groups To create a custom group, you select the specific items in the pivot table report that you want to include in the group. Then, you go to the Pivot Table Analyze ribbon and select the Group Selection button. Excel will create a new group (initially named generically, like “Group1”). You can rename this group directly in the pivot table report. Excel also creates a new field in the Pivot Table Fields pane corresponding to this custom group (e.g., “Item Type2” if you grouped based on ‘Item Type’). It is recommended to rename this new field as well (e.g., “Food and Drink”) for consistency. This can be done by clicking the drop-down arrow for the field in the Rows area and selecting “Field Settings,” or by right-clicking the field name in the Rows area and selecting “Field Settings”.
Expanding and Collapsing Groups When grouping is applied, items in the pivot table report often display with little plus and minus symbols next to them. These symbols allow you to collapse or expand the details within a group, letting you focus on summary levels or drill down into specifics. You can toggle the display of these buttons on or off from the Pivot Table Analyze ribbon in the Show group.
Multi-Dimensional Analysis Grouping contributes significantly to creating multi-dimensional pivot tables. By adding more fields and grouping them in the Rows or Columns areas, you can analyze your data by multiple factors simultaneously (e.g., analyzing profit by region, item type, and sales channel).
Ungrouping Data If you need to revert a group, you can select an item within the group in the pivot table and click the Ungroup button on the Pivot Table Analyze ribbon.
Grouping and Layout The report layout options can interact with grouping. For example, the Compact Form layout maintains the grouping structure. Adding blank rows using the “Blank Rows” option on the Design ribbon will insert a blank line after each grouped item, which can help emphasize groups and improve readability.
Excel Number Formatting Explained
Based on the sources and our conversation, number formatting is a crucial aspect of data cleaning and analysis in Excel, particularly to improve readability and consistency of your data. It involves ensuring that values in your cells are displayed in a way that accurately reflects their type and makes them easy to interpret.
Here’s a breakdown of the key points about number formatting discussed:
Purpose of Number Formatting:
To make your data a lot easier to read.
To ensure consistency in how numbers are displayed, such as the number of decimal places and the presence of currency symbols.
A currency symbol, for example, always makes monetary values a lot easier to read.
Applying Formatting in Standard Worksheets:
Formatting is applied using the Home tab in the Number group.
A drop-down menu provides common formatting options (e.g., General, Number, Currency, Accounting, Short Date, Long Date).
You can access more detailed formatting options by clicking “More Number Formats” at the bottom of the drop-down or by using the Ctrl+1 keyboard shortcut to open the “Format Cells” dialog box.
The appropriate format depends on the type of information in the column.
Examples discussed include:
Applying Text formatting to columns containing text.
Applying Date formatting to columns containing dates. Excel stores dates as numbers (days since January 1, 1900), and date formatting is needed to display them as calendar dates. If not formatted as a date, you might see the underlying numeric value. “Short date” and “long date” are common options. Custom date formats are also available via “More number formats” but are considered advanced.
Applying Number formatting to columns like “Units Sold,” where you might need to control the number of decimal places (e.g., reducing to zero using the Increase/Decrease Decimal buttons or “Format Cells”).
Applying Currency or Accounting formatting to monetary columns like “Unit Price,” “Total Revenue,” or “Total Profit” to add a currency symbol and control decimal places. The key difference is that Accounting format aligns the currency symbols and decimal points in a column, which is often considered easier to read, especially in long lists of numbers, whereas Currency format places the symbol right next to the value and doesn’t align decimals. The sources suggest Accounting format is frequently used.
Formatting and Data Cleaning Steps:
When initially cleaning data, steps like using “Clear Formats” can remove all formatting, including desirable number formatting. Therefore, you might need to reapply the correct formatting after this step.
Helper columns created for text cleaning formulas (like UPPER, TRIM, CLEAN, SUBSTITUTE) might inherit the formatting of surrounding columns, sometimes defaulting to “Text”. To see formula results correctly, these columns might need to be changed back to “General” format before applying the formula.
Identifying numbers stored as text is important. Indicators include the number being aligned to the left side of the cell and a little green triangle in the corner. You can convert these using the warning symbol option “Convert to Number” or by using the VALUE formula.
Number Formatting in Pivot Tables:
When you build a pivot table, the numbers in the values area are initially unformatted and inconsistent.
It is NOT recommended to apply number formatting directly to the cells in a pivot table using the Home ribbon. This is because pivot tables are dynamic; the fields and their locations can change when you rearrange or “pivot” the data. Formatting applied to a static cell will not move with the number it was applied to if the layout changes.
The correct method for applying number formatting in a pivot table is to apply it to the number itself, which ensures it moves with the data regardless of the layout.
This is done by right-clicking on a number within the pivot table and selecting “Number Format”.
Alternatively, you can access this through the Value Field Settings for the specific field in the Values area, and then clicking the “Number Format” button at the bottom.
Both methods open the familiar “Format Cells” dialog box, allowing you to choose formats like Accounting or Currency.
Custom number formatting is also available through this pivot table method.
If you configure your pivot table to show zero for empty cells, these zeros will also display with the number formatting applied to that values field (e.g., showing “$ -“).
In essence, applying consistent and appropriate number formatting is a vital step, first during general data cleaning and preparation, and then specifically within pivot tables using the recommended methods to maintain accuracy and readability as you analyze your data.
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This text comprises excerpts from a tutorial on using Microsoft Excel and its add-ins for data analysis. The tutorial covers data manipulation techniques, including formatting, sorting, and filtering, using functions and formulas for calculations and analysis (like median, average, and standard deviation), and creating visualizations (histograms, bar charts). It also explores pivot tables and pivot charts for data aggregation and summarization, demonstrates the use of Power Query for data cleaning and transformation, and introduces Power Pivot for data modeling and the creation of measures and calculated columns. Finally, the tutorial discusses methods for sharing completed projects.
Excel for Data Analysis: Study Guide
Quiz
What are the limitations of using Excel on a Mac operating system for this course? Mac users will not be able to complete the advanced chapters on power query and power pivot, as well as the final project. Also, Mac does not have as many data sources to pull from.
What are the two major Microsoft 365 plans recommended for this course? The two main plans recommended are the family plan, which can be shared with up to six people, and the personal plan, which is for individual use. Additionally, the family plan has a one-month free trial.
What is a key limitation of using the free Microsoft 365 online version for this course? The free online version of Microsoft 365 has limitations on power query and power pivot, which will restrict the user’s ability to follow along in the advanced chapters. The layout is also much different than the desktop app, and the course won’t provide specific support for navigating the online version.
Explain the difference between saving a file versus save as. When a new file is created and saved, both save and save as will act the same, allowing the user to designate the file name and location. However, if a file has been previously saved and is being modified, using save will overwrite the original, whereas save as creates a new file while keeping the original.
Describe what the “ribbon” is in Microsoft Excel. The ribbon is the area at the top of the Excel interface that contains the different tabs and commands. It’s where you can find options for formatting text, working with data, and inserting formulas.
What is a nested IF statement and why might it be less ideal than using AND/OR functions? A nested IF statement is when an IF statement is placed inside another IF statement. While functional, it can become hard to read and difficult to debug. Logical functions like AND and OR simplify complex conditions, making the formulas easier to understand.
What are the three major functions for statistical analysis covered in the course? The major functions covered include COUNT, which tallies the number of cells in a range containing a number, SUM, which calculates the total of numerical values in a range, and AVERAGE, which computes the mean of a set of numbers.
Why is the standard deviation function, STDEV.S, used over STDEV.P in the course? STDEV.S is used because the data being analyzed is considered to be a sample of the total population rather than the entire population. STDEV.P is used when analyzing an entire population.
What are the main differences between the QUARTILE.INC and QUARTILE.EXC functions? The main difference is that QUARTILE.INC is inclusive and can specify the Min and Max, which QUARTILE.EXC does not have the capability to do. Instead, QUARTILE.EXC excludes those outliers.
Explain the use case of the TEXTJOIN function covered in the course. The TEXTJOIN function is used to combine values from multiple cells into a single text string, using a specified delimiter. This is helpful in aggregating text data and creating longer strings based on multiple values.
Essay Questions
Discuss the importance of selecting the correct version of Microsoft Excel for data analysis tasks, specifically when using advanced features. What factors should a user consider when choosing between Microsoft 365, Microsoft Office Home & Student, and Microsoft 365 online?
Analyze the role of logical functions (IF, AND, OR) in data analysis within Excel. Provide examples of how these functions can be used to categorize and filter data based on multiple criteria, and discuss their advantage over nested IF statements.
Compare and contrast the use of math and statistical functions like COUNT, SUM, AVERAGE, and standard deviation in the context of exploratory data analysis (EDA). How do these functions aid in understanding the distribution and central tendencies of a dataset, and why is it important to use descriptive statistics during EDA?
Explore the importance of text functions in Excel, particularly LEFT, RIGHT, MID, FIND, and TEXTJOIN, in the context of data cleaning and preparation for analysis. Explain with examples how these functions can be used to extract, manipulate, and format text data from messy raw data.
Discuss the various what-if analysis tools available in Excel including Scenario Manager, Goal Seek, Solver, and Data Tables. How do these tools assist in decision making, and how do they aid in the evaluation of different possible outcomes?
Glossary of Key Terms
Power Query: A data transformation and preparation tool in Excel that allows users to import, clean, and shape data from various sources.
Power Pivot: An add-in in Excel that enables users to build data models, perform complex analysis, and manage large datasets with relationship tables.
Microsoft 365: A subscription service that provides access to a suite of Microsoft applications such as Excel, Word, and PowerPoint.
Microsoft Office Home & Student: A one-time purchase of Microsoft Office applications for home and student use.
Ribbon: The interface at the top of an Excel window containing tabs and commands for managing spreadsheets.
Nested IF statement: An IF statement that is placed inside another IF statement.
Logical Function: A function that tests conditions and returns a result based on whether those conditions are true or false such as IF, AND, and OR.
COUNT Function: A function that counts the number of cells in a range that contain numbers.
SUM Function: A function that adds together all numerical values in a given range.
AVERAGE Function: A function that calculates the arithmetic mean of a set of numbers.
Standard Deviation: A measure of the amount of variation or dispersion of a set of data values using the functions of STDEV.S for sample population, and STDEV.P for population.
Quartile: A measure of division of a data set into four equal groups such as QUARTILE.INC and QUARTILE.EXC for inclusive and exclusive outliers respectively.
MODE Function: A function that returns the most frequently occurring value(s) in a data set.
Text Functions: Functions that allow for the manipulation of text such as LEFT, RIGHT, MID, FIND, and TEXTJOIN.
Data Validation: A tool that restricts the values or data types that can be entered in a cell.
Date Functions: Functions in Excel used to manipulate dates and times such as TODAY, YEAR, and MONTH.
What-If Analysis: A set of tools in Excel that allow users to test different scenarios and see how changes in input values affect the output.
Scenario Manager: A tool that allows users to create and save different scenarios in a spreadsheet.
Goal Seek: A tool that finds the input value needed to achieve a specific target output value.
Solver: A more advanced what-if analysis tool that can find optimal solutions while managing constraints.
Data Table: A way to see how changing a value will affect the result of a formula.
Slicer: A visual control that can be used to filter data in a pivot table or data table
Conditional Formatting: An Excel feature that allows formatting to be applied dynamically based on cell value.
Data Analysis Toolpak: An add-in that allows you to perform more advanced statistical analysis.
Histogram: A chart showing the distribution of numerical data.
Rank & Percentile: Statistical functions to rank values and find their percentiles in a data set.
Moving Average: A tool used to reduce the fluctuations in data and identify a more generalized trend.
Power Pivot Data Model: A relational database within Excel that allows you to connect multiple tables together.
DAX (Data Analysis Expressions): A formula language used in Power Pivot for calculations and data analysis.
Explicit Measure: A DAX expression that is explicitly defined in Power Pivot for use in calculations.
Implicit Measure: A calculation done by just simply putting in a variable into the values of a pivot table
Filter Function (DAX): A function used to limit the values or context that can be evaluated.
Calculate Function (DAX): A function to evaluate an expression in a modified filter context.
Relationship Functions (DAX): DAX functions used to manage relationships between tables in Power Pivot such as CROSSFILTER.
GitHub: A web-based platform for version control and collaboration using git.
Git: A distributed version control system that tracks changes in files and code.
Repository (Repo): A storage location for your project files.
ReadMe.md: A text file containing descriptive information about your project, written in markdown.
Markdown: A lightweight markup language used to format text in readmes and other documents.
Mastering Excel: Data Analysis & Project Deployment
Okay, here’s a detailed briefing document summarizing the key themes and ideas from the provided text, including relevant quotes.
Core Idea: The course requires a specific version of Excel for full functionality, particularly for the “Advanced” chapters covering Power Query and Power Pivot.
Platform Compatibility:Windows: Microsoft 365, Microsoft Office Home & Student, or older versions up to 2010 are compatible for the entire course.
Mac: Excel installed directly on a Mac will have limitations, particularly in the “Advanced” chapter. Power Query and Power Pivot are not fully supported.
Microsoft 365 Online: This version is free but also lacks full functionality for the “Advanced Data analysis” section and has a different layout. “the layout on the web browser version of this app is much different from that that’s installing your computer so I’m not going to be providing any support on this course on actually actually how to navigate this”.
Recommendation: The instructor recommends Microsoft 365 family plan as it “includes all the different features that I need” and is cost-effective when shared.
Trial Option: Microsoft 365 offers a one-month free trial, which could allow users to complete the course for free (if cancelled before the trial ends). “if money is an issue Microsoft 365 family offers this free one-month trial which I think you can complete this course within a month”.
2. Excel Interface & Navigation
Ribbon Exploration: The course focuses on understanding the Excel ribbon, specifically the Home tab (formatting) and the Formulas tab (functions).
File Menu: This includes options for saving, printing, exporting, and closing files. It also contains account information, themes, feedback, and advanced options.
Sheet Manipulation: The course covers adding, deleting, renaming, and moving/copying sheets within and between workbooks.
Context Menus: Right-clicking on cells and objects will expose a lot of functions for various context specific actions.
3. Excel Formulas and Functions
Core Concepts: Formulas are used for calculations and data manipulation; Functions are pre-built formulas for specific tasks.
Insert Function Tool: Helps users find and understand functions.
Logical Functions (IF, AND, OR): These are critical for conditional analysis.
Example of if statement “if it has The Logical test that we want to actually evaluate so I’m going to put in P3 in this case as it’s going to return true or false and then from there the next value in there is value if true which what do we want to return if it is true well that our goal is met and then if it’s not met we want to have well not met”.
Nested If statements should be avoided as they’re “hard to read” instead using and and or which are a lot clearer.
IFS is used for multiple condition evaluations, especially for bucketing data, but requires practice.
Math & Statistical Functions: COUNT, SUM, AVERAGE, MIN, MAX, STDEV.S, QUARTILE, MODE. These are important for Exploratory Data Analysis (EDA).
The P stands for population and the S stands for sample.
“if we went above and below the average by one standard deviation around 68% which is a heck a lot of data is within this one standard deviation”.
Text Functions: LEFT, RIGHT, MID, LEN, FIND, TEXTJOIN, TEXTSPLIT are key for data extraction and manipulation, as often times data is messy.
Date & Time Functions: YEAR, MONTH, DAY, DATE, NOW, TODAY are used for working with date data. “a value of one is added when I put into it plus one basically takes it to the next date”.
Error Handling: The course includes a section to identify and address common Excel formula errors with chatbots being recommended. “The biggest time saer I’ve found with any of these errors is using some sort of chatbot specifically me I’m going to go to something like chat GPT or even claw they’re going to be able to provide really quick help in understanding what an error is and what I need to do to fix it”.
4. Data Analysis & Visualization Techniques
Data Tables: One and two input data tables for sensitivity analysis.
Tables: Converting ranges to tables unlocks sorting, filtering, and slicer functionalities.
Slicers: Used for interactive data filtering and dashboard creation.
Conditional Formatting: Highlights trends and patterns in data using color scales, data bars, and icon sets. “but you’re going to notice it basically does these bands but it does this entire table all formatted together and this is not what we necessarily want of course the total road is going to be the highest I want to look through that row and actually see where I should be actually looking”.
Analysis Toolpak: Includes Descriptive Statistics, Histogram, Rank and Percentile, Moving Average for deeper data analysis.
Charts: Creation of charts based on specific dataset with the x-axis as data range and the y-axis as frequency. “anyway I really like this because now look at this control we were able to minimize it not to go past 40,000 and have all these outliers and everything else that has past 40,000 is put into this basically more value”.
Solver, Goal Seek and Scenario Manager: For “what if” analysis and finding optimal solutions by changing input variables, even with constraints.
5. Power Query & Data Import
Data Import: Importing data from various sources including text files (CSV), multiple Excel workbooks, web data.
Power Query Editor: Clean, transform, and combine data from different sources.
Loading Data: Option to load data into Tables or Pivot Tables.
Error Handling: Power Query has its own errors and notifications.
6. Power Pivot & Data Modeling
Data Model: Linking multiple tables through relationships.
DAX (Data Analysis Expressions): Using DAX functions to create explicit measures for complex calculations and data aggregation.
Filter Functions: Used to modify filter contexts for complex aggregations, calculate provides that filter option.
Relationship Functions: CROSSFILTER is used for relationship issues.
Pivot Tables with Power Pivot: Creating interactive visualizations that summarize data from the data model.
7. Project & GitHub Integration
Project Structure: The course includes two projects: Salary Dashboard and Salary Analysis with a GitHub repo containing a readme for each with markdown.
GitHub: Used for sharing and version control of Excel projects.
Git: The core technology behind GitHub used for version control.
GitHub Desktop: An application that allows easy management of git repos.
Markdown: A markup language used to create formatted text in readmes, used in conjunction with Github.
File Management: Using a file system to organize project folders with their Excel files and readmes.
Pushing and Pulling: Demonstrates the workflow of pushing local changes to the remote repository (GitHub) and pulling remote changes to a local repository.
8. Project Documentation & Sharing
README.md Files: Using Markdown syntax (headings, lists, bold/italics, links, images) to document project steps and insights.
Project Sharing: GitHub is used for sharing projects, and LinkedIn for showcasing completed work.
One drive is not recommended for projects that use power query or power pivot features.
Screen Captures: Using system tools (command shift 4 for mac and windows shift + s for windows) to capture relevant visualizations for readmes.
Key Quotes:
“the layout on the web browser version of this app is much different from that that’s installing your computer so I’m not going to be providing any support on this course on actually actually how to navigate this”
“if money is an issue Microsoft 365 family offers this free one-month trial which I think you can complete this course within a month”
“if we went above and below the average by one standard deviation around 68% which is a heck a lot of data is within this one standard deviation”
“The biggest time saer I’ve found with any of these errors is using some sort of chatbot specifically me I’m going to go to something like chat GPT or even claw they’re going to be able to provide really quick help in understanding what an error is and what I need to do to fix it”
“but you’re going to notice it basically does these bands but it does this entire table all formatted together and this is not what we necessarily want of course the total road is going to be the highest I want to look through that row and actually see where I should be actually looking”
“anyway I really like this because now look at this control we were able to minimize it not to go past 40,000 and have all these outliers and everything else that has past 40,000 is put into this basically more value”
Overall Theme:
The course is a comprehensive guide to using Excel for data analysis, emphasizing not only the technical aspects of using the software but also the practical skills needed to conduct analysis, document findings, and share work effectively with GitHub.
Mastering Microsoft Excel: Data Analysis and Power Query
1. What are the different versions of Microsoft Excel, and which one is recommended for this course?
There are several ways to access Microsoft Excel. These include:
Microsoft 365: A subscription service offering access to various Microsoft applications, including Excel, Word, and PowerPoint. It comes in family (up to six users) and personal plans. College students or those in large corporations may have free access. A free one-month trial is also often available. If you cancel before the trial ends, you can retain the view-only functionality.
Microsoft Office Home and Student: A one-time purchase that provides keys to install Excel, Word, and PowerPoint.
Microsoft 365 Online: A free, web browser-based version of Excel with limitations.
The course recommends using either Microsoft 365 (family or personal plan) or Microsoft Office Home and Student. These versions allow for full functionality and access to advanced features such as Power Query and Power Pivot. The online version does not include the advanced features needed for the entire course and has a different UI.
2. What are the limitations of using Excel on a Mac operating system?
If you are using a Mac operating system, you’ll have limitations in the advanced chapters. You will not be able to complete sections on Power Query and Power Pivot or the final course project. These features are available in the Windows version of Excel, where Microsoft invests most of its resources. The Mac version has a reduced number of data sources available in the data tab and lacks power pivot.
3. What is the purpose of the “Ribbon” in Excel, and what kind of tasks can you perform there?
The ribbon is the area at the top of the Excel interface that contains various tabs and tools. It is designed to perform different tasks and functionalities. It contains multiple tabs such as “Home,” “Insert,” “Page Layout,” “Formulas,” and “Data,” each with options for formatting, inserting elements, setting up the page, using formulas, and handling data, respectively. The Home tab is used for formatting text and how things appear in the spreadsheet, like fonts, colors, and cell styles. The ribbon allows you to customize various aspects of a spreadsheet.
4. How do I manage different sheets and workbooks?
In Excel, you can manipulate different sheets and workbooks in various ways. To move a sheet, you can right-click on its tab and select “Move or Copy,” then choose to move it to another workbook or create a copy. You can open and work with multiple workbooks simultaneously. You can also copy and paste cells or groups of cells between different sheets or workbooks.
5. How do formulas and functions work in Excel, and what are some key examples?
Formulas and functions are the building blocks of calculations and analysis in Excel. Formulas always start with an equal sign (=), followed by values, operators, and references to cells. Functions are pre-built calculations that perform specific tasks, like SUM, AVERAGE, or COUNT. The lecture specifically uses COUNTIF which takes a range of cells and calculates based on specific criteria. Other basic functions covered are also AND and OR. You can insert a function using the Insert Function button which is very useful if you don’t know the specific function name you’re looking for.
6. What are logical functions and how are they used?
Logical functions in Excel test a condition and return a result based on whether the condition is true or false. The most popular of these are IF, AND, and OR. An IF statement checks a condition and returns one value if it’s true and another if it’s false. Nested IF statements can evaluate multiple conditions, but AND and OR are better for combining criteria. For example, AND returns true only if all its conditions are true, while OR returns true if at least one condition is true. The IFS function allows for multiple logical tests and outputs a different result for each scenario.
7. How do you use math and statistical functions to perform Exploratory Data Analysis (EDA)?
Math and statistical functions are used to perform EDA on a dataset. Common functions include COUNT, SUM, AVERAGE, MIN, MAX, STDEV.S (sample standard deviation), and QUARTILE.INC (inclusive quartiles), and MODE. These functions help you calculate descriptive statistics like measures of center (mean, median, mode), spread (standard deviation, quartiles), and range (min, max). Quartiles divide the data into four equal parts. The lecture also demonstrated AVERAGEIF to calculate an average based on a specific criteria. The RANK function returns the rank of a number in a list of numbers. The analysis tool pack can be used to provide descriptive statistics along with histograms.
8. How does Power Query work, and how can I connect it to multiple data sources?
Power Query is a tool in Excel that allows you to connect, transform, and load data from multiple sources. To connect to data, go to “Data” -> “Get Data” and select your data source (e.g., from file, database, or the web). Power Query loads the data into a query editor, where you can apply various transformations like filtering, sorting, and data type conversions. You can combine data from multiple files or tables into a single table. Once transformed, you can load the data into an Excel sheet or data model. When you refresh your data, it automatically updates with those transformations. You can also use parameters to change the inputs in the query, such as changing a date filter.
Spreadsheet and Chart Data Formatting
Data formatting in spreadsheets involves several techniques to ensure data is presented clearly and is easily understood [1]. Here’s an overview of some key formatting methods mentioned in the sources:
Centering Titles: Titles can be centered at the top of a column to clearly indicate the data below it [1].
Number Formatting: Columns containing numerical data, such as salary, can be formatted as currency or accounting numbers [1].
Decimal Places: You can adjust the number of decimal places displayed, which is useful when dealing with large numbers [1].
Date Formatting: Date columns can be converted to short date formats, which is useful when dealing with columns such as job posting dates [1].
Conditional Formatting: This type of formatting allows cells to be highlighted based on a specific rule [2].
Rules can be created to highlight cells based on their value [2, 3].
Color scales can also be applied to cells, with different colors indicating high or low values [3].
Data bars can visually represent values within cells [3].
Icon sets can be used to make data more dynamic [3].
Format Painter: This tool allows you to copy the formatting from one cell to another [3].
Custom Number Formats: Custom number types can be created to format numerical values in a certain way [4].
For example, a custom number format can be created to display values in thousands with a “k” at the end (e.g., 9.6k) [4].
Axis Formatting: Chart axes can be formatted to display numbers in a more readable format [4, 5].
This includes things such as displaying numbers in thousands with a “k” at the end [4, 5].
Minimum and maximum values on the axes can be changed, in order to more clearly display the data [4, 5].
The sources also demonstrate how to format visualizations:
Chart titles should provide context or ask a question [6].
Axis titles should be descriptive, especially for the y-axis which may not be self-explanatory [5, 6].
Chart elements such as axes, titles, data labels, gridlines, legends and trendlines can be added or removed [6].
Quick layouts can be used to quickly try out different themes for charts [6].
Colors can be customized to highlight specific information in a chart [6].
Chart elements such as data labels can be customized to display the data in a variety of ways [4].
These formatting techniques are intended to improve data visualization, making it easier to analyze and present [1, 6].
Spreadsheet Data Filtering Techniques
Data filtering is a powerful feature in spreadsheets that allows you to narrow down the data displayed based on specific criteria [1]. Here’s a breakdown of filtering techniques discussed in the sources:
Basic Filtering:
Filters can be applied to columns to show only data that matches a given condition [1].
For example, you can filter a job title column to show only “data analyst” roles [1].
Multiple filters can be applied to different columns to further refine the data. For example, you can filter for “data analyst” jobs that are “full-time” and in the “United States” [1].
Filters can also be applied to dates [1].
Filters can be cleared from columns to view all the data again [1].
Custom Filters:
Custom filters can be created to filter for data that meets certain conditions, such as values greater than zero and less than a specified value [2].
For example, a custom filter can be used to remove “NA” values from a column of median salaries [2].
Filtering in Tables:
When data is converted to a table, it automatically provides filter arrows at the top of each column [3].
These filter arrows allow for quick filtering based on text, dates, or numerical values [3].
Multiple values can be selected when filtering, such as selecting both “data analyst” and “business analyst” roles [3].
Filtering in Pivot Tables:
Pivot tables allow filtering by dragging fields into the “Filters” area [4].
You can filter rows or columns by selecting or deselecting specific values [4].
Label filters can be used to filter data based on text within labels, such as selecting jobs that contain the word “data” [4].
Value filters can be used to filter data based on numerical values, such as showing jobs with a count greater than 100 [4].
Filters can be cleared from tables to view all the data [4].
Slicers:
Slicers are a visual way to filter data in tables and pivot tables [3].
They provide buttons that can be clicked to filter data, making it easier for others to use the spreadsheet.
Slicers can be created for multiple fields and can be customized [3].
Multiple values can be selected by using multi select feature on slicers [3].
Timelines:
Timelines allow filtering of data by date and can be used in pivot tables or pivot charts [5, 6].
Timelines allow filtering by months, quarters, or years [6].
Filter Connections:
Filter connections can be used to connect filters from one pivot table to another [6].
This is especially useful when you want to have filters applied to multiple pivot tables simultaneously [6].
Filtering is a crucial step in data analysis, allowing you to focus on relevant data and gain insights more effectively [1]. It can be used in combination with data sorting and formatting to help you better understand your data [1].
In addition, the sources note a key limitation of filtering: filters are directional [7, 8]. When using relationships between tables, it is important to remember that filters are applied in the direction of the relationship [7, 8]. The sources provide a workaround for this limitation using Dax functions [8].
Data Analysis Techniques and Methods
Data analysis, as presented in the sources, involves a variety of techniques to explore, understand, and draw conclusions from data. Here’s a comprehensive overview of the key concepts and methods:
1. Exploratory Data Analysis (EDA)
Descriptive Statistics: EDA often begins with calculating descriptive statistics such as mean, median, mode, standard deviation, minimum, and maximum [1]. These can be used to get a sense of the distribution of numerical data [1, 2].
Histograms: Histograms are used to visualize the distribution of data [1, 2]. They show the frequency of values within specified ranges [1, 3].
The width of the “bins” (the ranges on the x-axis) can be adjusted to better visualize the data [3].
Histograms are great for understanding the distribution of numerical data, and determining whether data is skewed or has outliers [1, 2].
Box and Whisker Plots: Box and whisker plots are used to visualize the distribution of data, especially when you want to compare different categories of data.
The box shows the interquartile range, which contains 50% of the data.
The line inside the box indicates the median [3].
Whiskers extend from the box to show the range of the data, and any outliers are shown as dots [3].
Scatter Plots: Scatter plots are used to compare two numerical values and identify any trends or correlations between them [4].
Map Charts: Map charts are used to visualize data geographically, such as showing median salaries by country [5].
Pivot Tables: Pivot tables are used to summarize and analyze data by aggregating it based on different categories [2, 6, 7].
Pivot tables allow you to quickly change the way data is displayed, by moving categories or filters.
Pivot tables can be used to calculate sums, averages, counts, and percentages [2, 6].
Data Analysis Toolpak: This Excel add-in provides tools to perform more advanced statistical analysis, including descriptive statistics, histograms, and rank and percentile calculations [8].
2. Data Aggregation & Calculation
Math Functions: Spreadsheets include functions for performing calculations such as sum, average, min, and max [2, 6].
Conditional Aggregation: Functions like AVERAGEIF and SUMIFS allow you to perform calculations based on specified criteria [1, 2].
Median: The median is the middle value in a dataset, and it is less affected by outliers than the average, making it useful for analyzing salaries [1, 2].
Quartiles: Quartiles divide a dataset into four equal parts, and they can be used to analyze the distribution of the data [1].
Standard Deviation: Standard deviation measures the spread of data around the mean, which is useful for understanding the variability in the data [1].
Mode: The mode is the most frequently occurring value in a dataset [1].
Ranking: Data can be ranked to show its position relative to other values. [1]
Percentiles: Percentiles divide a dataset into 100 equal parts, and they can be used to show where a specific data point falls relative to others in the dataset [8].
Moving Average: A moving average is used to smooth out fluctuations in time series data [8].
3. Data Transformation
Data Type Conversion: Data types can be changed to ensure that data is treated appropriately (e.g. changing text to a number) [9].
Data Grouping: Data can be grouped together based on common characteristics for analysis [6, 10].
Manual grouping allows you to create custom groups.
Automatic grouping uses hierarchies to group dates or other similar data.
4. Advanced Analysis with DAX and Power Pivot
Data Modeling: Power Pivot allows you to model relationships between data from multiple tables [11].
Measures: Measures are formulas that are used to perform calculations on data in the data model [11].
Measures can be implicit or explicit. Implicit measures are created when you drag a field into the values area of a pivot table, whereas explicit measures are defined using DAX formulas. [12]
Calculated Columns: Calculated columns allow you to create new columns in your data model, based on formulas and expressions [12].
DAX (Data Analysis Expressions): DAX is a formula language that is used to create measures and calculated columns in Power Pivot [11, 12].
Aggregation Functions: DAX provides many functions for summarizing data, such as AVERAGE, COUNT, MAX, MIN, MEDIAN, and SUM [13].
Filter Functions: DAX provides filter functions, such as FILTER, and CALCULATE, which allow you to create measures that only perform calculations on subsets of your data [13]. CALCULATE evaluates an expression in a modified filter context [14].
Logical Operators: Logical operators, such as equal (=), not equal (<>), greater than (>), and less than (<), can be used in DAX formulas to create more complex filters.
Relationship Functions: DAX provides functions such as CROSSFILTER, which allows you to control the direction of filters [15].
5. Visualizing Data
Charts: Charts are used to visually represent data, making it easier to identify patterns and trends [2, 6].
Common chart types include column charts, bar charts, histograms, scatter plots, and map charts [2-6].
Customization: Charts can be customized to improve their appearance and readability [3, 4, 6].
This includes adding titles, axis labels, data labels, legends, and gridlines [3, 4].
Number formats can also be customized for data labels.
Slicers: Slicers are interactive controls that allow you to filter pivot tables and pivot charts [7].
In summary, data analysis involves a cycle of exploring, cleaning, transforming, calculating, and visualizing data. The sources demonstrate a range of techniques, from basic descriptive statistics and charting to more advanced techniques using DAX and Power Pivot. These tools enable you to gain a deeper understanding of your data and communicate your findings effectively.
Mastering Pivot Tables: A Comprehensive Guide
Pivot tables are a powerful tool for summarizing and analyzing data, allowing you to aggregate data based on different categories [1, 2]. Here’s a breakdown of key aspects of pivot tables, according to the sources:
Creating Pivot Tables
Pivot tables can be created from a table or range of data [1].
When creating a pivot table, you can choose whether to place it in a new worksheet or an existing worksheet [1].
The data source for a pivot table can be changed, and the table can be refreshed to include new data [1, 2].
It is possible to add data from multiple tables to a data model and analyze it using pivot tables [1, 3].
Pivot Table Layout
Pivot tables have different areas: filters, rows, columns, and values [1].
Fields dragged into the “rows” area appear as rows in the pivot table [1].
Fields dragged into the “columns” area appear as columns in the pivot table [1].
Fields dragged into the “values” area are aggregated using a specified calculation [1].
Fields dragged into the “filters” area can be used to filter the entire pivot table [1].
The layout of the fields can be adjusted to show them in stacked or in separate areas. [1]
Pivot tables can be displayed in compact, outline, or tabular form [4].
Pivot Table Functionality
Data Aggregation: Pivot tables are used to summarize data by aggregating it based on different categories [1].
Pivot tables can perform calculations such as sums, averages, counts, and percentages [1].
The type of aggregation can be changed in the “value field settings” [1].
Value field settings also allow you to change the number format and name of the column [1, 2].
Filtering: Pivot tables allow you to filter data based on multiple categories [1].
Filters can be applied to the rows, columns, or values [1, 2].
Label filters can be used to filter data based on text, such as selecting jobs that contain the word “data” [2].
Value filters can be used to filter data based on numerical values, such as showing jobs with a count greater than 100 [2].
Grouping: Pivot tables can group data based on a hierarchy [4].
This allows you to analyze data at different levels of detail, such as by country and then by job title [4].
Automatic grouping allows you to group data by year, month, and day [4].
Manual grouping allows you to create custom groups of data [5].
Sorting: Pivot tables allow you to sort data based on different columns [6].
You can sort data by row labels or by values in a specific column [4, 6].
Calculated Fields and Items: Calculated fields and items can be added to a pivot table [5, 7].
Pivot Table Design
Pivot tables can be styled with different colors and formats [6].
Options such as banded rows or columns, and row or column headers can be toggled on or off [6].
Grand totals for rows or columns can be toggled on or off [6].
Field headers can be toggled on or off [1, 6].
Pivot Charts
Pivot tables can be used to create pivot charts [7, 8].
Pivot charts are dynamic and automatically update when the pivot table is modified [8].
Pivot charts include field buttons that allow you to filter the data within the chart [7].
Slicers and timelines can be added to pivot charts, to provide interactive filtering [7].
Pivot charts can be customized with different chart types and formatting options [7].
Key Benefits of Pivot Tables
Dynamic Data Analysis: Pivot tables make it easy to analyze and explore data from different perspectives [1, 8].
Flexibility: Pivot tables can quickly be reconfigured to show different aggregations or perspectives of your data [1].
Efficiency: Pivot tables allow you to quickly calculate and summarize large amounts of data without complex formulas [1].
Interactivity: Pivot tables can be used to create interactive reports with slicers and timelines [7].
Data Relationships: Pivot tables can be used with data models to explore relationships between different data sets [9, 10].
In summary, pivot tables provide a versatile and efficient way to analyze and present data in spreadsheets. They are especially useful for summarizing large datasets and creating interactive reports [1, 2, 6]. Pivot tables can be used in combination with pivot charts to visually represent trends and patterns in your data. The sources also note that measures created with DAX are often more powerful than calculated fields within a pivot table [7, 9].
Creating Effective Charts in Excel
Chart creation in Excel, as detailed in the sources, involves several steps, from selecting the right chart type to customizing it for clarity and impact. Here’s a breakdown of the chart creation process:
1. Understanding Chart Types
Line Charts: These are best for time-series data, showing trends and connections over time [1].
Pie Charts: Pie charts are useful for comparing proportions of a whole, especially when there are two categories to visualize [2].
Column and Bar Charts: Column charts (vertical bars) and bar charts (horizontal bars) are used to compare values across categories [3].
Column charts are often used when categories have short names and the focus is on comparison by height.
Bar charts are useful for categories with longer names, to avoid overlapping labels [3].
Scatter Plots: Scatter plots are used to compare two numerical values and identify any correlations between them [4].
Map Charts: Map charts are used to visualize data geographically, such as showing median salaries by country [5].
Histograms: Histograms are used to visualize the distribution of numerical data, showing the frequency of values within specified ranges [5].
Combo Charts: Combo charts combine two or more chart types (e.g. column and line) to display different data sets [6, 7].
2. Chart Creation Process
Data Selection: Begin by selecting the data you want to visualize, including both the categories and the values [1]. It is important to select only the data you want to plot, especially when using pie charts [2].
Inserting Charts: Go to the “Insert” tab in Excel and select the chart type you want.
You can start with “Recommended Charts” for suggestions [1].
The “All Charts” tab allows you to select a specific chart type and customize it further [1].
Chart Elements:Chart elements such as axes, titles, data labels, and legends can be added or removed using the “+” icon next to the chart, or in the “Chart Design” tab [2].
The chart title can be used to summarize the data or to ask a question that you want the reader to understand from the chart [2].
Axis titles are used to clarify what the values on the x and y axes represent, especially for the y-axis, if the values are not self-explanatory [2].
Chart Design Tab: The “Chart Design” tab allows for customization of the chart with different layouts, themes, and colors [2].
3. Chart Customization
Titles and Labels: Chart titles and axis labels should be descriptive, and should clarify the purpose of the visualization.
Data Labels: Data labels can be added to display values directly on the chart [2].
The position, color, and formatting of the labels can be customized [2].
Trendlines: Trendlines can be added to charts to show trends in the data. Different options include linear, exponential, linear forecast, and moving average [2].
Color: Colors can be adjusted to highlight particular data or to make the chart more visually appealing [2]. Monochromatic color palettes may help focus the viewer on certain elements, such as using darker colors to emphasize certain parts of a pie chart [2].
Axes: The scale and bounds of the axes can be adjusted to better fit the data and eliminate visual clutter [4].
Number formats on the axes can also be customized to improve readability, such as using thousands separators and abbreviating with “k” [3, 4].
Legends: Legends can be used to show what different colors or shapes represent on the chart, especially when the chart has more than one data series [2].
4. Chart Best Practices
Appropriate Chart Choice: Select a chart type that best represents your data, taking into account the type of data and the message you are trying to convey [1].
Data Ordering: Order the categories in a way that makes the data easier to compare, for example, from high to low [3].
Simplicity: Charts should be clear and concise, avoiding too much complexity or clutter [2].
Too many colors can be confusing [2].
Too many data labels can be overwhelming [2].
Consistent Formatting: Use consistent formatting across all of your charts, including titles, labels, colors, and fonts.
Minimize Overlap: Ensure that data labels, titles, and other elements are properly positioned to minimize overlap and maintain readability [2, 4].
5. Interactive Charts
Slicers: Slicers are interactive controls that can be used to filter charts and pivot tables [8].
Slicers can be added from the pivot chart analyze tab [9].
Slicers can be connected to multiple charts [9].
Timelines: Timelines are interactive controls that can be used to filter charts that contain date information [9].
Timelines are inserted from the pivot chart analyze tab [9].
In summary, chart creation is an iterative process that requires attention to detail. Choosing the correct chart type, customizing the visual elements, and understanding your audience are all essential for creating charts that are both effective and insightful. Charts should be designed to tell a story, to draw attention to key aspects of your data, and to help your audience gain a better understanding of the data itself.
Excel for Data Analytics – Full Course for Beginners
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This extensive text provides a detailed tutorial on using Excel and Power BI for data analysis, emphasizing how to convert raw data into actionable insights. It covers fundamental techniques like sorting, filtering, and using Flash Fill in Excel, then moves to more advanced tools such as Pivot Tables for summarizing data and Power Query for importing, cleaning, and transforming data. The document highlights how Power Query is particularly useful for handling data from external sources and combining multiple files, positioning it as a significant advancement in data manipulation capabilities. It then introduces Power Pivot and the concept of a data model to manage relationships between multiple tables and handle large datasets more efficiently, contrasting it with the limitations of relying solely on worksheet formulas like XLOOKUP. Finally, the text explores Power BI Desktop and Power BI Online for creating interactive visualizations and reports, demonstrating how to import data, build data models, write DAX formulas, and share insights, showcasing the power of these tools for analyzing large volumes of data and creating dynamic dashboards.
Excel and Power BI Data Analysis Tools
Based on the sources, Data Analysis is defined as the process of converting raw data into useful information. The purpose of this conversion is to gain insight and make decisions. The source mentions that there are various synonyms for data analysis, including data analytics, analytics, business intelligence, and data science.
The sources highlight that almost every tool used for data analysis requires a proper data set. A proper data set generally includes a field name at the top and empty cells all the way around.
Several tools are presented in the sources as being used for data analysis in Excel and Power BI:
Sort and Filter: These are fundamental tools available in Excel tables, Pivot Tables, Power Query, Power Pivot, and Power BI Desktop. Sorting organizes records in a table, for example, from smallest to largest (a to z) or largest to smallest (z to a). You can sort by one column or multiple columns. Filtering shows only certain records based on one or more conditions. Filters can use various logical tests like AND, OR, or BETWEEN. A particularly helpful use of filtering in the Excel worksheet is to extract specific records. Filtering can also be data type specific, offering different options for dates, text, and numbers. When filtering, the records that match the criteria are shown, and the rest are hidden.
Flash Fill: This is a one-time, simple data cleaning tool in Excel. It can be used if there’s a consistent pattern in the data. You provide an example by typing the desired output next to the original data, and then Flash Fill attempts to apply the pattern to the rest of the column. It’s not recommended for tasks that need to be repeated or refreshed with new data; for those, formulas or Power Query are suggested.
Pivot Table: This is an amazing tool to build reports and charts. It’s particularly useful for summarizing data, such as survey results, showing counts and percentages. Standard pivot tables are suitable for small data sets within Excel and simple calculations like count and percent of total. They allow you to drag fields to areas like Rows and Values to create unique lists and calculations. Pivot tables can connect to data from various sources, including tables or ranges in the worksheet, external data sources, data models in Power Pivot, and data models in Power BI online. A key point is that with standard pivot tables, you have to repeatedly add number formatting to fields.
Power Query: Described as the greatest Excel tool invented since the pivot table. It excels at importing data from outside of Excel (like text files, other Excel files, databases, websites), cleaning data (e.g., splitting columns, extracting information), transforming data (e.g., removing columns, calculating new columns, combining tables), and loading data to the Excel worksheet, the pivot table cache, or the Power Pivot data model. Power Query is also present in Power BI Desktop, functioning the same way. Power Query memorizes the steps applied during importing, cleaning, and transforming, allowing for easy refreshing of data. These steps form the foundation of a Power Query query. Power Query has a functional language called M code, which is used for data mashup.
XLOOKUP function: A worksheet formula that can be used in data analysis, particularly when you need to look up values from one table and bring them into another column in your main table. It’s presented as a modern replacement for older lookup functions like VLOOKUP. XLOOKUP is appropriate when the data is already in the Excel worksheet, the data set is not excessively large (e.g., 43,000 rows is considered not a lot), and the solution can be created using standard pivot tables and Excel charts.
Power Pivot: An Excel feature that creates data model pivot tables. It allows for creating relationships between related tables, which helps avoid using many lookup formulas like XLOOKUP. Power Pivot enables the use of more than one table in a pivot table report. It is also capable of importing large amounts of data into a behind-the-scenes columnar database that compresses the data and can hold millions of rows. Power Pivot allows for the creation of reusable, formattable formulas called DAX measures, which are used in data model pivot tables. In Power Pivot, DAX measures are built in the measure grid below the fact table.
DAX Formulas: Data Analysis Expressions, a function-based formula language used in Power Pivot and Power BI Desktop. There are two types: DAX measures (reusable formulas dragged into data model pivot tables) and DAX calculated columns (formulas that add a new column to a table). Dax measures calculate based on the conditions or criteria (filter context) in the pivot table. This filter context makes calculations efficient, especially with large data sets. In Power Pivot, the assignment operator for DAX measures is a colon followed by an equal sign. In Power BI Desktop, it’s just an equal sign.
Data Model: Created in Power Pivot or Power BI Desktop, it involves multiple tables with relationships defined between them. Dimension or lookup tables, which contain unique lists (the “one” side of a relationship) and attributes, are related to fact tables, which contain repeating values (the “many” side of a relationship). Creating relationships in the data model replaces the need for lookup formulas like XLOOKUP and allows dragging and dropping fields from any related table into reports. The data model is stored in a behind-the-scenes columnar database.
Power BI Desktop: A free Microsoft tool designed for creating data models, visualizations, and reports. It contains the same Power Query and Power Pivot tools found in Excel. Power BI has a wider variety of visuals and reporting tools compared to Excel, and its visuals are interactive. Data models created in Excel Power Pivot can be imported into Power BI Desktop.
Power BI Online: Requires a license and allows users to upload Power BI Desktop files or Excel files with Power Pivot data models. This makes reports, visuals, dashboards, and data models shareable and universally available to assigned groups, serving as a single source of truth for data. Dashboards in Power BI Online are specific locations where you can pin important information (tables, charts, visuals, etc.) from various reports and workbooks for easy presentation and sharing. Dashboards should present information needed for good decisions.
The sources provide examples illustrating these tools:
Example 1 shows using Sort, Filter, and Flash Fill.
Example 4 (from video 3) shows summarizing survey results with a Pivot Table.
Example 5 demonstrates using Power Query to import, transform, and refresh data from a website CSV file.
Example 6 shows using Power Query to combine multiple files into one table, calculate a new column, and load it to the Pivot Table cache.
Example 7 illustrates solving a data modeling problem (needing data from multiple tables) using worksheet formulas like XLOOKUP to add helper columns before creating standard Pivot Table reports and charts. This approach is suitable for smaller data sets.
Example 8 shows solving the same data modeling problem as Example 7 but using Power Query to import data from an external Excel file and load it directly to the Power Pivot data model. This approach is better for larger data sets and allows creating relationships between tables and reusable DAX measures. It also introduces concepts like the one-to-many relationship and hiding fields in the data model.
Example 9 uses Power BI Desktop for the same data source as Example 8, demonstrating importing data with Power Query, loading it to the data model in Power BI Desktop, and creating interactive visuals and dashboards. This approach is preferred for interactive and shareable visuals.
Example 10 shows importing 7 million rows of data from an SQL database into Power BI Desktop using Power Query. It discusses the efficiency of the columnar database for handling big data and creating calculated columns and measures using DAX formulas (including the concept of iterator functions like SUMX) to calculate values like revenue and cost. It also covers creating a date table using DAX and marking it as a date table.
In essence, data analysis, as presented in the sources, is about transforming data for insight and decision-making using a range of tools in Excel and Power BI, from basic sorting and filtering to advanced data modeling with Power Query, Power Pivot, and Power BI Desktop, often involving calculated formulas using XLOOKUP or DAX. The choice of tool often depends on the size of the data, the source of the data, the complexity of transformations needed, and the desired output (e.g., simple report vs. interactive dashboard).
Mastering Power Query: Data Transformation in Excel and Power BI
Based on the sources, Power Query is highlighted as a fundamental and highly valuable tool in the process of Data Analysis, which involves converting raw data into useful information to gain insight and make decisions. It is described as the greatest Excel tool invented since the pivot table.
The primary reason for Power Query’s significance is that while tools like the Pivot Table were amazing for building reports and charts, there was a missing piece for importing data into Excel and fixing or cleaning bad data. Power Query fills this gap.
Power Query is not exclusive to Excel; it is also available in Power BI Desktop and functions the same way in both applications.
Key Capabilities of Power Query:
Importing Data: Power Query excels at bringing data into your analysis environment from various sources outside of Excel. These sources include:
Text files (like CSV, TXT)
Other Excel files
Databases (like SQL databases)
Websites
Folders (to combine multiple files)
Many other data sources
Cleaning Data: It provides tools to fix issues or extract specific parts of your data. Examples include:
Splitting columns (e.g., splitting first and last name)
Extracting information (e.g., extracting a date from a date time field)
Handling delimiters (e.g., tab delimiters in text files)
Transforming Data: Power Query allows you to reshape and modify data before loading it. Examples include:
Removing unwanted columns
Calculating new columns (e.g., multiplying Units by Price to get Sales)
Combining multiple tables into one table
Changing data types
Filtering data (e.g., filtering files by extension in a folder import)
Transforming text (e.g., changing text case to lowercase for filtering)
Removing relational columns automatically added during database import
The Power Query Editor:
Transformations are performed in the Power Query Editor, which is a separate window on top of the Excel or Power BI Desktop window. The Editor provides a preview of the data.
Applied Steps: One of the most important features is the recording of Applied Steps. Power Query memorizes every step applied during importing, cleaning, and transforming. These steps are rerun automatically when the data is refreshed. You can view the data preview at each step of the process.
M Code: Behind the user interface and applied steps is a functional language called M code, which Microsoft calls the data mashup language. While Power Query writes this code automatically when you use the user interface, you can view it in the formula bar or the Advanced Editor, and even write your own M code. M code is case-sensitive, which is different from the Excel worksheet.
Loading Data:
After cleaning and transforming data in the Power Query Editor, the results need to be loaded. The loading destination depends on whether you are using Excel or Power BI Desktop and the purpose of the analysis.
In Excel:
The default is to load the data as an Excel Table on a new worksheet.
Using Close & Load To, you can control the destination:
Load as a Table to a specified worksheet location.
Load to the Pivot Table Cache (for creating Pivot Tables directly from the query output without first putting it on a worksheet).
Load to the Power Pivot Data Model (used when working with multiple tables and relationships).
Only Create a Connection: This option stores the query definition in the Power Query Editor but does not load the data anywhere visible in the worksheet. This is the crucial option when importing data for the Data Model, especially when combining it with the Add this data to the Data Model option. It prevents duplicating the data source by putting it in a worksheet table and the data model.
In Power BI Desktop:
The Power Query Editor has a Close & Apply button. This closes the editor, applies the steps, and loads the data only to the columnar database in the Data Model. There is no option to load directly to a worksheet as in Excel, as the primary destination is always the data model for creating visuals and reports.
Benefits and Use Cases:
Automation and Refreshing: Because Power Query memorizes the steps, when the source data updates (e.g., a new monthly file is added to a folder, or a website CSV changes), you can simply click Refresh, and Power Query will re-import, re-clean, re-transform, and reload the data automatically. This eliminates repetitive manual tasks.
Handling Different Data Structures: Power Query is adept at handling various delimiters (comma, tab) and structures (single tables, multiple files in a folder).
Data Modeling: Power Query is essential for importing data from external sources into the Power Pivot or Power BI Data Model. This allows for building relationships between tables and avoiding the need for numerous lookup formulas like XLOOKUP in the worksheet, especially when dealing with data from multiple tables.
Big Data: Power Query is used to import large amounts of data (e.g., 7 million rows from an SQL database) into the compressed columnar database used by Power Pivot and Power BI Desktop.
Examples from Sources:
Example 5: Power Query is used to import, transform, and load data from a website CSV file to an Excel worksheet table that can then be easily refreshed.
Example 6: Power Query imports and combines data from multiple text files in a folder into a single table, adds a calculated ‘Sales’ column, and loads it directly to the Pivot Table cache, ready for reporting and charting.
Example 8: Power Query imports data from tables within an external Excel file and loads them directly to the Power Pivot Data Model using the “Only Create Connection” and “Add to the Data Model” options, preparing the data for creating relationships and data model pivot tables.
Example 10: Power Query connects to an online SQL database with 7 million rows, imports selected tables using credentials, checks and changes data types, removes unnecessary columns in the Power Query Editor, and loads the data to the Power BI Desktop Data Model.
In summary, Power Query is a robust, user-friendly, and essential tool for modern data analysis in both Excel and Power BI Desktop, providing powerful capabilities for connecting to, cleaning, and transforming data from a wide range of sources, automating repetitive data preparation tasks, and enabling advanced data modeling.
The Art of Excel Pivot Tables
Based on the sources, Pivot Tables are a cornerstone tool in data analysis, designed primarily for building reports and charts. They are considered one of the most significant tools invented in Excel, with Power Query being highlighted as the greatest since the pivot table.
Here’s a discussion of Pivot Tables based on the information provided:
Core Purpose and Functionality Pivot Tables allow you to convert raw data into useful information by summarizing and organizing records in a table. They provide an interactive way to analyze data by dragging fields into different areas (like Rows, Columns, and Values) in the Pivot Table Fields task pane. They use the same sorting and filtering conventions as Excel tables.
Standard Pivot Tables (Working with One Table) This type of pivot table is used when you have your data in a single table, such as an Excel worksheet table or a “flat table” created by adding lookup columns using functions like XLOOKUP. They perform calculations using built-in options like “Summarize Values By” (e.g., Count, Sum) and “Show Values As” (e.g., Percent of Column Total, Difference From Previous).
They are appropriate for data already in Excel, when there isn’t a lot of data (e.g., 43,000 rows is considered manageable, but 100,000-500,000 rows might slow down).
A limitation is that if you use the same number field in multiple reports, you have to reapply number formatting each time.
Standard pivot tables can automatically group dates into months and years.
Data sources can be a table or range directly in the worksheet, or data loaded into the Pivot Table Cache from Power Query. You can access data directly from the Pivot Table Cache using the “from external data source” option.
Data Model Pivot Tables (Working with Multiple Tables) Introduced with tools like Power Pivot and Power BI Desktop, Data Model Pivot Tables work with multiple tables loaded into a behind-the-scenes columnar database called the Data Model.
Relationships: Instead of using lookup formulas like XLOOKUP in the worksheet, relationships (often one-to-many) are created between related tables in the Data Model (e.g., linking a fact table with sales data to dimension tables like products, sales reps, or dates). This allows you to drag and drop fields from any related table into the pivot table report.
DAX Measures: Calculations are performed using reusable DAX measures that you create. A significant advantage is that you can include number formatting in the DAX measure, and this formatting will apply automatically whenever the measure is used in a pivot table.
Filter Context: DAX measures calculate efficiently using a concept called Filter Context, where the measure automatically filters the data based on the conditions in the pivot table (rows, columns, filters) before performing the calculation.
Handling Big Data: The Data Model, using a columnar database, can handle importing and analyzing large amounts of data (millions of rows), which is much better than handling such volumes directly in an Excel worksheet.
Data Loading: Data is typically loaded into the Data Model using Power Query, often selecting the “Only Create a Connection” and “Add this data to the Data Model” options to avoid duplicating data in the worksheet.
Date Tables: Unlike standard pivot tables, Data Model pivot tables do not automatically group dates. A dedicated date dimension table with a unique list of dates and attributes (like month, year) is required and linked via a relationship. The date table must be marked as a date table in the Data Model to function correctly.
Implicit vs. Explicit Measures: It is recommended to use explicit (user-created) DAX measures rather than implicit measures, which are automatically created when you drag a raw number field into a Data Model pivot table. Implicit measures are hidden, read-only, cannot be formatted or reused, and do not transfer when connecting live to data models in Power BI Desktop.
You can hide unnecessary fields in the Data Model so they don’t appear in the pivot table field list, making it less cluttered.
Integration with Power Query Power Query is essential for getting data from external sources and cleaning/transforming it before it is used in a pivot table. Power Query output can be loaded directly to the Pivot Table Cache for standard pivot tables or to the Data Model for data model pivot tables. This eliminates repetitive manual data preparation steps, as refreshing the query automatically updates the pivot table report.
Integration with Power Pivot and Power BI Desktop Power Pivot in Excel and Power BI Desktop share the core Data Model technology, enabling the creation of Data Model Pivot Tables. Power BI Desktop has a visual called a “Matrix” which is similar to an Excel pivot table and is used for cross-tab reports from the Data Model. You can also connect Excel pivot tables directly to data models stored online in Power BI Service.
In summary, Pivot Tables are powerful tools for data summarization and reporting, evolving from the standard type working with single tables to the more advanced Data Model type capable of handling multiple tables and large datasets using DAX formulas and relationships, often populated and managed with the help of Power Query and the Data Model environment.
Understanding the Data Model for Power Tools
Based on the sources and our previous discussion about Pivot Tables, the Data Model is a fundamental component used in conjunction with Data Model Pivot Tables and tools like Power Pivot and Power BI Desktop.
Here’s a discussion of the Data Model:
What it is: The Data Model is a behind-the-scenes columnar database that stores and compresses data. It is the underlying structure used by Power Pivot in Excel and Power BI Desktop.
Purpose and Benefits:
Handles Large Datasets: A key advantage of the Data Model is its ability to import and analyze large amounts of data (millions of rows) much more effectively than an Excel worksheet. The columnar database design helps compress the data, making it possible to work with volumes that would overwhelm Excel’s row limit or performance.
Works with Multiple Tables: The Data Model allows you to bring data from multiple tables together for analysis in a single pivot table report.
Relationships: Instead of using lookup formulas like XLOOKUP to combine data in the worksheet, you create relationships (typically one-to-many) between related tables directly in the Data Model. This linking of tables (like a fact table with sales data and dimension tables with product or sales rep details) is crucial for working with data spread across different sources. These relationships replace the need for adding helper columns with lookup formulas in your source data.
DAX Calculations: Calculations are performed using reusable formulas called DAX measures. These measures are built in the Data Model and can be easily dragged into a pivot table. DAX measures calculate efficiently using Filter Context, meaning the formula automatically considers the filters and conditions applied in the pivot table or visual (like rows, columns, or slicers) before performing the calculation.
Reusable Formatting: A significant advantage of DAX measures is that number formatting can be applied directly to the measure itself, so it only needs to be set once and will apply automatically whenever the measure is used in any report. This contrasts with standard pivot tables where number formatting must be reapplied each time the same field is used in a different report.
Organized Reporting: You can hide fields in the Data Model that you don’t intend to use in your pivot table reports (like foreign keys or raw number columns that will be used in measures), making the pivot table field list less cluttered.
How Data is Loaded: Data is typically loaded into the Data Model using Power Query. When loading Power Query output, you often select the “Only Create a Connection” option and then “Add this data to the Data Model”. This prevents the data from being loaded into the Excel worksheet and the Data Model, avoiding duplication and potential performance issues. Data can come from various sources, including Excel files containing tables or external databases.
Working with Dates: Unlike standard pivot tables that can auto-group dates, Data Model pivot tables require a dedicated date dimension table. This table contains a unique list of dates and related attributes like month name, year, etc.. This date table needs to be linked to the fact table using a relationship and marked as a date table in the Data Model tools to function correctly and prevent issues like inefficient date grouping or the creation of hidden date tables.
Implicit vs. Explicit Measures: When using a Data Model, it is strongly recommended to create your own DAX measures (explicit measures) rather than relying on the hidden implicit measures automatically created when dragging raw number fields into a pivot table. Implicit measures have limitations: they are hidden, read-only, cannot be formatted or renamed easily, and do not transfer when connecting live to data models in Power BI Service. Explicit measures offer control, reusability, and formatting.
Interface:
In Excel’s Power Pivot window (which opens when you manage the data model), there’s a Diagram View where you visualize tables and create relationships by dragging fields. There’s also a Data View to preview the data in each table and a Measure Grid at the bottom of the fact table to write DAX measures.
In Power BI Desktop, the corresponding views are Model View (similar to Diagram View) and Data View. Measures are typically created by right-clicking the table in the fields list or using buttons in the table/measure tools.
Integration: Data Models built with Power Pivot in Excel can be imported into Power BI Desktop. Both Excel Data Models and Power BI Desktop Data Models can be uploaded to Power BI Online (Power BI Service), making them available as a single source of truth for connecting to from other Excel or Power BI Desktop files.
In essence, the Data Model is the powerful engine behind advanced data analysis in Excel and Power BI, enabling efficient handling of large, multi-table datasets through relationships and flexible calculations via DAX.
Introduction to Power BI
Based on the sources and our conversation history, let’s discuss Power BI.
Power BI Desktop is a free Microsoft download that serves as a tool for data analysis, creating reports, and designing interactive visuals. It shares many core functionalities with Excel’s Power Pivot and Power Query. Power BI is specifically designed to offer more varied visuals and reporting tools and better shareability compared to Excel.
Here are some key aspects of Power BI:
Core Components and Workflow: Power BI Desktop integrates several tools:
Power Query: This is the tool used to import data from external sources (like databases, web files, other Excel files) and then clean and transform it. The Power Query Editor looks and functions very similarly to the one in Excel. The cleaned data is then loaded into the Data Model.
Data Model: Like Power Pivot in Excel, Power BI Desktop utilizes a behind-the-scenes columnar database called the Data Model to store and compress data. This model is crucial for handling large amounts of data, potentially millions of rows, much more effectively than a standard Excel worksheet. Within the Data Model, you create relationships between related tables (like fact and dimension tables) to link them for analysis, avoiding the need for lookup formulas in the source data. The Data Model in Power BI Desktop looks almost exactly the same as in Power Pivot. Power BI Desktop has a Model View (similar to Power Pivot’s Diagram View) for visualizing tables and creating relationships, and a Data View (similar to Power Pivot’s Data View) for previewing table data.
DAX Formulas: Calculations within the Data Model are performed using Data Analysis Expressions (DAX). You create reusable DAX measures to perform calculations like Sum or Average. A key advantage of DAX measures is that number formatting can be applied directly to the measure, and this formatting will be automatically applied whenever the measure is used in a report or visual. DAX measures calculate efficiently using Filter Context, meaning they automatically consider the filters applied by the visual (like rows, columns, slicers) before performing the calculation. While Power Pivot focuses on measures, Power BI Desktop also allows creating DAX calculated columns and entire DAX tables. It is strongly recommended to use explicit (user-created) measures rather than implicit measures (automatically created by dragging raw number fields), as implicit measures have limitations such as being hidden, read-only, and not transferring to Power BI Service when connecting live. Fields that are not needed for reporting (like foreign keys or raw number columns used in measures) can be hidden in the Data Model to keep the fields list cleaner in the reporting interface. In Power BI Desktop, hidden fields are indicated by an eyeball icon with a line through it.
Visualizations and Reporting: Reports are built in the Report View, which is comparable to an Excel worksheet where you might place pivot tables and charts. Power BI offers a wide array of visualizations. Examples include line charts, clustered column charts, a Matrix visual (similar to an Excel pivot table for cross-tab reports), slicers, cards, and maps. A defining feature is the interactivity of these visuals; clicking on one visual can filter or highlight data in other visuals on the page. You can control how visuals interact (filter, highlight, or none). Tooltips can be customized to show multiple measures when hovering over data points.
Power BI Online (Service): This is the cloud-based component that requires a license and enables sharing and collaboration.
You can publish Power BI Desktop files (containing the report and data model) or Excel files with Power Pivot data models to Power BI Online.
Uploaded data models appear as datasets. These datasets can serve as a single source of truth for multiple users and reports, allowing others to connect live to the data model from their own Excel or Power BI Desktop files without needing to share the original file.
Reports published from Power BI Desktop can be viewed and interacted with in Power BI Online.
Dashboards are a specific feature in Power BI Online, allowing you to pin visualizations from different reports and workbooks into a single view for easy access and sharing. Dashboards provide a high-level summary of key metrics.
Sharing is managed through workspaces, where groups of users with organizational emails can be granted access to reports, dashboards, and datasets.
Relationship with Excel Tools: Power BI Desktop and Power Pivot share the same Data Model engine. Many features learned in Power Query and Power Pivot in Excel are directly transferable to Power BI Desktop. While Excel (especially with Power Pivot) is capable of building data models and reports, Power BI Desktop is generally preferred for its superior visualization capabilities, interactivity, and the ease of sharing and collaborating via Power BI Online. Data models built in Power Pivot can be imported into Power BI Desktop.
Excel & Power BI Data Analysis Complete Class in One Video – 365 MECS 04
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This presentation offers an introduction to creating interactive dashboards in Excel, focusing on techniques suitable for users who may not have access to Power BI. The speaker guides viewers through the process of data preparation and organization, emphasizing the importance of putting data into a table for easy updates. Key steps covered include designing a wireframe layout, building various chart types like line, map, bar, and donut charts from pivot tables, and incorporating calculated metrics using formulas. The demonstration highlights how to assemble these elements onto a single dashboard, add interactive filters called slicers, and connect them to the charts.
Building Dynamic Excel Dashboards
Based on the provided source, an Excel dashboard is essentially a report that helps you display important data or information in a single place. The primary purpose is to allow your audience to easily see key metrics or statistics that are important to them at a glance.
Creating a dashboard in Excel is useful for analyzing data, extracting data, and presenting key metrics, highlighting pain points in a much more visual way. Dashboards use charts and color to get across the story of your data. When designing a dashboard, it is crucial to consider what questions you are trying to answer and what your audience wants to know.
While applications like Power BI are popular for creating reports and visualizations, Excel remains a preferred choice for many people for several reasons. One significant factor is cost, as Power BI requires an additional payment and is not part of a standard Microsoft 365 subscription. Excel is also a familiar application for most people, making them feel more comfortable using it when first learning about dashboards. Additionally, some users find Excel to be more flexible than Power BI, particularly when it comes to dashboard design and achieving a desired look.
Building an Excel dashboard involves several steps:
Data Preparation and Organization: It’s important to start with clean and consistent data. While the provided session’s data was already clean, cleaning techniques were covered in a previous webinar. A critical step is to put your source data in a table if you want your dashboard to update easily. You can check if data is in a table by looking for the “Table Design” ribbon, or by pressing Ctrl+T if it’s not. Standardizing naming conventions is also recommended for organization. For instance, naming tables with “TBL_”, charts with “CHT_”, and pivot tables with “PVT_” makes it easier to identify elements when linking them later. The source data used in the example was a downloaded sales data set with about 5,000 rows. Organizing different components (data, wireframe, charts, calculations, dashboard) into separate, color-coded tabs can help manage complexity.
Wireframing: Planning the dashboard layout is a good idea before starting. This involves noting exactly what metrics you want to display and how you want to lay out your dashboard, including where charts, slicers, and formatted sections will go. This plan, or wireframe, can be sketched on paper or in an Excel tab using shapes.
Creating Core Components: The dashboard often includes pivot tables and pivot charts built from the source data. The session demonstrated creating four specific charts:
A line chart showing total profit by year. Line charts are often suitable for time-based data.
A map chart showing average unit sold by country. Map charts colorize geographical regions based on data. Note that map charts cannot be created directly from pivot table data; the data must be copied out first, and then the chart’s data source needs to be pointed back to the pivot table. Not every chart type is suitable for all data; for example, too many countries would make a column chart unreadable, but are fine for a map chart.
A bar chart showing revenue by sales channel and item type. For charts with a lot of data categories, like many items, applying a filter to show only the top items (e.g., top 3 or 5) can make the chart more manageable and readable. Bar charts can be made more visually effective by increasing the bar width and adding data labels instead of using a horizontal axis for values.
A donut chart showing the count of orders by region. Donut charts (and pie charts) are generally best for representing a small number of items (e.g., two or three), as they can become confusing with more data.
Incorporating Calculations: Dashboards can display key metrics that are not represented in charts, often shown as “cards” or summary statistics at the top. These metrics need to be calculated on a separate worksheet and then linked to the dashboard. Examples of calculations shown included finding the most profitable item, most profitable region, count of cancelled orders, and top sales channel. This involves using functions like UNIQUE (to get a list of distinct values), SUMIF (to sum values based on a condition), COUNTIF (to count items based on a condition), MAX (to find the highest value), and INDEX/MATCH (to look up corresponding text for a value). Linking these calculations ensures the dashboard updates dynamically when the source data changes.
Assembling and Formatting: To make the dashboard look professional, it’s recommended to turn off grid lines on the dashboard sheet. Components like charts and calculated metrics (often placed inside shapes) are then brought onto the dashboard sheet and arranged. Basic formatting includes resizing elements, changing background fills, applying consistent fonts and colors, and using alignment tools. Using company branding colors is also a good practice. Removing chart borders can help them blend into the dashboard. Adding custom headings using shapes allows for consistent formatting across all elements.
Adding Interactivity (Slicers): Slicers are interactive filters that can be added to the dashboard. They are inserted from the PivotChart Analyze tab. Slicers represent column headings from your data, allowing users to click buttons to filter the displayed information. Slicers can be formatted (e.g., changing the number of columns, removing headers, modifying styles). Crucially, slicers need to be connected to the specific charts or pivot tables you want them to control using the “Report Connections” setting (also called “Filter Connections”). If not connected, a slicer may only control the first chart it’s associated with.
Updating the Dashboard: If your source data is in an Excel table, adding new data to the bottom should automatically expand the table. To update the dashboard components (pivot tables, charts, calculations), you can use the Refresh All button, typically found under the PivotChart Analyze tab. This process aims to provide a one-button update for the entire dashboard, though the live demonstration encountered an issue due to the source data not being properly formatted as a table initially.
Mastering Excel Pivot Tables for Dashboards
Based on the source provided, Pivot Tables are presented as a core component when building interactive dashboards in Excel. They are described as a straightforward way to analyze and summarize data.
Here’s a discussion of Pivot Tables based on the source:
Purpose in Dashboards: Pivot Tables are crucial for creating the underlying data or information that will be displayed on a dashboard, particularly in Pivot Charts. The dashboard demonstrated is “pretty heavy on pivot tables and charts”. They are used to extract and present key metrics from your raw data.
Creation Process:
They are created from your source data, which ideally should be in an Excel table to allow for easy updates.
To create a Pivot Table, you click within your data, go to the Insert ribbon, and select Pivot Table.
It’s strongly recommended to place each Pivot Table on a new worksheet to help organize a complex dashboard workbook.
It’s important to rename both the Pivot Table worksheet and the Pivot Table itself using a consistent naming convention (e.g., starting with “PVT_”) to make them easier to identify later, especially when connecting them to slicers.
Working with Pivot Tables:
The Pivot Table Fields area lists all the column headings from your source data.
You build the report by dragging and dropping these fields into four areas: Filters, Columns, Rows, and Values. The source demonstrates using Rows and Values frequently.
The Values area is typically where fields containing numbers (like profit or units sold) are placed. By default, Excel often tries to sum values, but you can change how the field is summarized (e.g., to Average or Count) using Value Field Settings.
Number formatting should be applied to the values in the Pivot Table to make them tidier and easier to read.
Excel can automatically group date fields (like splitting a date column into Years, Quarters, and the original Date), which you can then manipulate by dragging out groupings you don’t need.
You can turn off Grand Totals if they are not needed for the chart or display you are creating.
For efficiency when building multiple pivot tables for a dashboard, you can copy an existing pivot table worksheet and then modify the fields and settings as needed, which is faster than starting from scratch each time.
Pivot Tables and Charts:
Pivot Tables are the basis for Pivot Charts, which are then used on the dashboard.
However, the source highlights a specific limitation: Map charts cannot be created directly from data inside a Pivot Table. To work around this, you must first copy the data out of the pivot table (pasting values only), create the map chart from the copied data, and then point the chart’s data source back to the Pivot Table’s data range.
You can apply filters within the Pivot Table (like the “Top 10 Filter,” which can be configured for any number, not just 10) to refine the data before creating a chart, especially when you have a large number of categories that would make a chart unreadable.
Interactivity and Updating:
Slicers, which are interactive filters, can be inserted from the Pivot Chart Analyze tab and are connected to Pivot Tables and Pivot Charts using Report Connections (also called Filter Connections). This allows users to filter the dashboard data by clicking buttons. Naming your Pivot Tables and charts correctly is important for easily selecting which ones a slicer should control.
If your source data is in a table and new data is added, the Pivot Table’s source range will update automatically. To update the dashboard components (including Pivot Tables), you use the Refresh All button, typically found under the PivotChart Analyze tab. This is intended to provide a quick way to update the entire dashboard based on new data.
In summary, Pivot Tables in the context of this source are powerful tools for summarizing and manipulating large datasets. They serve as the foundation for creating the visualizations and summary statistics that populate an Excel dashboard, allowing for dynamic reporting and analysis. Effective use of Pivot Tables involves careful data organization, understanding how to summarize and filter data within them, and properly linking them to charts and slicers for interactivity and dynamic updates.
Creating and Using Excel Pivot Charts
Based on the source provided, Pivot Charts are a fundamental element in creating interactive dashboards in Excel, specifically designed to visually represent the summarized data from Pivot Tables. The dashboard demonstrated in the source is described as being “pretty heavy on pivot tables and charts”.
Here’s a discussion of Pivot Charts based on the source:
Purpose and Connection to Pivot Tables: Pivot Charts serve to display important data and key metrics from your raw source data in a visual format on a dashboard. They are inherently linked to Pivot Tables; you create a Pivot Chart directly from an existing Pivot Table. Visualizing data through charts is highlighted as a way to “get across the story of your data” and highlight “pain points”.
Creation Process: Once you have created a Pivot Table populated with the data you want to visualize, you create a Pivot Chart by clicking within the Pivot Table, navigating to the “Pivot Table Analyze” ribbon, and selecting “Pivot Chart”. From there, you can choose the desired chart type.
Types of Pivot Charts Demonstrated: The source demonstrates creating four specific types of pivot charts for the dashboard:
A Line Chart, used to show total profit by year. Line charts are noted as often suitable for “time based data”.
A Map Chart, intended to show average unit sold by country.
A Bar Chart, used to display revenue by sales channel and item type.
A Donut Chart, created to show the count of orders by region. Donut charts (and pie charts) are generally suggested as “good for when you have maybe two or three things” to represent, as more data can make them confusing. Column charts are mentioned as generally suitable for most data.
Suitability of Chart Types: The source emphasizes that “not all charts are created equally” and some are “more suited to certain types of data”. For example, while a map chart works well for visualizing data across many countries, a column chart with that much data would be “absolutely horrendous and nobody would be able to read it”.
Limitations and Workarounds (Map Charts): A significant point raised is that you cannot create a Map chart directly from data inside a Pivot Table. The workaround involves copying the data out of the pivot table (pasting values only), creating a regular Map chart from this copied data, and then importantly, pointing the chart’s data source back to the Pivot Table data range using the “Select Data” option on the “Chart Design” ribbon. This ensures the chart updates when the pivot table data changes.
Formatting and Customization: Pivot Charts offer various formatting options to enhance their appearance and readability on the dashboard:
Hiding “gray filter buttons” (field buttons on chart) to make the chart look cleaner.
Removing the legend if it doesn’t add valuable information.
Adding or modifying chart titles.
Changing chart colors and styles.
Formatting axes (e.g., changing bounds to adjust the visual range).
Formatting the data series (e.g., changing bar width, varying bar colors by point, adjusting donut hole size).
Adding and formatting data labels (e.g., position, color, boldness), sometimes used instead of displaying values on an axis.
Deleting grid lines within the chart area.
Adding a border around the chart’s data series (e.g., bars).
On the dashboard itself, removing the default chart borders helps charts blend into the background.
Using custom headings added with shapes on the dashboard instead of the chart’s built-in title allows for consistent formatting across the dashboard.
Efficiency in Creation: When creating multiple pivot charts, copying the worksheet containing an existing pivot table and chart, then deleting the chart and modifying the pivot table, is suggested as a quicker method than creating each one from scratch from the source data.
Interactivity with Slicers: Pivot Charts are designed to work interactively with Slicers. Slicers act as visual filters that allow users to dynamically change the data displayed in the chart by clicking buttons. To connect a Slicer to specific Pivot Charts (or their underlying Pivot Tables), you must use the “Report Connections” (or “Filter Connections”) setting found by right-clicking the slicer. Properly naming your Pivot Charts and Pivot Tables helps in identifying them when establishing these connections. If connections aren’t made, a slicer may only control the first chart it’s associated with.
Updating: Once the source data is updated (ideally in a table format), the Pivot Charts can be updated automatically by refreshing the linked Pivot Tables. This is done using the “Refresh All” button, typically found under the “Pivot Chart Analyze” tab. The goal is a “one-button update” for the entire dashboard.
In essence, Pivot Charts translate the powerful data summarization capabilities of Pivot Tables into visual insights, forming the central graphical components of interactive Excel dashboards, while requiring careful handling, especially with chart types like maps.
Visualizing Data in Excel Dashboards
Based on the source provided, Data Visualization is presented as a key trend and a fundamental aspect of analyzing and presenting data effectively, particularly in the context of building interactive dashboards in Excel.
Here’s a discussion of Data Visualization based on the source:
Purpose of Data Visualization: The popularity of analyzing and extracting data, and presenting key metrics is rising, with a focus on doing so “in a much more visual way than we ever have done before”. The goal is to “really get across the story of your data” and highlight “pain points” to the audience. Dashboards themselves serve to “display important data or information in a single place so that your audience can easily see key metrics or statistics that are important to them”.
Methods of Visualization: Data is presented visually “using charts using color”.
Role in Dashboards: Data visualization, particularly through charts and pivot charts, is a central component of the dashboard creation process discussed. The dashboard built in the source is described as “pretty heavy on pivot tables and charts”. These visualizations allow users to quickly see key metrics and statistics.
Specific Chart Types: The source demonstrates creating several types of charts for the dashboard, all linked to underlying pivot tables:
A Line Chart to show total profit by year.
A Map Chart to show average units sold by country.
A Bar Chart to display revenue by sales channel and item type.
A Donut Chart to show the count of orders by region.
Column charts are mentioned as generally suitable for most data.
Chart Suitability: The source emphasizes that “not all charts are created equally” and some are “more suited to certain types of data”. For instance, a map chart is good for geographical data across many countries, whereas a column chart with that much data would be “absolutely horrendous and nobody would be able to read it”. Donut and pie charts are suggested as “generally good for when you have maybe two or three things that you want to kind of represent”.
Using Color: Color is used as part of visualizing data. It can also be used for design purposes on the dashboard and to help organize tabs in the workbook. Using company branding colors is also suggested for consistency.
In essence, Data Visualization, primarily through the use of charts derived from summarized data (often via Pivot Tables), is presented as a crucial technique for making data analysis accessible, insightful, and actionable within the context of Excel dashboards. It’s about transforming raw data into visual elements that tell a clear story and highlight important information for the audience.
Excel Dashboard Data Organization Principles
Based on the provided source, Data Organization is highlighted as a crucial element when building interactive dashboards in Excel, particularly because dashboards can become quite complex with potentially “lots and lots of different tabs”. Effective organization helps manage this complexity and ensures the dashboard functions correctly and updates easily.
Here are the key aspects of Data Organization discussed in the source:
Starting with Clean Data: The source emphasizes that the raw data used for the dashboard should be “nice and tidy” and “consistent”. Data downloaded from third-party systems or websites may not be in the perfect format and might require cleaning using Excel functions. While the source doesn’t detail cleaning methods, it mentions that a previous webinar covered these techniques.
Using Excel Tables for Source Data: A “really important point” for organization and dashboard functionality is to put your Source data in a table. If your data is in a table, it allows your dashboard to “update with the click of one button” when new data is added. You can check if data is in a table by looking for the “Table Design” ribbon when clicked inside the data, and you can convert data to a table using the keyboard shortcut Ctrl + T.
Standardizing Naming Conventions: It is “really important” to name your table and standardize your naming conventions for different elements. This means using prefixes like TBL_ for tables, CHT_ for charts, and PVT_ for pivot tables, followed by a descriptive name (e.g., TBL_sales_data). This standardized naming makes it “easier to identify the different elements in your dashboard”, which is particularly helpful when linking elements like tables and charts to slicers.
Organizing Worksheets/Tabs: With potentially many components (source data, extra data, wireframe, calculations, pivot tables, charts, dashboard), organizing your tabs is essential. The source recommends putting each pivot table and pivot chart on a new worksheet to avoid confusion. Furthermore, using color-coded tabs helps separate different groups of worksheets, such as data tabs, calculation tabs, and chart tabs, making it “easier for me to organize all of these different tabs”.
Separating Calculations: Calculations used for key metrics displayed on the dashboard (like most profitable item or region, count of cancelled orders) are housed on a dedicated “calculations worksheet”. These calculations link back to the source data, ensuring they update when the source data changes.
Handling Data Extraction for Specific Charts: For certain chart types, like Map charts, you cannot create them directly from Pivot Table data. The workaround involves copying the data out of the pivot table and pasting it as “values only” onto a separate range. While this extracted data isn’t automatically linked, the chart created from it is then pointed back to the original Pivot Table data range using the “Select Data” option.
In summary, effective data organization in Excel dashboards, as described in the source, involves ensuring source data is clean and in a Table format, adopting standardized naming conventions for key elements, strategically organizing components onto separate, color-coded worksheets, and managing calculations and specific chart data appropriately. This structured approach helps maintain clarity and enables the desired interactivity and easy updating of the dashboard.
Building Dashboards in Excel: A Guide
Microsoft Excel Dashboards: A Study Guide
I. Introduction to Dashboards in Excel
What is a Dashboard?A report that displays important data and information in a single place for easy understanding of key metrics and statistics.
Designed to answer specific questions for a target audience.
Often incorporates visual elements like charts and color to tell the story of the data.
Excel vs. Power BIPower BI is a dedicated application for creating reports and dashboards, often considered the “latest buzzword” in data analysis. It is a paid product outside the Microsoft 365 subscription.
Excel is a familiar and widely used application. Many prefer it for dashboard creation due to its cost (often included in existing subscriptions), user comfort, and perceived flexibility in design and layout.
Purpose of Dashboards:Visualize data and highlight key metrics.
Identify pain points or areas of interest.
Present data in a more visual and digestible way than raw data or traditional reports.
Enable informed decision-making.
II. Building a Dashboard: The Process
Agenda for the Session:Introduction to dashboards.
Viewing a completed example.
Data preparation and organization.
Creating a wireframe (planning the layout).
Setting up information using pivot tables and charts.
Incorporating calculations with formulas.
Assembling the dashboard.
Basic formatting.
Adding interactivity with slicers.
Updating the dashboard.
Example Dashboard Components:Title
Key statistics/metrics (displayed as “cards” or highlighted areas).
Inspiration and Design:Look at examples from others (e.g., Pinterest) for ideas on structure and design.
Consider company branding and theme colors.
Use color to separate and organize different groups of information (e.g., colored tabs for data, wireframe, calculations, dashboard).
Remove gridlines on the dashboard sheet for a cleaner look.
III. Data Preparation and Organization
Source Data:Use a clean and consistent data set. Data may need cleaning using Excel functions before analysis.
Ensure the data is in an Excel Table. This is crucial for automatic updating when new data is added.
To put data in a table: Select data and press Ctrl + T.
Naming Conventions:Standardize naming for different Excel elements (tables, charts, pivot tables).
Prefixes like TBL_ for tables, CHT_ for charts, and PVT_ for pivot tables help with identification.
Proper naming makes it easier to link elements (e.g., connecting slicers to charts).
IV. Planning the Dashboard: The Wireframe
Purpose: To plan the layout and content of the dashboard before beginning the building process.
Process:Determine the key metrics and information to be displayed.
Sketch out the desired arrangement of elements (title, key statistics, charts, slicers).
Can be done on paper or using shapes in an Excel tab.
V. Setting Up Information: Pivot Tables and Charts
Creating Pivot Tables:Start with your data in a table.
Go to Insert > PivotTable.
Choose your table range (automatically selected if clicked within the table).
Create the pivot table on a new worksheet for organization.
Rename the pivot table worksheet and the pivot table itself using standardized naming conventions (e.g., PVT_line, PVT_line_chart_effect).
Build the pivot table by dragging fields into the Rows, Columns, Values, and Filters areas.
Example Pivot Tables:Profit by Year (Order Date in Rows, Total Profit in Values).
Average Unit Sold by Country (Country in Rows, Unit Sold in Values, change summary function to Average).
Revenue by Sales Channel and Item Type (Sales Channel and Item Type in Rows, Total Revenue in Values).
Count of Orders by Region (Region in Rows, Order ID in Values, change summary function to Count).
Pivot Table Settings:Automatically splits date fields (Years, Quarters, Dates). Can remove unwanted levels.
Change the summary function (Sum, Count, Average, etc.) using Value Field Settings.
Apply number formatting to values.
Turn off Grand Totals if not needed for charting.
Creating Pivot Charts:Click inside the pivot table.
Go to PivotTable Analyze > PivotChart.
Choose the appropriate chart type for the data.
Line Chart: Good for time-based data.
Map Chart: Good for geographical data.
Bar Chart: Suitable for most data, but be cautious with too many categories.
Donut Chart: Limited to a few categories; shows parts of a whole.
Map Chart Consideration: Cannot create a Map Chart directly from a pivot table. Need to copy and paste the pivot table data as values, create a regular map chart from that, and then point the chart’s data source back to the pivot table range.
Chart Formatting:Remove gray filter buttons (right-click, Hide All Field Buttons on Chart).
Remove unnecessary legends.
Add a chart title (can be replaced later on the dashboard).
Change colors and chart style using the Design tab.
Format data series (e.g., gap width for bar charts, donut hole size).
Add and format data labels.
Delete gridlines.
Remove chart borders on the dashboard for a cohesive look.
Copying Worksheets: Hold down Ctrl and drag a worksheet tab to create a copy, saving time when creating multiple similar pivot tables/charts.
VI. Incorporating Calculations with Formulas
Purpose: To display specific key metrics as individual values on the dashboard.
Process:Create a separate worksheet for calculations.
Use Excel formulas to extract and calculate the desired metrics from the source data.
Example Formulas:UNIQUE: To get a list of unique values from a column.
SUMIF: To sum values based on a condition.
COUNTIF: To count entries based on a condition.
MAX: To find the maximum value in a range.
INDEX and MATCH (or XLOOKUP in newer versions): To perform lookups and return corresponding values based on a condition (e.g., finding the item type associated with the maximum profit).
Link the results of these calculations to the dashboard.
VII. Assembling and Formatting the Dashboard
Bringing Elements Together:Copy and paste pivot charts from their respective worksheets onto the dashboard sheet.
Arrange charts according to the wireframe plan.
Link key metric “cards” (shapes) to the cells containing the calculation results on the calculations sheet using the formula bar (=).
Apply basic formatting to the dashboard elements (colors, fonts, alignment).
Consider adding text boxes or shapes for consistent headings instead of relying on chart titles.
VIII. Adding Interactivity with Slicers
What are Slicers? Interactive filters that allow users to easily filter the data displayed on the dashboard by clicking on buttons.
Inserting Slicers:Click on a chart connected to a pivot table.
Go to PivotChart Analyze > Insert Slicer.
Choose the fields you want to use for filtering (e.g., Sales Channel, Order Priority).
Formatting Slicers:Use the Slicer contextual ribbon to change the number of columns.
Right-click the slicer and go to Slicer Settings to deselect “Display header” for a cleaner look.
Modify the Slicer Style to change colors and remove the white background to blend with the dashboard design.
Connecting Slicers to Charts:By default, a slicer inserted from a chart only controls that specific chart.
Right-click the slicer and go to Report Connections (or Filter Connections).
Select the checkboxes for all the pivot tables (and consequently their linked charts) that you want the slicer to control. Ensure pivot tables are named correctly to easily identify them.
IX. Updating the Dashboard
Requirement: Source data must be in an Excel Table.
Process:Add new data to the bottom of the existing data within the source data table. Excel Tables automatically expand to include new data.
Go to the dashboard or any pivot table.
Go to PivotTable Analyze > Refresh (or Refresh All).
The dashboard, including pivot tables, charts, and linked calculations, should update automatically to reflect the new data.
Quiz (Short Answer)
Answer each question in 2-3 sentences.
What is the primary purpose of creating a dashboard in Excel?
Why might someone choose to create a dashboard in Excel instead of Power BI?
What is a wireframe in the context of dashboard design?
Why is it important for your source data to be in an Excel Table for dashboard creation?
Suggest a standardized naming convention for an Excel Table containing sales data.
What is the advantage of creating each pivot table for a dashboard on a separate worksheet?
Describe a scenario where a Map Chart might be a suitable visualization for your data.
What workaround is necessary to create a Map Chart using data from a pivot table in Excel?
How do you link a shape on your dashboard to a calculation result on a separate worksheet?
What is a slicer and how does it enhance the interactivity of a dashboard?
Answer Key (Quiz)
A dashboard in Excel helps to display important data and key metrics in a single, visually appealing place. This allows users to easily understand information and can aid in decision-making by highlighting trends or pain points.
Users might choose Excel over Power BI because Excel is often more familiar and comfortable to work with. It is also generally included in a Microsoft 365 subscription, avoiding the additional cost of Power BI.
A wireframe is a plan or sketch of how the dashboard will be laid out. It helps organize thoughts about which information to include and where different elements like charts and slicers will be positioned before starting the building process.
Having source data in an Excel Table is crucial because tables automatically expand when new data is added. This allows the dashboard to be easily updated by simply adding new data and refreshing the pivot tables.
A standardized naming convention for an Excel Table containing sales data could be TBL_Sales_Data. This prefix indicates it’s a table, and the rest of the name describes its content.
Creating each pivot table on a new worksheet helps to keep the workbook organized, especially for complex dashboards with many components. It prevents pivot tables from getting mixed up with the source data or other elements.
A Map Chart is suitable for visualizing geographical data, such as sales performance by country or region. It uses color gradients on a map to quickly show variations across different locations.
To use pivot table data for a Map Chart, you must first copy the data from the pivot table and paste it as values onto a new range. Then, create a regular Map Chart from this copied data and finally, update the chart’s data source to point back to the original pivot table range.
To link a shape on your dashboard to a calculation result, select the shape, then go to the formula bar. Type an equals sign (=), navigate to the worksheet containing the calculation, click the cell with the result, and press Enter.
A slicer is an interactive filter that appears as a set of buttons. Clicking on a button in a slicer filters the connected charts and data on the dashboard, allowing users to easily explore different segments of the data.
Essay Format Questions
Compare and contrast the strengths and weaknesses of using Excel versus Power BI for creating interactive data dashboards, considering factors like cost, user familiarity, design flexibility, and updating capabilities based on the provided text.
Discuss the importance of data preparation, organization, and standardized naming conventions in the process of building a complex Excel dashboard. Explain how these steps contribute to efficiency, maintainability, and the functionality of interactive features like slicers and automatic updates.
Elaborate on the role of the wireframe in the dashboard design process. Explain how planning the layout beforehand can impact the effectiveness and clarity of the final dashboard, including considerations like the target audience’s needs and the selection of appropriate chart types.
Describe the different types of pivot charts demonstrated in the source material and explain for each type the kind of data it is best suited to visualize. Include a discussion of any specific challenges or workarounds mentioned for creating these charts from pivot table data in Excel.
Analyze how calculations using formulas, such as SUMIF, COUNTIF, UNIQUE, MAX, INDEX, and MATCH, are integrated into the dashboard process. Explain how creating a separate calculations worksheet and linking these results to the dashboard contributes to its dynamism and usefulness.
Glossary of Key Terms
Dashboard: A report that displays important data and information in a single place, often using visualizations, to provide an overview of key metrics and statistics.
Power BI: A separate Microsoft application designed specifically for creating reports and dashboards, often used for data analysis and visualization.
Microsoft 365: A subscription service from Microsoft that includes various applications like Excel, Word, and PowerPoint; Power BI is not typically included.
Key Metrics: Important data points or statistics that are central to understanding performance or trends.
Pain Points: Areas or issues highlighted by the data that require attention or investigation.
Wireframe: A preliminary sketch or plan of the layout and content of a dashboard.
Pivot Table: A tool in Excel used to summarize, analyze, explore, and present summary data from a larger data set.
Pivot Chart: A chart that is linked to a pivot table and visually represents the summary data from the pivot table.
Line Chart: A chart type often used to display data over time or in a continuous sequence.
Map Chart: A chart type that uses geographical regions (like countries or states) and shades them based on data values.
Bar Chart: A chart type that uses rectangular bars to represent data values, often used for comparing categories.
Donut Chart: A chart type similar to a pie chart, showing parts of a whole, with a hole in the center.
Calculations Worksheet: A separate sheet in a workbook dedicated to performing formulas and calculations that are then used on the dashboard.
UNIQUE function: An Excel function that returns a unique list of values from a range.
SUMIF function: An Excel function that sums values in a range that meet a specified criterion.
COUNTIF function: An Excel function that counts the number of cells within a range that meet a specified criterion.
MAX function: An Excel function that returns the largest value in a set of values.
INDEX function: An Excel function that returns a value or the reference to a value from within a table or range.
MATCH function: An Excel function that searches for a specified item in a range of cells, and then returns the relative position of that item in the range.
Lookup (using INDEX and MATCH or XLOOKUP): A method of finding and returning a value from a table based on a matching criterion.
Slicer: An interactive visual filter that allows users to easily filter data in pivot tables and pivot charts by clicking on buttons representing different categories.
Report Connections (Filter Connections): A setting for slicers that determines which pivot tables and charts the slicer will control.
Excel Table: A structured range of data in Excel that has specific features, including automatic expansion when new data is added, which is beneficial for dynamic dashboards.
Number Formatting: Applying specific display formats to numbers (e.g., currency, percentage, reducing decimal places) to improve readability.
Standardized Naming Conventions: Using a consistent system for naming different elements within an Excel workbook (e.g., tables, charts, sheets) for better organization and ease of reference.
Data Labels: Values displayed directly on a chart element (like a bar or point on a line) to show the exact data value.
Gap Width: A formatting option for bar charts that controls the spacing between the bars in a data series.
Donut Hole Size: A formatting option for donut charts that controls the size of the central hole.
Refresh (Refresh All): A command used to update pivot tables and connected charts and elements when the source
Briefing Document: Interactive Dashboards in Excel
Subject: Review of key concepts and practical steps for building interactive dashboards in Microsoft Excel, covering the advantages of Excel over Power BI, data preparation, planning (wireframing), creating pivot tables and charts, incorporating calculations, and adding interactivity with slicers.
Summary:
This briefing document summarizes the key takeaways from a webinar on building interactive dashboards in Excel. The session, led by IT trainer Deborah Ashby, highlights the increasing popularity of visualizing data and the benefits of using Excel for dashboard creation compared to Power BI, primarily due to cost and familiarity. The core of the webinar focuses on the practical steps involved, including data preparation, using pivot tables and charts (line, map, bar, and donut), integrating calculations using formulas, and making the dashboard interactive with slicers. The importance of planning (wireframing) and standardized naming conventions is also emphasized. While a final demonstration of refreshing the dashboard with new data encountered a technical issue, the overall process and key concepts for creating dynamic Excel dashboards were clearly outlined.
Main Themes and Important Ideas/Facts:
Rising Popularity of Data Visualization: The trainer notes a significant increase in the trend of analyzing and presenting data visually using charts and colors to convey insights and highlight pain points. “One thing that I’ve definitely noticed over the last few years is the R in popularity of analyzing data extracting data and presenting key metrics highlighting pain points in a much more visual way than we ever have done before so we present our data using charts using color so that we can really get across the story of our data.”
Excel vs. Power BI for Dashboards:Power BI: Described as the “latest buzzword” for data analysis and visualization, used for creating “really nice looking reports and dashboards and visualizations.” However, it is an additional cost and “does kind of live outside of the Microsoft 365 family.”
Excel: A popular alternative for creating dashboards due to its familiarity (“most of us use Excel or have used excel at some point or another”) and cost-effectiveness. The trainer also personally finds Excel “a little bit more flexible than powerbi particularly when when it comes to dashboard design and getting my dashboard to kind of look exactly as I want it to look.”
What is a Dashboard?: A dashboard is defined as a report that displays “important data or information in a single place so that your audience can easily see key metrics or statistics that are important to them.”
Audience-Centric Design: A crucial aspect of dashboard design is considering the audience and the questions the dashboard aims to answer. “One thing that is really important when you’re designing dashboards you need to think to yourself what questions am I trying to answer with this dashboard what do people want to know what does my audience want to know.”
Planning (Wireframing): Creating a wireframe before starting the dashboard design is highly recommended. This involves noting the desired metrics and planning the layout and placement of charts, slicers, and other elements. The trainer demonstrates using shapes in an Excel tab as a wireframe. “It is a good idea to kind of have a plan as to what you want to go onto your dashboard before you even begin.”
Data Preparation and Organization:Clean Data: Having clean and consistent source data is essential. While not covered in this session (referencing a previous webinar), it’s acknowledged as a necessary first step.
Data in a Table: Putting the source data into an Excel Table is “a really important point if you want your dashboard to update with the click of one button.” This allows the table to automatically expand when new data is added.
Standardized Naming Conventions: Naming elements like tables, charts, and pivot tables using a consistent system (e.g., TBL_, CHT_, PVT_) is vital for organization and ease of use, especially when linking elements to slicers. “It’s so important to have like a standard nameing convention so it’s easy to identify the different elements in your dashboard is because it’s going to make your life a lot easier when we start having to link our tables and our charts to things like slices.”
Using Color for Organization: Employing color coding for different types of tabs (data, wireframe, charts, calculations, dashboard) can significantly improve organization, especially in complex workbooks.
Key Components of the Dashboard (as demonstrated):Title: A clear heading for the dashboard.
Summary Statistics/Cards: Displaying key metrics (e.g., most profitable item, number of cancelled orders) at the top of the dashboard. These are often derived from calculations performed on a separate sheet.
Charts: Visual representations of data. The webinar demonstrates creating:
Line Chart: Used to show “total profit by year,” suitable for time-based data.
Map Chart: Used to show “average unit sold by country,” suitable for geographical data. Important Note: Map charts cannot be created directly from pivot table data and require copying and pasting values before creating the chart and then re-pointing the chart’s data source back to the pivot table. “You can’t create this chart type with data inside a pivot table… you have to pull it out of the pivot table first of all.”
Bar Chart: Used to show “revenue by sales channel and item type.” Suitable for comparing categories. The trainer advises using filters (e.g., Top 3) for large datasets to avoid overcrowded charts.
Donut Chart: Used to show the “count of orders by region.” The trainer expresses a preference for other chart types for more than a few categories.
Pivot Tables and Pivot Charts: The dashboard heavily relies on pivot tables as the source for the charts. Each pivot table and chart is ideally placed on its own sheet for clarity and organization.
Calculations: Demonstrates using Excel formulas like UNIQUE, SUMIF, MAX, INDEX, MATCH, and COUNTIF on a separate sheet to derive key metrics for the summary statistics displayed on the dashboard. These calculations are linked to the source data to ensure dynamic updates.
Formatting: Basic formatting techniques are discussed, including removing grid lines, adding shape outlines, and customizing data labels and axis. The use of company branding colors is also mentioned.
Interactivity with Slicers: Slicers are described as “interactive filters” that allow users to easily filter the data displayed on the dashboard. They can be customized in appearance.
Report Connections: Slicers need to be connected to the specific charts they are intended to control. This is done via the “Report Connections” (or “Filter Connections”) option, emphasizing the importance of naming charts correctly to easily identify them.
Updating the Dashboard: The intended workflow for updating a dashboard involves adding new data to the source table (which auto-expands) and then using the “Refresh All” button on the “Pivot Chart Analyze” tab. A technical issue prevented a successful demonstration of this step in the webinar, but the principle was explained. “If you have your data in a table when you build your dashboard if you add data into the bottom the dashboard can be updated simply by clicking on refresh and everything will pull through nicely.”
Key Quotes:
“Dashboards can get really complex and you might find yourself with lots and lots of different tabs so we want to try and organize that as best as we can.”
“It’s always good to sort of go away and take a look at what other people are doing… sites like Pinterest just to give yourself some inspiration.”
“If you want your dashboard to update with the click of one button you want to make sure that you put your Source data in a table.”
“The reason why it’s so important to have like a standard nameing convention… is because it’s going to make your life a lot easier when we start having to link our tables and our charts to things like slices.”
“You have to think to yourself okay how am I going to design my dashboard so before you even begin you want to make sure that you make a note of exactly what you want to display on that dashboard.”
“When you’re putting together a dashboard you want it to kind of look as clean and professional as possible.”
“When you’re um creating charts not all charts are created equally some charts are more suited to certain types of data.”
“If you have time based data like we do here 2015 to 2022 that’s often nicely represented in the form of a line chart.”
“You can’t create this chart type with data inside a pivot table and that’s really important if you want to use pivot table data in a map chart you have to pull it out of the pivot table first of all.”
“If you do have a lot of data and you’re trying to cram it into a certain chart type one little thing I would say to do is maybe start applying a filter to just show the top three or the top five.”
“Pie charts and donut charts in general are not my favorite types of chart they’re quite limited with how much data you can actually display in them.”
“Because of the way I’ve constructed this [calculations] and everything links back to that Source data if anything changes it’s going to feed through to my calculations worksheet which is in turn going to feed through to my dashboard.”
“Slicers just act as little filters which we can click on and it changes what’s displayed on our dashboard.”
“With these slices they will not be connected to all of your charts… we need to right click on the slicer and Report connections.”
Conclusion:
The webinar provides a comprehensive introduction to building interactive dashboards in Excel, emphasizing planning, data organization, utilizing pivot tables and charts, incorporating calculations for key metrics, and adding dynamic filtering with slicers. Despite a minor technical issue with the final refresh demonstration, the session successfully conveyed the fundamental principles and practical steps required to create visually engaging and informative dashboards in Excel. The trainer’s insights on comparing Excel to Power BI, the importance of naming conventions, and the utility of wireframing are particularly valuable.
What is an interactive dashboard in Excel?
An interactive dashboard in Excel is a single-page report that helps you display important data and information visually using elements like charts, tables, and key metrics. The goal is to present crucial statistics in one place for easy audience understanding. A key feature is interactivity, often achieved through elements like slicers, allowing users to filter and explore the data presented on the dashboard dynamically.
Why are dashboards useful, particularly in Excel?
Dashboards are useful for visually highlighting key metrics and pain points within data, telling a story with the numbers. They allow audiences to quickly grasp important information without sifting through raw data. While dedicated tools like Power BI exist, many prefer Excel due to its familiarity, cost-effectiveness (often included in Microsoft 365 subscriptions), and perceived flexibility in design.
What are some key steps involved in building an Excel dashboard?
Building an Excel dashboard involves several key steps. Initially, it’s crucial to prepare and organize your data, ensuring it’s clean and structured, ideally in an Excel table for easy updates. Planning is also vital, often done through creating a wireframe or sketch to outline the desired layout and content. The process then involves creating components like pivot tables and charts from your source data, assembling them onto the dashboard sheet, formatting for clarity and visual appeal, adding interactive elements like slicers, and finally, connecting these elements to ensure the dashboard updates with new data.
How does data preparation impact the creation of an effective dashboard?
Proper data preparation is fundamental to creating an effective dashboard. The source data should be clean, consistent, and ideally structured in an Excel table. Using a table allows the dashboard to update automatically when new data is added. Without clean and well-organized data, building accurate pivot tables and charts, and ensuring the dashboard functions dynamically, becomes significantly more challenging.
What is the importance of naming conventions and organization in Excel for dashboards?
Standardizing naming conventions for tables, charts, and other elements is crucial for organization, especially as dashboards can become complex with numerous components and worksheets. Using prefixes like TBL for tables, CHT for charts, and PVT for pivot tables helps in easily identifying and referencing these elements. This organization is particularly helpful when linking different parts of the dashboard, such as connecting slicers to specific charts.
What role does a wireframe play in dashboard design?
A wireframe serves as a planning tool before you start building the dashboard in Excel. It involves sketching out the layout and deciding where different elements like the title, summary statistics, slicers, and charts will be placed. This planning stage helps to ensure that the dashboard is designed in a logical and user-friendly way, preventing unnecessary rework during the creation process.
How are calculations and key metrics displayed on an Excel dashboard?
Key metrics and top-level statistics that aren’t best represented by charts can be displayed using linked cells or formulas on the dashboard. Calculations, such as finding the most profitable item or counting canceled orders, are often performed on a separate worksheet using formulas like UNIQUE, SUMIF, COUNTIF, MAX, INDEX, and MATCH. The results of these calculations are then linked to shapes or text boxes on the dashboard using cell references in the formula bar, ensuring they update automatically when the source data changes.
How do slicers provide interactivity in an Excel dashboard?
Slicers are interactive filtering tools that can be added to an Excel dashboard. They act as visual buttons that, when clicked, filter the data displayed in the connected pivot tables and charts. To connect a slicer to multiple charts, you need to use the “Report Connections” feature (or “Filter Connections” in newer versions) by right-clicking on the slicer. This allows users to easily explore different subsets of the data by simply clicking on the desired filter options within the slicer.
Interactive Dashboards in Excel: Microsoft Excel Crash Course
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These sources offer an extensive exploration of data analysis and PowerBI, focusing on the role of a data analyst and the process of transforming raw data into valuable insights. They cover essential concepts like data sourcing, cleaning, modeling, and visualization, emphasizing the importance of effective communication of findings. The texts also introduce advanced topics such as DAX calculations, performance optimization, and the integration of PowerBI within a larger enterprise data flow, highlighting the potential of data to drive strategic business decisions. Furthermore, they touch upon the application of generative AI in data analysis and provide guidance on preparing for the Microsoft PL-300 certification exam, offering real-world scenarios and career insights through examples of aspiring data analysts.
Foundations of Data Analysis
Data analysis is a multifaceted process crucial for turning raw data into meaningful insights and informed decisions for businesses and organizations . It involves identifying, cleaning, transforming, and modeling data to discover meaningful and useful information. Data analysts use various techniques to explore, interpret, and draw meaningful conclusions from processed data “.
The Importance of Data Analysis
Data is an essential business component, but raw data is only meaningful after proper interpretation and analysis . **Data analysts are crucial because they help organizations make sense of the vast amounts of collected data, turning it into insights that inform decisions**. This analytical work helps businesses identify growth opportunities, improve operations, gain a competitive advantage , identify the cause of problems, uncover trends, and make decisions that can improve business performance. Ultimately, data analysis drives strategic decision-making and can significantly impact an organization’s success “.
The Data Analysis Process
The data analysis process typically involves several interconnected stages “:
Identifying the analysis purpose or defining the business problem: This is the foundational step, determining what you aim to achieve or the questions you need to answer with the analysis . Gathering the right data is fundamental to ensure the analysis is relevant and useful, and understanding the purpose informs the type and scope of data needed. Consulting with stakeholders is key to determining the purpose “.
Data Collection and Preparation: Data is gathered from various sources . This raw data is often unorganized and may have missing values or inconsistencies. Data preparation involves cleaning, standardizing, organizing, and transforming the data into a usable format for analysis . The Extract, Transform, Load (ETL) process is a common method for processing data, involving extracting data from sources, transforming it to make it consistent and ready for analysis, and loading it to a suitable destination. Data wrangling is another term for this process of processing, cleaning, and transforming data “.
Data Processing and Modeling: Processing transforms raw data . Data modeling organizes data to make sense of the information and generate insights. This can involve understanding basic concepts, using tools like DAX to create calculations, and optimizing model performance . Common data schemas include star and snowflake schemas, which organize data into fact and dimension tables.
Data Analysis, Visualization, and Interpretation: This stage involves exploring processed data and generating insights . Data analysis uses various techniques to explore, interpret, and draw meaningful conclusions from the processed data. Analytical techniques include statistical analysis, hypothesis testing, and identifying patterns, trends, and relationships . Data visualization is a powerful tool used to communicate these insights. Visualizations (like charts and graphs) transform complex data into understandable representations, helping to spot patterns, anomalies, and trends at a glance . Interpretation involves understanding what the patterns and trends reveal.
Reporting and Sharing Data Insights: Insights are communicated to stakeholders through reports and dashboards . Dashboards consolidate critical information visually on one screen to achieve specific objectives. Sharing reports requires considering factors like accessibility, visual appeal, and security . Effective communication and storytelling are essential to convey findings responsibly and ethically.
Implementing Insights and Recommendations: Informed decisions are made based on the analyzed data, guiding actions and adjustments within the business to achieve objectives “.
This data flow process – collection, processing, analysis, and decision-making – is a fundamental concept in business “.
Roles in Data Analysis
The data analysis process involves various roles that collaborate to achieve datadriven success “:
Data Engineer: Designs and constructs data infrastructure, including pipelines, cleaning, pre-processing, and transforming raw data for analysts and scientists “.
Data Analyst: Examines data sets to identify trends, patterns, and insights . They use tools to visualize and present data, making it digestible for stakeholders, and work closely with teams to align analysis with business goals. The data analyst is often a central figure in the process “.
Data Scientist: Dives deeper into data, creating predictive models using machine learning and statistical techniques to identify hidden patterns and optimize decisions . They often collaborate with data analysts.
Database Administrator (DBA): Works on the maintenance, performance, and security of databases, ensuring data is stored efficiently and accessible “.
Data Architect: Creates the blueprint for data management systems, designing data models and strategies for storage, integration, and retrieval “.
Business Intelligence (BI) Analyst: Transforms data into actionable insights, focusing on Key Performance Indicators (KPIs) using BI tools to visualize and present data to stakeholders and collaborating with business leaders to understand their goals “.
These roles are essential for providing organizations with the information they need for informed, data-driven decisions “.
Skills for Data Analysts
To succeed, data analysts require a mix of technical and non-technical skills “:
Technical Skills: Proficiency with tools like Microsoft Excel and Microsoft PowerBI . Experience with programming languages such as R and Python is used for analysis and visualization. Understanding SQL (Structured Query Language) is vital for interacting with databases . Key technical activities include data wrangling (cleaning and transforming data), data modeling (organizing data for analysis) , creating calculations using languages like DAX, data visualization (creating charts and reports) , and using statistical functions. Other important technical skills mentioned include data profiling , managing data storage modes, creating aggregations , joining and merging data, grouping and binning data , and performance optimization.
Non-Technical (Soft) Skills: These are crucial for connecting with and influencing stakeholders . Essential skills include **effective communication** to present complex information clearly and concisely to various audiences, diplomacy for navigating disagreements and maintaining relationships , **understanding end-user needs** to tailor analysis and provide relevant insights, and being a technical interpreter to translate complex concepts for non-technical stakeholders . **Strategic thinking, awareness of impact, and understanding the business context** are also important. The ability to use data to tell a story or narrative is also highlighted “.
By developing these technical and non-technical skills, data analysts can collaborate effectively, create actionable insights, inspire change, and make lasting impacts “.
Tools and Techniques Used in Data Analysis
Data analysts utilize a range of tools and techniques “:
Software and Tools: Microsoft Excel is used for designing and managing spreadsheets and preparing data . **Microsoft PowerBI** is a powerful tool for processing, analyzing, and sharing data, known for its user-friendly interface, rich visualizations, and advanced analytics capabilities . The PowerBI workflow includes PowerBI Desktop, PowerBI Service, and PowerBI Apps. Power Query Editor within PowerBI is used for data preparation, cleaning, transformation, and ETL tasks . SQL Server and other databases are used for data storage. Programming languages like R and Python are used for data analysis and visualization “.
Techniques:ETL (Extract, Transform, Load): A fundamental process for preparing data “.
Data Wrangling/Cleaning/Transformation: Making raw data consistent and usable “.
Data Modeling: Organizing data into structured formats like star or snowflake schemas “.
DAX (Data Analysis Expressions): A formula language used to create custom calculations and measures within data models “.
Calculations and Statistical Functions: Performing mathematical operations and applying functions like average, median, count, min, and max to data to reveal insights “.
Data Visualization: Creating graphical representations of data such as charts, graphs, scatter plots, bubble charts, dot plots, and tables to make complex information understandable . Interactive features like filtering, sorting, slicers, and bookmarks enhance visualizations.
Data Profiling: Examining data sets to evaluate accuracy, completeness, and statistical distribution . Tools analyze column quality, distribution, and profile statistics.
Grouping and Binning: Organizing data points into chosen groups or equal-sized segments “.
Clustering: Identifying similarities in data attributes to divide data into subsets or clusters “.
Time Series Analysis: Analyzing data in chronological order to identify trends “.
Performance Optimization: Modifying data models and reports to improve speed and efficiency, especially with large data volumes . Techniques include filtering, sorting, indexing, aggregation, and choosing appropriate storage modes. The Performance Analyzer tool helps diagnose issues “.
Data Storage and Management: Understanding different data types (structured, unstructured, semistructured) and appropriate storage solutions , as well as concepts like normalization and indexing in databases.
Connecting to Data Sources: Using methods like Import mode or Direct Query mode to bring data into tools like PowerBI “.
These tools and techniques empower data analysts to extract insights, support business intelligence, and facilitate data-driven decision-making . The sources frequently use the example of Adventure Works, a fictitious bicycle company, to illustrate how data analysis is applied in real-world business scenarios.
Mastering Microsoft PowerBI for Business Intelligence
Microsoft PowerBI is an interactive data visualization product and a comprehensive business analytics solution. It is considered an essential resource for many organizations across various industries.
Importance in Business
PowerBI plays a crucial role in helping businesses make sense of the vast amounts of collected data, transforming it into actionable insights that inform decisions. It enables organizations to harness the full potential of data to uncover insights, identify patterns, trends, and insights, and drive strategic decision-making. PowerBI supports data-driven decision-making and is vital for providing organizations with the information they need for informed decisions [Introduction]. For companies like Adventure Works, PowerBI is used to extract insights from large amounts of data.
Components and Workflow
Microsoft PowerBI has multiple components that work together. The main components are PowerBI Desktop, PowerBI Service, and PowerBI Apps. Other related components include PowerBI mobile, PowerBI report server, and PowerBI embedded.
PowerBI Desktop is a Windows-based application used by data analysts or report designers to clean, transform, and load data, create a data model, design reports, and publish them.
PowerBI Service is the cloud-based service (SaaS) part of PowerBI, used by report users and administrators. It offers advantages like accessibility, scalability, collaboration tools, and data backup and recovery features.
PowerBI Apps are the native mobile applications available on iOS, Android, and Windows. They allow access to insights on the go.
A typical workflow in PowerBI often starts with the creation of a report in PowerBI Desktop. Report designers and developers are primarily responsible for this task. When the report is ready, you publish it to the PowerBI service, where administrators can assign permissions and specific users can consume the report. You can also share reports with colleagues, your whole organization, or external stakeholders who need to draw insights. Insights are also communicated through dashboards, which consolidate critical information visually. PowerBI Service and PowerBI mobile can be used to view dashboards.
Key Capabilities and Features
PowerBI offers a wide range of features and capabilities for data analysis and business intelligence:
Data Connection and Preparation:
PowerBI supports a wide range of data sources, including traditional databases, Excel spreadsheets, cloud-based services, on-premise databases, external enterprise applications, and APIs. PowerBI connector is used to access these sources.
Data preparation is crucial for making raw data usable. This involves cleaning, standardizing, organizing, and transforming data.
The Extract, Transform, Load (ETL) process is fundamental for preparing data in PowerBI. Power Query Editor in PowerBI is a tool used for data preparation, cleaning, transformation, and ETL tasks. Data wrangling is another term for processing, cleaning, and transforming data [Introduction, 1, Introduction].
Techniques include data profiling, joining and merging data [Introduction], and grouping and binning data to classify or segment data points.
Data Modeling:
Data modeling is creating visual representations of your data in PowerBI to organize it and make sense of the information. It involves understanding how different data elements interact and outlining the rules that influence these interactions.
PowerBI allows you to identify or create relationships between data elements. You can define relationships between tables and assign data types.
Common data schemas include star and snowflake schemas, which organize data into fact and dimension tables [Introduction, 7, 43].
DAX (Data Analysis Expressions) is a powerful language used to create custom calculations, calculated measures, columns, and tables within data models. DAX is fundamental to data analysis in PowerBI.
Performance Optimization is important, especially with large data volumes. Techniques include modifying models, reports, queries, filtering, sorting, indexing, aggregation, and choosing appropriate storage modes. The Performance Analyzer tool helps diagnose issues.
Aggregations in PowerBI enable diving deeper into data without compromising speed and performance. They involve summarizing or consolidating large volumes of data into manageable summary tables.
Understanding different Data Storage Modes (Import, Direct Query, Dual, Composite) is vital as they determine where data is stored and how queries are sent. Import mode stores data in PowerBI’s in-memory storage, Direct Query keeps data in the source, and Dual mode can act as either. Composite mode allows combining different storage modes.
Creating Hierarchies (date, product, geographical) is a significant feature allowing analysis at different levels of granularity within the same visual using drill down.
Analysis Techniques:
PowerBI empowers you to transform raw data into meaningful insights through various advanced tools and functionalities.
Calculations are the foundation of data analysis in PowerBI and are created using DAX. Common calculations include aggregations and statistical functions like average, median, count, min, and max [Introduction, 21, 22, 23].
PowerBI offers analytics capabilities to add significant value to visualizations. This includes using statistical summary tools.
Identifying patterns, trends, and anomalies is crucial. Scatter charts can help identify outliers.
Time Series Analysis involves analyzing data in chronological order to identify trends. PowerBI supports time series forecasting to predict future trends.
Clustering identifies similarities in data attributes to divide data into subsets.
The Analyze feature automatically detects relationships and connections, providing automated insights. You can right-click on a data point to analyze fluctuations like increases or decreases.
PowerBI leverages AI capabilities and machine learning algorithms to provide insights. This includes AI visuals like Key Influencers and Decomposition Trees for understanding drivers behind outcomes, sentiment analysis, and key phrase extraction.
The Q&A feature is a natural language processing tool allowing users to ask questions about data in plain English and get answers as visuals. It learns and adapts over time.
Quick Insights automatically searches datasets to discover and visualize potential patterns, trends, and outliers using machine learning and statistical functions.
Dynamic reports can facilitate using What-If parameters for interactive adjustments and scenario analysis.
Metrics and Scorecards are critical for tracking progress towards specific objectives and providing a comprehensive view of performance.
Visualization:
Data visualization is a powerful tool for communicating insights. Visualizations transform complex data into understandable representations, helping to spot patterns, anomalies, and trends [Introduction, 11].
PowerBI offers a variety of built-in visualization types, such as bar charts, maps, tables, cards, multirow cards, gauges, KPI visual, scatter plots, bubble charts, and dot plots. Heat maps, tree maps, and 3D visualizations are also discussed for handling high-density data. Coropleth and shape maps are common map visuals.
Custom visuals can be imported from the PowerBI marketplace or created using Python or R.
Design principles are important for creating effective visualizations. This includes considering color theory, appropriate positioning and scale, maintaining cohesion and consistency, and avoiding clutter.
Accessibility is crucial in report design, including features like alt text, sufficient color contrast, keyboard navigation, and compatibility with screen readers. PowerBI has built-in tools to support this.
Visualizations can be interactive, allowing users to drill down, filter, and sort data.
Visual interactions determine how selecting data in one visual affects others. The primary types are filter (filters other visuals), highlight (dims non-selected data), and none (no interaction).
Slicers help users drill down to deeper insights and can be synchronized across report pages to improve user experience.
The Selection Pane helps manage report elements, allowing naming, grouping, and layering visuals. Bookmarks can also be used to create a smooth narrative.
PowerBI allows optimizing report layouts for mobile devices to ensure proper display on smaller screens.
Sharing and Collaboration:
Insights are communicated through reports and dashboards. Publishing reports to PowerBI Service makes them accessible and collaborative.
PowerBI Workspaces are specialized areas that hold assets like reports, dashboards, and datasets. They help organize assets, provide security, enable collaboration, and allow quick updates. There are personal and shared workspaces.
Workspace roles (viewer, contributor, member, admin) determine how individuals interact with content. Permissions can be managed.
You can share Workspace assets as an app, which can have multiple audience groups with tailored access.
Data security is important for safeguarding sensitive data. PowerBI offers authentication tools, sharing links with controlled permissions, sensitivity labels, and data permissions.
Row-Level Security (RLS) controls which individuals can view data based on predefined roles and rules, enhancing security and user experience.
You can promote and certify datasets to establish trust and standardize data quality, helping users find the most accurate data.
Data Gateways establish a secure connection between PowerBI cloud services and on-premises data sources. Types include on-premises data gateway (standard mode), on-premises data gateway personal mode, and Azure virtual network data gateway. They help sync data and keep datasets up to date via schedule refresh.
Subscriptions and Alerts provide automated delivery of data snapshots (emails/notifications) and notifications when specific conditions are met. They enhance user engagement and support real-time decision-making.
Overall, PowerBI transforms raw data into actionable intelligence, acting as a toolkit with mapping techniques and navigation support to help users cut through data noise and interpret patterns. It is a central tool in the data flow process within a business, moving from collection, processing, analysis, and decision-making.
PowerBI Data Transformation Explained
Data transformation is a fundamental process in Microsoft PowerBI, essential for preparing raw data for analysis and generating meaningful insights. It involves altering the structure, format, or values of data to make it suitable for analysis. This often includes cleaning, structuring, and enriching the data.
Why is Data Transformation Necessary?
Raw data, as collected from various sources, is often untidy, incomplete, inconsistent, scattered across different systems, or may have missing values or duplicate entries. Working with such data can lead to inaccurate or misleading analysis results and, consequently, poor business decisions. Data transformation addresses these issues by ensuring the data used for analysis is accurate, clean, consistent, and reliable. It standardizes data across multiple sources and organizes it to be more understandable.
Where Transformation Happens in PowerBI
Within PowerBI, data transformation is primarily handled by Power Query Editor. Power Query is a powerful ETL (Extract, Transform, Load) tool integrated into PowerBI Desktop. It provides a graphical user interface (GUI) for connecting to various data sources, cleaning data, and performing transformations with ease.
Key Data Transformation Techniques and Capabilities
Power Query Editor offers a range of tools and features for transforming data:
Data Cleaning: This involves identifying and correcting errors and inconsistencies. Techniques include removing duplicate entries, handling or filling in missing values (nulls), fixing incorrect data types, and standardizing formats (e.g., ensuring consistent spelling or capitalization). Filtering data is also a key cleaning method.
Structuring and Shaping Data: This prepares data for analysis. Operations include removing unwanted columns or rows, splitting or merging columns (e.g., combining first and last names into a full name), changing data types (e.g., text to numeric, date, or decimal), and sorting data. Promoting header rows is also a common shaping task. Grouping data allows manually dividing data points, while binning automatically separates data points into segments based on number or size.
Combining Data: It is common to need to combine data from multiple sources.
Append: Adds rows from one table to another. This is useful for consolidating data that has the same columns but spans across different files or databases (e.g., monthly sales files).
Merge: Consolidates data from multiple sources into a single table based on matching criteria or key columns, similar to joining tables in a database. This is used when data needs to be combined horizontally based on relationships between tables.
Reshaping Data Structures:Unpivot: Transforms data from a “wide” format (many columns) to a “narrow” format (fewer columns), often converting column headers into row values. This is useful for data normalization and making comparisons easier.
Pivot: Transforms data from a “narrow” format to a “wide” format, converting rows into columns based on specific values.
Adding Calculated Columns: Power Query allows adding new columns based on calculations performed on existing columns, such as calculating total price by multiplying quantity and unit price. DAX is used for calculations within the data model, but calculated columns can be created during the transformation stage in Power Query using its own formula language or features.
Query Management: Power Query’s Applied Steps list is a critical feature, visually representing every transformation applied to a query. This list can be reviewed, modified, deleted, or reordered, ensuring transparency and allowing for easy undo or redo functionality. Referencing a query creates a new query based on an existing one, inheriting its steps. Changes to the original query automatically update the referenced query, which is useful for maintaining complex transformation workflows. Duplicating a query creates an independent copy that can be modified without affecting the original.
Relationship with Data Loading and Profiling
Transformation is typically performed after data extraction and before data loading into the PowerBI data model. The loading process brings the transformed data into PowerBI for analysis and visualization.
Before transforming or loading data, it is essential to inspect and profile the data. Power Query Editor provides tools like Column Quality, Column Distribution, and Column Profile to evaluate the data’s accuracy, completeness, validity, distribution, and identify anomalies or outliers. This profiling step helps identify where transformations are needed.
Benefits of Data Transformation
Effective data transformation is crucial for generating accurate reports and gaining valuable insights. It improves data quality and consistency, enhances performance by preparing data efficiently, simplifies data management, and helps organizations make informed decisions based on reliable information.
PowerBI Data Visualization Fundamentals
Data Visualization in PowerBI
Data visualization is a graphical representation of data. In Microsoft PowerBI, it is much more than simple graphical depictions; it involves converting raw data into a visual format to help identify patterns, trends, and insights that might not be apparent in text-based data. Visualizations enable you to communicate complex data and insights in a simple, appealing way by presenting data graphically. This process makes it easier for stakeholders to grasp key insights, trends, and patterns that may be difficult to identify from row data or tables.
Why is Data Visualization Important?
Data visualization is crucial for generating accurate reports and gaining valuable insights. It enhances business intelligence, particularly in complex and dynamic business environments. Key benefits include:
Revealing Patterns and Trends: Data visualizations can reveal patterns, trends, and correlations hidden in raw data. For example, a bar chart could visualize sales data demonstrating geographic regions where sales are highest.
Making Data Accessible: Visualizations make data more accessible to a broader audience, as most stakeholders can understand a well-designed chart or graph. This encourages engagement with data and contributes to data-driven decision-making.
Powerful Communication Tool: Visualizations are a powerful communication tool that can tell a compelling story with data, making insights more memorable and persuasive.
Driving Data-Driven Decisions: By providing clear, interactive displays, visualizations act like a navigation system through complex data, helping businesses make informed decisions based on reliable information.
Real-time Analysis: Visualizations can enable real-time data analysis. For example, as sales figures are updated, visualizations in PowerBI can update automatically, providing up-to-date insights.
Where Visualization Happens in PowerBI
Visualizations are primarily created in the Report View of PowerBI Desktop. This is the primary canvas where you design and create your visualizations, adding and arranging different visual elements. Reports can have multiple pages organized using tabs at the bottom of the window. Once created in reports, visualizations can also be pinned to Dashboards in the PowerBI service, which provide a consolidated, one-page summary of the most important metrics or key performance indicators (KPIs).
Workflow for Creating Visualizations
Creating visualizations in PowerBI typically follows a workflow:
Connecting to data sources.
Using Power Query Editor to extract, transform, and load the data.
Loading the refined data into PowerBI’s data model.
Representing this processed data in visualizations.
Key Components and Concepts
Several key components and concepts are involved in creating and using visualizations in PowerBI:
Visualizations Pane: Located on the right side of the window, this pane contains a gallery of visual elements you can add to your report canvas. You add visuals by clicking or dragging them onto the report view.
Fields Pane (or Data Pane): Also on the right side, this pane displays the data tables and fields available for your report. You use this pane to populate your visualizations with data by dragging fields onto the visual or specific field wells.
Field Wells: These are sections within the visualizations pane where you drag data fields to define how they are used in the visual, such as axes, legend, values, or tooltips.
Axes (X and Y): These represent the data points you want to compare or analyze.
Categorical Axes: Used to represent discrete, non-numeric data points (categories). PowerBI automatically arranges data points in the order they appear in the dataset or allows sorting. Common in bar charts and column charts.
Continuous Axes: Designed to represent numerical data points with an inherent order along a continuous scale. Ideal for visualizing quantitative information to identify trends and patterns. Common in line charts, area charts, and scatter plots.
Legend: Controls the color coding or grouping of elements in your chart, helping differentiate between different categories or subgroups. It makes it easier to understand which color represents which item.
Tooltips: Display data or extra information when you hover over the data points of a chart. Tooltips can be customized to include additional fields.
Formatting: PowerBI offers extensive options to format the appearance and feel of visualizations to improve their aesthetic appeal, readability, and align with branding. This includes options for colors, fonts, grid lines, titles, backgrounds, and more. Formatting options are found in the ‘Format visual’ tab of the visualizations pane.
Common Visualization Types
PowerBI offers a wide variety of visualization types:
Charts:Column Charts: Compare different categories in a vertical orientation, useful for demonstrating changes over time or comparisons, generally with fewer than 10 categories.
Bar Charts: Similar to column charts but horizontal, useful for comparing larger quantities or categories with lengthy labels.
Line Charts: Best suited for showing trends over time by connecting individual numeric data points, particularly effective for large datasets.
Area Charts: Similar to line charts but with the area beneath the line filled, helping compare quantities and show part-to-whole relationships over time or across categories. Stacked area charts emphasize the total across several categories.
Pie Charts: Circular graphics divided into slices to illustrate numerical proportions of a whole. Each slice represents a category, and its size is proportional to its quantity. Less effective with too many categories.
Donut Charts: Similar to pie charts but with a blank center. Ideal for showing a dataset as a proportion of a whole.
Scatter Charts: Use dots to represent values for two numeric variables, plotting them along two axes to illustrate how one factor is affected by another, representing correlations and helping identify anomalies or outliers.
Bubble Charts: A variation of scatter plots where a third variable is represented by the size of the bubble. They can depict multi-dimensional data in a single view.
Funnel Charts: Present sequential or staged data, such as a sales conversion process, helping identify trends and bottlenecks.
Combo Charts (Line and Column): Combine line and column charts to display complex and related data points seamlessly.
Tree Maps: Use nested rectangles to display hierarchical or proportional data. Useful for visualizing larger datasets without becoming overly complex compared to pie charts.
Tables: Display raw, detailed data and exact numbers in columns and rows, providing a comprehensive numerical view. Useful for examining exact figures and making precise comparisons.
Maps: Visualize geographical data.
Shape Maps: Color-code geographical regions based on data values to reveal insights.
Coropleth Maps (Filled Maps): Similar to shape maps, shading or patterning geographical areas (countries, states, regions) to illustrate quantitative data values.
Heat Maps: Use color gradients to represent the density and distribution of data across geographical regions or grids. Not a core PowerBI visual but can be imported or created with Python.
ArcGIS Maps: Rich in map visualization features.
KPI Visuals: Specifically designed to display key performance indicators. Include Cards (single value), Multirow Cards (multiple values per row), Gauges (progress toward a target), and the KPI visual (performance against target with trend line).
Advanced Visualization Techniques
PowerBI offers advanced capabilities for visualizing complex data:
Handling High-Density Data: Techniques include using aggregations and summarization, drill through and drill down, color coding (like heat maps), and using 3D and custom visualizations.
Hierarchies and Drill Down/Through: Organizing data into hierarchies (like Date, Product, Geography) allows users to explore data from a general overview level down to specific details within the same visualization. Drill down allows navigating through these hierarchy levels. Drill through is a technique for creating summary pages with high-level insights.
Custom Visualizations: User-defined visual elements for specific requirements. They can be imported from the PowerBI marketplace (AppSource).
Python/R Visuals: Integration with Python and R programming languages allows creating dynamic and sophisticated custom visualizations. This requires specialist expertise and has limitations on data size.
Key Influencers Visual: An advanced analytics feature that uses AI algorithms to identify key contributors behind increases or decreases in a metric, such as sales.
Decomposition Tree: Another specialized analytics tool to navigate through data hierarchy levels to understand how a final value is influenced by different categories.
Clustering: Using algorithms (like in scatter plots) to group data points based on patterns and identify hidden relationships.
Interactions: Visualizations can be configured to interact with one another.
Filter: Selecting a data point in one visual filters the data displayed in others.
Highlight: Selecting a data point highlights related data in other visuals while dimming the rest, maintaining context.
None: Disables interaction, useful when visuals should function independently.
Slicers: Visual filters that allow viewers to segment and filter the data in real-time.
Data Visualization and Data Storytelling
Data visualization is a crucial part of data storytelling. Data storytelling involves leveraging narrative, data, and visualizations to communicate insights effectively. Visualizations act as a bridge between raw data and actionable insights, supporting the narrative and making complex information accessible and engaging for the audience. By choosing appropriate and effective data visualizations, analysts can allow viewers to quickly grasp information and identify trends, patterns, and insights.
Accessibility
When designing reports and visualizations, it is important to consider accessibility. This means creating reports that can be easily used and understood by all individuals, including those with disabilities. Features supporting accessibility in PowerBI include providing alt text for visuals, ensuring sufficient color contrast, enabling keyboard navigation (Tab Order), using markers on lines, and ensuring compatibility with screen readers. High-contrast themes are also available.
Essential Concepts in Data Security
Based on the sources provided, here is a discussion of data security:
Data security is considered paramount in our digital age, like safeguarding your most valuable possessions in a vault with a strong lock. Data, being the lifeblood of modern organizations, is subject to a range of threats, including cyber attacks, breaches, and unauthorized access. Ensuring the security of this “digital gold mine” is not just a choice, but a necessity. In the world of data visualization, ensuring data security is of utmost importance. This includes protecting sensitive information and maintaining data integrity. Incorporating robust security measures is crucial throughout the visualization process.
Why Data Security Matters
Data security is crucial for generating accurate reports and gaining valuable insights. It enhances business intelligence, particularly in complex and dynamic business environments [Source 1 – my previous response, not directly from the provided sources]. Working with data often involves handling sensitive information, such as customer data, financial records, or proprietary business insights. Ensuring the security of this data is essential to:
Maintain trust.
Comply with regulations.
Protect against unauthorized access or data breaches.
Safeguard the company’s reputation and success.
Prevent potential harm to the company and its stakeholders.
Mishandling sensitive data can lead to serious consequences, including financial loss, legal troubles, brand damage, and competitive disadvantage. It can also damage the relationship between an organization and its workforce if employee data is leaked.
Identifying Sensitive Data
Sensitive data contains important information about a business or its stakeholders that, if mishandled, could cause harm or misuse. A simple rule is: if it’s information that could damage the company’s reputation, finances, or stakeholder privacy, it’s sensitive data. Examples include:
Customer details.
Financial records (including profit margins).
Employee information.
Proprietary business knowledge or insights.
Product designs.
Vendor contracts.
Any information that offers intimate knowledge not meant for circulation can be classified as sensitive.
Measures for Safeguarding Data
PowerBI offers various measures to ensure data security:
Access Control & Authentication: Controlling access to data is vital to ensure only authorized individuals can view or interact with specific data sets. Before a user can access a report, they need to prove who they are through an authentication system. Once authenticated, the system determines what data they are permitted to access. This helps protect organizations like Adventure Works from internal leaks and unauthorized external breaches. PowerBI allows defining roles for users with specific permissions tied to them, ensuring data is distributed on a need-to-know basis. Regularly reviewing and updating these roles is essential. Access logs and audit trails can also track and monitor data usage.
Role-Level Security (RLS): RLS is a powerful data governance capability that controls which individuals can view data based on predefined roles and rules. It allows restricting data visibility so each user can only access data they are authorized to view, ensuring data integrity and confidentiality.
Benefits: Precise control over data visibility, prevention of accidental data leaks, safeguarding sensitive data, easier handling of complex data access needs as data scales, assistance with compliance and auditing, and a reduced risk of data breaches.
Types:Static RLS: Uses predefined rules based on user roles and is suitable for a fixed set of users or a simple logic. You configure this in PowerBI Desktop by managing roles, adding filters using DAX expressions, testing, and then assigning users to these roles in the PowerBI service.
Dynamic RLS: Adjusts real-time data access based on user roles and attributes stored in the data itself, using DAX expressions like USERPRINCIPALNAME() to filter data dynamically. This is ideal when user access is based on varying criteria, such as region-specific data access.
Considerations: Both types require thorough testing to ensure accurate and secure visibility. Dynamic RLS can potentially slow down data retrieval and requires regular maintenance.
Data Anonymization and Masking: These techniques protect privacy by removing personally identifiable information or replacing it with pseudonyms. Techniques include generalization, suppression, or noise addition. Data masking specifically allows working with obscured versions of sensitive data, balancing transparency and security, for example, viewing only the last four digits of a credit card number. These are used for analysis and visualization while preserving privacy, especially when sharing data with external partners.
Data Integrity: Maintaining data integrity is crucial to ensure the accuracy and reliability of the visualized information. Key aspects include data validation, error detection, and consistency checks. Implementing data validation rules and performing regular audits helps identify and rectify anomalies. Encryption techniques can also prevent unauthorized modifications and tampering.
Secure Data Transmission: When transferring data or sharing visualizations, it is essential to prioritize secure data transmission using encrypted connections such as HTTPS or SSL/TLS. These protocols ensure data is encrypted during transit, making it difficult for unauthorized individuals to intercept or manipulate it. Other secure methods include using VPNs, two-factor authentication (2FA), enterprise cloud storage solutions, secure protocols like SFTP, and secure cloud-based platforms for distribution. Sharing reports externally requires secure embedding methods like publish to web or embed code, chosen carefully based on data sensitivity.
Data Sensitivity Labels: PowerBI’s data sensitivity labels allow categorizing data to safeguard company reputation and trust. They act like digital tags indicating the required level of confidentiality. Applying these labels properly ensures data protection, especially when sharing or exporting. The sources mention six categories: Personal, Public, General, Confidential, Highly Confidential, and Restricted. These labels can also include encryption settings, preventing access even if a file is inadvertently shared.
Sharing Permissions and Link Management: PowerBI’s link sharing feature allows distributing reports via a URL. However, this poses security risks, so access must be carefully managed. PowerBI offers different sharing options for links (e.g., people in your organization, specific people). Configuring sharing permissions is vital to safeguard data by determining who can access it and what they can do. Permission types include Read (view only), Build (use data for analysis/reports but not change source), Reshare (distribute to authorized users), Write (alter data sets), and Owner (comprehensive control). These permissions can be configured using the ‘Manage permissions’ option in the PowerBI service. When sharing externally, it is important to carefully control what information is shared and maintain strict security measures. Safe links with clear permissions, expiration dates, and limitations to specific users enhance report security. User licensing also needs to be considered for external partners.
External Sharing Settings: PowerBI administrators can adjust settings to enable external sharing while maintaining security standards, such as authorizing users or groups, setting content restrictions, controlling link expiration, and mandating authentication.
PowerBI Gateways: Data gateways, such as the on-premises data gateway, bridge the gap between PowerBI’s cloud services and on-premises data sources, allowing secure use of on-premises data in the cloud. The connection is outbound, which helps reduce security vulnerabilities.
Data Security in the Data Flow
Security considerations are relevant throughout the data flow stages: collection, processing, analysis, and decision-making. Processes within a business govern how data is acquired, stored, manipulated, and shared to support operations. Safeguarding data is important during data preparation (cleaning, transformation) [Source 1 – my previous response, not directly from the provided sources] and ensuring accurate data (data refresh). Planning for data storage and management involves considering security and implementing measures to protect data against unauthorized access, theft, tampering, and emerging threats.
Roles and Responsibilities
Various roles are involved in ensuring data security. Data analysts often work with sensitive data and must handle it with care. Database administrators safeguard the security and overall health of an organization’s databases. Data architects design strategies for data storage, integration, and retrieval, collaborating with other data professionals to align designs with business needs and support security objectives. BI analysts transform data into actionable insights and must work closely with other data professionals, considering data security when presenting to stakeholders. PowerBI Administrators control organizational settings related to security, including external sharing. Workspace roles (viewer, contributor, member, admin) define levels of interaction and access to assets.
In conclusion, security is a fundamental aspect of data visualization in PowerBI, crucial for protecting sensitive information, maintaining trust, ensuring data integrity, and complying with regulations. By implementing measures such as access control, RLS, data anonymization, secure transmission, sensitivity labels, and proper sharing permissions, organizations can build trust, protect sensitive information, and deliver reliable insights to stakeholders.
Microsoft Power BI: Data Analysis Study Guide
Quiz
What are the three key pieces of information required to construct an IF function formula in Excel? An IF function requires a logical test, a value to display or perform if the test is true, and a value to display or perform if the test is false.
Explain the primary difference between a nested IF function and an IFS function in Excel. A nested IF function involves placing one IF function inside another as an argument, typically in the “value if false” section. An IFS function is designed to handle multiple logical tests sequentially without requiring nesting.
According to the source material, why is gathering the right data crucial in the data analysis process? Gathering the right data is essential because it ensures the analysis is focused, relevant, and useful for the end user. Using irrelevant data will not provide insights needed for informed decisions.
What is the primary purpose of data profiling in Power BI, and what are two tools available in the Power Query editor for this? Data profiling identifies potential issues and anomalies within a dataset, enabling informed decisions about data cleaning and transformation. Column quality and column distribution are two tools in the Power Query editor for data profiling.
Define the terms “unique” and “distinct” as they are used in data profiling within Power BI, according to the source. “Unique” refers to the total number of values that appear only once in a column. “Distinct” refers to the total number of different values in a column, regardless of how many times each value appears.
What is DAX (Data Analysis Expressions) and what is its primary function in Power BI? DAX is a programming language used in Power BI (among other Microsoft tools) to create custom calculations on data models and generate additional information not present in the original data.
Explain the concept of “row context” in DAX calculations. Row context refers to the current row of a table being evaluated within a calculation. When a DAX expression is evaluated for a specific row, it considers the values in that row as the context for the calculation, allowing for row-level operations.
What are “calculated columns” in Power BI, and how do they differ from standard columns? Calculated columns are new columns added to an existing table in Power BI that display the results of a DAX formula. Unlike standard columns which are populated by imported data, calculated columns are generated dynamically based on existing data.
Describe the purpose of the CALCULATE function in DAX. The CALCULATE function in DAX evaluates an expression within a context that is modified by specified filters. It allows you to alter the filter context of a calculation, enabling more focused analysis.
What is the primary requirement for a table to be marked as a “date table” in Power BI for time intelligence calculations to function correctly? For a table to function correctly as a date table for time intelligence calculations, it must contain one record for each day, have no missing or blank dates, and span from the minimum to the maximum date present in the data.
Answer Key
Logical test, value if true, value if false.
Nested IF places IF functions inside each other as arguments; IFS handles multiple tests sequentially without nesting.
It ensures the analysis is focused, relevant, and useful for the end user and provides necessary insights for informed decisions.
To identify potential issues and anomalies within the dataset; Column quality and Column distribution.
Unique: Total number of values that appear only once. Distinct: Total number of different values regardless of frequency.
A programming language used for creating custom calculations and generating additional data not in the original model.
The current row being evaluated in a calculation, considering the values in that specific row.
New columns added using DAX formulas; they are calculated dynamically, while standard columns are from imported data.
To evaluate an expression in a filter context modified by specified filters.
One record per day, no missing or blank dates, and spans from minimum to maximum date.
Essay Format Questions
Compare and contrast the star schema and snowflake schema data models in Power BI. Discuss their key characteristics, advantages, disadvantages, and when you might choose one over the other.
Explain the concept of evaluation context in DAX. Discuss how row context and filter context interact and impact the results of DAX calculations, providing examples of each.
Describe the different types of measures in Power BI (additive, semi-additive, and non-additive). Provide examples of each and explain how the approach to aggregation differs for each type.
Discuss the importance of effective data visualization in Power BI for conveying insights to stakeholders. Describe at least three different visualization types mentioned in the source material and explain how they can be used to display key performance indicators (KPIs).
Explain the process of creating and utilizing data hierarchies in Power BI. Discuss why hierarchies are beneficial for data analysis and reporting, and describe how you can create your own custom hierarchies using different data fields.
Glossary of Key Terms
Autofill: A feature in Excel that allows you to quickly copy formulas or data down a column or across a row.
Logical Function: A function in Excel or Power BI that performs a calculation based on whether a condition is true or false.
IF Function: A logical function in Excel that returns one value if a condition is true and another value if it’s false.
Logical Operators: Symbols used in logical functions to compare values (e.g., =, >, <, >=, <=, <>).
Nested IF: An Excel formula where one IF function is placed inside another IF function’s arguments.
IFS Function: An Excel function that checks multiple conditions and returns a value corresponding to the first true condition.
Serial Numbers: How Excel interprets and stores dates for calculation purposes.
AutoFill Double-click Shortcut: A quick method in Excel to copy a formula down a column by double-clicking the fill handle.
DAX (Data Analysis Expressions): A programming language used in Power BI, Excel Power Pivot, and SQL Server Analysis Services for creating custom calculations and data analysis.
Data Modeling: The process of creating visual representations of data and defining relationships between data elements in Power BI.
Schemas: Structures used to organize data in a data model, such as star and snowflake schemas.
Relationships: Connections between tables in a data model, typically based on common key columns.
Cardinality: The nature of the relationship between two tables (e.g., one-to-one, one-to-many, many-to-many).
Cross-filter Direction: The direction in which filters propagate through relationships in a Power BI data model (e.g., single, bidirectional).
Calculated Tables: New tables created in a Power BI data model using DAX formulas based on existing data or combinations of data sources.
Cloned Tables: Exact copies of existing tables in a Power BI data model, often created to manipulate data without affecting the original table.
Calculated Columns: New columns added to an existing table in a Power BI data model that display the results of a DAX formula.
Measures: Dynamic calculations or metrics created in Power BI using DAX to summarize, analyze, and compare data across dimensions.
Additive Measures: Measures that can be meaningfully summed across any dimension (e.g., total sales quantity).
Semi-additive Measures: Measures that can be summed across some dimensions but not all, often problematic with the time dimension (e.g., inventory balance).
Non-additive Measures: Measures that cannot be meaningfully summed across any dimension (e.g., profit margin percentage).
Row Context: In DAX, the current row being evaluated within a calculation.
Filter Context: In DAX, the set of filter constraints applied to the data before it’s evaluated by an expression.
CALCULATE Function: A powerful DAX function that evaluates an expression in a context modified by specified filters.
Time Intelligence Functions: Specialized DAX functions designed to work with date and time data for temporal analysis (e.g., TOTALYTD, DATESBETWEEN, DATEADD).
Common Date Table (Date Dimension): A dedicated table in a data model containing a continuous list of dates, required for time intelligence calculations.
Data Granularity: The level of detail captured in a data set or data field (high granularity means more detail).
Data Profiling: The process of examining and summarizing data to understand its structure, content, and quality.
Column Quality: A data profiling feature in Power BI that categorizes values in a column as valid, error, or empty.
Column Distribution: A data profiling feature in Power BI that shows the frequency and distribution of values in a column.
Append Queries: A process in Power Query to combine rows from two or more tables with the same column structure into a single table.
Merge Queries: A process in Power Query to combine data from two or more tables based on matching values in common columns (similar to SQL joins).
Join Type: Determines how rows from two tables are combined during a merge query based on matching criteria (e.g., left outer, inner).
Primary Key: A column or set of columns in a table that uniquely identifies each row.
Foreign Key: A column or set of columns in one table that establishes a relationship to the primary key in another table.
Data Hierarchy: A structured way to organize data fields into levels, allowing for drill-down analysis in visualizations.
Drill Down/Up: Features in Power BI visualizations that allow users to navigate through different levels of a data hierarchy.
Bookmarks: A feature in Power BI reports that captures the current state (filters, slicers, visual state) and allows users to quickly return to that state.
Key Performance Indicators (KPIs): Measurable values that indicate the effectiveness of a company or department in achieving business objectives.
Card Visualization: A Power BI visual that displays a single data point or value.
Multi-row Card Visualization: A Power BI visual that displays one or more data points, with each data point on a separate row.
Radial Gauge: A Power BI visual that displays a single value measuring progress toward a goal or target.
KPI Visual: A Power BI visual specifically designed to track the performance of a metric against a target, often including a trend line.
Histogram: A type of bar chart used to visualize the frequency distribution of data, grouping values into ranges or bins.
Top N Analysis: A method to filter data to show only the top or bottom specified number of values based on a criterion.
Geo Hierarchy: A data hierarchy based on geographical locations (e.g., continent, country, state, city).
Custom Visualizations: Visualizations in Power BI created using programming languages like Python or R or developed to meet specific analytical or aesthetic needs.
Workspace Apps: A feature in Power BI Service that allows you to package and share an entire workspace (data sets, reports, dashboards) with specific users or teams.
Impact Analysis: A tool in Power BI Service to view which workspaces, reports, or dashboards are affected by a data set.
Lineage View: A view in Power BI Service that shows the connections and dependencies between different items in a workspace.
Permissions: Settings in Power BI Service that control who can access and interact with data sets, reports, dashboards, and workspace apps.
Use Relationship Function: A DAX function that allows you to activate an inactive relationship between tables for a specific calculation.
Role-Playing Dimension: A single dimension table in a data model that can play multiple roles in relationships with a fact table (e.g., a Date table related to both Order Date and Ship Date).
Briefing Document: Excel and Power BI Data Analysis Techniques
Summary:
This document summarizes the key concepts and techniques presented in the provided source material, focusing on fundamental data manipulation in Excel and various advanced data analysis and visualization capabilities in Microsoft Power BI. The sources cover Excel’s date/time and logical functions (IF, nested IFs, IFS), and delve into Power BI topics such as data modeling, DAX (Data Analysis Expressions), data preparation (profiling, cleaning, transforming, loading, merging, appending), visualization types, hierarchical data, bookmarks, and performance optimization. The importance of non-technical skills, data quality, and understanding analysis objectives is also highlighted.
Key Themes and Important Ideas:
1. Excel Fundamentals:
Working with Dates and Time: Excel interprets dates as serial numbers, allowing for calculations like subtraction. Functions like TODAY(), NOW(), DAY(), MONTH(), YEAR(), and DATE() are used to extract or combine date components and create dynamic date/time formulas.
“Excel interprets stored dates as serial numbers…”
“you can separate the date into its component parts so that you can focus on the year element type an equal sign the word year and an open parenthesis in cell H5…”
“…you also reviewed functions for creating dynamic formulas that calculate time and date values these include the today and now functions…”
“…you can also divide a date entry into its component parts using day month and year or return these components as a single date with the date function…”
Logical Functions (IF, Nested IFs, IFS): Logical functions allow Excel to perform actions based on conditions or logic, essentially asking “yes” or “no” questions about data.
“when working with Excel you might need to execute a function under certain conditions or logic in these instances you can use a logical function calculation like an if function…”
“You can use logical functions to ask yes or no questions about your data if the function returns yes as its answer then you can direct Excel to perform the required action however if the function returns an answer of no then Excel can be directed to perform a different action…”
Logical Operators: These operators are crucial for logical tests within formulas and compare values against specified criteria. Examples include =, >, <, >=, <=, and <>.
“for these tests to work the formula must contain logical operators the logical operators determine what kind of question the formula is asking and what value it needs for its answer these operators can be used to compare both text and numeric entries…”
“The equal sign is the first of the mathematical operators that Excel uses in logical functions excel uses this operator to check if the value of one item is equal to that of another item…”
“finally a very useful set of logical operators is not equal to this is when the less than and greater than symbols are typed back to back this combination of operators is interpreted by Excel as not equal to…”
IF Function Syntax: The IF function requires three arguments: a logical test, a value if true, and a value if false.
“when constructing the if function formula you need to give Excel three pieces of information the first piece of information is called the logical test… The next instruction tells Excel what to do or what to display if the test returns a result of true… The third and final argument is what Excel should do or display if the logical test returns the result of false…”
Nesting IF and IFS Functions: Nested IF functions allow for multiple conditions to be tested sequentially, with subsequent IF functions embedded within the value if false argument of the previous one. The IFS function provides an alternative, designed to run a series of tests without nesting, executing the action for the first test that returns true.
“what if you need to test for multiple conditions? You can use nested if and ifs functions…”
“nesting functions is the technique of adding another function to the formula as an argument for the original function in other words you can place one function inside another to expand its functionality…”
“One approach would be to create what is known as a nested if formula the formula begins with an if that performs an initial logic test if the test turns out to be true then the formula will simply process whatever action is specified in the value if true argument however the result of the logical test could also be false if so then another if function in the value of false argument could run another test and process different actions…”
“The second approach is to use a function called ifs an ifs function is designed to run a series of tests that don’t require you to nest other functions the ifs function steps through the tests checking each one if a test is false it continues to move through the tests until it finds one that is true when a logical test returns true as a result the formula performs or displays whatever is in the value if true for that test it then stops running tests…”
2. Power BI – Data Modeling and DAX:
Data Modeling: Creating visual representations of data and defining relationships between data elements to generate insights. Power BI is a key tool for this.
“data modeling is creating visual representations of your data in PowerBI you can use these representations to identify or create relationships between data elements by exploring these relationships you can generate new insights into your data to improve your business…”
“microsoft PowerBI is a fantastic tool for creating data models and generating insights and you don’t need an IT related qualification to begin using it…”
Schemas (Flat, Star, Snowflake): Different ways to structure data models. Star and Snowflake schemas are common, organizing data into fact and dimension tables.
“you’ll learn to identify different types of data schemas like flat star and snowflake…”
“when deciding on the data schema you plan to use for your analysis the most common schema types are star and snowflake schemas you may recall that in these schemas data is broken down into fact and dimension tables…”
Relationships: Connecting tables based on common keys (primary and foreign keys). Cardinality (one-to-one, one-to-many, many-to-many) and cross-filter direction are important aspects of relationships.
“you’ll create and maintain relationships in a data model using cardality and cross- filter direction…”
“a table relationship is how two tables are connected to each other…”
“in the products table the product ID column is what’s known as a primary key each value in the product ID column is unique… in the sales table the product ID column is what’s known as a foreign key it’s not the primary key of the table but instead it establishes a relationship to the products table…”
“Now that you know how to establish a relationship between two tables the next important aspect is the cardality of the relationship in PowerBI there are three types of cardality one many to one or one to many and many to many…”
DAX (Data Analysis Expressions): A programming language used in Power BI (and other Microsoft tools) to create custom calculations and generate information not present in the original data model. It uses functions, operators, and constants.
“if it’s possible to derive the data from the original model you can use DAX data analysis expressions to create custom calculations to generate the data…”
“dax is a programming language used in Microsoft SQL Server analysis services Power Pivot in Excel and PowerBI it is a library of functions operators and constants used in formulas or expressions to create additional information about the data not present in the original data model…”
“to master DAX you need to understand its syntax different data types the operators and how to refer to columns and tables using functions…”
DAX Syntax: Typically involves specifying the name of the new calculation, an equal sign, the DAX function name, and arguments within parentheses (often referencing table and column names).
“first write the name of your new calculation then add the equal sign operator next write the name of your DAX function then parenthesis that contain the logic of your formula write a table name enclosed in single quotes followed by the column name enclosed in square brackets…”
Operators in DAX: Used for various calculations and comparisons, including arithmetic, comparison, logical, and concatenation.
“dax formulas rely on operators there are many different types of operators they can be used to perform arithmetic calculations compare values work with strings or test conditions…”
DAX Functions: Reusable pieces of logic for tasks like aggregations, conditional logic, and time intelligence calculations. Examples include SUM, AVERAGEX, and SUMMARIZE.
“functions are reusable pieces of logic that can be used in a DAX formula these functions can perform various tasks including aggregations conditional logic and time intelligence calculations…”
“commonly used DAX formulas and functions include calculate sum and average…”
Row Context and Filter Context: DAX formulas are evaluated within a context. Row context refers to the current row being evaluated in a calculation. Filter context refers to the constraints applied to the data before evaluation, determining the subset of data used for calculations.
“dax computes formulas within a context the evaluation context of a DAX formula is the surrounding area of the cell in which DAX evaluates and computes the formula this surrounding area is determined by the set of rows and filters to be evaluated in a DAX expression it determines which subset of data is used to perform calculations…”
“row context refers to the table’s current row being evaluated within a calculation…”
“filter context refers to the filter constraints applied to the data before it’s evaluated by the DAX expression…”
CALCULATE Function: A powerful DAX function that can alter the filter context of a calculation. It evaluates an expression within a context modified by specified filters.
“calculate along with its companion calculate table is the only DAX function that can alter the filter context during a DAX calculation…”
“the calculate function evaluates an expression in a context modified by the specified filters…”
“from the examples you have learned the calculate only modifies the outer filter context by applying new filters this is done by either overriding the existing filter or by combining new filters with the existing ones…”
Measures: Calculations or metrics that generate meaningful insights from data, often using DAX. They are essential for quantitative analysis and can be categorized as additive, semi-additive, and non-additive.
“a measure is a calculation or metric that generates meaningful insights from data measures are an important aspect of data analysis and play a lead role in creating calculated tables and columns…”
“there are three different types of measures additive semi-additive and non-additive which type of measure is used depends on the needs of your data and its dimensions…”
Additive, Semi-Additive, and Non-Additive Measures:Additive: Can be meaningfully aggregated across any dimension (e.g., total sales).
Semi-Additive: Can be aggregated over some dimensions but not all, often time (e.g., inventory balance).
Non-Additive: Cannot be meaningfully aggregated across any dimension (e.g., profit margin percentage).
Statistical Functions in Measures: Functions like AVERAGE, COUNT, DISTINCTCOUNT, MIN, and MAX are used in measures to calculate values related to statistical distributions and probability.
“a key element of measures is statistical functions statistical functions calculate values related to statistical distributions and probability to reveal information about your data several common statistical functions are used in measures like average median and count…”
Calculated and Cloned Tables/Columns: Calculated tables and columns are new elements created within a data model using DAX formulas. Calculated tables can combine data from multiple sources or normalize dimension tables. Cloned tables are exact copies used for manipulation without altering the original. Calculated columns add derived data to existing tables.
“you can use calculated and cloned tables to enhance your data sets and improve your analysis…”
“a calculated table is a new table created within a data model based on data from different sources a calculated column is a new column added to an existing table that presents the results of a calculation…”
“cloning a table can be extremely useful for manipulating or augmenting data without affecting the original table…”
“calculated columns are custom data columns that are created within a Microsoft PowerBI data model using data analysis expressions or DAX language…”
Time Intelligence Functions: Specialized DAX functions for working with date and time data to perform advanced temporal analysis, including period-to-date calculations, comparisons, and moving averages. A common date table is a prerequisite.
“time is the dimension that virtually underpins all data analysis and for this reason time intelligence functions hold a position of paramount importance time intelligence functions are specialized functions designed to work with date and time data enabling users to perform advanced temporal analysis and gain deeper insight into historical data…”
“a common date table or date dimension is a prerequisite for time intelligence calculations you can’t execute them without a date dimension…”
“important time intelligence DAX functions is total year-to- date… date year-to- date function… dates between… same period last year… date add function…”
Common Date Table: A critical dimension table for time intelligence calculations, requiring one record per day, no missing or blank dates, and covering the full date range of the data. Can be created in Power BI using Power Query or DAX (CALENDAR, CALENDARAUTO).
“a common date table or date dimension is a prerequisite for time intelligence calculations…”
“the date dimension must meet the following requirements there must be one record per day there must be no missing or blank dates and it must start from the minimum date and end at the maximum date corresponding to the fields in your parameters…”
“you can create a date dimension in PowerBI using either Power Query or DAX this is useful when working on large data sets with complex calculations you can create a date dimension with DAX using the calendar and calendar auto functions…”
USE RELATIONSHIP Function: Used within other DAX functions (like CALCULATE) to override or activate an inactive relationship between two tables for a specific measure calculation.
“with the cross filter function you can change the cross filter direction for a specific measure while maintaining the original settings… Fortunately Adventure Works can use the cross filter function to alter the direction while maintaining the original settings…”
“the cross filter function changes the cross filter direction between two tables for a specific measure while maintaining the original settings…”
“you can only use use relationship within DAX functions that take filter as an argument for example calculate calculate table and total YTD…”
“the use relationship function in DAX overrides this relationship and establishes a temporary relationship between the date column of the date table and the shipping date column of the sales table this inactive relationship becomes active only during the current calculation when using the use relationship function there are some essential points to consider…”
3. Power BI – Data Preparation and Transformation:
Importance of Gathering the Right Data: The objective or purpose of the analysis informs the data collection process, ensuring the data is focused, relevant, and useful for the end user.
“gathering the right data is crucial for conducting a successful analysis however before you can start collecting data it’s essential to determine and understand the purpose or goals of the analysis you can then collect the appropriate data to conduct an analysis that is focused relevant and useful for the end user of the analysis…”
“the purpose of your analysis will inform what is the right data to collect including the type and scope of the data to gather and use in the analysis…”
Data Profiling: Analyzing data to understand its structure, content, quality, and patterns. Helps identify potential issues and anomalies for cleaning and transformation. Power BI’s Power Query Editor offers Column Quality, Column Distribution, and Column Profile tools.
“data profiling is the process of examining and analyzing a data set to understand its structure content quality and patterns…”
“data profiling enables the identification of potential issues and anomalies within the data set this proactive approach allows you to make informed decisions about data cleaning transformation and enrichment ultimately leading to improved data quality…”
“microsoft PowerBI offers the following two profiling tools in the Power Query editor column quality and column distribution…”
“column quality focuses on valid error and empty rows on each column allowing you to validate your row values…”
“column distribution provides a set of visuals underneath the names of the columns that showcase the frequency and distribution of the values in each of the columns…”
“another type of profiling in PowerBI is column profile column profile provides column statistics such as minimum maximum average frequently occurring values and standard deviation…”
Unique vs. Distinct: In Power BI, “unique” refers to values that appear only once, while “distinct” refers to the total number of different values regardless of frequency.
“before delving into data profiling tools let’s first consider two important factors in data profiling unique and distinct in PowerBI unique is known as total number of values that only appear once distinct is known as total number of different values regardless of how many of each you have…”
Data Cleaning: Addressing inconsistencies, errors, and missing values identified during profiling.
“you explored evaluating data data statistics and column properties reviewing why data evaluation is crucial Power Query’s profiling capabilities and different evaluation methods through an interactive activity you practiced analyzing a data set for anomalies and statistical irregularities preparing you for real world scenarios as a PowerBI data analyst you also explore data inconsistencies unexpected or null values and data quality issues you may encounter as a PowerBI data analyst as well as resolving data import errors…”
Transforming and Loading Data: Shaping data into a usable format and loading it into the data model. Includes creating and transforming columns, changing data types, and applying query steps.
“next you explored the transforming and loading data you reviewed creating and transforming columns understanding the importance of selecting appropriate column data types and how to transform columns and create calculated columns in Power Query you brushed up on shaping and transforming tables and applying query steps to shape the data exploring reference queries you recaped when to use reference or duplicate queries and also unpacked the differences between merge and append queries and explored the different types of joins…”
Merge vs. Append Queries:Append: Combines rows from multiple tables into a single table (stacking data). Works best when tables have the same column structure.
Merge: Combines columns from multiple tables based on a common key (joining data). Requires selecting a join type (left outer, right outer, full outer, inner, left anti, right anti).
“Append queries are a great way to consolidate data from multiple sources into a single table… append queries works well when the columns in the data source are well aligned and the desired resulting table should match the format of the data sources however you may encounter more complex scenarios requiring the merging of data from different sources this is where merge queries comes in…”
“to merge two tables you need to tell the merge query which type of join you would like to use the join type informs PowerBI how to merge the two tables a join requires that there is a common column between the two tables… this is known as the join key…”
“powerbi supports the following join types left outer right outer full outer inner join left anti-join and right anti- join…”
4. Power BI – Visualization and Presentation:
Visualizing KPIs: Displaying key performance indicators using Power BI visuals like Cards, Multi-row Cards, Radial Gauges, and the dedicated KPI visual. KPIs differ from regular charts by aligning with strategic business objectives.
“kpis differ from regular charts and metrics because they align directly with strategic business objectives instead of simply presenting raw data KPIs offer insight into how that data impacts overall business goals and progress…”
“microsoft PowerBI offers a range of visualizations to display KPIs including cards multirow cards gauges and the KPI visual…”
Card Visuals: Display a single value or data point, ideal for essential statistics.
“the card visualization displays one value or a single data point this type of visualization is ideal for representing essential statistics you want to track on your PowerBI dashboard or report…”
Multi-row Card Visuals: Display one or more data points, with one data point per row.
“next is the multirow card visualization that displays one or more data points with one data point for each row…”
Radial Gauge Visuals: Circular arcs displaying a single value, measuring progress toward a goal.
“another visualization you can use is the radial gauge this visual is a circular arc that displays a single value measuring progress toward a goal or target or indicates the health of a single measure…”
KPI Visual: Tracks a metric’s performance against a target and includes a trend line.
“lastly the KPI visual in PowerBI is a powerful tool for tracking the performance of a metric against a target the KPI visual also includes a trend line or chart to show the data’s trajectory over time…”
Data Granularity: Refers to the level of detail captured in a data set or field. High granularity provides deeper, more precise insights. The appropriate level of granularity depends on the analysis objectives.
“data granularity refers to the level of detail or depth captured in a certain data set or data field granular data provides deeper and more precise insights this delivers more nuanced and valuable findings…”
“data granularity isn’t about always having the highest level of detail it’s about having the appropriate level of detail before you begin your analysis ask yourself do you require high granularity or low granularity the decision should depend on the specific requirements and objectives of the analysis…”
Histograms: Visualizations illustrating the frequency distribution of data by grouping data points into ranges or bins. Often use bar or area charts.
“a histogram is a way to visualize a topend data query result while the topend function in PowerBI is a built-in DAX function that retrieves the topend records from a data set based on specific criteria it compares the parameters provided and returns the corresponding rows from the data source the n in top n refers to the number of values at the top or bottom data points are grouped into ranges or bins making the data more understandable a histogram is a great way to illustrate the frequency distribution of your data…”
Top N Analysis: Filtering data to display only the top or bottom ‘n’ values based on specific criteria, enabling quick identification of significant data points.
“the top end analysis prevents this by sorting the data to display according to a category’s best or worst data points this enables stakeholders to quickly identify the top or bottom values in the data and make datadriven decisions efficiently…”
Data Hierarchies: Structured ways to organize data (e.g., geographical, product categories) to allow users to drill down into data at different levels of detail. Can be created automatically by Power BI (for dates) or manually.
“PowerBI offers a way to unravel this mystery by creating a data hierarchy hierarchies provide a structured way to organize and visualize data allowing users to uncover hidden insights and tell a compelling story…”
“PowerBI has automatically created a hierarchy with all the date fields such as estimated delivery date and order date… How can you create a hierarchy of your own? Let’s create a hierarchy for product related data using the product category product subcategory color and product name fields…”
Map Visualizations: Used for visualizing geographical data. Requires correctly formatting geographical columns as data categories (Country, State/Province, City) and can benefit from using latitude and longitude coordinates for precision. Geo hierarchies enhance map visualizations.
“for map visualizations defining a precise location is especially important this is because some designations are ambiguous due to the presence of one location name in multiple regions for example there is a Southampton in England Pennsylvania and New York adding longitude and latitude coordinates solves this issue but if the data set does not have this information you will need to make sure to format the geographical columns as the appropriate data category…”
“adding depth to map visualizations leverages geo hierarchies you can drill down from country to state state to city and so on…”
Bookmarks: Capture and save the current state of a report (filters, slicers, display properties, current page, visual selection) to share specific views with others or for easy navigation.
“bookmarks in PowerBI are a way to capture the current state of the report you are viewing and share this state with other viewers…”
“when adding a bookmark there are four state options that you can save data properties such as filters and slicers display properties such as visualization highlighting and visibility current page changes which present the page that was visible when you added the bookmark and selecting if the bookmark applies to all visuals or selected visuals…”
Using Variables for Troubleshooting: Variables in DAX store values or tables temporarily, allowing for breaking down complex formulas into smaller, manageable parts. This aids in debugging and understanding the calculation process.
“maybe the weight of potential inaccuracies weighs on you mistakes mean mistrust in data and mistrust in data can lead to poor business decisions in this video you’ll learn how to use variables in DAX to troubleshoot issues like this one…”
“to recap a variable in DAX lets you store a value or a table to be used later in your formula think of them as placeholders or temporary storage units for your data by breaking down your DAX formula into smaller pieces and storing parts of the calculation in variables you can keep track of each step making the process more comprehensible and easier to debug…”
Power BI Service – Dashboards: Dashboards provide a single page view of key metrics and visuals from one or more reports. They are available in Power BI Service and mobile, but not Desktop. Tiles from reports or other dashboards can be pinned to dashboards.
“a PowerBI dashboard is a single page view of key metrics and visuals from one or more reports…”
“you can create and copy dashboards you must use the Microsoft PowerBI service you can view dashboards in Microsoft PowerBI service and in Microsoft PowerBI mobile dashboards are not available in PowerBI desktop…”
Duplicating Dashboards and Pinning Tiles: Dashboards can be duplicated in Power BI Service. Tiles from reports or other dashboards can be pinned to existing or new dashboards to consolidate visuals.
“to create a copy of a dashboard you must be the creator of the dashboard… you cannot pin tiles from dashboards shared with you only from dashboards created by you…”
“to duplicate a dashboard log into your PowerBI service and open the workspace that contains your dashboard… to pin a tile from one dashboard to another open the product sales dashboard from my workspace and hover the cursor on the tile to pin then select more options and select pin tile from the dropdown…”
Custom Visualizations (Python/R): Power BI allows for creating custom visualizations using Python or R programming languages for more advanced or specific needs. Requires installing Python/R and enabling scripting in Power BI.
“you can create custom visualization in PowerBI using Python or R programming languages these visualizations are imported from a file on your local computer you can also develop PowerBI visuals to meet your analytical or aesthetic needs…”
“using R or Python to develop your own PowerBI visuals or to customize existing ones is an optional expertise you may wish to pursue it if you have a coding background a familiarity with Python or want to extend your skill set into this area…”
Data Access and Permissions in Power BI Service: Power BI Service allows for managing data access and permissions at the dataset level and through workspace apps. Lineage view helps understand the impact of a dataset on reports and dashboards.
“effective data access and permission management is crucial to ensure that the right individuals have the appropriate level of access to sensitive data and reports…”
“with data set level permissions PowerBI service enables you to assign specific permissions to data sets while sharing you can ensure that although colleagues can access and utilize the data they cannot make changes to it this ensures the sanctity of vital data sets…”
“workspace apps in PowerBI allow you to share entire workspaces including data sets dashboards and reports ia workspace app is a full data package that can be shared with specific users or teams ensuring a comprehensive sharing experience…”
“to check how many workspaces reports or dashboards are affected by a data set you can perform what is known as impact analysis to do this you go to your workspace and hover on a data set then select the more options three dots next to it and select show lineage…”
Using Microsoft Copilot in Bing for DAX Assistance: Copilot can help troubleshoot DAX formulas, suggest corrections, and offer alternative approaches for complex calculations like nested IFs.
“Microsoft Copilot in Bing can also be a valuable companion in troubleshooting and improving your DAX formulas…”
“microsoft Copilot in Bing can help guide you through the correct structuring of calculate formulas suggest how to perform dynamic aggregations and even detect and suggest fixes to syntax errors…”
“Copilot can simplify this by suggesting straightforward alternatives or helping restructure these nested conditions into manageable components…”
5. General Concepts:
Importance of Non-Technical Skills: Developing non-technical skills like understanding end-user needs, relaying findings to stakeholders, collaboration, and creating actionable insights are crucial for data analysts.
“non-technical skills are equally vital these include a keen understanding of the needs of end users and the ability to relay findings and concepts to stakeholders of varying technical knowledge by developing these non-technical skills you can better collaborate with stakeholders create actionable insights inspire change and make lasting impacts enriching your own career and contributing to the growth and success of those around you…”
Data Quality: Emphasized throughout the data preparation process, focusing on completeness, accuracy, uniqueness, and consistency.
“data profiling enables the identification of potential issues and anomalies within the data set this proactive approach allows you to make informed decisions about data cleaning transformation and enrichment ultimately leading to improved data quality…”
This briefing document provides a high-level overview of the key topics and concepts covered in the provided source material, offering a foundation for understanding essential data analysis techniques in both Excel and Power BI.
Excel Functions and Power BI Data Modeling
How do Excel’s logical functions, such as the IF function, work and what are they used for?
Excel’s logical functions are used to ask yes or no questions about your data. Based on the answer to that question (true or false), Excel can be directed to perform different actions or display different values. The IF function is a common example, requiring three pieces of information: a logical test (a condition to check, often using logical operators), what to do if the test is true, and what to do if the test is false. For example, you could use an IF function to check if a sales figure is greater than or equal to a target; if true, award a bonus, and if false, award nothing. Logical operators like =, >, <, >=, <=, and <> (not equal to) are essential components of these tests.
When might you need to use multiple conditions in Excel logical functions, and what are the approaches?
You might need to test for multiple conditions when a simple yes/no question isn’t sufficient. For instance, determining different bonus levels based on varying sales thresholds. There are two main approaches: using nested IF functions or using the IFS function. A nested IF involves placing an IF function within another IF function’s “value if false” argument to perform a subsequent test if the initial one is false. The IFS function is designed to run a series of tests without nesting, stepping through each condition until one is true and then performing the corresponding action.
What is Data Analysis Expressions (DAX) in Power BI and what are its key components?
DAX is a programming language used in Power BI, SQL Server Analysis Services, and Power Pivot in Excel. It’s a library of functions, operators, and constants used to create additional information or custom calculations on data models that isn’t present in the original data. Key components of DAX include syntax (defining calculations, often starting with a name, equals sign, and function), operators (for arithmetic, comparison, logic, and concatenation), functions (reusable logic for tasks like aggregation, conditional logic, and time intelligence), and understanding the data model (tables, relationships, and context).
How do row context and filter context influence DAX calculations in Power BI?
DAX formulas compute values within a context. Row context refers to the current row being evaluated within a calculation. This allows calculations to be performed row by row, which is useful for tasks like creating calculated columns where a calculation is applied to each row independently. Filter context refers to the filter constraints applied to the data before a DAX expression is evaluated. This determines which subset of data is used for calculations. Changes in filters (like selecting a specific product category or region) will alter the filter context, leading to different results for the same DAX measure.
What are measures in Power BI, what types exist, and why are they important for analysis?
Measures in Power BI are dynamic calculations or metrics used to generate insights from data. They are essential for quantitative analysis and summarizing, calculating, and comparing data. There are three main types: additive measures (which can be meaningfully summed across all dimensions, like total sales), semi-additive measures (which can be summed across some dimensions but not all, particularly time, like inventory balance), and non-additive measures (which cannot be meaningfully summed across any dimension, like percentages or ratios). Measures are important because they compute values on the fly based on the current filter context, allowing for dynamic analysis and reporting.
What are calculated and cloned tables in Power BI and when would you use them?
Calculated tables are new tables created within a Power BI data model using DAX expressions, often based on data from existing tables or even multiple sources. Cloned tables are exact copies of existing tables. You would use calculated tables to combine data from different sources, normalize dimension tables (like in a snowflake schema), create a common date dimension table, or generate summary tables from large datasets. Cloned tables are useful when you need to manipulate or augment data without affecting the original table, especially if the original data is refreshed periodically.
How do data granularity and geographical hierarchies contribute to data analysis in Power BI?
Data granularity refers to the level of detail captured in a dataset or data field. High granularity provides deeper and more precise insights, while low granularity offers a more summarized view. Choosing the appropriate level of granularity depends on the analysis objectives. Geographical hierarchies in Power BI (like Country > State > City) provide a structured way to organize and visualize data based on location. They allow users to drill down into data from a broad overview to a more detailed level, enabling the analysis of trends and performance at different geographical scales.
What is the significance of data modeling, schemas (Star and Snowflake), and table relationships in Power BI?
Data modeling in Power BI involves creating visual representations of your data and defining relationships between data elements to generate new insights. Schemas, such as the Star and Snowflake schemas, are common structures for organizing data into fact tables (containing measurements and metrics) and dimension tables (providing contextual attributes). Table relationships, established using primary and foreign keys, define how these tables are connected. Understanding and correctly configuring cardinality (one-to-one, one-to-many, many-to-many) and cross-filter direction in these relationships is crucial for accurate data analysis and filter propagation in Power BI calculations.
Power BI Tutorial For Beginners To Advanced | Master Power BI From Beginner to Expert, By Microsoft
The Original Text
data is an important part of your day-to-day existence think about how many times you collect and make use of data every day for example you may have recently compared the cost of flights to find the best value for your vacation or you might have asked your friends to let you know what dates they’re available to meet for a party so that you can find a day that suits everyone in the group so how do data analysts make use of information just like when you plan your vacation or party they identify and gather important data then study and analyze the data to generate the insights that they need data analysts carry out these tasks using a range of techniques tools and software like Microsoft Excel and Microsoft PowerBI these might sound like complicated technologies but it’s possible to approach them from an entry-level stage and develop competency and this high demand at an organizational level for individuals who can demonstrate proficiency with these tools the career opportunities available for data analysts include a range of roles from business analyst to data scientist to database administrator with increasing digitization of all aspects of life the demand for these roles across all business sectors is greater than ever with the right knowledge and skills you could be the next data analyst an organization is looking for you might be keen to pursue a career in data analytics but you might also be concerned that you don’t have a relevant university degree or prior experience or maybe the cost is just too high don’t let these concerns hold you back if you’re fascinated by the world of data and willing to join us then we’re offering you a chance to embark on a learning journey that prepares you for an exciting career in data analytics this Microsoft PowerBI analyst professional certificate consists of a series of courses that act as a solid foundation of fundamental knowledge that imparts the skill set required for an entry- level job in data analytics in addition finishing this program also prepares you for the exam PL300 Microsoft PowerBI data analyst earning a Microsoft certification provides industry endorsed evidence of your skills and demonstrates your willingness to stay on top of the latest trends and demands and stand out in a fast changing industry you’ll begin this program with an overview of how to design and manage spreadsheets using Microsoft Excel this overview begins with a guide to Excel elements and techniques along with guidance on how to organize data you’ll then learn how to prepare data for analysis using different functions this overview of Excel will help you to understand the importance of sourcing and organizing data so you’ll follow it with an exploration of the different stages and roles in the data analysis process you’ll begin by learning about essential data analysis concepts and the role of the data analyst you’ll then review the tools required to source gather transform and analyze data effectively sourcing data is important but so is preparing it for analysis that’s why you’ll also learn how to bring data into PowerBI and clean and transform it for analysis you’ll begin by learning about different data sources in PowerBI you’ll then learn techniques for importing the data lastly you’ll discover how to clean and transform data once you’ve imported your data you then need to organize it so that you can make sense of the information to generate insights so you’ll also review techniques for modeling data you’ll start by developing an understanding of basic data modeling concepts you’ll then learn how to use DAX in PowerBI to create calculations finally you’ll discover how to optimize the performance of a data model in PowerBI the ability to generate insights from your data is great but you also need to be able to communicate these insights that’s why you’ll also explore the techniques and tools used to create visual presentations of data you’ll begin by exploring visualization concepts and you’ll also learn how to create reports next you’ll learn how to ensure your reports contain navigation and accessibility elements you’ll then explore how to bring data to the user by managing access and creating dashboards finally you’ll review methods and techniques for identifying patterns and trends in your data another important skill you’ll require is the ability to make use of available PowerBI assets so you’ll also learn how to create use monitor and manage a workspace and you’ll discover how to manage share and secure data sets in PowerBI not only do you need to be able to visualize your data but it’s also important that you can use it to tell a story or narrative during this program you’ll explore how to design robust and compelling visualizations to communicate your data with stakeholders you’ll start by exploring key principles of design and the importance of narrative you’ll then learn techniques for designing report pages with powerful visuals and you’ll review design principles and techniques for dashboards you’ll complete a final capstone project where you’ll put your new skills to use by developing a PowerBI dashboard in the final course you’ll prepare for the PL300 exam by undertaking a practice exam this exam covers all the main topics of the Microsoft Certified Exam PL300 so it’ll also help you determine if you’re ready for the real thing once you complete the program it’s time to start exploring potential careers and don’t forget to share your Corsera Professional Certificate to get that extra advantage congratulations on your decision to become a data analyst and to help make sense of data for others now let’s get started have you ever faced the challenge of making decisions or providing insights based on large amounts of data this can be quite a daunting task especially if the data is difficult to read and understand fortunately you’ve come to the right place this course on preparing data for analysis in Microsoft Excel will equip you with the skills you need to work with large blocks of data and make it easier to read and understand data analysis is a process that involves defining the purpose of the data gathering cleaning and analyzing it to gain insights businesses often use data analysis to obtain usable relevant information that can assist them in making educated business decisions however this is usually done with large amounts of data that you need to cleanse transform and analyze you will often have to present this data in charts tables and graphs that provide relevant insights your data insights will help organizations to lessen the risks associated with making business decisions microsoft Excel can assist you in analyzing data for your business and you don’t need an IT related qualification to do this the preparing data for analysis with the Microsoft Excel course is designed for anybody that’s interested in learning about preparing data for analysis within a business context it also establishes a foundation for anyone striving to have a career in data analytics through data analytics in Excel you will be able to collect store and delve deeper into your business’s data you will also learn to harness the power of data using tools for sourcing gathering transforming and analyzing data now let’s go over a brief overview of what you will learn over the next few weeks to kickstart your learning journey you’ll discover the fundamental and essential Microsoft Excel elements and techniques for creating workbook content these techniques include entering formatting managing and adding data to worksheets you’ll then learn how to read large blocks of data and review the steps for sorting and filtering data in Excel next you’ll discover how to use formulas and functions to perform calculations in Excel then you’ll learn how to prepare data for analysis using functions you’ll explore functions that are used to clean or standardize text to prepare it for effective analysis you’ll then investigate the use of date and time functions in Excel so that you can complete actions like creating timeline information in a spreadsheet you’ll also review the logical functions like if and ifs and you’ll learn how to use these logical functions to generate content like data columns in the last module you’ll undertake a final project in this project you’ll create a worksheet with an executive summary of a business’s month-by-month profit margin performance compared to the previous year this project will help you prepare for the final capstone project at the end of this program finally you’ll have a chance to recap on what you’ve learned and focus on areas you feel you can improve on throughout the course you will encounter many videos that will gradually guide you toward a solid understanding of preparing data for analysis watch pause rewind and re-watch the videos until you are confident in your skills then consolidate your knowledge by consulting the course readings and measuring your understanding of key topics by completing the different knowledge checks and quizzes by the end of the course you’ll be equipped with the necessary skills to work effectively with data in Microsoft Excel good luck as you start this exciting learning journey the Microsoft PowerBI Analyst program is an excellent resource to start your career whether you’re a beginner or a seasoned professional looking to improve your skills data is the driving force behind this everchanging modern world shaping and developing industries and society it has transformed the way institutions operate from banks and hospitals to schools and supermarkets and for businesses data is everything it informs decisions and helps create value for customers content streaming services analyze data to decide what content to promote social media services analyze data to determine what products their customers are interested in and your local supermarket gathers and analyzes data to ensure the products you want are available the result of having all this data is that professional analysts are required to process and sort it to gain the insights that drive both the business and social worlds are you intrigued by this career field and wondering how to get started let’s meet two other students who have just begun their careers in entry- levelvel positions discover how and why they have chosen to embark upon career paths in this field with Microsoft and Corsera lucas a recent information technology graduate is currently searching for his first IT job he is eager to secure a position in the IT sector that offers good earning potential and a quick career progression he wants to work full-time in data analysis as he feels this career would offer both benefits during his degree he found working with and analyzing cloud-based data to be the most enjoyable element hence his focus on this career path lucas currently works shifts in a warehouse environment so he will need the flexibility of self-paced learning his earnings are low so he wants to achieve the qualification using the same basic laptop he relied upon as a student despite being a beginner Lucas has already mapped out his career and certification path and has enrolled in the Microsoft PowerBI analyst program he plans to apply for an entry- levelvel position as a data analyst once he has successfully completed the program and passed the PL300 exam as a data analyst he will inspect data identify key business insights for new business opportunities and help solve business problems amelia has been working as an administrative assistant in sales and marketing since leaving high school now that a few years have passed she is ready to embark upon a new career path in her current role Amelia has seen PowerBI reports and dashboards created by colleagues and shared with the team she was impressed at how the information was used to shape and focus the sales campaigns this sparked an interest in a career in data analysis amelia’s job requires her to work long hours so the ability to structure her own learning path is vital she also has a long commute so would like to access e-learning through her smartphone or tablet pursuing the PowerBI analyst qualification will showcase her dedication and help her apply for more senior roles in the department in the short term amelia doesn’t have a scientific background but she finds IT concepts logical and easy to understand so she’s embarking on the Microsoft PowerBI analyst program as it doesn’t assume a pre-existing high level of technical knowledge in the long term she hopes to secure an entry-level role as a PowerBI analyst as a PowerBI analyst she will be responsible for building data models creating data assets like reports and dashboards and ensuring data requirements are met you may be in a similar position to Lucas and Amelia and possess an interest in this exciting field of data analysis like them you can begin your career in this field by enrolling in the Microsoft PowerBI analyst program this will be the start of your new adventure good luck with your learning journey generative AI stands at the forefront of a transformative era reshaping our interaction with data and redefining the boundaries of creativity across diverse sectors this innovative tool utilizes sophisticated statistical techniques to generate content across text images and code empowering individuals and industries with remarkable capabilities in this video you’ll gain an understanding of the multifaceted landscape of generative AI exploring its vast capabilities industry implications and the career opportunities it presents before we get into more detail let’s answer the question what is generative AI examples of these models are generative adversarial networks or GANs and transformer models with these models generative AI can create outputs that closely mimic humanmade content using generative AI as an assistant can make a positive contribution across multiple industries for example imagine a trendy clothing store using generative AI to design unique patterns and styles based on customer preferences with GANs the AI could generate lifelike images of clothing designs enabling the store to offer personalized options to each customer this application not only enhances the shopping experience but also streamlines the design process illustrating how generative AI is reshaping industries through its creative capabilities now that you’re up to speed on what generative AI is let’s explore some of its capabilities across different functions firstly there’s text generation where generative AI models like generative pre-trained transformer or GPT can compose essays generate creative writing automate customer support and more imagine how generative AI can bring the store collection to life for shoppers effortlessly crafting engaging product descriptions captivating social media posts and personalized customer communication that mimics the tone and style of human interaction next there’s image creation generative AI can transform textual descriptions into stunning visual representations for the retail store this means converting text into realistic images of new apparel designs from elegant evening gowns to casual streetear providing the store’s creative team with endless inspiration and flexibility in bringing their vision to life this capability is revolutionizing fields such as graphic design video game development film production and marketing and branding where custom visuals can be created quickly and at scale with audio production the store’s marketing and branding department uses generative AI’s audio ability to synthesize speech compose music and create sound effects generative AI produces captivating audiovisisual content for advertising campaigns captivating audiences and enhancing brand visibility in addition to its applications in creative fields like fashion generative AI also showcases its capability in code generation imagine the retail store leveraging generative AI to optimize its online presents ai would aid the store’s programmers by suggesting improvements completing lines of code or even creating entire programs this would not only streamline website development but also enhance user experience ensuring seamless navigation and captivating visuals for online shoppers finally there is data synthesis in the fashion world staying ahead of the curve is crucial and generative AI aids the store in achieving just that it utilizes extensive data sets on fashion trends customer preferences and style influencers the store can conduct market research and analyze customer behavior ethically and responsibly by generating synthetic data sets that maintain statistical properties without compromising individual privacy this application is crucial for training more AI models where access to real data might be restricted or unethical so what are the industry implications of this emerging technology the deployment of generative AI across various industries indicates a major shift in operational dynamics in healthcare AI generated models can predict patient outcomes personalize treatment plans and automate administrative tasks in finance AI can manage risk assessment automate trading and personalize banking services the creative industry is seeing an explosion of innovation and inspiration as generative AI aided tools are contributing hugely to the fields of art music and literature pushing the boundaries of traditional creativity as AI evolves its impact on the workforce and industry standards will be significant the demand for AI knowledge is growing and learning to work with AI will be crucial for career advancement in all fields jobs that traditionally didn’t involve technology will start using AI tools more often this shift will require professionals in most fields to develop new skills and undergo additional training to effectively integrate generative AI into their work as a result educational programs and workshops focusing on generative AI and its applications are becoming increasingly important offering valuable resources for those looking to stay relevant and excel in their careers both businesses and individuals need to understand and adapt to generative AI’s capabilities to fully harness its potential generative AI is not just a tool for creating and automating it is a catalyst for innovation and transformation across all areas in this video you gained an understanding of the capabilities of generative AI and its implications for various industries you also explored some of the career opportunities it will create as we continue to explore and expand these technologies capabilities the opportunities for advancement and creativity are limitless welcome to the age of generative AI where everyone has the chance to redefine the boundaries of what is possible generative AI is transforming businesses today by gathering information and creating all kinds of content changing how businesses operate let’s imagine a renowned restaurant called Chef’s Table as chef Andre strives to innovate and delight his patrons with new dishes he turns to generative AI to enhance his culinary creations the technology behind this ability involves using models trained on huge sets of data to do tasks such as text generation image creation and even code synthesis in Chef Andre’s kitchen Generative AI acts as his trusty sue chef assisting him in developing innovative recipes crafting visually stunning presentations and even optimizing kitchen workflows just like Chef Andre relies on his sue chef to complement his skills and creativity generative AI compliments businesses by providing them with new insights ideas and efficiencies in this video you’ll explore the technical foundations and potential applications of generative AI in businesses like Chef’s Table you’ll also assess its limitations and examine the ethical considerations that arise when using it first let’s gain some insight into the technical foundations of generative AI it operates primarily through two types of models generative adversarial networks or GANs and transformer-based models guns involve two neural networks the generator and the discriminator working in tandem to produce highly realistic outputs these two components are known as the generator and the discriminator imagine the generator as a chef preparing a new dish and the discriminator as a food critic tasting it the chef the generator creates new dishes while the food critic the discriminator evaluates them if the critic cannot distinguish between the chef’s creations and dishes from renowned restaurants then the chef has succeeded this collaborative process results in the creation of highly realistic and refined outputs transformers used by models like generative pre-trained transformer or GPT and birectional encoder representations from transformers or BERT use attention mechanisms to create text that is contextually relevant and stylistically coherent attention mechanisms play a crucial role in the model’s functionality these mechanisms enable the model to focus selectively on various parts of the input data much like a chef carefully chooses the best ingredients for a dish this selective focus allows the model to highlight important information and maintain a clear grasp of the context imagine a chef who not only selects fresh ingredients but also keeps the recipe and cooking techniques in mind to craft a delicious and well- balanced meal similarly attention mechanisms ensure that the text generated by the model is coherent and contextually appropriate rather than a random assortment of words these technologies rely on deep learning needing a lot of computer power and data to train them how well a generative AI model works depends on the quality and variety of its training data which affects its ability to generalize new information without upholding biases so you’ve learned about the technical foundations of generative AI but what are its practical applications in various business functions in marketing and customer engagement generative AI can craft personalized content at scale from email marketing campaigns to dynamic web content think of this as a chef preparing a personalized menu for each diner based on their preferences creating unique and delightful dining experiences ai models can enhance engagement and conversion rates by analyzing existing customer data and tailor messages that resonate on an individual level additionally generative AI assists in optimizing operational efficiencies and logistics for instance AI can forecast demand trends simulate supply chain scenarios and recommend adjustments this is like a chef estimating the number of diners planning the menu and ordering ingredients to minimize waste and make customers happy this predictive capability enables Chef’s Table to make informed decisions reduce costs and improve service delivery in the area of human resources AIdriven analysis of job descriptions and applicant data helps streamline the recruitment process by generating and evaluating diverse job descriptions AI can attract a wide range of candidates potentially reducing biases often found in manual processes additionally generative AI can simulate training scenarios providing personalized learning experiences for employees think of this as a chef conducting cooking classes tailored to the skill levels and learning styles of each student ensuring everyone learns effectively another application of generative AI is document management and technical writing it can analyze extensive data sets of documents to learn and replicate the necessary formatting style and technical language specific to different business sectors for example AI models trained on legal documents can help to draft contracts that comply with current laws and regulations furthermore models trained on medical texts can help in preparing accurate clinical trial reports the technologies ability to understand and generate technical content is like Chef Andre mastering the preparation of complex dishes ensuring consistency and high standards without extensive manual effort one of the standout features of generative AI is its capacity to mimic specific writing styles this capability is particularly useful in marketing and customer communications where maintaining a consistent brand voice is crucial by training on a company’s historical communication data AI can generate content that aligns with the brand’s tone style and audience engagement strategies additionally it can adapt to different styles as needed much like a versatile chef who can cook various cuisines to cater to diverse tastes and cultural preferences finally the ability of generative AI to produce coherent and contextually relevant text has wide ranging application in business for instance it can generate product descriptions marketing copy or news articles with little to no human input significantly speeding up the content creation process moreover in customer service AIdriven chat bots can handle inquiries and provide responses in real time improving customer experiences and operational efficiency these applications demonstrate the potential of generative AI to take over repetitive and time-conuming tasks enabling employees to focus on more strategic activities much like a chef relying on a well-trained kitchen staff to handle routine tasks while focusing on creating innovative dishes despite its capabilities generative AI is not without limitations and may raise some ethical concerns the quality of output can vary significantly depending on the model’s training inaccuracies can emerge especially when the AI encounters data or requests outside its training scope moreover there’s the potential for AI to reinforce or amplify biases present in the training data leading to unfair outcomes or ethical dilemmas this is similar to a chef needing to ensure their ingredients are fresh and free from contaminants as any issue can affect the final dish ethical concerns that must be addressed include issues such as data privacy intellectual property and the potential for misuse therefore businesses must establish clear guidelines and ethical frameworks to govern AI use ensuring that AI generated outputs align with legal and moral standards think of it as a chef adhering to food safety regulations and ethical sourcing practices to ensure every dish is not only delicious but also responsibly made in this video you learned how generative AI offers substantial benefits across various business functions enhancing productivity decision making and customer engagement however to leverage this technology effectively businesses must understand its technical foundations potential applications and limitations you also gained insight into how responsible use of generative AI guided by strong ethical principles is essential to harness its full potential while reducing associated risks as businesses continue to integrate AI into their operations the focus must remain on creating value responsibly ensuring that AI solutions are deployed in a manner that is both effective and ethical like a master chef businesses must blend innovation with responsibility to create a successful and sustainable future picture a future where machines not only grasp our language but also craft it with remarkable finesse where creativity knows no bounds as artificial minds effortlessly generate images and ideas this isn’t the stuff of sci-fi dreams it’s the emergence of generative AI a tool that will complement and benefit us in both our work and our everyday lives to gain a better understanding of generative AI it is crucial to dive into its foundational technologies such as machine learning models and their architectural nuances let’s get started by exploring the distinguishing features of generative AI unlike traditional AI which typically focuses on analysis and classification generative AI is proactive in creating new content this shift from passive analysis to active creation is transformative especially in handling complex tasks such as natural language processing or NLP and synthetic image generation nlp enables machines to read understand and generate human language while synthetic image generation involves creating fake images using computer programs and algorithms it’s like a digital artist creating a convincing picture of a landscape they’ve never seen before the introduction of transformers a type of model architecture that relies on mechanisms called attention and self attention has revolutionized NLP models like Google’s birectional encoder representations from transformers or BERT and Open AI’s GPT series use these transformers they learn the relationships between words in a text but not in the usual order from start to end instead they can understand different parts of the text at the same time it’s like reading a mystery novel and being able to pick up on clues scattered throughout the book all at once this way of learning allows for more things to be processed at the same time making the training quicker and more efficient so those are some of the distinguishing features but what are the technical foundations of generative AI it primarily operates through two types of machine learning supervised and unsupervised in supervised learning models are trained on labeled data sets allowing them to learn a function that can map input data to desired outputs for example a model might be trained to generate text summaries by learning from a data set of articles paired with their respective summaries unsupervised learning on the other hand involves training models on data without explicit labels here the aim is for the models to discover inherent patterns and relationships in the data this approach is particularly beneficial for generative AI as it allows the model to learn to create content that is not bound by predefined labels enabling more innovative and adaptive applications next let’s take a closer look at some of the core technologies behind generative AI at the heart of its capabilities are neural networks particularly generative adversarial networks or GANs and variational autoenccoders or VAEs variational autoenccoders or VAEEs encode input data into a compressed representation and then decode it back to reconstruct the input the process involves optimizing the parameters of the encoder and decoder so that the output closely matches the input allowing the model to generate new data samples from learned representations language models are constantly evolving so it’s important to keep up to date with these advancements language models such as GPT3 and BERT demonstrate significant advancements in generative AI these models use transformer architectures which rely on self-attention mechanisms to process sequences of data like sentences in ways that consider the context provided by other parts of the sequence this is crucial for generating coherent and contextually appropriate text word tovec another critical technology involves vectorizing words into a geometric space where words with similar meanings are located close to each other this enables more nuanced understanding and generation of text based on semantic similarities rather than just syntactic rules generative AI has many business applications and can revolutionize several key areas let’s explore some in more detail firstly there’s content generation gpd models excel in generating written content by leveraging transformer architecture which allows them to understand context and generate coherent and contextually appropriate text these models are pre-trained on a wide variety of internet text and fine-tuned for specific applications enabling them to create highquality articles blogs and other written materials next is personalization the process starts with collecting user data from sources like websites apps and social media integrated data pipelines using tools like Apache Kafka or Google Cloud Data Flow consolidate this data in real time realtime analytics platforms such as Apache Spark streaming or AWS Kinesis process the data to extract insights which feed into a personalization engine that generates tailored recommendations content and communications these personalized interactions are delivered using APIs integrated with various platforms to ensure low latency responses edge computing technologies like AWS Green Grass or Azure IoT Edge process data closer to the user additionally there’s automation ai models trained on large data sets and using advanced algorithms automate these processes improving efficiency and reducing costs the technical backbone includes robotic process automation or RPA for executing repetitive tasks AI powered software tools for intelligent decision making and cloud services that provide the necessary scalability and support continuous learning and adaptation of the models this infrastructure ensures that AI systems remain upto-date and can handle increasing volumes of work effectively and finally innovation generative AI fosters innovation by simulating and modeling various scenarios to predict outcomes aiding businesses in developing new products and services with higher success rates this involves using advanced AI models for predictive analytics scenario planning and risk assessment including techniques like regression analysis time series forecasting Monte Carlo simulations Beijian networks and stress testing large data sets from diverse sources are processed using tools like Apache Hadoop and Apache Spark simulation tools such as digital twins and optimization algorithms are used to predict performance and find optimal solutions from what you have learned in this video it is clear that generative AI is a powerful tool that when leveraged responsibly can provide significant advantages to businesses by automating tasks personalizing customer experiences and driving innovation you’ve gained an understanding of how generative AI continues to evolve providing useful business applications as the technology continues to evolve it will likely become an even more integral part of the digital business landscape it’s no secret that generative AI has significantly transformed various job functions in the workplace from automating routine tasks to enhancing creative processes these systems use vast amounts of data to create new content make predictions and even make decisions despite its revolutionary potential generative AI is not without its pitfalls and shortcomings which raise several risks challenges and ethical considerations that must be carefully managed in this video you will gain further insight into these challenges and limitations but first let’s explore how generative AI can be integrated into different job functions in many sectors generative AI tools are employed to streamline operations and enhance productivity for example in roles such as content creation AI can produce drafts suggest edits and generate creative ideas which allows human workers to focus on more strategic aspects of their work similarly in software development AI can write code debug and even test software streamlining the development process and reducing time to market a significant shortcoming of generative AI was highlighted by the use of Open AI’s GPT3 in generating medical advice in one instance GPT3 was used to provide mental health support and it suggested to a simulated user experiencing distress to commit self harm this incident underscored the danger of relying on AI for sensitive tasks without robust safeguards the model generated harmful advice because it lacked the nuanced understanding and ethical judgment required in mental healthcare relying instead on patterns learned from its training data this example demonstrates the potential risks and severe consequences of deploying AI without adequate human oversight and ethical considerations these capabilities not only optimize efficiency but also offer significant cost savings and scalability for growing businesses however the integration of AI into these roles is not always seamless the reliance on AI can lead to job displacement as roles traditionally failed by humans become automated furthermore the quality of AI generated outputs can be inconsistent while AI excels in generating structured content it struggles with tasks requiring deep understanding or emotional intelligence often producing outputs that are awkward or contextually inappropriate earlier you learned that businesses need to adopt ethical considerations given the potential for bias in AI generated content since AI models learn from data they inherently acquire the biases found in their training data sets this can result in discriminatory practices such as favoring one demographic group over another when AI is used in HR for resume screening or job recommendations maintaining the privacy of personal data is a primary objective for businesses when using generative AI systems to interact with personal data care must be taken to ensure confidentiality and user privacy these systems can inadvertently expose sensitive information or even be used to generate deep fakes contributing to misinformation and potentially harming individuals reputations next let’s examine some of the challenges of reliability and accountability when using generative AI ai systems are notorious for their blackbox nature meaning the processes they use to reach conclusions are not always clear this lack of transparency can lead to reliability issues where businesses find it challenging to understand or predict the AI’s behavior this is particularly problematic in highstakes environments like healthcare or finance where unexpected AI decisions can have serious consequences accountability is another challenge when errors occur it’s difficult to determine responsibility between the AI developers the users and the AI itself this complicates legal and regulatory frameworks which are often illequipped to handle the novel implications of AI technology despite their advanced capabilities generative AI systems often lack common sense reasoning a basic human ability to make practical judgments about everyday situations ai can generate plausible sounding responses or content that upon closer examination is nonsensical or impractical this limitation is due to the AI’s reliance on pattern recognition instead of understanding underlying principles or contexts implementing generative AI in a workplace context involves various hurdles these include the technical challenge of integrating AI with existing IT systems the need for significant investment in technology and training and the ongoing requirement to update and maintain AI systems to adapt to new data or changing conditions additionally if an organization is resistant to change and its staff are doubtful about AI this can also make it harder to implement effectively to reduce potential harm and ensure ethical AI deployment it is crucial to adhere to guidelines like those set by major technology companies including Microsoft these guidelines emphasize fairness reliability privacy inclusiveness accountability and transparency organizations must commit to rigorous testing and auditing of AI systems to identify and correct biases protect data privacy and ensure that AI systems perform as intended without infringing on ethical norms in this video you’ll learn that while generative AI presents remarkable opportunities for transforming workplace operations and enhancing productivity its implementation must be approached with a nuanced understanding of its limitations and potential risks by prioritizing ethical considerations and responsible use organizations can harness the benefits of generative AI while mitigating its shortcomings this balanced approach is essential for realizing the full potential of AI technologies in a manner that respects human values and social standards at this point in the course you might view Microsoft Excel as a complicated software application or believe it’s only used for working with financial data however Excel is designed to be very userfriendly and can assist with many different types of data and tasks in this video you’ll discover Excel’s primary purpose and use cases and explore key parts of the software’s user interface including the command tabs adventure Works a multinational manufacturing company that produces and distributes bicycles and accessories globally needs to input some data into Excel to assist with this task the company has recruited you and your several new employees however before starting the task the company has decided to train you to use the software so that you can improve your experience with Excel this training will help you better manage and analyze the data required for the task at hand let’s begin by understanding what Excel can do for Adventure Works microsoft Excel is a software application that businesses use to store data like financial figures and create calculations based on this data users can interpret the data they store by creating visuals or using Excel’s built-in analysis features they can then use the insights derived from these interpretations to inform business strategies or influence decisions with Adventure Work’s vast product line and global presence Excel’s capabilities will be crucial in managing and analyzing its data efficiently before you can start using Excel it’s essential to understand how to navigate the software’s user interface and locate the features you need excel’s user interface is designed to be accessible and includes various elements that help you interact with the software effectively the first of these elements is the title bar it’s located at the top of the Excel window and displays the name of your file the search option and other essential features the worksheet is the primary area where you can input data into cells using either the keyboard or other input devices the command tabs are located below the title bar and provide quick access to Excel’s hundreds of commands which are organized in areas called tabs or ribbons to find the command you need click in the relevant tab to reveal the related commands let’s take a few moments to explore these features and discover how you can use them to input data one of the main areas of Excel is the grid this area contains the worksheet which is where you enter data or information it’s divided into rows and columns and you input information into cells where a column and row intersect just above the worksheet is the formula bar when you type information into a cell in the spreadsheet it appears in both the cell and the formula bar when you create a calculation the result appears in the cell while the formula that drives the result appears in the formula bar in other words the formula bar always shows the actual contents of the cell there is a green title bar at the top of the screen on the left is the autosave button in the browser version of Excel you can find the app launcher button here which you could use to access other Microsoft 365 programs the title bar also contains a useful undo button when autosave is turned on creating a new Excel document automatically assigns the name book to your new file you can view the file name within the title bar to rename a file select the title bar and type an alternative name file names can contain spaces and capital letters you can also use punctuation marks however it is best to avoid the use of punctuation marks as some characters are not permitted also file names can contain a maximum of 255 characters but it’s recommended that you use 31 characters at most you can select the same box to manage the location in which you store the file to the right of the file name is the search feature select the search box and then select find to open a dialogue box where you can search for content like text or figures in your files you can use the options choice in the bottom right of the dialogue box to refine and control Excel searches you can also search for a recent action you’ve applied to a cell next let’s explore the command tabs excel has hundreds of commands organized in storage areas called tabs or ribbons you can select a tab heading to view its ribbon and related commands let’s review the most frequently used tabs the home ribbon is the first ribbon that appears when you open a file it contains the most frequently used commands you’ll rely on for standard everyday tasks like formatting and sorting data you can use the commands on the insert ribbon to add different elements to a file like charts or comments the draw ribbon offers you drawing tools for marking your worksheet while the page layout ribbon lets you alter the appearance of a spreadsheet when printed the formulas ribbon contains commands that you can use to manage more complex calculations you can use the data ribbon to perform different actions with data such as transform query sort and filter operations adventure works are expected to work with large blocks of information and the data ribbons sort and filter commands are useful for these tasks you’ll mostly use the commands on the review ribbon once you’ve created a spreadsheet for example you can use them to manage security settings or collaborate with colleagues the view ribbon offers Excel users commands to make it easier to view large spreadsheets such as the freeze pane which keeps titles visible when moving through data blocks there are also extra tabs called contextual tabs that appear during specific actions or when certain items are selected for example if you add a bar plot to your worksheet then the chart design and format tabs appear on screen these extra tabs contain commands relevant to the tasks you’re working on this demonstration provided only a brief overview of Excel’s interface and it’s completely normal if you feel like you need more help with this information learning any new software requires time and practice so don’t worry if you don’t fully understand everything just yet as you continue through the course you’ll have more opportunities to explore these commands and features in greater depth and you’ll become more comfortable with Excel’s interface by learning about its key elements including the command tabs you’ve built a solid foundation of Excel’s primary purpose and use cases keep up the good work excel is a powerful tool for organizing and analyzing data but sometimes when you’re dealing with large amounts of information it can be difficult to make sense of it all that’s where formatting comes in in this video you’ll discover how to enter and format data in Excel to improve its readability adventure Works has created a list of its offices using Excel however important information is missing from these files it’s also difficult to read the data because it’s not correctly formatted let’s help Adventure Works to add and format its data the green cursor box is in the top leftand corner of the worksheet you can move the cursor by pointing and selecting on a cell the cell location indicator shows you where you are on the sheet you can also use the arrow keys on the keyboard to move the cursor as you type the entry appears in the cell and on the formula bar you can use the backspace key to delete any typing errors the office location is missing from cell C21 select on C-21 type Delaware and then press enter to confirm your entry the entry appears in the cell and formula bar the data lines up to the left of the cell to indicate that it’s text type the number 130422 and confirm it in cell E21 the entry sits to the right of the cell in Excel text aligns to the left of the cell and numbers to the right excel treats an entry that contains both letters and numbers as text you can also manually set the alignment with the alignment buttons on the home ribbon excel also offers an autocomplete feature as a shortcut for entering data for example column D already contains several instances of the word partner so if you type the letter P in cell D21 then Excel suggests the word partner as a possibility press enter to accept the suggestion you can also ignore it by continuing to type an alternative word next New Jersey needs to be added type the word new in C16 this prompts an incorrect suggestion so you must type New Jersey in full now if you type new in C17 Excel waits to see what letter is typed next before suggesting a word because there is more than one entry beginning with new in the browser version of Excel you’ll be presented with a drop-own list of multiple suggestions from which you could select New Jersey column C contains state names this results in a floating dialogue called convert to geography to appear select in the dialogue to instruct Excel to recognize text entries as geographic locations you can select on the card symbol to the left of the entry to interact with Bing to generate information about the location keep in mind that if you print your worksheets the card symbols beside the entries will appear on the print like other Microsoft 365 apps Excel has an undo feature in the desktop version this feature is located on the title bar in the browser version it is located to the left of the home ribbon select the undo feature to reverse recent actions in this case you’ll remove the geographic locations tag and return the entries to normal text the next action is to type New York in full in C18 autocomplete has no suggestions as New York hasn’t appeared in the column before a different shortcut called autofill can be used to add New York to C19 and C20 with the cursor still on C18 position the mouse pointer over the bottom right hand corner of the cell the pointer changes to a narrow black cross now hold down the mouse button and drag it down this action autofills the entry into the cells underneath now that you’ve entered the data in your spreadsheet you need to format it formatting data makes it easier to read and correct formatting on numeric entries prevents misunderstandings here the numbers in E2 and H21 are financial data to make this clear highlight the numbers by selecting all the data from E2 to H21 then select on the currency button in the number group the currencies are available on the drop-own menu alternatively you can use the comma format to display a comma separator and two decimal places you can use the increase or decrease decimal buttons to customize the number of decimal places the percentage button is both a format and an action button it adds the percentage symbol and it also multiplies the cell content by 100 select undo to reverse this the dropown above these buttons presents other number formats these formats include dates as dates are treated as numbers in Excel your next task is to format the column titles so that they stand out type the heading state code in B1 the text overflows into the adjacent empty cell once you add state in C1 two characters of the B1 heading are masked however the formula bar confirms that the whole heading is still there the column’s title is partially hidden you need to make the full title visible from the home ribbon choose wrap text to stack the words in the cell you can also format a heading to stand out using font options in this example the size of the heading has been increased to Calibbri 12 and a blue background color has been applied you can also center the heading using the alignment section of the ribbon another Excel shortcut is the format painter which is found on the left of the home ribbon this shortcut copies format settings from one cell to another select in the format painter to display a paintbrush and copy B1’s style then highlight A1 to H1 to paint those cells with a copied format this action also copies the wrap text and center alignments you should now be familiar with the different methods and shortcuts you can use to enter and format data in Excel this video also demonstrated how this knowledge can be applied to help Adventure Works complete and format their Excel sheet great work reading and editing the contents of a large spreadsheet with hundreds or even thousands of data entries can seem like a large task thankfully Microsoft Excel offers several features and keyboard shortcuts that help you navigate and edit your spreadsheets over the next few minutes you’ll explore these features and shortcuts and learn how to use them adventure Works has sent you a large inventory file they need you to check the current information in the file and add some new data there are over 100 entries in the file to navigate through however you can quickly review these entries and add new ones through Excel’s navigation features and keyboard shortcuts there are several useful navigation and editing features available in Excel the freeze panes feature for example keeps an area of the screen static you could use it to freeze a specific row the static area remains on screen while you scroll freely through the other content you can use the new window option to open a second viewpoint of your file with this feature you can keep one part of the file within view as you work in another area name box is another useful Excel feature the name box is the title of an area located between the ribbon and the worksheet to the left of the formula bar when you type a cell reference in this box and press enter the cell cursor moves to that position on the sheet the name box can also be used to assign a name to a cell finally there are also several keyboard shortcuts that you can use to speed up the navigation and editing of a spreadsheet let’s discover more about how these features and shortcuts operate by helping Adventure Works first you need to freeze key rows to give yourself a more efficient view of the data from the window group of the view ribbon you can access several options two of these include freeze panes and new window select the freeze pane drop-down to view three choices freeze panes freeze top row and freeze first column select freeze top row to turn the row currently visible at the top of the screen static be aware that row one isn’t always the top visible row a horizontal line appears under the top row to indicate the static area the selected frozen row remains static while the other rows below it scroll off screen you can also select freeze first column to turn the first column currently visible on screen static in this case it’s the category column again the first column column A isn’t always the one that becomes static selecting the freeze first column option automatically turns off the freeze first row option once you’ve frozen an area of the screen the first choice in the freeze panes drop-down menu changes to unfreeze pane select the unfreeze pane to release all static areas on screen what if you need to freeze the screen in two directions at the same time for example to help Adventure Works view its worksheet more clearly you need to make sure that all row titles and the data in columns A and B are visible to do this you first need to select on C2 to move the cursor to that position then in the freeze panes dropdown select the freeze panes option once this option is selected Excel identifies the cursor position and freezes everything above and to its left your cursor is currently on C2 so Excel freezes columns A and B along with row one again you can use the unfreeze panes option on the freeze panes dropdown to release all areas of the screen you must also have the totals in row 152 available on the screen while editing other areas of the spreadsheet you can use the new window command to open another view of the file in a new window this window isn’t a separate copy of the file it’s just a different view of the same file with both views visible you can now review the totals data in row 152 while editing the cells in other areas of the spreadsheet to close this second view just select the X in the top right hand corner of its window you can also move quickly around the worksheet using keyboard shortcuts let’s take a moment to explore some keyboard shortcuts available to Windows users press control and home to jump to cell A1 at the top left of the worksheet if on the freeze panes top row choice is turned on the cursor will instead jump to cell A2 but what if you need to move to the end of your work to continue data entry press control and end to move the cursor to the last cell in the worksheet that contains content rather than simply moving the cursor hold down the shift key while pressing either the control and home or the control and end combinations excel selects the entire block as it moves the cursor you can also use the name box to move quickly to specific cells the name box is located to the left of the formula bar the box typically displays the cell reference for your cursor’s current position however if you type a different cell reference and press enter your cursor jumps to the specified cell the name box is also a useful method for assigning names to cells a cell name helps users to identify data content since it’s more descriptive than just a cell reference adventure Works needs you to rename cell 152 to units in stock so position the cursor on the cell then in name box type the text units underscore in underscore stock and press enter cell names must be unique and cannot contain spaces you can use the underscore symbol to substitute for spaces if the cell is referenced in a calculation its name and reference are visible you can view the name from the drop-own list in the name box you can check which cell the name is assigned by selecting the name manager on the formula ribbon in the dropown select the cell name to move the cursor to the cell you can use these same steps to view and access this cell from any sheet in the workbook for example from the products two sheet selecting the units in stock cell name from the name box dropdown brings you back to that cell on the products one sheet you should now know the Excel features and shortcuts to help you navigate and edit spreadsheets you can use these tools to assist you in any Adventure Works Excelbased assignments well done have you ever opened a Microsoft Excel worksheet only to find the content structure difficult to interpret perhaps it contains irrelevant entries or needs too much scrolling to navigate in this video you’ll learn how to use Excel’s sort and filter features to organize content so you can read and identify data quickly and efficiently over at Adventure Works the company checked its inventory data for records related to a specific supplier however the Excel file that contains the data is poorly structured and difficult to navigate adventure Works needs your help to sort and filter the information so that only the suppliers data is visible before you begin helping Adventure Works let’s examine the concepts of sorting and filtering in Excel excel offers users a series of sort and filter commands these commands change the position of data in the worksheet window so that it’s easier to understand in other words they don’t change the data they change how it’s displayed it’s also important to remember that the sort and filter commands are not the same they work on data in different ways you need to understand these differences to prevent any misreading of the data let’s begin with the sort feature the sort feature is found in the sort and filter group in the data ribbon this feature reorders the worksheet by physically moving rows into new positions to return the data to its original position you must use the undo command however if a sort was not your last action you may inadvertently reverse other steps you should also be careful if saving your workbook after applying a sort once your changes are saved the sort order applied to the data is permanent and an undo is no longer possible now that you’re familiar with the sort feature let’s focus on filtering filtering refineses the data displayed based on the criteria of your choosing however unlike with sort the rows are not repositioned instead Excel hides all the rows that don’t match your chosen criteria this leaves a subset of rows visible this subset can be reduced further by applying more filters let’s learn more about how these actions work by helping Adventure Works restructure its inventory Excel file the Adventure Works inventory Excel file is currently sorted by category you need to restructure it using the sort and filter commands access these commands from the sort and filter group in the data ribbon the sort ascending and sort descending commands are shortcut choices when you select one Excel checks the location of your cursor it then uses the column in which the cursor is located as the key for the sort place your cursor on column B which is the date entered column then select sort ascending which is now called oldest to newest the rows are now organized in date order excel interprets dates as numbers so it has performed a numeric sort had you placed the cursor in the supplier column Excel would have performed a textbased sort you can select undo on the title bar to restore the previous row order adventure Works has requested that the data be sorted by supplier the data in column D and that the most recent entry is visible first within each block of supplier data sorting by the supplier and then sorting by the date won’t work here because one sort would cancel out the other instead you need to perform a multi-level sort this technique lets you sort data in two ways simultaneously first from the sort and filter group of the data tab select the sort button to open a sort dialogue box at the top right of the dialogue box you need to confirm that there’s a tick in the my data has headers box this instructs Excel to exclude the first row from the sort next use the drop-own menu under column to instruct Excel to perform the first sort by supplier you can retain the defaults of sort and sell values and sort A to Z then select the add button to display additional sort fields use these fields to configure the second sort level by data entered again retain the defaults of sort and cell values but change the order to newest to oldest then select okay to exit the dialogue box and sort the data as required you have now sorted the data by supplier and date entered select undo on the title bar to reverse the sort next Adventure Works needs you to filter the records to view only the data related to the supplier called Cycles the first step when filtering is to turn on the filtering feature select the filter button on the sort and filter group of the data tab to add filter arrows to each column heading you can now filter the data using the arrows next to each heading to open drop-own lists each filter arrow also has an additional submen to allow for more precise filtering excel recognizes the type of content in the column and generates contextsensitive choices such as equals does not equal begins with and more select the arrow next to the supplier column heading to display a list of suppliers a tick mark beside an entry indicates that its rows are currently visible remove the tick marks next to list entries except for cycles as then select apply excel hides all other rows in the worksheet so that only the cycles as data is visible there are now only 10 rows visible in the sheet all of which relate to cycles as you can confirm this by checking the bottom left of the Excel screen here it states that 10 records were found select the arrow next to the unit price to apply another filter from the drop-down put a tick in the box to the left of item seven then select the apply button the filter only works on the 10 visible records so you have now displayed only rows where cycles as is the supplier and seven is the unit price you might ask yourself how do I know if data has been filtered in Excel there are two ways to determine if data has been filtered the first is to check the filter arrow to the right of the column heading if there is a funnel symbol on the filter arrow then your list is filtered the other method is to check for breaks in the sequence of row numbers on the left hand side of the display area for example a row sequence of 8 9 112 indicates that rows 10 to 111 have been filtered out so how can you remove filtering to make other data visible again in the column header select the arrow or arrow and funnel symbol then select the clear filter option from the drop-own menu to clear a specific filter while retaining the others you can also select the clear choice in the sort and filter group of the data tab to clear all filters you’ve now removed all filters and restored the full data display thanks to your help Adventure Works has the inventory data it needs and you should now be familiar with using the sort and filter actions to organize and identify data quickly and efficiently well done congratulations on reaching the end of the first week in this course on preparing data for analysis with Microsoft Excel in this week you explored the fundamentals of Microsoft Excel by learning how to create workbook content and work with blocks of data in Excel let’s take a few minutes to recap the key skills you gained during this week’s lessons you began with an introduction to the program in which you discovered what topics you will learn about as you progress through the different courses you were also given guidance on how to be successful in this course this guidance included helpful tips on how to structure your study and ways in which you can approach the learning material you were then introduced to other learners in a meet and greet session during which you explained why you’re taking this course and what you hope to achieve from it finally you explored a list of valuable resources you can use to succeed in the course in the second lesson you learned how to create workbook content you began this module with an introduction to Microsoft Excel you developed an understanding of the importance and function of the application including how it’s used in everyday business to store calculate and gain insights from data you then learned how to navigate Excel using its user interface or UI the UI is comprised of three key areas there’s the title bar which contains the name of your file the search option and other primary features the worksheet is the main area used to input data into cells and the command tabs provide quick access to Excel’s commands which are organized in areas called tabs or ribbons you then learned how to enter and format data in Excel you explored the different ways data can be added to a worksheet you discovered how to use formatting to improve the readability of a spreadsheet and you reviewed keyboard shortcuts for data entry and formatting next you learned how to manage worksheets you then undertook an exercise where you demonstrated your new skills by adding data to a worksheet this was followed by a knowledge check which tested your understanding of the material finally you explored additional resources to enhance your learning in the third and final lesson of this week you focused on working with blocks of data in Excel you began the lesson by learning how to read large data blocks in Excel you explored Excel’s navigation and editing features such as the freeze panes feature the new window feature and the name box feature and keyboard shortcuts you then developed an understanding of the concepts of sort and filter you learned how to identify the key differences between both and you learned how to sort and filter data in Excel so that you can organize and identify data quickly and efficiently you then explored different methods for sorting data in a worksheet including alpha numeric sort and the multi-level sort feature and you discovered how to use the filter feature to control data visibility in a worksheet next you undertook an exercise in which you demonstrated your new skills by sorting and filtering data in a worksheet this was followed by a knowledge check and module quiz both items tested your understanding of the material by presenting questions focused on the key concepts you explored you should now be familiar with the fundamentals of Microsoft Excel you should be capable of creating workbook content and using different methods for working with blocks of data great work i look forward to guiding you through the lessons next week in which you’ll learn how to use formulas and functions in Excel analyzing data often involves making calculations however when working with large blocks of data calculations can quickly become confusing luckily Microsoft Excel can calculate numerical information using formulas you can solve real life data analysis problems in Excel with a little bit of planning and some basic math over the next few minutes you’ll learn how
Excel processes calculations and how to create a formula using the correct syntax over at Adventure Works the accounting staff are amending a spreadsheet that records orders placed with suppliers their first task is to update the prices and order amounts they need to work out the purchasing cost by creating a calculation in the data but first they need to understand how Excel reads interprets and implements calculations let’s take a few minutes to explore formulas and calculations and then help Adventure Works a formula in Excel is a calculation performed on the values in a range of cells in your worksheets examples of these calculations include addition subtraction multiplication and division once the calculation is completed the formula returns a result even if it’s an error now that you’re familiar with what a formula is let’s find out more about how they work all formulas begin with an equal sign it is then followed by a calculation or function formulas can contain numbers or cell references for example this formula instructs Excel to add the values in cells A1 and B1 excel usually reads the formula from left to right characters are used to indicate the type of calculation Excel should perform the plus character is used for addition and the minus character for subtraction the asterisk is used for multiplication and the forward slash character is used for division the formula bar shows the formula in the cell you are working in the worksheet shows the result of the formula in the formula bar this is important to take note of when you are creating or working with calculations a formula can also be static or dynamic a formula containing fixed numbers will be static and always generate the same result for example the formula in E2 is static because it contains specific numerical values it will not update if any of the monthly figures in cells A2 B2 or C2 change on the other hand a formula that contains cell references is dynamic based because Excel always uses the current value in the cell the formula in E3 is dynamic because it includes cell references a formula can also include a reference to a cell which itself contains a formula this creates a chain of calculations for example the formula in E1 refers to cell C1 cell C1 also contains a formula that calculates the data in cells A1 and B1 if the values in cells A1 and B1 change then the formulas in cells C1 and E1 will both change in other words a change at one end affects all other formulas in the chain a formula can also refer to a cell in another sheet this reference must include the worksheet name followed by an exclamation mark this other worksheet can be in the same workbook or in another Excel file references to cells in other workbooks are called links or external references the formula in this screenshot references the product sheet within the same workbook for example this formula states that what is in this cell is equal to the contents of H2 in the product sheet plus the contents of A1 in this sheet now that you’re familiar with the basics of a formula let’s view it in action by helping Adventure Works determine the cost of the items it’s ordering from its supplier begin by positioning the cursor on K3 which is the cost column this is the cell where the results should be displayed then type an equal sign to determine the cost of the order you need to multiply the contents of I3 the unit price by the contents of J3 the number ordered select cell I3 to add that reference to the formula the equal sign and the cell reference are displayed in both the result cell K3 and in the formula bar next type an asterisk symbol to represent multiplication then select cell J3 this reference is colored red on the formula bar and the cell is highlighted in red press the enter key to complete the formula this creates a result of 79,050 which is now visible in K3 adventure Works decide to make a change to its order it wants to reduce the number of units that it ordered by 250 so how can Adventure Works update the formula with this new information amend the figure in J3 and press enter this causes the formula in K3 to recalculate and generate a new result of $65,875 if you double click on a cell such as K3 this opens edit mode while you’re in edit mode Excel places colored highlights around the cells referenced in the calculation it’s easy to begin to edit a cell accidentally with a double click if the cell contains a formula particularly one you didn’t create this can be a little worrying pressing the escape key is a safe way to cancel an edit without amending any of the information within a cell you have explored how calculations in Excel can be useful in data analysis by now you should know how Excel processes calculations and how to create a formula using the correct syntax you will learn more about formulas as you progress in your learning journey well done microsoft Excel doesn’t just store data it also assists with calculations a fundamental component of Excel and data analysis so it’s important that your calculations are correct and reliable in this video you’ll learn how Excel processes calculations discover how to construct the syntax for calculations and edit your syntax to avoid errors jamie at Adventure Works is working on a purchase sheet it has been updated to include information on new orders placed with suppliers she now needs to create calculations that correctly display the difference between purchasing costs and sales amounts the formulas she creates will contain a mixture of multiplication and subtraction and she needs to be confident that those operations are happening in the correct sequence let’s take a few minutes to explore how these formulas work beginning with operators the symbols that are used to indicate mathematical actions in Excel are known as operators operators are used for actions like addition subtraction multiplication and division for example you can use operators to add the values of two cells together or divide the value of one cell by another when working through a formula Excel does not always calculate the expressions or steps in a formula from left to right excel handles the operators in a calculation according to a key mathematical principle called the order of precedence the order of precedence assigns greater importance to some of the mathematical symbols over others this means that Excel calculates formulas according to the hierarchical position of each symbol within the order of precedence don’t worry if you don’t fully understand what the order of precedence is this is covered in a later reading in terms of importance Excel tries to process division and multiplication symbols before addition and subtraction however you can control how Excel executes calculations by using parenthesis in your formulas this is a key technique in creating formulas that generate reliable results parenthesis instruct Excel as to which part of a calculation must be executed first even if this would contradict the order of precedence let’s explore the use of parenthesis in formulas you want Excel to add the numbers two and three together and then multiply the subtotal result by 4 so you type this formula as equal sign 2 + 3 * 4 however Excel will not process this calculation left to right instead Excel will first multiply 3 by 4 which gives a result of 12 it will then add two giving a formula result of 14 this is because the multiplication symbol has a higher priority in the order of presidents adding parentheses to the calculation allows you to instruct Excel to do this bit first so you could rewrite your calculation by placing part of the formula in this instance 2 + 3 in parenthesis now you’ve directed Excel to add 2 and 3 as its first step and then multiply the result of that addition by four the result of this calculation would be 20 and not 14 as it was previously it is important to have a clear understanding of where to put parenthesis in a calculation placing parentheses in the wrong position in a formula or not including them at all could change how Excel understands and implements the calculation an incorrect calculation result may not always be obvious as it may seem plausible there are also times when you may need to reproduce cell entries and formulas within a worksheet when a formula is copied it is important to consider the appearance of the cell references there are two ways a cell reference can appear in a calculation these are relative and absolute a relative cell reference means that if you copy a formula to a new cell Excel will adjust the row numbers or the column initials in the cell references to update the formula relative to its new location this ensures that the formula is correct for the row or column it has been copied to for example the formula in K3 which reads equal sign I3 multiplied by J3 is copied down using the autofill feature excel adjusts the cell references for each row but what if a cell reference needs to say the same when the formula is copied elsewhere for this to happen you must make the cell reference into an absolute reference when Excel copies a formula it keeps absolute references constant and does not adjust them for example if the formula in L3 is copied down through the column then the reference for the cell that contains the exchange rate needs to say the same when the formula in L3 is copied down the K3 reference in the formula will adjust to include a different row number however the N2 reference in the formula should not change since the exchange rate is only mentioned in that one cell to make a cell reference absolute add a dollar sign before the column initial and before the row number this instructs Excel to keep the cell reference constant during the copy operation this means that all copies of the formulas will contain the original cell reference don’t worry if you find these concepts difficult to follow you’ll explore how to control calculations in more detail in a later video there are also additional resources available at the end of the lesson excel will also recalculate and update all formula results when a file is opened files that contain a lot of complex calculations will be slower to open fully on screen than ones that only contain data fortunately you can turn the automatic recalculation feature off just remember to switch it on when you are done working with the file to change the recalculation mode select the calculation options dropown on the formulas ribbon then on the dropown select the recalculation mode you need for your file well done you now know how to control how Excel works through the steps in a formula you’re also able to identify the correct syntax to use if calculations are going to be copied elsewhere in the spreadsheet great work a Microsoft Excel formula can be complex and include many steps in this video you’ll explore the correct syntax for Excel calculations that contain multiple steps and discover how to adjust a formula to ensure that it copies a calculation correctly amy at Adventure Works is preparing a price quote in a worksheet for the client Kontoso Bikes the client wants to order bicycle parts for their retail outlets let’s find out more about how Amy can control her worksheet calculations to ensure that the prices are correct for the client amy has already listed the required items and their respective prices adventure Works are offering a 10% discount to the customer adventure Works charges different prices for delivery based on the region that the customer outlet is in contoso Bikes has four retail outlets two in region A and two in region B the spreadsheet also shows data for region C however this region is not the focus of this video amy must ensure that two different delivery rates are used in her formulas let’s help her create calculations firstly cell G6 must show the result of the cost per unit multiplied by the quantity ordered position the cursor on cell G6 and type an equal sign to begin the first calculation select cell E6 and type a star from multiplication then select F6 press enter to complete the calculation and generate the subtotal next Adventure Works needs to calculate the client’s 10% discount select cell H6 and type an equal sign select the subtotal amount in G6 to work out the 10% amount you need to divide by 100 and multiplied by 10 add the forward slash symbol for divide and type 100 then add the star symbol for multiply and type 10 excel processes these calculations from left to right it first divides the figure in G6 by 100 and then multiplies the result by 10 press enter to get the discount figure now you need to work out the total cost excluding delivery select cell I6 and type an equal sign then select G6 to select the subtotal and type a minus symbol to subtract the discount select cell H6 to select the discount however before pressing enter to complete the calculation there’s another step to consider this order needs to be duplicated for each of Ktoso bikes four outlets so the total cost excluding delivery needs to be multiplied by the value in cell I2 to calculate this type a star select cell I2 and press enter but something has gone wrong with the result of this formula because the total amount is less than the subtotal select I6 to return to edit mode in your formula the multiplication operator has higher priority or precedence than the minus operator in other words the multiplication operator is higher in the order of precedence so Excel takes the discount in H6 multiplies it by the value in I2 and then subtracts that value from the total to work around this add an opening parenthesis before G6 and a closing parenthesis after H6 this ensures that Excel processes the subtraction operator before the multiplication operator press enter to execute the formula and generate the correct value next you need to calculate the total amount if it is to include the cost of delivery remember there are two different prices for delivery one price for each region so there must be subtotals in this formula the formula in the cell also requires a mixture of addition and multiplication symbols so you need to use parenthesis to work with the order of precedence select cell J6 and type an equal sign select I6 to include the total cost if excluding delivery then type a plus symbol type an opening parenthesis and the number two add a star symbol and then select cell M2 include a closing parenthesis type another plus symbol add an opening parenthesis a number two and a star select cell M3 type the closing parenthesis press enter to calculate the result the total cost when delivery is included is $22,930 amy now needs to calculate these same costs for all the remaining categories in the worksheet you could help her by using the autofill feature to copy the formulas that you’ve created to save time however some cell references will need to be made absolute to prevent the autofill process from changing them select cell I6 type a dollar sign in front of the letter I and another dollar sign in front of the number two press enter the formula in J6 also requires a dollar sign this time instead of typing out each dollar sign let’s use a shortcut method enter edit mode on cell J6 position the cursor on the M2 reference this is the region A delivery charge press the F4 key on the keyboard to bring up the dollar signs repeat this action for the M3 reference the region B delivery charge then press enter to complete the formula it’s now safe to use autofill to copy these formulas as the required cell references will remain absolute position the cursor on G6 a shortcut for autofill is available because there is a block of data to the left position the mouse pointer on the bottom right hand corner of the cursor so that it becomes a black cross then double click the mouse button excel uses the block of data to the left as a reference and copies the formulas down to G15 repeat this process on cells H6 I6 and J6 to complete the worksheet you have now helped Amy to calculate the various costs for Kontoso bikes orders you should now be able to recognize situations in which you need to adjust the syntax in a formula to control how it’s processed in Excel you’ve also learned some useful shortcuts for absolute references and autofill these shortcuts will help you to work more quickly and efficiently on your worksheets at this stage of the course you should be familiar with creating and working with formulas but you don’t always have to create your own formulas as you’ll soon discover Excel offers predefined formulas called functions that you can use to perform calculations in this video you’ll discover what function formulas are explore their syntax and learn how to use them to perform calculations over at Adventure Works the company is approaching the end of its financial year lucas in accounts has been tasked with calculating the total quarterly sales for each regional sales team you can help Lucas carry out this task using Excel function formulas but first you need to learn what functions are and recognize their syntax let’s begin by defining a function a function is a predefined formula that performs a calculation based on values specified by the user for example a simple function could total the values in two cells or a more complex function could calculate repayments on a bank loan functions are useful because they allow for more complex calculations they also facilitate dynamic content that responds to changes in the worksheet excel contains many built-in functions these built-in functions are grouped into different categories which can be accessed from the formulas tab or ribbon there are several categories visible when you access this ribbon select the more functions option to view the others these categories are organized so that you can locate the functions most relevant to your day-to-day requirements for example Excel offers functions for financial date and time and math calculations you’ll explore each of these categories in more details as you progress through the course you can also refer to the Microsoft page Excel functions by category article link in the additional resources so now that you know what a function is let’s explore its elements the first element of a function formula is the name of the function this takes the form of a single word such as sum the sum function adds all the values within a selected range of cells the second element of the formula is the arguments as you’ve just learned a function calculates data this data or information is referred to as an argument the data it accepts is also custom you can add your own information to the formula to direct and control the action of the function it’s important to remember that each function requires a different list of arguments some arguments are mandatory a function can’t carry out its task without them however other arguments are optional they exist to provide different choices around additional elements like formatting your results so how do you construct a function formula like any other calculation a function formula begins with an equal sign you then need to write the function name for example equals followed by sum the next step is to write the arguments arguments are contained within a pair of parenthesis so begin by typing an open parenthesis then list the arguments as an example you could follow a sum function with the argument open parenthesis C2 colon C4 make sure to separate arguments from one another using characters such as commas or colons instead of spaces or periods when you finish typing your arguments end your function formula with a closing parenthesis you now have an argument that instructs Excel to add all data in cells C2 to C4 when executed this formula returns a result that calculates the values within this cell range function formulas can contain more complex arguments but this simple example is a great starting point to help familiarize you with the syntax now that you know how to construct a basic function formula let’s make use of your new skills and help Lucas create a sum function to obtain the totals for Adventure Works sales figures adventure Works sales data is contained in an Excel workbook called annual sales totals the workbook contains a worksheet called sheet one this sheet contains five columns the first column lists the months of the year one month per row the other four columns contain the names and data for each regional sales team each column contains 12 sales totals one for each month let’s begin by calculating the sales totals for team A first you need to place the cursor on the cell where the result of your function must appear place your cursor on cell B14 underneath the sales data for team A this is the cell where the overall sales total must appear now you can write your function first type an equal sign then type the name of your function in this instance you need to add the data so you can use a sum function function names are not case sensitive you can type them in upper or lower case once written Excel displays them in uppercase as you type the word sum a list of suggested functions appears this list is a useful shortcut for accessing functions quickly but for now you can continue typing the formula now that you’ve stated the name of your function you need to outline your arguments type an open parenthesis a floating help message appears with argument prompts if the prompt is in bold then the argument is required if the argument is in square parenthesis then it is optional in other words it’s not required for the function to work in this instance you’re writing a custom argument type B2 colon B13 then type a closing parenthesis to end your argument the sum function and your custom argument instruct Excel to calculate or add numeric total of all data in cells B2 to B13 just like the example you explored earlier press enter to execute the function the result shows that team A sales total for the year was $971,000 now that you’ve calculated the sales total for team A you can copy the function formula to the other cells in the row using the autofill shortcut select cell B14 position the mouse pointer over the bottom right hand corner of the cursor to turn it into a black cross hold down the mouse button and drag the cursor to the right as far as cell E14 as it copies the data from cell to cell excel also adjusts the formula to total the cells in each column for the remaining teams lucas now has the sales totals for each of Adventure Works sales teams thanks to your help Lucas successfully created the function formula he needed to complete his task and having assisted Lucas you should now know what functions are be able to read the syntax of a function and know how to use a function to perform a calculation creating a formula with a function for the first time can be intimidating how many arguments does it require what’s the correct syntax thankfully Excel offers a useful insert function tool that provides a framework for creating a function formula in this video you’ll explore the insert function tool and function categories and learn how to create a function over at Adventure Works the company is busy calculating the annual sales total for each regional team the sales data is contained in a worksheet called sheet one the worksheet lists all four teams and their respective sales totals for each month let’s help Adventure Works calculate each team’s total sales using the insert function feature begin by positioning the cursor on cell B14 this is the cell in which your sales total must appear for team A now you can access the insert function feature there are two ways to open this feature the first is by selecting the insert function button on the left hand side of the formulas ribbon or you can select the insert function option on the worksheet screen to the left of the formula bar selecting either one of these options opens the insert function dialogue box in the middle of this dialogue box is a list of functions you can navigate through these functions using the scroll bar however this is a brief list that doesn’t contain all available functions above this list is a drop-own box with the heading most recently used to the left of this dropdown is a prompt called or select a category because the category choice is set to most recently used the list underneath contains functions that you’ve recently used in your worksheet formulas as you work through Excel you’ll most likely make frequent use of the same functions over time this list will populate with your most used functions providing a useful quick access shortcut you can select each function in the list to display a short description of its purpose in the bottom left of the dialogue box is a blue hyperlink called help on this function this is a contextsensitive link select it to visit the help page for your selected function on the Microsoft support site if your required function isn’t on this list then select the drop-own arrow to the right of most recently used you can select another category to open a different list of functions for example you need to use the sum function to complete the calculation task for adventure works you can access a sum function from the math and trigonometry category when you select this category the list of available functions changes you can learn more about which functions correspond to which categories in the additional resources remember that you can select a function for an explanation of what it does or you can highlight a function name in the list then select the blue help hyperlink for more detail the function list is arranged alphabetically so scroll down to the S section select sum and then select okay this action opens another dialogue box called function arguments there are two boxes at the top of this dialogue labeled number one and number two respectively notice that the text number one is bolded this indicates that an entry is required here the text number two is not bolded which indicates that it is optional however you might use it in a situation where you require a total for blocks of numbers at separate locations in the spreadsheet in the adventure worksheet Excel has identified the block of numbers directly above your cursor position so it’s suggesting that you include the cell range B2 to B13 in your total in the background on the formula bar Excel has already constructed the calculation for you it has included not only the cell references but also the equal sign the parenthesis and the colon if Excel has suggested the wrong block of cells then you can select the navigate button to select a different range or edit the formula the navigate button is an arrow pointing upwards at the right of the number one box selecting this arrow temporarily collapses the dialogue box and returns you to the spreadsheet so that you can change the selection the navigation arrow to the right of the number box is now an arrow pointing downwards selecting this arrow restores the full function arguments dialogue box just above the blue help link on this dialogue is a formula result which in this case is a total you should also be aware of warning messages that could appear here these warnings are often generated by errors that are created when working with more complex function formulas you’ve now selected the required function and you’ve made sure that the syntax is correct and targets the required data select okay to add the completed formula to the worksheet when executed this function formula generates a sales total of $971,000 for team A adventure Works can copy this formula across the row to generate sales totals for the other teams thanks to your use of the insert function feature Adventure Works now have the required sales data and you should now be familiar with the function tool understand its categories and be able to make use of the tool to create a function formula congratulations on reaching the end of this second week in this course on preparing data for analysis with Microsoft Excel this week you explored how to create and work with formulas and functions in Excel let’s take a few minutes to recap what you learned in this week’s lessons you began the first lesson by learning about formulas you learned that a formula in Excel is a calculation performed on the values in a range of cells in your worksheets examples of these calculations include addition subtraction multiplication and division once the calculation is completed the formula returns a result even if it is an error you then learned how formulas work different characters or operators are used to indicate what type of calculation Excel should perform examples of operators and calculations include addition subtraction multiplication and division the formula bar shows the formula in the cell you are working in while the worksheet shows the result of the formula in the formula bar formulas can also be static or dynamic a static formula means that the numbers are fixed so it always generates the same results a dynamic formula is one in which the results depend on the current values in the reference cells a formula can also include a reference to a cell that itself contains a formula creating a chain of calculations and a formula can also refer to a cell in another sheet this reference must include the worksheet name followed by an exclamation mark you then learned how to control calculations you learned that when working through a formula Excel handles the operators according to the order of precedence this means that Excel calculates formulas according to the hierarchical position of each symbol within the order of precedence the hierarchy is as follows excel first calculates division and multiplication operators it then calculates addition and subtraction operators however you also discovered that you could control a calculation using parenthesis in formulas parenthesis instruct Excel as to which part of a calculation must be executed first even if this would contradict the order of precedence there are also times when you may need to reproduce cell entries and formulas within a worksheet when a formula is copied it is important to consider the appearance of the cell references there are two ways that a cell reference can appear in a calculation relative and absolute a relative cell reference means that Excel adjusts the cell reference of a copied formula relative to its new location to make sure it’s correct and an absolute reference means that Excel keeps the reference constant it doesn’t adjust it you learned that to make a cell reference absolute you must add a dollar sign before the column initial and before the row number you also explored different percentage calculations and you learned how to create reliable percentage formulas using the correct syntax throughout the lesson you put your new knowledge to use by assisting Adventure Works with many different calculation tasks one of these tasks was in the exercise in the exercise you calculated Adventure Works profits and margins in preparation for a presentation to complete this task you created a calculation that relied on the company’s revenue data and you made sure that your calculation followed the best practices you had explored during the lesson you then undertook a knowledge check in this item you proved your understanding of the concepts you encountered by answering a series of questions finally you explored a list of additional resources designed to help you improve your knowledge of the topics in this lesson in the second lesson of this week you learned how to get started with functions you began by learning that a function is a predefined formula that performs a calculation based on values specified by the user you then discovered that Excel contains many built-in functions grouped into separate categories which can be accessed from the formulas tab or ribbon you then explored the two elements of a function the first element of a function is the name such as sum next is the arguments an argument is the data a function accepts arguments are mandatory but the data can be custom you then learned how to construct an argument in Excel like any other calculation a function formula begins with an equal sign you then need to write the function name for example equals followed by sum the next step is to write the arguments within a pair of parenthesis when you finished typing your arguments end your function formula with a closing parenthesis you also learned that you could create a function using the insert function tool the tool is a framework for building functions it’s accessed using the formulas ribbon or from the worksheet screen the tool lets you build a function from a series of drop-own lists and it provides useful tips for building functions and warnings for when they’re incorrect you then explored the autosum shortcut the autosum shortcut is a method of adding formulas in Excel it provides quick access to core functions that Excel users make daily use of the functions it provides access to include the sum function which adds all values within a selected range of cells the average function used to calculate the average of the selected range and the different versions of the count functions these are useful methods of counting the numbers of cells in a given range that contain or don’t contain specified values there’s also the max function which displays the cell with the largest value from a given range and finally the min function this function displays the cell with the lowest value from a given range you can also reproduce calculations quickly and easily in a worksheet using the autofill feature just like in the previous lesson you put your new knowledge to use by assisting Adventure Works with many different functions this included the exercise item in the exercise you helped Adventure Works to prepare a monthly sales report to complete this task you prepared the report using a series of functions and you made sure that your calculation followed the best practices you had explored during the lesson you then undertook a knowledge check and a module quiz in which you proved your understanding of the concepts you encountered by answering a series of questions you’ve now reached the end of this module summary it’s time to move on to the discussion prompt where you can discuss what you’ve learned with your peers you’ll then be invited to explore some additional resources to help you develop a deeper understanding of the topics in this lesson best of luck we’ll meet again during next week’s lessons you check the results of a recently performed data analysis only to discover the results are wrong a quick inspection of the data set reveals errors in the data raw data needs to be correct and trustworthy because this information influences decisions so you always need to check for errors and resolve any you find in this video you’ll explore the common data errors in Microsoft Excel and discover how they could negatively impact data analysis jamie at Adventure Works is working on a spreadsheet that contains a large amount of customer and sales information she’s assessing if the contents are reliable enough to be used for data analysis to deliver new insights on customer behavior however the spreadsheet contains some common errors these errors must be resolved before she can make use of the data let’s take a few minutes to examine the types of errors that Jaime should be checking for many common errors or mistakes that you might find in your data set are often made by those who entered the data they might be unfamiliar with the software or technology or they’re just not paying attention a common mistake is that a name or keyphrase is misspelled in that case Excel might not link the entry to other important details as it should or it might not find the entry in a search for example Jaime’s spreadsheet tracks sales figures by region column C tracks the city in which each sale was made if she types the city Chicago as the latest entry without the A or types it in the wrong column Excel would ignore that entry when asked to summarize or total the sales results for that city entries can be misidentified during the data analysis process if they contain unnecessary characters for example Jaime types a dollar character before the numbers in her entries these entries are considered text excel would not include those amounts in a number calculation in a wider data analysis process they might be ignored altogether remember in Excel a currency amount should always be typed as numbers in the cell first then you should apply the currency symbol or the comma separator using a number format unnecessary spaces before or after entries can also create difficulties they don’t stand out on screen in the same way as other text or number characters but Excel is aware of them for Excel the word Chicago followed by a single space is different from Chicago typed without the space for calculation and analysis purposes it considers them to be two separate cities finally an entry might be placed in the wrong column or under an incorrect heading in a spreadsheet for example Jaime might type an entry under the wrong heading in her spreadsheet the city named Chicago is entered in the sales price column so that row item might be mclassified other examples of common errors or mistakes can be caused by an inconsistent layout or content it’s important that data is presented consistently throughout a worksheet so that it always remains accurate and reliable poor or inconsistent layouts can give rise to errors when creating an Excel file keep in mind the way in which information will be used like if a spreadsheet only has a single column for an address this column then contains all the address elements like city region or area code this means that it’s difficult to break down these results separately by city or by region during data analysis because they’re not in separate columns instead you should format information like addresses across multiple columns so that it’s easier to process and analyze the data abbreviations and acronyms can also generate errors in data analysis it’s usually better to include a full word or title instead of an abbreviation or acronym in the following spreadsheet there are multiple variations of common abbreviations like Mr Miss and doctor this will cause serious issues during data analysis the best approach for data analysis is to standardize the approach for writing abbreviations particularly for titles like these another important feature of data analysis is the ability to break down results and information by date or calendar interval this means that dates must be entered in a particular way in a spreadsheet so that Excel recognizes them as calendar items the component elements like the month day and year must be typed as numbers and separated by a forward slash or a dash if you type dates with incorrect separator characters then Excel won’t interpret them as numbers instead it processes them as text so you won’t be able to conduct time analysis of your data a final common error to be aware of is duplicate information duplicate information in a data block distorts analysis results items can be counted multiple times and numeric results can be artificially inflated checking for duplicate data is an important step before performing data analysis duplicated entries in data are often the result of human error where entries are typed multiple times data could also be repeated accidentally if imported or created using a copy and paste operation for example Jamie might add sales figures from the previous week to the spreadsheet if her colleague doesn’t check for duplicate data then those sales figures could be included in the results a second time so how could you avoid the risk of duplicate data aim for an efficiently designed spreadsheet for example if you’re including dates in your spreadsheet then sort the sheet in date order this makes it easier to identify the time entries already added likewise if you’re including address data then assign a different column to each element of an address this helps others to identify entries by searching for house numbers street names or cities like an entry for apartment 1 2 36 on North Street Miami jamie has identified the common errors in her data set she can now resolve them and start analyzing the data and you should also now be able to recognize common data errors and how they can have a negative impact on data analysis results you’ll be able to identify and fix the most common errors in the data before submitting it for analysis well done every day you calculate dates and times asking questions like “How long do I have to get to work?” or how many days do I have available to complete that project data analysts also ask date and timebased questions about their data sets and they can calculate answers using Excel’s date and time functions and formulas in this video you’ll learn about the importance of these date and time calculations how they can generate new data and explore some business use cases over at Adventure Works distribution hub Jaime is overseeing both the stock that Adventure Works are purchasing from suppliers and the items dispatched to fulfill customer orders jaime needs to create a spreadsheet with date and time formulas that track the delivery times dates and date intervals before you discover how Jaime can make use of these formulas let’s find out how date and time information provides businesses with an essential framework for planning date and timebased calculations are useful tools in helping businesses to plan for increased demands for products and demands on resources such as staff and equipment they also help businesses plan towards key dates or deadlines you can also use Excel to plan toward key dates where there will be an increased demand on your business take the example of a building company contracted to build a new office block the project manager needs to create schedules and plans for all stages of the building process for planning purposes they need to determine how many working days there are between the project’s proposed start and end dates excel can be used to create formulas to calculate how many hours calendar days or work days there are for important deadlines these formulas can be set up in a dynamic way so that they update as the clock or the calendar changes by monitoring daily results over a specific time interval businesses can identify dips and peaks in performance for example a management team might notice that during one period there was a significant drop in sales if the results are organized by date they can identify the factors internal or external that might have caused this date and time calculations are also useful for tracking results and performance business transactions are usually recorded against dates and in some cases against time now that you’re familiar with some of the benefits of date and time calculations let’s explore date and time functions and formulas in Excel it is important to understand how Excel tracks dates and how it is used in calculations let’s begin with serial numbers the method Excel uses for tracking calendar days in Excel each date entry is formatted to appear as a calendar item however behind each date is a number that Excel uses to keep track of calendar days this number is known as a serial number excel assigns a serial number to each date starting from the 1st of January 1900 this date was given serial number one excel uses the system clock on your computer to track time and it increments the serial number by one when a 24-hour period has elapsed a date in the past will have a smaller serial number than one in the future you can view the serial number behind any date by changing the format from date to general in this example the two entries in A2 and B2 are formatted to display as dates if the same entries in A4 and B4 are formatted as general it is possible to display the serial numbers behind these dates the later date has a larger serial number excel uses these serial numbers in calculations using serial numbers one date can be subtracted from another to calculate a specific number of days for example the today formula can be used to always display the current date in a spreadsheet over at Adventure Works Jamie needs to display the current date in her spreadsheet she can use the today function to generate this result the syntax for this formula is an equal sign followed by the word today and parenthesis this creates a dynamic date display in a spreadsheet that updates every 24 hours a similar function called now can also be used to display both the current date and time the syntax for this function is an equal sign the word now followed by parenthesis when executed this function displays the current date and time in your spreadsheet this makes it more useful than the today function which just shows the date you can also use functions to extract the component elements of a date these actions can be carried out using the month day and year functions each function extracts a specific component of the date the month day or the year you will learn more about these functions and the others you’ve just reviewed later in the course finally there’s also the date function the date function is the opposite of month day and year either of these operations may be necessary to prepare date information for data analysis you will learn more about these functions and the others you’ve just reviewed later in the course jamie can use these date and time formulas to track delivery times and dates for Adventure Works purchases from suppliers and to track items dispatched to their customers and you should now understand how date and time calculations are used to generate new data in Microsoft Excel you’ve also learned how to identify key business case uses for date and timebased information well done as a data analyst you’ll often have to input large volumes of time and date based data into your spreadsheets and it can be difficult to manually keep this data aligned with your project thankfully with Excel you can create dynamic date and time entries that update automatically over the next few minutes you’ll learn how to create dynamic time and date entries in a worksheet and separate dates into component parts adventure Works are preparing a new advertising campaign which will launch in multiple countries they need to use Excel to track progress toward key dates the milestone dates for the project are contained in a worksheet called regional dates the worksheet tracks information about the products that are part of the campaign alongside the campaign launch dates for each country adventure Works needs to calculate how many project days are available for each campaign another calculation in the spreadsheet must show on a rolling basis how many days are left until each launch date the development of this campaign will spread over two years so Adventure Works also need to record the accounting period for the project launch date for each country let’s help Adventure Works to complete their spreadsheet using date and time formulas entries in column D and E are formatted as dates you can select any cell in the range D5 to E19 and check the number format box on the home ribbon to confirm this remember that these dates are actually serial numbers so you can switch the format on cells D5 and E5 to general access the home tab and select general from the drop-own menu to display the serial numbers notice that the serial number for the date in E5 is larger than the one for the date in D5 select undo to restore the date format now you need to calculate the number of project days you can complete this task using a simple subtraction formula select F5 to input your calculation begin the calculation with an equal sign then take the date in E5 the larger serial number and subtract the date in D5 the smaller serial number press enter to generate the result there are 63 days assigned to the timeline for this first project note that because this calculation is a subtraction Excel doesn’t include the start date in cell D5 in its count however if required you can ask Excel to include the start date by adding a plus one to the formula the result in F5 remains static because the dates in D5 and E5 won’t change now you need to work out the days to launch figure for cell G5 the formula for this figure takes the launch date in E5 and subtracts a current date figure in cell E1 the current date in E1 must also be created using a formula if E1 always displays the current calendar date then the formula in G5 recalculates daily to show the decreasing numbers of days to the launch date you need to use the today function in your formula in E1 to make sure that the date updates every 24 hours to the current date with the cursor in E1 type an equal sign the word today and an open parenthesis you might notice that the help prompt is empty this is because the function doesn’t require any arguments there still needs to be parenthesis after the function name but no arguments should be included press enter to produce a dynamic date result that updates every 24 hours to show the days to launch figure in G5 the formula takes the campaign launch date in E5 and subtracts the current date in E1 the E1 cell reference must have dollar signs before the column initial and the row number this is to make sure that the reference stays constant when the formula is copied the today formula will now change the current date in cell E1 every day this means that the formula in G5 also recalculates daily so the days to launch figure reduces by one each day as the timeline gradually progresses your next task is to show the year for the campaign launch date excel recognizes three elements in a date the month the day and the year you can use the year function to identify and display the year element from a date in another cell in other words you can separate the date into its component parts so that you can focus on the year element type an equal sign the word year and an open parenthesis in cell H5 a help prompt appears on screen and states serial number this is because Excel interprets stored dates as serial numbers select E5 type a closing parenthesis and then press enter to generate the result in H5 this campaign is set to launch in 2023 you’ve calculated the required campaign information for row 5 you can now copy these formulas down through the spreadsheet to calculate the remaining campaign dates use the autofill doubleclick shortcut on each formula to copy it down through the column to row 19 and complete the spreadsheet you should now understand how Excel works with dates in calculations and be able to create some common dates and time tracking formulas thanks to your work in these formulas Adventure Works now have a clearer picture of how much time is available for each stage of this project well done when working with Excel you might need to execute a function under certain conditions or logic in these instances you can use a logical function calculation like an if function in this video you’ll explore the purpose of logical functions review some common use cases and learn the syntax for creating a logical function formula using the if function over at Adventure Works Lucas is reviewing the monthly sales reports he needs to find out if any of the sales staff are entitled to a monthly bonus as a reward for exceeding their sales targets you can help Lucas to identify which sales team members deserve a bonus by using an if function formula but before you can help Adventure Works you’ll need to find out more about how logical functions work you can use logical functions to ask yes or no questions about your data if the function returns yes as its answer then you can direct Excel to perform the required action however if the function returns an answer of no then Excel can be directed to perform a different action for example you can direct adventure works if function formula to ask the question has this salesperson met their target if the answer is yes then they’ll be awarded their bonus if the answer is no then they’re not awarded a bonus when logical functions such as if run a test they determine the answer by comparing the value in a cell against a specified criterion for these tests to work the formula must contain logical operators the logical operators determine what kind of question the formula is asking and what value it needs for its answer these operators can be used to compare both text and numeric entries let’s review some examples of these operators the equal sign is the first of the mathematical operators that Excel uses in logical functions excel uses this operator to check if the value of one item is equal to that of another item for example a formula that tests if one equals 1 would return the value of true the logical symbols greater than and less than are used by Excel to test if one value is larger or smaller than another an Excel formula that performed the logical tests two is greater than one and one is less than two would return an answer of true for both tests the greater than and less than symbols can also be combined with the equals sign this combination lets Excel confirm if a value is greater than or equal to or less than or equal to another value let’s take a formula where Excel checks to see if the value in cell D2 is the same as or larger than the value of 400 if even one of these arguments were true then the test would return the value of true finally a very useful set of logical operators is not equal to this is when the less than and greater than symbols are typed back to back this combination of operators is interpreted by Excel as not equal to in other words you’re asking Excel to determine that value A does not equate to value B for example the result of the logical test 1 is not equal to two would be true because the two numbers are different values so you’ve discovered how an if function formula works but how do you make use of one when constructing the if function formula you need to give Excel three pieces of information the first piece of information is called the logical test for the logical test you need to identify the cell that contains the value to be checked you also need to specify the test to be carried out in relation to this value this is the if keyword followed by parenthesis it’s within these parentheses that you must type the logical test for example Lucas needs Excel to check the total sales of each team member to determine if they meet their monthly target the next instruction tells Excel what to do or what to display if the test returns a result of true in Lucas’s case if his test returns a value of true then the team member is awarded a bonus the third and final argument is what Excel should do or display if the logical test returns the result of false if Lucas’ test returns a value of false for a team member then Excel returns a value of zero in other words that person is not awarded a bonus now that you’ve reviewed the elements of an if function formula let’s make use of your new skills and help Lucas create a formula to check the sales team’s monthly figures and determine which employees are entitled to a bonus the data set Lucas requires is in a workbook called monthly sales the workbook contains four sheets one for each sales team for this exercise let’s just focus on the results for team A the worksheet lists the name of each team member their total monthly sales and their monthly target the bonus amounts must be calculated and listed within column E any team member who meets or exceeds their target is awarded the bonus figure in cell H4 let’s begin by finding out if team member Michelle Cook is entitled to a bonus position the cursor on cell E4 type an equal sign the keyword if and an opening parenthesis you need to place your arguments for the if function within parenthesis notice the floating help message prompting you for the three arguments that the function needs select cell C4 for Michelle’s monthly sales data type a greater than symbol followed by an equal sign then select cell D4 and type a comma this instructs Excel to check if Michelle’s sales figures for this month are greater than or equal to her assigned target however as you can see from the bold prompt text the formula is still incomplete you now need to instruct Excel on what bonus value to award you must also include what action Excel should take if the result of the logical test is true or yes and what to do if the result is false or no select cell H4 for the value if true add a dollar sign before the column initial and the row number this dollar sign prevents Excel from adjusting it when copied then type a comma followed by a zero for the value if false this zero indicates that Michelle doesn’t receive a bonus if Excel doesn’t return the required value finally type a closing parenthesis to end your arguments press enter to execute the if function formula the results show that Michelle has met her sales target and has earned a bonus of $500 for this month copy the formula down the column and executed to determine how the other team members have performed the results show that three team members met their sales targets and could be awarded a bonus two team members did not reach their targets so should not receive a bonus thanks to your help Lucas successfully created the IF function formula he needed to complete his task and having assisted Lucas you should now know how if functions work and recognize the correct syntax to create a logical formula using if well done you may be familiar with using a logical function to test for conditions in your data sets but what if you need to test for multiple conditions you can use nested if and ifs functions in this video you’ll explore the concept of nested if and ifs functions and learn how they can be used to perform a series of elimination tests and generate a final result over at Adventure Works Lucas is calculating bonuses for sales team B lucas needs to calculate each team member sales total and determine what level of bonus they should be awarded lucas can complete this task using nested if and ifs functions let’s find out more about these functions and then help Lucas complete his task at this stage of the course you’ve encountered many examples of function formulas but a formula doesn’t have to make use of just one function in fact a formula can contain several functions that work together to achieve a result logical functions work this way by interconnecting with one another nesting functions is the technique of adding another function to the formula as an argument for the original function in other words you can place one function inside another to expand its functionality for example you might need to create a formula that performs a series of elimination tests before it generates the final result you could design this formula in two ways one approach would be to create what is known as a nested if formula the formula begins with an if that performs an initial logic test if the test turns out to be true then the formula will simply process whatever action is specified in the value if true argument however the result of the logical test could also be false if so then another if function in the value of false argument could run another test and process different actions for example a nested if formula could check if a member of the adventure work sales team meets a specific bonus band if the result is false then a second argument could check the value against another band and so on the second approach is to use a function called ifs an ifs function is designed to run a series of tests that don’t require you to nest other functions the ifs function steps through the tests checking each one if a test is false it continues to move through the tests until it finds one that is true when a logical test returns true as a result the formula performs or displays whatever is in the value if true for that test it then stops running tests in the case of Adventure Works the IFS function can continually check each sales team member’s sales results against the different bonus bands until it identifies a suitable amount to award them now that you’ve learned about the basics of nested if and ifs functions let’s put your knowledge to use by helping Lucas to calculate the bonus bands for the sales team the sales data sets are contained in the team B worksheet in a workbook called monthly sales figures the team B worksheet lists the names of each team member and their monthly sales result it also lists their sales targets and the amount they achieved above their targets the bonus amounts must be listed in column F using the bonus bands data in column I and J adventure Works also needs a formula in F3 that checks the sales data in cell E3 it must then calculate which bonus band is applicable to the team member Olivia King and display the correct bonus amount let’s begin by typing the formula position the cursor on F3 type an equal sign an if and an opening parenthesis next select E3 to add that cell as a cell reference then type a greater than symbol followed by an equal sign type 20,000 which is the first bonus band and then a comma finally select cell J3 to add it as the value if true argument then type a comma this first part of the formula provides Excel with the following instruction if the figure in cell E3 is greater than or equal to 20,000 then the staff member is owed the bonus amount in cell J3 but what if one or more of the amounts in column E are less than 20,000 if the amount in E3 is less than 20,000 there are still two other bands from which a bonus can be assigned to test for these bands you need to add another if function as the value if false argument in the formula you can nest this function within the first one first type an if in this instance you don’t need another equal sign then type an opening parenthesis so you can begin writing your arguments this second occurrence of the if will need its own opening and closing parenthesis the parenthesis must contain three arguments a logical test a value if true and a value if false let’s create the logical test first select E3 to assign it to your argument then type a greater than symbol and an equal sign then type 10,000 and add a comma next you need to assign the value if true so if the amount in E3 is over 10,000 then the bonus amount awarded will be the value in J4 select cell J4 and type a comma to assign it to your argument finally you need the value if false if it’s not true that the amount is over 10,000 then the bonus amount awarded will be the value in J5 select cell J5 to assign it to your argument each instance of if also needs its own closing parenthesis type two closing parenthesis and press enter to execute the function the results of your function show that the logical test for the first if failed so Excel moved on to the second if the second logical test was true so Excel correctly displayed the bonus amount of $1,000 in cell J4 changing the monthly sales figure for Olivia to 67,140 would change the result in F3 because both if functions would have returned a false result so the result would have been the value in cell J5 this formula is now a nested formula because there is a second if inside the first one let’s delete this result and recreate the formula using the ifs function when you type equals an ifs and an opening parenthesis Excel only provides prompts for two arguments a logical test and a value if true as you learned earlier you can use ifs to specify a series of tests and the value if true for each one let’s step through this process select cell E3 then type a greater than symbol an equal sign and a value of 20,000 type a comma and then select J3 as the band to be assigned if the first test is met when you type a comma prompts appear for another logical test and a value if true for the second logical test select E3 again this time you must follow it with a greater than sign and an equal sign then select J4 now you need to tell Excel that the final value of true should be the result of the formula so type true and a comma then select J5 adding the word true here prevents Excel from producing a hash NA error message you also need to add dollar signs to the J3 J4 and J5 references you can now copy this formula down through the column to calculate the bonus amount for each team member thanks to your help Lucas has now determined what bonus band should be awarded to each team member and you should now understand the difference between a nested if function formula and a calculation that uses ifs you’ve explored the different syntax for both types of formula so you can decide which you find easier to understand and replicate congratulations on reaching the end of the third week in this course on preparing data for analysis with Microsoft Excel this week you explored how to use functions to prepare data for analysis in Excel let’s take a few minutes to recap what you learned in this week’s lessons you began the first lesson by discovering how inconsistent data affect analysis and the common mistakes people make examples of these errors include misspellings unnecessary characters and spaces and incorrectly place entries you now know that errors such as these have a negative impact on data analysis you were also able to fix these errors in your data before submitting it for analysis you then learned how you can use different functions to standardize text data the left mid and right functions are used to return a specific number of characters from either the left the middle or the right side of a cell entry typically these functions are used in situations where you need to transfer parts of the cell content to a different column many data analysts use the left mid and right functions to split the contents of a column into three separate columns the trim function removes empty spaces from text strings except for the spaces between words this is useful for when you suspect that there are random spaces at the beginning or end of an entry it’s also a useful way to tidy up a column of text before beginning any analysis using the wrong case in text data can make a summary or report appear untidy or unprofessional there are three functions you can use to standardize the case used in text entries these are upper lower and proper lastly you can use the concat function to combine entries from different cells in a spreadsheet into a single cell entry and in this lesson you put your new knowledge of functions to use by helping adventure works you used your knowledge of functions to help Adventure Works standardize its data for analysis one of these tasks was in the exercise in the exercise you had to clean up Adventure Works spreadsheet so that it could be used for data analysis to complete this task you used formulas to remove inconsistencies or errors from the data and you made sure that your formulas followed the best practices you had explored during the lesson you then undertook a knowledge check in this item you proved your understanding of concepts you encountered by answering a series of questions finally you explored a list of additional resources designed to help you improve your knowledge of the topics in this lesson in the second week you learned how to use date and time functions in Microsoft Excel to generate new data you explored different examples of how the data generated from date and time calculations can be used for example date and time data can be used to create a framework for planning track business performance and display important results you then learned how Excel interprets and works with dates in a spreadsheet all dates have serial numbers which is how Excel interprets them with these serial numbers you can use dates to perform calculations like subtracting one date from another you also reviewed functions for creating dynamic formulas that calculate time and date values these include the today and now functions and you discovered that you can also divide a date entry into its component parts using day month and year or return these components as a single date with the date function throughout the lesson you put your new knowledge to use by assisting Adventure Works you helped the company to plan its projects by using different date and time calculations one of these tasks was in the exercise in the exercise you gathered date and time information for one of Adventure Works advertising campaigns you completed this task using the date and time calculations you learned about these functions helped you to generate new milestone data for Adventure Works you then undertook a knowledge check in this item you proved your understanding of the concepts you encountered by answering a series of questions finally you explored a list of additional resources designed to help you improve your knowledge of the topics in this lesson in week three you learned about logical functions such as if and ifs you learned that logical functions can be used to ask yes or no questions about your data if the function returns yes as its answer then you can direct Excel to perform the required action however if the function returns an answer of no then Excel can be directed to perform a different action next you learn that for these tests to work the formula must contain logical operators the logical operators determine what kind of question the formula is asking and what value it needs for its answer you discover that these operators make use of if formulas and this formula needs three pieces of information to work it requires a logical test a true value and a false value you also learned that nesting functions is the technique of adding another function to the formula as an argument for the original function in other words you can place one function inside another to expand its functionality there are two approaches you can use the nested if function or the ifs function you learned that the nested if formula begins with an if that performs an initial logic test if the test turns out to be true then the formula will simply process whatever action is specified in the value if true argument however the result of the logical test could also be false if so then another if function in the value if false argument could run another test and process different actions the second approach is to use the ifs function you discover that the ifs function steps through the tests checking each one if one test is false then the function continues to move through the remaining tests until it finds one that is true when a logical test returns true as a result the formula performs or displays whatever is in the value if true for that test it then stops running tests just like in the previous lessons you put your new knowledge to use by helping adventure works in this lesson you determined the financial performance of the sales team using if and ifs functions this included the exercise item in the exercise you helped Adventure Works to generate additional information from a customer’s spreadsheet to complete this task you generated the required information by using if and ifs functions and you made sure that your calculation followed the best practices you had explored during the lesson you then undertook a knowledge check and a module quiz in which you proved your understanding of the concepts you encountered by answering a series of questions you’ve now reached the end of this module summary it is time to move on to the discussion prompt where you can discuss what you’ve learned with your peers you’ll then be invited to explore some additional resources to help you develop a deeper understanding of the topics in this lesson best of luck we’ll meet again during next week’s lessons you’re nearing the end of this course on preparing data for analysis in Microsoft Excel you’ve put great effort into this course by completing the videos readings quizzes and exercises you should now have a stronger grasp of several foundational concepts for understanding data analysis these include the fundamentals of working with data in Microsoft Excel creating and using formulas and functions in Excel and preparing data for analysis using functions you’re now ready to apply your knowledge in the exercise and the final course assessment the assessment is a graded quiz that consists of 30 questions that are related to topics you covered throughout the course but before you start let’s recap on what you’ve learned in the first week you were introduced to Microsoft Excel you learned how to use Excel by exploring how to enter and format data manage worksheets read large blocks of data and sort and filter data microsoft Excel is a useful data analysis tool it is used in everyday business to store calculate and gain insights from data you learned how to navigate Excel using its UI for example the title bar that displays the name of your file and search option and the command tabs which are organized into tabs and ribbons you also learned that a worksheet is where you input data into cells data can be added to worksheets by importing it or creating it manually data isn’t always easy to read but you’ve learned how to use formatting to improve the readability of a spreadsheet you also explored the keyboard shortcuts for data entry and formatting excel has various features that help you to read large blocks of data you learn that you can use the freeze panes new window name box features and keyboard shortcuts to make it easier to read your data you can use the sort and filter feature to organize and sort data quickly and efficiently there are also different sort methods such as alpha numeric sort and multi-level sort that you can use to sort your data the filter feature helps you to control data visibility in a worksheet and provides information on how many rows match a specific criteria in the following week your focus shifted to functions and formulas in Excel you discovered that a formula in Excel is a calculation performed on the values in a range of cells in your worksheets examples of these calculations include addition subtraction multiplication and division once the calculation is completed the formula returns a result even if it is an error you then explored how formulas work along with the operators they use formulas can be static or dynamic a static formula means that the numbers are fixed so it always generates the same results a dynamic formula is one in which the results depend on the current values in the reference cells and it reacts to any changes in the values by updating the result you also learned how to control calculations here you learned that Excel controls calculations using the order of precedence this means that Excel processes the mathematical operators in formulas according to the hierarchical position of each symbol within the order of precedence you learned about the hierarchy of symbols and discover that you can also control a calculation using parenthesis next you explored the relative and absolute cell references these concepts relate to how a
cell reference appears in a calculation a relative cell reference means that Excel adjusts the cell reference of a copied formula relative to its new location to make sure it’s correct an absolute reference means that Excel keeps the reference constant in other words it doesn’t adjust it you also learned about functions which are predefined formulas built into Excel you explored popular functions such as sum average and count and learned how to create formulas with them using features such as the autosome shortcut and the insert function wizard you also explored different percentage calculations and you learned how to create reliable percentage formulas using the correct syntax the third week was all about preparing data for analysis using functions you started off by exploring how inconsistent data affects analysis and the mistakes that can be made when inputting data examples of these errors include misspellings unnecessary characters and spaces and incorrectly placed entries you now know that errors such as these have a negative impact on data analysis you also learned how to fix these errors in your data before submitting it for analysis it is important to standardize text data before analyzing it you can do this using functions the left mid and right functions are used to return a specific number of characters from either the left the middle or the right side of a cell entry typically these functions are used in situations where you need to transfer parts of the cell content to a different column the trim function removes empty spaces from text strings except for the spaces between words this is useful for when you suspect that there are random spaces at the beginning or end of an entry you also learned that there are three functions upper lower and proper that you can use to standardize the case used in text entries your reports will look tidy and professional if you standardize the case you can also use the concat function to combine entries from different cells in a spreadsheet into a single cell entry next you discover that dates are important for data analysis without date and time data it is more difficult to analyze and compare results over time you explored functions such as today or now which help you add dynamic date and time information to your worksheet you also learned that other functions such as year month or day can be used to split dates into their component parts to facilitate analysis finally you learned how logical functions such as if and ifs add another dimension to calculations because they ask Microsoft Excel to check for criteria and perform different actions depending on the result you then explored how other functions such as the or and the and functions make the logical formulas you create even more efficient and versatile you also learned how to produce specific and targeted formulas by using functions such as sum if average if and count if these functions combine the if functionality with the actions of standard functions such as sum now that you’ve built a solid understanding of the fundamentals of Excel formulas functions and learned how to prepare data for analysis you’re ready to test your knowledge by undertaking the exercise and the final course assessment best of luck congratulations you have made it to the end of the preparing data for analysis in Microsoft Excel course your hard work and dedication have paid off you’re off to a great start with your data analysis learning journey and you should now have a thorough understanding of the fundamentals of Microsoft Excel working with blocks of data in Excel formulas and functions and how to prepare data for analysis using functions you can also identify common errors made in data analysis and you know how to deploy different strategies to make sure you have reliable data but that’s not all you’ve also gained valuable insight into the functions and formulas you can use to create in-depth data for analysis you’ve explored various calculations deepened your knowledge of how data analysis can be performed and reviewed scenarios where it is used and let’s not forget the process of preparing data for analysis you now understand the critical role that reliable data plays as a central focal point of data analysis you should now have a firm knowledge of how Microsoft Excel works and how it can be used for data analysis think about everything you can do with this new knowledge well done for taking the first steps towards your future data analysis career by successfully completing all the courses in this program you’ll receive a Corsera certification this program is a great way to expand your understanding of data analysis and gain a qualification that will allow you to apply for entry- levelvel jobs in the field all the courses in this program including the one you just completed will help you prepare for the PL300 exam by passing the exam you’ll become a Microsoft certified PowerBI data analyst it will also help you to start or expand a career in this role this globally recognized certification is industry endorsed evidence of your technical skills and knowledge the exam measures your ability to perform the following tasks prepare data for analysis model data visualize and analyze data and deploy and maintain assets to complete the exam you should be familiar with Power Query and the process of writing expressions using data analysis expressions or DAX you’ll learn about the syntax later in this program you can visit the Microsoft certifications page at http://www.learn.microsoft.com/certifications to learn more about the PowerBI data analyst associate certification and exam this course has enhanced your knowledge and skills in the fundamentals of data analysis but what comes next there’s more to learn so it’s a good idea to register for the next course on harnessing the power of data in Microsoft PowerBI the next course will cover various ways data analysis is used in business you’ll learn about the role of a data analyst and how to use data to solve business problems and you’ll learn how to process and analyze data then you’ll move on to learn about the tools needed to analyze data efficiently whether you’re just starting out as a novice or you’re a technical professional completing the whole program demonstrates your knowledge of analyzing data in PowerBI you’ve done a great job so far and you should be proud of your progress the experience you’ve gained will show potential employers that you are motivated capable and not afraid to learn new things it’s been a pleasure to embark on this journey of discovery with you best of luck in the future hello and welcome to the harnessing the power of data with PowerBI course this course covers the core concepts of data analysis and introduces the main features of Microsoft PowerBI many of your normal digital activities generate data this can happen when you use services such as car parking traveling by rail or air or from your shopping socializing or fitness activities of course it’s not just you that’s contributing data your friends family and colleagues in fact almost everyone adds content to the data pool businesses and organizations also use many other sources such as government financial economic health and scientific data to name a few gathering and storing a vast amount of data is the first phase then comes the challenge of its analysis this is why there is a growing demand for data analyst professionals businesses need data analysis more than ever and as a data analyst you’ll be ideally placed to begin harnessing the power of data in this learning path you will learn about the life and journey of a data analyst and the skills tasks and processes they go through in order to tell a story with data you’ll discover how getting that data analysis story correct enables businesses to make informed decisions let’s get an overview of the main topics covered in this course you may have already learned about one crucial topic preparing data using Microsoft Excel you also need to understand other elements involved in the career of data analysis including learning about the stages in the data analysis procedure and the roles involved recognizing key issues and concerns when conducting analysis and sharing results and knowing different types of data sources and connection types this course will give you a solid foundation in these topics and introduce you to the component elements of Microsoft PowerBI software that helps to process analyze and share data let’s now quickly summarize the course material to give you an overview of all your study in this course this course will introduce you to data analysis in business data sources and data ingestion to begin you’ll learn about the role of a data analyst key data analysis concepts and how data plays an essential role in business you’ll then be briefly introduced to PowerBI as a tool for data analysis you will also learn about data sources and the exact transform load or ETL process you’ll learn the importance of identifying and evaluating data sources and following this you will learn about transforming and cleaning data in PowerBI you’ll get to distinguish between the different query and scripting languages to consolidate your learning and put it into practice you will complete a practical assignment where you will use data to determine the cause of a recent decrease in sales practical exercises in the course are based on a fictional business called adventure works during the exercise you must identify stakeholders locate data sources perform data transformation and distribute reports after this hands-on learning you will complete a final graded assessment be assured that everything you need to complete the assessment will be covered during your lesson with each lesson made up of video content readings and quizzes to assist your learning you will also get to apply your newly gained skills in exercises quiz questions and self- reviews in addition discussion prompts allow you to share knowledge and discuss difficulties with other learners these discussions are also a great way to grow your network of contacts in the data analysis world so be sure to get to know your classmates and stay connected during and after your course is this the course for you hopefully the outline of the course content and topics will help you decide and it’s important to mention that you don’t need an IT related background to take this course it’s for anyone who likes using technology and has an interest in data analysis whatever your background to complete this course you need to have access to some resources you need a laptop or desktop computer with a recommended 4 GB of RAM an internet connection and a Windows operating system version 8.1 or later it should have a .NET framework version 4.6.2 to or later install and a subscription to Microsoft Office 365 you’ll also need to install PowerBI desktop available as a free download you’ll find further details about these and other requirements in the additional resources item at the end of this lesson this program prepares you for a career in data analysis when you complete all the courses in the Microsoft Power BI analysis professional certificate you learn a Corsair certificate to share with your professional network taking this program not only helps you become job ready but also prepares you for an exam PL300 Microsoft PowerBI data analyst in the final course you’ll recap the key topics and concepts covered in each course along with a practice exam you’ll also get tips and tricks testing strategies useful resources and information on how to sign up for the exam finally you’ll test your knowledge in a mock exam mapped to the main topics in this program and the Microsoft Certified Exam PL300 ensuring you’re wellprepared for certification success earning a Microsoft certification is evidence of your real world skills and is globally recognized a Microsoft certification showcases your skills and demonstrates your commitment to keeping pace with rapidly changing technology it also positions you for increased skills efficiency and earning potential in your professional roles the topics covered in the practice exam include prepare data model data visualize and analyze data and deploy and maintain assets in summary this course introduces how a data analyst uses data to create a compelling story through reports and dashboards using Microsoft PowerBI it also explores the need for true business intelligence in the enterprise i hope you are ready to get started with your data analysis journey data is an essential business component with organizations using many methods to collect their data however raw data is only meaningful with proper interpretation and analysis that’s where the work of a data analyst is crucial because data is often used to inform decisions that can significantly impact an organization’s success data analysts are essential to business they help organizations make sense of the vast amount of collected data in this video you will explore the role of a data analyst the flow of data in an organization and how an analyst achieves data insights that inform decisions you’ll also learn about the importance of data analysis in modern organizations and the vital role of a data analyst data analysts help organizations make sense of the data they collect turning it into insights that inform decisions let’s explore the responsibilities of a data analyst and discover how they achieve data insights imagine you work for an online retail company every day your company collects data on customer purchases website traffic and social media engagement however the data is not organized which makes it difficult to analyze the inability to interpret the data means your company fails to identify opportunities to improve customer experience increase sales and stay ahead of the competition this is why a data analyst is needed the data analyst is responsible for collecting organizing and analyzing the data to generate insights that inform business decisions for example the data analyst may identify trends in customer behavior that could inform marketing campaigns or website design they may also identify areas where the company can cut costs or improve efficiency strategic thinking awareness of impact and understanding of context are crucial skills for a data analyst to succeed in their role here’s why each skill is important strategic thinking helps data analysts prioritize tasks allocate resources efficiently and make datadriven decisions that contribute to long-term success by considering both short-term and long-term implications data analysts can ensure their work has a meaningful impact on the organization being aware of the potential impact of their analysis is critical for data analysts to ensure they communicate their findings responsibly and ethically this involves understanding the consequences of datadriven recommendations considering potential biases and ensuring data privacy and security awareness of impact also helps data analysts advocate for datadriven decision making and fosters a culture of evidence-based strategy within the organization data analysts need to have a deep understanding of the context in which they are working including the industry market trends and the organization’s goals and challenges this knowledge allows them to tailor their analysis to the specific needs of the business and provide actionable insights data analysts use various tools and techniques to collect and analyze data these include programming languages like R and Python r is used specifically for data analysis while Python is a generalpurpose programming language that can be used for a wide range of applications including statistical analysis data visualization tools like Microsoft PowerBI and databases like SQL Server data analysts are expected to be proficient in these tools and technologies and to possess excellent analytical skills a data analyst collects data from many resources including customer sales financial and operational data departments within an organization such as marketing sales finance and operations provide this data the data is then processed cleaned and transformed into a usable format for analysis this process is known as data wrangling once the data is wrangled it is loaded into a data warehouse or data lake where data analysts can access and analyze it the data is organized into tables or data sets each containing a specific data type data analysts then use this data to generate insights that inform business decisions data analysts play a critical role in our datadriven world they help organizations make sense of the large amounts of collected data turning it into insights that inform decisions using their skills data analysts help organizations identify growth opportunities improve operations and gain competitive advantage someone at the party asks you “What do you do?” You reply “I work with data.” Does that help them data roles are a mystery most people don’t understand the value and variety of positions in the data analysis process let’s demystify data analysis roles and responsibilities in this video by exploring various roles and describing how they contribute to the success of datadriven organizations you’ll also learn about the importance of each role and how roles collaborate the data analysis roles and responsibilities that you’ll explore are data engineer data analyst data scientist database administrator data architect and business intelligence analyst commonly called BI analyst to understand a data engineer’s role imagine you’re creating a garden the data engineer is like the person who designs and constructs the irrigation system delivering water to each plant they build and maintain the data infrastructure including designing constructing and integrating data pipelines they clean pre-process and transform raw data into a format that can be used by data analysts and data scientists in our gardening analogy the data analyst is like the gardener who meticulously observes the growth of each plant and makes recommendations for improvement data analysts examine data sets to identify trends patterns and insights to inform decision-m they use various tools and techniques to visualize and present data making it easily digestible for stakeholders data analysts work closely with other team members to align their analysis with business goals and objectives think of a data scientist as a botanist using their plant biology knowledge to optimize the growth and health of the garden they dive deeper into the data to create predictive models using machine learning algorithms and statistical techniques they seek to identify hidden patterns and correlations that help organizations make better datadriven decisions data scientists often work closely with data analysts sharing insights and collaborating on projects to maximize the value of the data after all at gardening you’ll want to safeguard the security and overall health of the garden that’s like the role of a database administrator or DBA database administrators work on the maintenance performance and security of an organization’s databases they ensure data is stored and retrieved efficiently implemented backup and recovery strategies and manage user access dbas play a crucial role in keeping data safe and accessible to those who need it to ensure a greatl looking garden a landscape architect designs the garden layout to maximize aesthetics and functionality in a similar fashion a data architect creates the blueprint for an organization’s data management systems they design data models establish database structures and create strategies for data storage integration and retrieval data architects collaborate with other data professionals to align their designs with business needs and support the objectives of data analysts and scientists the business intelligence or BI analyst is like the garden consultant who helps you make informed decisions about the type of plants to grow where to place them and how to care for them based on data and analysis pi analysts transform data into actionable insights that drive business growth and improve decision-making they work closely with data analysts and data scientists to extract meaningful insights from complex data sets focusing on key performance indicators and using various BI tools to visualize and present data to stakeholders bi analysts also collaborate with business leaders to understand their goals and objectives ensuring that their analysis is relevant and impactful so the next time you’re at a party and someone asks about your role what will you say you should be able to highlight the importance and variety of data analysis positions you could discuss the data engineer who is responsible for building and maintaining the data infrastructure the data analyst who identifies trends patterns and insights in the data the data scientist who creates predictive models to optimize decision-m the database administrator who ensures the security and performance of databases the data architect who designs the blueprint for data management systems and the business intelligent analyst who transforms data into actionable insights for decision makers your party friends will then understand what each role does in the data analysis process providing organizations with the information they need to make informed datadriven decisions jamie the CEO at Adventure Works has asked you to analyze customer data to identify trends and make recommendations for improving the customer experience after weeks of working through the data creating detailed visualizations and uncovering valuable insights you now need to present your findings to various stakeholders these include your team marketing sales and company executives for your project to be successful you need to effectively communicate your findings and collaborate with people at all organizational levels to succeed as a data analyst you need a strong foundation in non-technical abilities like these in addition to technical skills in this video you will explore some essential non-technical or soft skills a data analyst should have nontechnical skills are important for data analysts these skills can help you connect with and influence stakeholders increasing your impact within your organization essential non-technical skills include effective communication diplomacy understanding end user needs and being a technical interpreter for nontechnical stakeholders let’s explore each skill in more detail the first soft skill is effective communication data analysts need to effectively communicate findings to various stakeholders with different degrees of technical knowledge for example when Jamie at Adventure Works asks you to analyze customer data you would need to present your findings to team members managers and executives to communicate effectively data analysts need to present complex information clearly and concisely imagine you have identified a trend in Adventure Works data that could significantly increase sales instead of overwhelming your audience with raw data you could visually represent this trend and use storytelling techniques to explain how it could impact the business another important non-technical skill is diplomacy which is the art of navigating delicate situations and maintaining positive relationships even when disagreements arise as a data analyst diplomacy may be essential for negotiating access to data mediating disagreements among stakeholders or presenting results that challenge existing beliefs for instance you might have to present a report that disagrees with a manager’s idea by being diplomatic you can share your findings in a way that maintains trust and respect while still communicating your insights collecting and analyzing data is not sufficient for making an organizational impact data analysts also need to understand the needs of the end user of their reports this will lead to findings that are relevant and useful to the stakeholders that will use them as a result stakeholders can use the insights from your reports to take action and make informed business decisions understanding the analytical needs of a business involves asking questions empathizing with the user’s perspectives and collaborating with stakeholders to identify the most valuable insight imagine you are analyzing customer data for a marketing team by understanding the marketing team’s goals and customer frustrations you can tailor your analysis to provide more useful and relevant insights because data analysts often serve as a bridge between technical and nontechnical stakeholders it’s important to be able to translate complex concepts into understandable terms this is especially so when relaying information to stakeholders who lack a technical background one way to do this is by using analogies or metaphors to explain technical concepts for example comparing machine learning algorithms to a chef who improves their recipes over time based on customer feedback ultimately becoming a successful data analyst goes beyond mastering technical skills it also requires effective communication diplomacy a total understanding of the needs of end users and the ability to relay findings and concepts to stakeholders of varying technical knowledge by developing these non-technical skills you can better collaborate with stakeholders create actionable insights inspire change and make lasting impacts enriching your own career and contributing to the growth and success of those around you i hope this thought will inspire you as you continue your journey to becoming the best data analyst you can be if you needed to assess the prospects for a new bicycle launch in the USA by Adventure Works you wouldn’t collect data about sports clothing from the European market would you no because no matter how great your analysis is this data will not provide insights that Adventure Works can use to make informed decisions about a product launch in the USA that’s why gathering the right data is an important part of the data analysis process in this video you’ll explore how the objective or purpose of analysis informs the data analysis process you’ll learn the importance of gathering data that is aligned with this purpose and how it influences the type of scope of data used gathering the right data is crucial for conducting a successful analysis however before you can start collecting data it’s essential to determine and understand the purpose or goals of the analysis you can then collect the appropriate data to conduct an analysis that is focused relevant and useful for the end user of the analysis to determine the purpose of your analysis you will need to consult with stakeholders and consider the questions you aim to answer with the analysis such as what are the recent sales figures for bike A and bike B and insights you hope to gain through the patterns trends or relationships that emerge from the analysis such as how the introduction of bike B to the market is affecting the sales of bike A for example in the case of Adventure Works you might need to brainstorm with marketing manager Renee and the sales and marketing team to determine what they hope to achieve with analysis the purpose of your analysis will inform what is the right data to collect including the type and scope of the data to gather and use in the analysis the type and scope of data used then influence the conclusions drawn and the decisions made let’s explore how the purpose of the analysis can influence the type and scope of data used in the analysis the type of data refers to the format or structure of the data for example sales figures and numerical data suppose through consultation you determine that the primary goal of the analysis for the sales and marketing team at Adventure Works is to determine which bicycle models are the most profitable in the USA in this case the type of data you might choose to focus your analysis on is sales data which includes information on the total sales of each bicycle model the number of units sold and the revenue generated by each model however if the team is more interested in understanding which products American customers are interested in buying and how to improve the product purchasing experience customer feedback data may be more useful than sales data this might involve collecting customer reviews ratings and comments on each bicycle model as this data can provide valuable insights into customer preferences and help identify areas for improvement these examples demonstrate the role identifying and defining the end goal or purpose of the analysis plays in determining what data is relevant and should be collected aside from considering the type of data appropriated from achieving the aims of your analysis you also need to define the scope of your data in relation to the analysis purpose considering the scope of your data in data analysis includes defining the boundaries or limits of the data you’ll collect and use in your analysis such as geographical regions time periods or product categories it can also include the size or amount of the data and number of variables considered in the data to illustrate if Adventure Works stakeholders would also like to use the analysis to inform the development of a new bike in the USA you might decide to analyze market trends competitor and sales data from the past two years focusing on mountain bikes and road bikes in North America by defining the scope of the data you can ensure that you collect data that is useful for understanding the relevant product market and identifying potential product development opportunities for adventure works ultimately by carefully defining the type and scope of your data based on the purpose of your analysis you can collect relevant data this helps ensure that your analysis is accurate and relevant to the needs of the business addressing the specific objectives or goals of the project this video highlighted the importance of identifying the purpose of your analysis and then gathering relevant data of the appropriate type and scope for successful analysis this ensures that the analysis results are meaningful and useful helping businesses like Adventure Works unlock insights and make informed decisions as you continue to develop your data analysis skills remember that the foundation of any successful analysis lies in gathering the correct data you might think that a business like Adventure Works is a great place for data analysis it has access to large amounts of data from a variety of sources like sales manufacturing purchasing and marketing however that data while valuable is often not in a form that is easily understandable or ready for analysis this is where the process of preparing and analyzing data comes in in this video you’ll learn about the importance of processing and analyzing data for transforming raw data into valuable insights that can drive strategic decisions you’ll be introduced to the extract transform load or ETL process a common method for processing data you will also learn how using calculations and visualizations during analysis can help uncover hidden patterns and trends in the data first let’s define what is meant by processing and analyzing data processing data refers to transforming raw data into a format that can be easily understood and analyzed analyzing data involves using various techniques to explore interpret and draw meaningful conclusions from the processed data for Adventure Works processing data might involve consolidating data from multiple sources such as sales transactions customer demographics and product inventory this is because the data in its raw form may be scattered across different databases spreadsheets and even paper records additionally the data may be in various formats have missing values or contain duplicate entries in this case processing the data would involve cleaning organizing and transforming the data into a format that is more suitable for analysis a common data processing method is the extract transform load or ETL process the ETL process involves extracting data from various sources such as databases or files transforming the data to make it consistent accurate and ready for analysis for example by cleaning and filtering the data and loading the transformed data to a suitable destination like data repositories databases or analytical tools for further analysis this process which you will learn about in greater depth later plays a crucial role in preparing raw data for analysis now that you have a general understanding of data processing let’s explore some methods of data analysis one effective way to analyze data is by performing calculations on the processed data to reveal new insights for example Adventure Works can calculate its products total revenue profit margin or average order value these calculations can help the company identify which products are performing well and which might need improvement another powerful technique for analyzing data is data visualization visualizations or graphical representations of data such as charts and graphs can communicate complex information in a simpler way and help make complex data easier to understand they can also help uncover patterns trends and relationships within the data that might not be apparent through calculations alone for instance Adventure Works could create a bar chart to compare the total sales of different product categories or a line chart to track monthly revenue over time visualizations like these can help the company quickly identify trends spot potential issues and make more informed decisions in summary processing and analyzing data is critical to transforming raw data into actionable insights through the ETL process data can be extracted transformed and loaded into a format that is suitable for analysis when the data is processed calculations and visualizations can then be used to explore the data uncover hidden patterns and generate new insights to drive strategic decisions as you progress in this course you will learn more about the various tools and techniques available for processing and analyzing data by mastering these skills you will be better equipped to help businesses like Adventure Works maximize the value of their data and make datadriven decisions that drive growth and success jaime Lee owner and CEO of Adventure Works is concerned that sales have been stagnant and wants to take her business to the next level she’s aware of the power of data insights to drive business decisions so she employs Adio Quinn a data analyst to help provide the answers she needs to grow her company in this video you’ll explore how data insights can be used in the final stage of the data analysis process to drive business using a case study you’ll discover how these insights can empower stakeholders like Jamie to make informed decisions and improve business performance data insights refer to the valuable and actionable information knowledge and understanding generated from analyzing data this is the final stage of data analysis where the insights can be used to identify trends patterns and opportunities these insights can then lead to actionable business decisions that can help businesses grow and stay ahead of the competition let’s explore how data insights can drive business decisions practically by considering how Jamie could use insights related to sales customer and competitor data to make decisions that improve business performance at Adventure Works by analyzing sales data collected over the past year ADIO identifies that certain types of bicycles sell more during specific seasons like mountain bikes in the spring and road bikes in the summer by using this data insight Jaime can make informed decisions about inventory and promotional efforts for example she could make sure that the warehouse is sufficiently stocked up with each bike type based on seasonal demand levels and have the marketing team offer special promotions to boost sales of the bikes in their off seasons by making decisions based on data insights Jaime can optimize her inventory management and increase overall profitability suppose Adio also discovers that customers belonging to particular age groups prefer specific bicycle types or respond more positively to particular marketing messages jamie can use this information to oversee the creation of targeted marketing campaigns offerings and communications that resonate with different segments of the company’s audience by personalizing marketing efforts based on customer data insights Jaime can increase customer satisfaction and loyalty and drive more sales and revenue imagine Addio’s analysis reveals a gap in Adventure Works current offerings with customer data indicating that customers are increasingly interested in electric bikes and unique design features with insight into this growth opportunity Jaime can explore the development of new products to meet these demands making decisions related to product development and innovation for Adventure Works this datadriven approach to product development ensures that businesses create products that cater to real customer needs increasing the likelihood of success another area where data insights could drive business decisions is pricing strategy sales data competitor pricing and customer feedback can help stakeholders like Jamie determine optimal price points for products balancing demand revenue optimization and market competitiveness for example say Adio finds that customers at Adventure Works are willing to pay a premium for certain highquality bicycles jaime can then adjust the company’s pricing strategy accordingly to capture more value from those sales however if some bicycles are priced too high and are hurting overall sales Jaime can consider lowering their prices to create demand by using data insights to inform pricing decisions businesses can optimize revenue and profitability stakeholders and data analysts alike can follow some best practices to enhance the use of data insights to drive business decisions for a comprehensive understanding of a business its operations and trends and patterns it’s important to gather data from multiple sources and regularly analyze it regular data analysis makes it possible to stay upto-date with trends and make timely informed decisions it’s also important to encourage a datadriven culture where data insights are valued and used to inform decision making at all levels likewise encouraging collaboration and insight sharing within an organization can lead to better decision-m finally investing in the right tools and technology like Microsoft PowerBI can help streamline the data analysis process making it easier to gain insights and make datadriven decisions you should now have a better understanding of how data insights can drive business by embracing a datadriven approach companies can stay ahead of the competition and make better business decisions ultimately the more stakeholders like Jamie understand their data the better equipped they’ll be to make informed strategic decisions that can optimize business performance for your company imagine navigating through a dark maze without a map searching for hidden treasure this is what it feels like to dive into a vast ocean of data without the right tools microsoft PowerBI offers a solution to the challenge of navigating large amounts of data and uncovering useful insights in this video you’ll learn about PowerBI’s role in data analytics and visualization its key features and benefits and navigating its user interface powerbi is a suite of business analytics tools to help organizations transform raw data into meaningful information and make datadriven decisions there are several products within the PowerBI ecosystem including PowerBI desktop the Windows application for creating reports and dashboards that you’ll use throughout this course and others such as PowerBI service PowerBI mobile PowerBI report server and PowerBI embedded these components work together to provide a comprehensive business analytics solution allowing you to connect to various data sources clean and prepare data create impactful visualizations and reports and share findings and insights effectively powerbi has become an essential resource for many organizations across various industries let’s explore why powerbi is userfriendly its easy to use intuitive interface makes it accessible to technical and nontechnical users alike with its drag and drop functionality you can create visualizations reports and dashboards simply and quickly another benefit of using PowerBI is data integration it supports a wide range of data sources including traditional databases Excel spreadsheets and cloud-based services this allows you to consolidate data from multiple sources and create a comprehensive view of their business performance powerbi simplifies data transformation with the Power Query Editor in PowerBI you can clean transform and reshape data as needed which is important to ensure that data is accurate consistent and ready for analysis there are also rich visualization options available in PowerBI with a variety of built-in visualization types such as bar charts and maps and custom visuals developed by the community these options make it easy for you to present data in a visually appealing and easy to understand way you can perform advanced analytics with PowerBI with data analysis expressions or DAX and built-in analytical capabilities you can perform complex calculations and data analysis leading to deeper insights and better decision- making plus you can easily collaborate and share reports and dashboards with colleagues both within and outside the organization powerbi is scalable and designed to grow with organizations its various licensing options and features can accommodate businesses of all sizes and the platform can scale to meet changing business needs finally PowerBI integrates seamlessly with other Microsoft products such as Excel SharePoint and Teams and offers a cost effective pricing model now that you have some insight into why PowerBI is one of the most popular data visualization and business intelligence tools let’s examine its user interface to get started with PowerBI you’ll need to download and install PowerBI Desktop the primary application for designing and creating reports and dashboards once you have PowerBI Desktop installed you can begin exploring the main areas of its user interface you can use the ribbon located at the top of the PowerBI desktop window to quickly access various tools and features to create and customize your reports and dashboards it contains several tabs such as home insert modeling and view each tab has its own collection of buttons and options for performing common tasks like connecting to data sources creating visualizations and formatting your reports in the left navigation pane you can select report to open report view report view is the primary canvas where you design and create your visualizations you can add and arrange different visual elements here like charts tables maps and more to build your report pages allow you to create multiple views of your data in a single report at the bottom of the PowerBI desktop window you’ll find a row of tabs you can use these to organize your visualizations based on themes or categories to add duplicate or remove pages use the tabs at the bottom of the report view the visualizations pane is located on the right side of the window and contains a gallery of visual elements that you can add to your report there are various types of visuals available that you can add to your report by clicking or dragging them from the visualization pane onto the report view also on the right side of the window is the fields pane it displays the data tables and fields available for your report as you learn to build reports in PowerBI you’ll use the fields pane to populate your visualizations with data the fields pane is organized into two sections the top section displays the available tables and the bottom section shows the fields within the selected table last the filter pane found on the right side of the window allows you to apply filters to your data at various levels such as the entire report individual pages or specific visualizations in this video you discover the benefits of using PowerBI as a business intelligence tool and explored its user interface by understanding its key features and capabilities you’re one step closer to using PowerBI to create reports that communicate your insights effectively and drive meaningful change businesses like Adventure Works often have a large amount of data but don’t know how to extract the insights hidden within in this video you’ll discover how calculations and visualizations in Microsoft PowerBI are used to analyze this data generate and communicate insights and empower businesses to make datadriven decisions you’ll learn the key concepts behind calculations using data analysis expressions or DAX and how visualizations can communicate complex data and insights in PowerBI calculations are the foundation of your data analysis and are created using a powerful language called data analysis expressions or DAX calculations allow you to perform specific operations on data manipulate it and create new calculated measures columns and tables that you can use in visualizations and reports to drive decision-m with custom calculations you can tailor your analysis to specific business requirements and address unique analytical needs some common calculations are aggregations where multiple values are combined or grouped into a single value to summarize large amounts of data for example summing up finding the average or counting data points based on specific criteria timebased calculations for comparing data across time periods such as month over month or year-over-year growth and ratios and percentages for calculating proportions or shares of a whole to understand the relative performance of different elements to illustrate with data on monthly sales Adventure Works could use DAX to calculate the average monthly sales determine the month with the highest sales or identify the percentage of sales coming from a specific product category after performing calculations with your data the next step is to represent the results visually visualizations enable you to communicate complex data and insights in a simple appealing way by presenting data graphically visualizations make it easier for stakeholders to grasp key insights trends and patterns that may be difficult to identify from row data or tables powerbi offers a wide range of visualization types such as different charts maps tables and even custom visualizations when choosing the most suitable visualization you should consider the type of data you’re working with for example whether the data is numerical or categorical consisting of non- numeric variables the purpose of your analysis such as comparing values showing distribution understanding relationships or tracking trends as well as the level of detail needed from highlevel summaries to granular insights now let’s explore how to create a visualization in PowerBI using a given data set suppose you are part of a team analyzing sales data and creating a report for Adventure Works you need to create a visualization that represents the number of orders across the different bite categories to create your visualization you first need to import your data to do this open Microsoft PowerBI desktop click on get data in the home tab then select text/ CSV and click connect navigate to the location of the CSV file containing the data you need in this case the Adventure Works bike sales data select it and click open once the data is loaded the data view will display the important data in a table format take a moment to familiarize yourself with the structure of the data the next step is to create a bar chart of the bike sales by category click on the report view which is the first icon on the left side of the PowerBI interface next click on the clustered bar chart visualization icon in the visualizations pane this is a bar chart with multiple bars after that drag and drop the product category field onto the y-axis section of the visualization pane then drag and drop the order quantity field onto the x-axis section of the visualization pane this bar chart visualization shows the total order quantity for each product category this can help Adventure Works quickly identify which bike categories have the highest or lowest number of orders they can use the insight to make informed decisions about inventory management marketing strategies and product development you’ve now gained a foundational understanding of calculations and visualizations in PowerBI and their role in generating results and insights from data you learned about using DAX calculations for data analysis and using visualizations to communicate data insights and help businesses make datadriven decisions congratulations on completing this first module on data analysis in business let’s recap some key concepts that you covered in lesson one you were introduced to the course and syllabus explored some tips for successfully completing the course and engage with your peers in the second lesson you learned more about the essential role data analysis play in businesses helping them collect organize analyze and understand their data data analysis can help businesses gain insights from their data identify the cause of problems uncover trends and make decisions that can improve business performance you are introduced to the stages of data analysis and the interconnected roles available within this process from data engineers to business intelligence or BI analysts you also explored some important skills data analysts need to succeed in their role including nontechnical skills like effective communication and understanding end user needs in lesson three you examine the stages of data analysis in more depth these stages include identifying the problem or purpose of the analysis collecting processing data and analyzing data data visualization and report sharing and implementing insights and recommendations you learned that gathering the right data is fundamental to an analysis that is relevant and useful understanding the purpose of your analysis will inform the type and scope of data that is correct for the analysis you then explore the processing and analyzing stages of data analysis some are more processing involves transforming raw data in preparation for analysis and analysis involves analyzing the processed data and generating insights you are briefly introduced to the extract transform load or ETL processing method and learned about DAX calculations and visualizations in data analysis you also learned about some factors to consider before sharing reports with stakeholders including the accessibility visual appeal and security of your report as well as data storage and refresh schedules you discovered the importance of understanding stakeholder experience and applying this to data visualization and analysis to more effectively convey data insights you learned how data insights can drive informed business decisions and lead to improvements like increased customer satisfaction you then explored some best practices for stakeholders and data analysts to follow to drive business decisions including collecting data from multiple sources regular data analysis encouraging datadriven culture and collaboration and insight sharing and investing in the right tools and technology you also had the opportunity to apply the knowledge gained in the lesson by evaluating an analysis process finally you were introduced to Microsoft PowerBI and its many benefits including its userfriendly interface rich visualizations and advanced analytics you learned how to navigate PowerBI’s users interface set up your own PowerBI desktop environment view a report and generate interactive visualizations you now know more about the role of a data analyst the data analysis process the role data analysts play in business and PowerBI as a tool for data analysis with the foundational knowledge you’ve gained you are ready to move on to your next lesson on harnessing the power of data in PowerBI in previous lessons you learned about the importance of data and the role it plays you discovered how organizations aim to derive meaningful insights from their collected data in this context it’s necessary to identify the collected data and evaluate which parts of it are required you could start a data project by first determining what is being measured and what are the critical issues you need to make decisions about the answers will help you to identify and evaluate the data correctly now let’s examine the process of data identification and evaluation in more detail this process includes understanding the importance of asking the right questions analyzing the required data for a business decision and data type classification by the end of this video you’ll understand data classification and modern data sources and you’ll learn how to use these in business decisions proper data valuation depends on the key skills of identifying data sources and asking the right questions let’s explore data evaluation at Adventure Works a fictitious large multinational company that makes and distributes bicycles and accessories to global markets jamie the CEO at Adventure Works wants to analyze sales data to reveal factors that influence the sales of their products a good place to start the analysis is to streamline the business requirement from complex to simple and then establish relationships between any multiple topics let’s take the example of identifying factors that affect sales to do this analysis you need first to determine the data to be measured and the potential factors that could influence it for instance this includes internal company data data from social media and sensor generated data such as product codes from barcode scanners or identity confirmation from facial recognition software sales data is the main area that Adventure Works wants to assess a critical source of this information comes from their enterprise resource planning or ERP system erp systems are designed to collect store manage and interpret structured data from various business activities structured data is data that is organized into a formatted repository typically a database so it’s easily searchable in the context of Adventure Works everything is a physical store from product shelves product categories to points of sale employees and customers and are all defined and stored in the table of the ERP database this kind of data structure creates a digital mirror of the real world store and provides a highly efficient and effective way for Adventure Works to analyze sales data from various periods such analysis could be based on product category or type of customer providing actionable insights into sales trends customer behaviors and product performance how you evaluate the ERP database depends entirely on your perspective and analysis evaluation questions could be are sales generally showing a downward or upward trend are there seasonal increases or decreases in certain categories how do holidays or special occasions affect sales have sales shown variability by age gender income level or customer geographic location on a product or category basis now let’s consider other potential data sources for Adventure Works in addition to the ERP data examining the situations that occur before or during the purchase are useful an excellent example of such a source is the sensors installed in the automatic doors of the store the data from these sensors revealing the number of people entering and exiting the store at any given time can be categorized as semistructured data semistructured data falls between structured and unstructured data while it doesn’t conform to the formal structure of data models as seen in an ERP system it contains tags or other markers to separate data elements and enforce hierarchies of records and fields within the data the data obtained from door senses might be tagged with information like timestamps store identifiers or locations allowing for more detailed analysis this data can be used to evaluate the store’s visit intensity over different periods offering an opportunity to correlate store traffic patterns with sales volume this analysis could lead to insight about peak selling times the effectiveness of promotions or how staffing levels relate to sales in addition Adventure Works can analyze unstructured data flowing from social media channels to gauge the company’s popularity and reputation this can include online messages related to the company social media check-ins photos and videos shared by customers unstructured data is information that doesn’t have a predefined structure or isn’t organized in a predefined manner making it less straightforward to analyze for adventure works this social media data can be evaluated from different dimensions such as the timing of posts or demographic characteristics of the audience interacting online with the company for instance by conducting trend analysis the company can gauge the popularity of its brands products or campaigns this analysis can inform marketing strategies customer engagement tactics and product development with a robust data identification and evaluation strategy to identify and evaluate the correct data sources companies like Adventure Works can harness the full potential of data to uncover actionable business insights each piece of data regardless of its type structured unstructured or semistructured holds immense value the true power of data lies not in its volume or variety but in its purposeful utilization remember data itself is not the end goal instead it’s a tool to help businesses make more informed decisions therefore it’s vital to understand why you’re using the data how it serves your purpose and what methods you’ll use for its evaluation what’s the best way to use Microsoft PowerBI as with other software you may have your own preferred way to use it and that’s okay however in this video you will explore key PowerBI components and discover their primary purpose to achieve the best results you must use these components in the proper order that sequence of use is known as a workflow over the next few minutes you’ll get to know how a common workflow operates in PowerBI microsoft PowerBI is an interactive data visualization product with multiple components you use its components and its rich visualization features to create meaningful reports from different data sources and types of data let’s explore the details of Microsoft PowerBI’s three main components powerbi Desktop PowerBI apps and PowerBI service powerbi Desktop is a Windows-based desktop application that is mainly used by data analysts or report designers to clean transform and load data create a data model design reports and publish these reports powerbi desktop uses PowerBI connector to access various data types and data sources connectors allow you to read data from various sources this includes resources located in the local file system such as Microsoft Excel or PDF documents conventional database systems hosted on internal servers called onremise databases cloud-based databases and even external enterprise applications and application program interfaces or APIs powerbi service is the cloud-based BI service or software as a service part of PowerBI it is used by report users and administrators powerbi apps is the native mobile application of PowerBI it’s available on iOS Android and Windows with these components and interfaces Microsoft PowerBI enables users from various disciplines such as report designers administrators and business users to use the product according to their roles as mentioned earlier the order in which you use these components is known as a workflow a PowerBI workflow can be described as the steps taken with data to create publish and share a typical workflow in PowerBI often starts with the creation of a report in PowerBI desktop report designers and developers are primarily responsible for this task when the report is ready you publish it to the PowerBI service where administrators can assign permissions and specific users can consume the report now let’s examine each step of the workflow in more detail create is about importing data and creating a report this step is when you import your data sources into PowerBI desktop clean transform and load your data in order to have targeted data for your reports use your filtered data to create a report and analyze and present your data using various visualizations and charts in your report then you move on to the publish step of the workflow where you publish reports and create dashboards that means you publish your report to the PowerBI service and share your data with others by creating dashboards and use different visualizations and filters to make your data more understandable in your dashboard the final step of this workflow is sharing in this step you share dashboards with users and manage access to your data share your dashboards with the users needed to make it easier to collaborate on projects manage access to your data by ensuring that dashboards have different user permission levels this is also where you consider mobile usage for instance using PowerBI mobile apps you can view and interact with reports and dashboards that have content pinned from reports anytime and anywhere you can use different features of the mobile apps to explore and share your data from different perspectives in summary a typical Microsoft PowerBI workflow sequences the requirements needed to choose data sources and types in step one and then step two is used to visualize the data the third and final workflow step presents the resulting reports and dashboards to cater to different user types and their requirements using such a workflow you combine different types of data from many sources using various components such as PowerBI desktop PowerBI service and PowerBI apps have you ever tried to solve a jigsaw puzzle when the pieces are scattered everywhere and you don’t even know those pieces belong to the same puzzle that’s what it can feel like as a data analyst tasked with extracting insights from data that spread across multiple sources formats and structures not to worry there’s a way to solve this problem the extract transform load or ETL process in this video you’ll build on your knowledge of the ETL process you’ll explore the three main components of the ETL process and how to apply them the benefits of using the ETL process and how it’s performed using Microsoft PowerBI as you learned earlier in this course ETL stands for extract transform and load the names given to the three main steps in the ETL process this process involves taking raw data from various sources preparing it for analysis and loading it into a repository or data storage and management system let’s explore each step of the ETL process in more detail and how they can be applied in the scenario of the manufacturing company Adventure Works which produces and distributes bicycles and accessories extract is the first step in the ETL process which involves retrieving and extracting raw data from different sources such as databases files or other data storage systems for example imagine that Adventure Works data is scattered across multiple systems as is the case with many organizations say customer data is stored in a data management system called customer relationship management or CRM sales marketing and manufacturing data is in an enterprise resource planning system or ERP and purchasing data is in spreadsheets the extraction process involves pulling the data from these different sources then you consolidate it into an easily accessible central location often a temporary intermediate storage location known as the staging area and prepare it for further processing in the next step once the data is extracted the second step is to transform it transforming the data involves cleaning structuring and enriching the data to make it more suitable for analysis this may involve removing duplicates handling missing values creating new calculated fields converting data types and standardizing measurement units in the case of Adventure Works let’s say that the sales and marketing data is in US dollars but the manufacturing and purchasing data is in different currencies depending on where in the world the sales or purchase take place as part of transforming the data you may need to convert all the currency values into a standard unit of measurement in this case US dollars to ensure consistency the third and last step involves loading the transformed data into the final storage system typically a data warehouse where it can be readily accessed and analyzed for example using tools like PowerBI depending on the organization’s needs the loading process can be a one-time event or scheduled to run regularly in the case of Adventure Works the cleaned and transformed data might be loaded into a cloud-based data warehouse making it accessible to the company’s data analysts and decision makers the ETL process ensures that the data analyze is accurate clean and consistent which in turn supports informed decision-m this process offers many benefits including data integration etl helps integrate data from different sources providing a unified view of an organization’s data making it easier for analysts to perform analysis and derive insights data quality etl processes involve data cleansing and validation which significantly improve data quality data consistency by transforming data into a standardized format ETL ensures consistency across various data sets enabling analysts to easily compare and analyze data from different sources enhance performance by aggregating summarizing or indexing data during the transformation process etl can improve query performance and reduce the load on data analysis systems and data governance etl can support data governance initiatives by helping organizations maintain a single source for their data ensuring that everyone has access to the same accurate information widely used in data analytics tools like PowerBI the ETL process helps you bring together refine and assemble different data pieces into a coherent picture that can drive business decisions powerbi is just one tool that comes equipped with built-in ETL capabilities enabling you to connect to many different data sources transform your data using Microsoft Power Query and load it into the PowerBI data model power Query is a powerful ETL tool within PowerBI providing a graphical interface and formula language called M to perform various data transformation tasks with Power Query you can extract data from multiple sources clean and structure it and load it into PowerBI for creating reports and visualizations the extract transform load or ETL process is essential for any datadriven organization the importance and benefits of ETL lie in its ability to turn raw data into accurate and consistent information in a centralized system that is easy to analyze and use in decision-m because data is critical to better decision- making embracing tools that can support the ETL process such as PowerBI can significantly impact business performance addio the data analyst at Adventure Works needs to analyze sales data from multiple channels including physical stores and e-commerce platforms he asks the data analytics team to gather and ingest the data a fundamental step before he can proceed with the later stages of the extract transform load or ETL process in this video you’ll explore data gathering and ingestion including different methods to gather and ingest data and their advantages and disadvantages let’s start by outlining data gathering and ingestion which typically take place in the extract step of the ETL process data can come from a variety of sources such as structured data from spreadsheets or databases unstructured data from text files or social media posts and streaming data from realtime data transmissions such as webcams or satellite navigation systems data gathering involves collecting or acquiring data from these different sources an example of gathering data is the data analytics team at his venture works collecting all their sales data ranging from spreadsheets to realtime streams data ingestion starts with data gathering and encompasses the process of obtaining and importing data from various sources for immediate use or storage such as in a database for example as a part of data ingestion the team at Adventure Works can go on to extract relevant data from each source such as customer data and sales metrics like revenue they can then load it into a central database where it can be accessed for further processing and transformation the data gathering and ingestion process is beneficial for organizations for various reasons with data volume velocity or speed of generation and variety in terms of types and sources constantly increasing it helps organizations consolidate their data this unified view of their data facilitates comprehensive analysis datadriven decision-m and innovation data ingestion improves operational efficiency through process automation proper ingestion practices can also help organizations meet regulatory requirements protect sensitive data and ensure data integrity now that you know more about data gathering and ingestion and its benefits let’s explore some common methods for gathering and ingesting data as well as their advantages and limitations these include manual data entry filebased ingestion database connections web scraping and data streaming manual data entry is the most basic method of data gathering and ingestion where data is manually inputed into a system for example an employee at Adventure Works may type in data from a physical customer order form into a customer relationship management or CRM system while manual data entry is straightforward and suitable for small amounts of data it is time consuming prone to errors and unsuitable for large scale data ingestion another method is filebased ingestion the process of importing data from files such as spreadsheets to illustrate Adventure Works might receive sales data from retail stores in Excel spreadsheets these files can be imported into the ETL process using tools that read and parse or interpret the file contents while filebased ingestion is common and requires less technical expertise than other methods it can become cumbersome when dealing with large numbers of files or frequent updates with the database connection method you access data directly from a database or data warehouse using tools that can connect to and query the source for example Adventureworks can create a database connection to access data from its sales database using SQL queries this connection enables the analytics team to extract necessary data by using SQL commands as well as transform and load it for further analysis later in the ETL process while database connections offer real-time access to data enabling instant insights and prompt decision- making they do require knowledge of database languages like SQL and may involve complex configuration or authentication process web scraping is a method of extracting data from websites using automated methods or software tools in the case of Adventure Works the analytics team can use web scraping to gather competitor pricing information or customer reviews web scraping is a powerful way to gather data from websites but it can require legal permission and be complex as it involves a range of technologies streaming data is continuous real-time data generated by sensors or other sources you can ingest data streaming using tools that connect to and process the data as it is generated for instance Adventure Works could use data streaming to monitor factory equipment track inventory levels or analyze real-time sales data data streaming allows for immediate analysis and decision-m but requires specialized tools and infrastructure to handle the continuous flow of data each data ingestion method has its advantages and limitations so it’s essential to choose the appropriate data ingestion method based on your specific use case and the nature of the data you’re working with in summary data gathering and ingestion involve obtaining and importing data from different sources generally in the extract phase of the ETL process data gathering and ingestion have many benefits for businesses from consolidating data to facilitating innovation by mastering the data gathering and ingestion methods introduced in this video you can help organizations like Adventure Works optimize their data for analysis due to rapid growth Adventure Works needs to store and manage increasing volumes of data from different sources the company must develop a comprehensive plan for data storage and management to handle its changing data needs in this video you learn about the role of data storage and management planning in the extract transform load or ETL process and for organizations in the short and long term you’ll also learn key considerations for effective data storage and management planning planning for data storage and management is involved throughout the ETL process during the extract step you need to consider what types of data you’ll be collecting how often and from which sources setting the foundation for data management in the transform step proper data management ensures the transform data is consistent accurate and complete planning for data storage is also necessary as the transformed data may need temporary storage before being loaded into its end destination finally in the load step planning for data storage and management like considering database or data warehouse structure facilitates efficient retrieval and analysis of stored data in a broader context planning for data storage and management impacts multiple aspects of an organization short-term data storage and management solutions address immediate data needs facilitating quick access to up-to-date data and collaboration for Adventure Works this is vital for daily operations like responding to customer inquiries and processing transactions long-term storage and management planning caters to strategic goals and compliance requirements for example long-term storage solutions will enable Adventure Works to analyze sales data customer feedback and market trends over time informing decision-m and improvement strategies when planning for data storage key considerations include storage capacity data access scalability security and backup and disaster recovery one of the first considerations is how much storage capacity you need this depends on factors like organization size data types and average file size required storage duration and anticipated data volume growth accurate estimation can prevent the cost of overprovisioning and lower underprovisioning risks like data loss and system performance issues it’s also important to consider how easily you and your team can access data when needed whether for daily operations and collaboration or long-term trend analysis planning for accessibility may involve organizing file structure implementing searchability and retrieval mechanisms and providing remote access options another factor is the scalability of your storage solutions or its ability to adapt to changes in data volume technology and data types planning for scalability helps ensure the storage infrastructure can support your organization’s data needs as they change over time without compromising performance requiring major infrastructure changes or incurring excessive costs next is security considering storage security is vital as data breaches can have serious consequences like financial loss planning and implementing security measures such as access controls and data encryption help protect your data against unauthorized access theft or tampering and emerging threats and vulnerabilities lastly a comprehensive backup and disaster recovery plan is essential for minimizing the impact of data loss due to unexpected events such as hardware failures or human error this involves creating regular data backups on site offsite or both implementing a recovery strategy that outlines how to restore data and resume operations and regularly testing and updating the recovery plan now that you’re familiar with data storage planning let’s focus on data management which involves organizing maintaining and protecting data to ensure its quality accuracy and accessibility key aspects of data management planning include data governance data quality data integration data security and privacy and data retention and archiving data governance establishes policies and procedures for data collection storage access and usage throughout your organization this helps prevent data silos or isolated sets of data ensures data accessibility and promotes data quality and responsibility among team members data quality considerations ensure accurate complete up-to-date data relevant to business needs you can implement processes for checking cleaning and enriching your data to maintain high quality data data integration plays an important role in the combination and consolidation of data from multiple sources and formats into a unified view facilitating data analysis and insights data security and privacy include planning measures such as access controls activity monitoring and compliance with data protection regulations implementing a data retention policy and archiving process to ensure data is retained for the appropriate time based on factors like legal or business requirements are important aspects of data management planning in conclusion data storage and management planning helps organizations develop comprehensive solutions to handle their current and future data needs even during periods of expansion as with adventure works by considering data storage factors like storage capacity and accessibility alongside aspects of data management from data quality to retention organizations can ensure efficient data storage management and use imagine you have a Microsoft Excel spreadsheet of raw data from various sources your task is to analyze it and generate insights to help Adventure Works make informed decisions as you start exploring the data set you realize that it’s filled with inconsistencies missing values and duplicate entries if you don’t address these issues your analysis will be flawed and potentially lead to costly mistakes this is where data cleaning and transforming comes into operation in this video you’ll explore data cleaning and data transformation discover how they impact the quality of your analysis and compare the implications of cleaning data at source and in PowerBI data cleaning is the process of identifying and correcting errors and inconsistencies in data sets this includes removing duplicate entries filling in missing values and fixing incorrect data types data transformation involves altering the structure format or values of the data to make it more suitable for analysis this may include aggregating data converting data types or normalizing values both cleaning and transformation are crucial to ensure the quality and reliability of your analysis for instance imagine you’ve been given a data set that contains information about customers products and sales transactions some customer names are written in all caps while others are in sentence case making it difficult to group or filter the data by customer name cleaning this data would involve standardizing the format of customer names an example of transforming this data is calculating the total revenue for each customer which would require aggregating the sales data by customer and multiplying the quantity of products sold by their respective prices inconsistent untidy or duplicate data entries can have a negative impact on data analysis these issues can lead to inaccurate or misleading results which can lead to poor decision-m for example if duplicate sales transactions are included in the data the total revenue might appear higher than it actually is this can result in overestimating the company’s performance and making illinformed decisions about resource allocation now let’s discuss the difference between cleaning data at the source and cleaning data in PowerBI cleaning data at the source involves addressing data quality issues directly within the source system such as a database or a spreadsheet this method ensures that any future analysis using this data will have a clean and consistent foundation however this approach may not always be possible especially if you don’t have direct access to the source system or if multiple systems are involved cleaning data in PowerBI involves importing the raw data and applying cleaning and transformation steps within the PowerBI environment this approach addresses data quality issues without modifying the original data source however this means that you may need to repeat the cleaning process each time you import the data into PowerBI which is time consuming and prone to errors let’s consider examples of data cleaning in PowerBI and data cleaning at the source the source refers to where your data is coming from for instance it could come from internal software like enterprise resource planning or ERP systems accounting software databases or Microsoft Excel let’s start by exploring how to clean data at the source adventure Works stores its sales customer and product information in a centralized database the data quality team decides to implement data validation rules and standardize the formatting of customer names directly in the database this ensures that any future analysis of this data has a consistent and accurate base by addressing the data quality issues at the source Adventure Works can save time and effort in future analysis as the data will already be clean and ready for use now let’s switch to an example of cleaning data in PowerBI rather than at the source imagine that Adventure Works stores its sales and data in multiple systems and the data quality team does not have direct access to all the source systems they choose to import the raw data into PowerBI and apply cleaning and transformation steps there while this approach allows them to address data quality issues and generate accurate insights it also means that they will need to repeat the cleaning process each time they import new data this is time consuming and if the cleaning steps are poorly documented it may lead to inconsistencies in future analysis in summary data cleaning and transforming are essential data analysis processes they help ensure your insights are accurate and reliable data cleaning involves identifying and correcting errors and inconsistencies in data sets data transforming involves altering the data structure format or values to make it more suitable for analysis now that you understand the implications of cleaning data at the source compared to EmpowerBI you can choose the most effective approach for your needs by improving your data cleaning and transformation skills you’ll be better equipped to tackle the challenges of errors and inconsistencies in data sets picture this you’re at your desk with your morning coffee your manager needs a comprehensive report on Adventure Works sales performance across all regions product categories and customer types and she needs it by the end of the day your heart races as you think about the vast amount of data you’d have to sift through scattered across numerous files databases and systems but you don’t panic you remember that Microsoft Power Query can help with Power Query you know you can efficiently connect to multiple data sources transform unclean data and create a structured data set for further analysis in PowerBI this video explores the capabilities and benefits of Power Query you’ll discover how Power Query helps you connect to multiple data sources clean and transform data and create structured and repeatable data preparation workflows for efficient data analysis microsoft Power Query more commonly known as Power Query is a data connectivity and data preparation tool built into Microsoft’s PowerBI suite it plays a crucial role in the data analysis process by enabling you to connect to a wide range of data sources clean and transform the data and then load it into PowerBI data models for analysis and visualization power Query streamlines and automates the process of preparing data for analysis making it easier for you to gain valuable insights from data power Query is designed to handle the extract transform load or ETL process an essential part of any data analysis workflow let’s explore how Power Query can help with the ETL step extract power Query can connect to various data sources such as relational databases Excel workbooks CSV files web pages and more once connected you can select the specific tables or data sets you want to work with transform with the data loaded Power Query provides a userfriendly interface for cleaning and transforming the data you can perform various transformations such as filtering sorting merging splitting grouping and aggregating data load once the data has been cleaned and transformed Power Query loads it into the PowerBI data model where you can further analyze visualize and share power Query is particularly useful in the following scenarios connecting to multiple data sources power Query simplifies the process of connecting to any consolidating data from different sources into a single data set for further analysis cleaning and transforming data power Query provides a wide range of tools and functions that help you clean reshape and transform data into a structured and usable format automating data preparation tasks power Query records the steps you take when transforming data creating a repeatable and editable process this feature not only saves time by automating repetitive tasks but also ensures consistency and accuracy during data preparation structured and collaborative workflows power Query’s ability to record and edit transformation steps makes it easy for you to share data preparation workflows with colleagues power Query also promotes a structured and repeatable approach to data preparation as you perform transformations it records these steps in an applied steps pane which allows you to review modify or delete any step in the process this makes it easy to fine-tune your data preparation workflow and ensures that you can consistently reproduce your results to illustrate the ability of Power Query let’s return to your task of creating a sales performance report for Adventure Works based on all sales regions in this situation your data is scattered across various sources such as Excel spreadsheets CSV files databases and even web pages with Power Query you can easily connect to these different sources extract the relevant data and consolidate it into a single data set once you’ve connected to your data sources Power Query provides a userfriendly interface that allows you to perform various data transformations such as removing unwanted columns or rows splitting or merging columns changing data types and filtering and sorting data power Query is ideal for extracting data from various sources cleaning and transforming it and then loading it into a PowerBI data model for further analysis and visualization this enables you to create a comprehensive Adventure Works sales performance report breaking down sales by region product category and customer type just as your manager requested part of the PowerBI suite Power Query is a versatile and powerful data connectivity and preparation tool by connecting to multiple data sources cleaning and transforming data and creating structured and repeatable data preparation workflows Power Query helps you at each stage of the ETL process turning raw data into valuable insights that drive informed decision-making as you continue to work with data and explore the world of PowerBI Power Query will become an indispensable tool in your data analysis toolbox imagine yourself as an artist standing before a canvas prepared to create a masterpiece the colors on your palette are your data and your brush is Microsoft PowerBI how you blend these colors the strokes you choose and your vision will determine the beauty of your final painting your business intelligence insights working through this week on the right tools for the job you learned the techniques to paint a masterpiece you covered the importance of identifying suitable data and evaluating data sources data gathering and ingestion transforming and loading the data in preparation for analysis and using the extract transform load or ETL capabilities of Microsoft PowerBI and Microsoft Power Query let’s revisit some of the key concepts you covered in the week you started your journey with an exploration of data collection identifying and evaluating the required data in the foundation for successful business decision-making you learn the importance of asking the right questions and analyzing the necessary data for business decisions illustrated through the scenario of adventure works you explore the need to understand the purpose of the data how it serves this purpose and how it should be evaluated learning about classifying data as structured unstructured and semistructured types you then continued to the workflow in PowerBI the artist’s brush in the earlier analogy you discover that PowerBI with its three main components PowerBI desktop PowerBI service and PowerBI apps is a powerful tool for creating meaningful reports from various data sources you were introduced to the PowerBI workflow to effectively sequence your work from importing data to creating dashboards sharing them and managing access permissions next you explored the ETL process and related concepts you learned about data gathering and ingestion the act of obtaining and importing data from different sources this process aids in data consolidation enabling enhanced decision-m and innovation you covered some common methods of data ingestion and gathering from less technical methods like manual data entry to methods that require specialized tools or knowledge like database connections you also learned more about data storage and management and their importance for datadriven organizations you explored key considerations for data storage planning such as storage capacity and data access needs as well as key aspects of data management planning from data governance to retention and archiving your journey then led you to data cleaning and transformation much like cleaning and preparing your paint brushes before creating a masterpiece data needs to be cleaned and transformed to ensure its quality and suitability for analysis you learned how data cleaning addresses inconsistencies missing values and duplicate entries in data sets while data transformation enhances data analysis through processes like aggregating data converting data types and normalizing values after that you explore the practical aspects of cleaning data at the source in Excel before importing it into PowerBI you discovered the importance of using key Excel functions like text functions data and time functions logical functions and lookup functions to ensure the reliability and accuracy of our data in the final part of the week you explored Microsoft Power Query in PowerBI a data connectivity and preparation tool that handles the ETL process you should now understand how Power Query helps in connecting to multiple data sources cleaning and transforming data automating data preparation tasks and creating structured and collaborative workflows this week you were introduced to some of the tools you can use to create data analysis masterpieces robust insightful and visually appealing business intelligence reports in future courses you’ll have the opportunity to develop practical skills in using these tools as you continue your PowerBI learning journey remember that like a skilled artist a successful data analyst must know their tools well understand their medium the data and have a clear vision of the end result the knowledge and skills acquired in this week will serve as a strong foundation to build on enabling you to create compelling data narratives that drive informed business decisions you’ve now reached the end of your learning journey for this harnessing the power of data with PowerBI course building a solid foundation in learning how to use Microsoft PowerBI to help businesses make the most of their data with Microsoft PowerBI in your data analysis toolkit you discovered how you can use data effectively to help stakeholders make informed business decisions you’ve put great effort into completing this course by working through a range of videos readings exercises and quizzes in the final course assessment you’ll apply what you’ve learned by completing tasks that simulate a real world data analysis scenario to consolidate your learning you’ll then take a final graded quiz to assess the knowledge and skills you gained throughout this course in this video you’ll review key learnings related to the data analysis process for businesses and the process of transforming data into valuable insights using PowerBI this will help you prepare effectively for your upcoming assessments now let’s get started by revisiting your first week of learning in the first week you learned about data analysis in business including the interconnected roles available to you in the world of data you primarily focus on the role of a data analyst when exploring the data analyst role you cover the skills data analysts need to collect process analyze and ultimately transform raw data into valuable business insights another key learning point was the stages of the data analysis process you learned that the data analysis process includes identifying the analysis purpose or defining the business problem data collection and preparation data processing and modeling data analysis visualization and interpretation and reporting and sharing data insights in relation to data processing you explored how you can use the extract transform load or ETL process to transform raw data in preparation for analysis you were introduced to data analysis expressions or DAX calculations and using visualizations during the data analysis stage you also explored some factors to consider when creating data analysis reports and best practices for supporting datadriven decision-making in businesses the importance of gathering the right data and engaging with the analysis purpose for successful data analysis was emphasized you learned the significance of understanding stakeholder experience you discovered how tailoring your data analysis and visualization with this in mind can enhance comprehension engagement and the relevance of data insights part of your learning included discovering how data insights can drive business decisions and how stakeholder engagement can facilitate this process you then went on to learn more about Microsoft PowerBI and its user interface components powerbi is a userfriendly but powerful tool for data analysis and visualization week two began with an exploration of data collection and the importance of asking the right questions to ensure you gather the right data this included learning about identifying suitable data by evaluating data sources and types you were introduced to the PowerBI workflow consisting of PowerBI desktop PowerBI service and PowerBI apps you learned that with the PowerBI workflow you can import data generate data insights create meaningful reports and dashboards and share and manage those reports and dashboards you then explored elements of the extract transform and load process in more depth as a part of this process you covered data gathering and ingestion which are integral to the data analysis as well as methods for performing them you also explored the importance of effective data storage and management which is involved throughout the ETL process data storage and management planning and considerations from storage capacity and data access needs to data retention and archiving were highlighted as crucial for datadriven organizations you then learned more about data cleaning and transformation essential steps to ensure data quality and accuracy prepare your data for analysis and enhance your analysis you discovered how to clean data at source in Microsoft Excel before you import it into PowerBI the week of learning concluded with an introduction to Microsoft Power Query Editor in PowerBI a data preparation tool with ETL capabilities you learn that Power Query can help you connect to multiple data sources clean and transform data automate data preparation tasks and create workflows as you embark on the final course exercise and graded quiz you can approach your assessments with confidence knowing that you’ve built a strong foundation of knowledge and skills by committing to your learning journey throughout the course however if you feel the need to review any of the concepts summarized for you in this video or require additional preparation remember that you have the flexibility to revisit any of the course items it’s now time to showcase your learning starting with an invaluable practical exercise in this exercise you’ll engage in key tasks that form part of the initial phases of the data analysis process for a product launch analysis wishing you the best of luck as you embark on the final week of this course congratulations on completing the harnessing the power of data with PowerBI course with your hard work and dedication you’ve made great progress in your data analysis learning journey you should now have a thorough understanding of the following topics the role of data in driving decisions and business outcomes how data is produced gathered and transformed into insights in businesses and organizations the stages in the data analysis process the role of the data analyst including related skills tasks and tools the components of Microsoft PowerBI and using PowerBI as a tool for data analysis and visualization this course provided you with a foundation in data analysis in Microsoft PowerBI you discovered the importance of data analysis in business with a deep dive into the role of a data analyst in supporting datadriven decision-m in organizations you’ve learned all about the data analysis process and how to ensure that the analysis you perform is useful for stakeholders whether you’re engaging with stakeholders to determine the analysis purpose or business problem gathering the right data or reporting the insights you now have a comprehensive understanding of each stage of the process you familiarize yourself with PowerBI including its user interface and components you had the opportunity to generate your own visualization a key skill for a data analyst you also learned about the PowerBI workflow and using Power Query Editor in PowerBI for transforming data the foundational knowledge you’ve gained represents a significant step towards using PowerBI effectively to generate valuable insights from data well done this course forms part of the Microsoft PowerBI analyst professional certificate these professional certificates from Corsera help you get job ready for in demand career fields the Microsoft PowerBI analyst professional certificate in particular is not only a way to broaden your understanding of data analysis but also gain a qualification that can serve as a foundation for a career in data analysis using Microsoft PowerBI plus the professional certificate will help you prepare for exam PL300 Microsoft PowerBI data analyst by passing the PL300 exam you’ll earn the Microsoft certified PowerBI data analyst certification this globally recognized certification is industry endorsed evidence of your technical skills and knowledge the exam measures your ability to prepare data model data visualize and analyze data and deploy and maintain assets to complete the exam you should be familiar with Power Query and the process of writing expressions using data analysis expressions or DAX of which you gain some foundational knowledge in this course you can visit the Microsoft certifications page at http://www.learn.microsoft learn.microsoft.com/certifications to learn more about the PowerBI data analysis certification and exam this course enhance your knowledge and skills in the fundamentals of data analysis in PowerBI but what comes next well there’s more to learn so it’s recommended you move on to the following course in the program whether you’re new to the field of data analysis or already have some expertise and experience completing the whole program demonstrates your knowledge of and proficiency in analyzing data using PowerBI you’ve done a great job so far and should be proud of your progress the experience you’ve gained will showcase your willingness to learn motivation and capability to potential employers it’s been wonderful to be a part of your journey of discovery wishing you all the best for the future hello and welcome to this course on extracting transforming and loading data in Microsoft PowerBI regular digital activities such as ordering food online reserving a trip and using a social media application generate a great deal of data now think about the billions of people who engage in these activities every single day then there are other organizations like universities and banks that perform many other transactions that may need to be stored in different ways businesses also need to gather data from different sources for example from their customers from other companies and from the government now imagine all that data living in different places and being stored in different ways how can a company make sense of all of this that’s where data analysts come in one of their jobs is to extract data from different sources transform it in a way that it can be used and load it into a tool to help the analysis process like PowerBI this is what you will learn in this course how to extract transform and load data a process also known as ETL before data can be used to tell a story it must first be processed so that it is usable as a story data analysis is the process of identifying cleaning transforming and modeling data to discover meaningful and useful information the data is then crafted into a story through reports for analysis to support the critical decision-making process in this learning path you will learn about the life and journey of a data analyst and the skills tasks and processes they have to master to tell a story with data you’ll discover how getting the data analysis story correct enables businesses to make informed decisions by now you should have learned how to harness the power of data in PowerBI and how it benefits an organization in this course you will get to explore various topics and elements involved in the career of a data analyst including identifying how to collect data from multiple sources and configuring it in PowerBI preparing and cleaning data for analysis and inspecting and analyzing ingested data to ensure data integrity this course will give you a solid foundation in these topics and offer you opportunities to practice extracting transforming and loading data into PowerBI now let’s briefly outline the course content so you can have an idea of what’s to come in your learning journey as you explore the extract transform and load process first you will learn about the extract portion of the ETL process you will focus on data sources and how to extract data and configure storage modes in PowerBI then you will move on to the transform portion of the ETL process you will practice cleaning and transforming data to prepare it for data modeling you will also learn about data cleaning using Power Query and how to use applied steps next you will cover the load portion of ETL and practice using data profiling and advanced queries you will also learn about referencing queries and data flows and using the advanced editor to modify code to assist your learning you will also get to apply your newly gained skills in exercises quiz questions and self- reviews to consolidate your learning and put it into practice you will complete a practical assignment in this assignment you will be provided a business scenario from Adventure Works a fictional business where you need to gather data from multiple data sources to clean and transform you will have the opportunity to apply the knowledge you gained in this course to join and merge these data sources identify and remove anomalies using profiling tools after this practical assignment you will complete a final graded assessment be assured that everything you need to complete the assessment will be covered during your learning with each lesson made up of video content readings and quizzes in addition you can share your knowledge and discuss challenges with other learners these discussions are also a great way to grow your network of contacts in the data analysis world so be sure to get to know your classmates and stay connected during and after your course this course is also a great way to prepare for the Microsoft PL300 exam by passing the PL 300 exam you’ll earn the Microsoft PowerBI data analyst certification the exam measures your ability to prepare data model data visualize and analyze data and deploy and maintain assets in this course you will learn the process of extract transform and load you will identify how to collect data from and configure multiple sources in PowerBI and prepare and clean data using Power Query you’ll also have the opportunity to inspect and analyze ingested data to ensure data integrity now that you have an overview of what this course is about it’s time to take the next step and prepare for a career as a data analyst using PowerBI these days businesses generate very large amounts of data through their activities and the data may come from different sources for example from different departments within the company or from clients the challenge is how to make sense of this data and extract valuable insights that can help improve business performance that’s where PowerBI comes in in this video you’ll explore the basics of data sources produced from business operations and learn how to combine them to gain business insights to begin let’s first review the data sources that you can connect to in PowerBI flat files are a common type of data source that can be used for ETL or extract load and transform in PowerBI examples of flat files include CSV TXT and Microsoft Excel files relational data sources such as SQL Server MySQL and Oracle databases are commonly used by large organizations because they provide a high level of reliability data integrity and security nosql databases such as MongoDB and Cassandra are becoming increasingly popular for ETL in PowerBI these databases are designed to store and manage large volumes of unstructured or semistructured data making them ideal for use in a wide range of applications don’t worry if you’re not familiar with all the terminology it will be discussed later in this course so no matter where your data is stored PowerBI has the flexibility to connect to a wide range of data sources next we will explore how combining data sources in PowerBI can optimize supply chain performance imagine you are a supply manager responsible for managing the new just in time system of your company ensuring that all parts and materials are sourced and delivered on time while meeting quality standards you closely collaborate with your team to ensure that the system runs and all suppliers meet their obligations by combining data from various sources such as sales figures inventory production and supplier information your department could gain valuable insights into customer behavior product performance and supplier performance for example by analyzing sales data alongside supplier data trends in customer demand can be identified and production and inventory levels adjusted accordingly on a company level analyzing supplier performance data helps to identify areas for improvement and work with them to enhance their performance and long-term collaboration in conclusion combining data sources can benefit different stakeholders in a business by providing valuable insights into customer behavior product performance and supplier performance this information can be used to make informed decisions leading to improved supply chain management reduced costs increased customer satisfaction and ultimately drive business success data integration can be a daunting task especially when you are working with multiple data sources that have varying formats structures and quality levels the combination of these sources can often lead to inconsistencies and errors making it difficult to derive meaningful insights and make informed decisions but you don’t need to worry tools like PowerBI simplify the process of combining data from different sources reducing the time and effort required to create a comprehensive view of your data it is designed to be userfriendly and accessible even for non-technical users with an intuitive interface and drag and drop functionality that makes it easy to create reports and visualizations powerbi also allows you to customize your reports and visualizations to suit your company’s specific needs you can choose from a wide range of pre-built templates and visualizations or create your own custom designs this flexibility makes it easy to create reports that are tailored to the unique needs of your business it also enables collaboration by allowing you to share your reports and visualizations with colleagues clients or stakeholders by sharing reports or embedding them in websites or apps this collaborative approach can improve communication and ensure that everyone is working with the same data ultimately driving business success combining data sources is a great method of providing valuable information that can lead to improved supply chain management reduced costs increased customer satisfaction and ultimately drive business success and it should not be a daunting task in this video you learned the basics of data sources produced from business operations and how to combine them to gain business insights tools like PowerBI with its built-in data connections can simplify the process of combining data from different sources reducing the time and effort required to create a comprehensive view of your business by leveraging the functionalities of PowerBI you as an aspiring data analyst along with other stakeholders can gain a competitive edge and unlock new opportunities for growth and success at Adventure Works every day businesses generate large amounts of data but where do they store it all many organizations store and export data as files such as flat files in this video you’ll learn how to set up and export a flat file data source your manager at Adventure Works Adio Quinn asked you to build a PowerBI report using a flat file that the human resources team has prepared the file contains some of Adventure Works’s employee data such as employee names hire dates positions and managers as well as data located in several other data sources so what is a flat file a flat file is a file type that contains a single data table with a uniform structure for every row of data and does not have hierarchies some examples of flat files include commaepparated value or CSV files delimited text or TXT files and fixed width files additionally output files from various applications such as Microsoft Excel workbooks can also be classified as flat files now that you know what a flat file is let me demonstrate how to set up a flat data source let’s help Adventure Works HR department set up a flat data source the first step is to determine which file location you need to use to export the data the file location is important because when it is changed PowerBI will not be able to refresh the data this can cause errors such as file not found or data source not found once you have located your file you can proceed in PowerBI to display available data sources in the home group of the PowerBI desktop ribbon select the get data button option or down arrow to open the common data sources list if the data source you want isn’t listed under common data sources select more to open the get data dialogue box in this example you need an Excel data source which is first on the list next a connection window displays where you select the employee Excel workbook that the HR team prepared and select open when your HR file is connected to PowerBI desktop the navigator window opens this window displays the tables available in your data source the Excel file in this example you can select a table to preview its contents and to ensure that the correct data is loaded into the model after selecting the check box of the table that you want to bring into PowerBI it activates the load button now you can select the load button to import your data into the PowerBI data set in case you need to change the location of your source file for a data source during development or if your file storage location changes you’ll need to update your connection strings in PowerBI to keep your reports up to date to do this in PowerBI desktop select file in the menu bar then select options and settings from the file menu and now select data source settings from the options and settings menu you can also change or clear the permissions by selecting edit or clear permissions respectively permissions cover the privacy level and credentials used for connecting to a data source remember that any structural changes to the file can break the reporting model so it’s important to reconnect to the same file with the same file structure by following these steps you’ll be able to ensure that your report uses the most accurate and up-to-date information available you’ve now helped Adventure Works HR department to store their data and you should now know how to set up and export a flat file data source great work as an aspiring PowerBI data analyst you’ll generate large amounts of data but where can you store this data fortunately PowerBI offers several storage options for its users over the next few minutes you’ll explore PowerBI’s storage modes and their impacts on report performance adventure Works need help with creating a report that displays the performance of different product categories over time this report will be a large sales transaction table with billions of rows so you need to optimize its performance so that the end users have fast access to the visuals but before taking on this task you first need to understand the different storage modes available in PowerBI and how they impact report performance let’s begin with an overview of PowerBI storage modes powerbi has two primary storage modes import mode and direct query mode it also includes a complimentary dual mode import mode is used to import small data sizes from various sources into PowerBI and it stores it in memory which enables quick access for example in import mode you can connect to an Excel file containing a data set of available categories this mode is ideal for the marketing department if they need to filter sales transactions by category in the report view on the other hand direct query mode allows you to connect directly to the data source and the data remains in the source system direct query mode is best suited for larger data sets where loading data into memory is not practical for instance if you have a card visualization that displays an aggregate summary of category sales from a sales table with this storage mode PowerBI will send a request to the data source and get the result back by using direct query the sales department can leverage the power of the external database to handle complex queries and aggregations while PowerBI only brings in the necessary data for visualizations there are many features in import mode not supported in direct query mode so it’s important to remember that you can’t switch from one mode to the other now that you’re familiar with the two primary storage modes in PowerBI import and direct query let’s explore the complimentary dual mode dual mode is a distinct mode that combines the benefits of import and direct query modes when you use dual mode the PowerBI service determines the most efficient mode to use for each query so if a table has similar data between import and direct query modes then using dual mode can be beneficial with dual mode you can import the data you need and still use direct query for additional data that is not available in the important data let’s explore the advantages and limitations of each of the storage modes in a little more detail starting with import mode import mode is a great option if you need to work with small to medium-siz data sets data is loaded into PowerBI to form the data model the data model organizes the data into tables columns and relationships making it more accessible and easier to work with all calculations are performed within the data model the data is stored in compressed form which optimizes memory usage one downside of import mode is that you must refresh the data manually this means that any changes you make to the source data will not be reflected in the report until the data is refreshed the next mode you’ll explore is direct query direct query mode connects directly to the data source and queries are sent to the source system in real time this means that the data is always up to date and there’s no need to refresh the data manually direct query mode is best suited for larger data sets as it does not require loading all the data into memory if you choose to import the data to a PowerBI file stored on your local computer it will require a significant amount of memory and resource overhead one downside of using direct query mode is that it can impact performance if the queries are complex or the data source is slow so you need to consider the benefits and drawbacks of each storage mode and select the one that best suits your needs the third option you need to be familiar with is dual mode this is where data is stored in memory but can also be retrieved from the original data source this is useful when you are working with dimension tables which can be queried with fact tables from the same source for instance Adventure Works might have a sales aggregate by customer loyalty table in import mode which is used to speed up query processing by storing a summarized and categorized version of customer data in memory simultaneously the larger sales transactions table could be set to direct query mode in this scenario setting the common dimension table such as date to dual mode can enhance the performance of the report when the dual mode table date is combined with an import mode table sales aggregate by customer loyalty it behaves like an import table and retrieves data from memory ensuring faster performance on the other hand when the dual mode table dimension date is combined with a direct query mode table sales the dual mode table dimension date behaves like a direct query table quering data directly from the source system when you use multiple data sources to create a data model it is called a composite model composite models enables you to combine multiple import modes into one unified data model using composite models can greatly enhance the functionality and performance of your reports and analytics workflow when building composite models in PowerBI it’s important that you specify the storage mode for each table in your data model the performance of your composite model depends on how you set it up for the best performance try to use import or dual mode tables they work faster because the data is stored in memory and can be retrieved quickly giving you faster results when creating reports it’s essential that you consider the size of your data set and determine if real-time access is a requirement before selecting a storage mode powerbi offers different storage modes and in this video you learned about the two primary storage modes in PowerBI import direct query as well as the complimentary dual mode as an aspiring data analyst it is important that you understand how these different storage modes impact a report’s performance in this video you explored the advantages and limitations of each of the storage modes great work data has the potential to help organizations make better business decisions but businesses generate such large amounts of data they have to sift through that it becomes difficult to see the story it tells luckily PowerBI is an excellent tool for visualizing and analyzing data however the slow loading time of data can be a significant issue especially when working with large data sets in this video you’ll learn how to configure import direct query and dual storage modes in PowerBI to optimize data retrieval and processing enhance report speed and guarantee that your reports always contain the most recent data renee Gonzalez the marketing manager at Adventure Works has asked you to create a report that displays sales at the cash registers as customers purchase products the point of sale system scans product barcodes at the cash register measuring purchase trends she’s concerned with the logistics of ordering stocking and selling products while maximizing profit as this is going to be a large sales transaction table with billions of rows you need to ensure that the report’s performance is optimized so that the end users have fast access to the visuals to complete this task successfully you have to select the best storage mode for the data and configure it in PowerBI to optimize data retrieval and processing let’s start by helping Adventure Works choose a storage mode in PowerBI desktop to do this select the data button on the home group of the PowerBI desktop written in the get data dialogue box search for the Azure SQL database connector once you’ve selected the Azure connector the data connectivity mode section displays where you can choose from two options import or direct query import mode stores data directly in PowerBI desktop’s memory while direct query retrieves data from your data source in real time powerbi also provides extra functionality to customize the storage mode for each table in your data set to get started select the model view icon near the left side of the window to display a view of the existing model model view displays all the tables columns and relationships in your model table card headers are colored to help you quickly identify which tables are from the same kind of source a table card header with no color indicates that these tables are in import mode tables from the same direct query source will display the same color in the table card header blue in our example select the sales order detail DW table and expand the properties pane by right-clicking on the table and selecting properties the properties pane displays various options for configuring the table you’ll find a drop-own menu labeled storage mode in the advanced section of the properties pane this is where you can set or adjust the table’s storage mode now let’s set up a dual import mode for your table by configuring the storage mode of the sales order details table this table is currently set to a direct query mode in the advanced section change the option to import mode the following warning message will display setting storage mode to import is an irreversible operation you will not be able to switch it back to direct
query this operation will refresh table setter import which may take time depending on factors such as data volume next select okay congratulations you now know how to configure storage modes to optimize your reports now that the storage modes are configured Renee and her team should experience a significant improvement in system performance for example reports will generate more quickly they can display real- time data and business users can access data more efficiently well done at this stage of the course you should be familiar with how businesses gather and generate large amounts of data in their daily activities this can include data from human resources accounting and sales you also learned that this data may be structured and stored in different ways as an aspiring data analyst at Adventure Works you will realize that the most important step is to determine how data will be structured and stored knowing your data types and the way it is structured gives you the correct data sets to create reports that suit the company’s needs allowing business insights that will help during decision-m furthermore identifying the best storage solution for your data can reduce costs and improve performance two aspects that any company has as top priorities by the end of this video you will be able to identify the difference between structured and unstructured data and what storage solution is ideal for each type as an aspiring data analyst at Adventure Works you’ve been assigned the task of determining the best storage solution for the online retail website at Adventure Works the website was built with three data sets used to run the business product catalog data image files and financial business data each data set has different requirements the key factors to consider in your task are data classification how your data will be used and how you can get the best application performance now let’s focus on data types there are three types of data structured unstructured and semistructured all of which are suitable for analysis but differ in the tools used for ingestion transformation and storage let’s start with structured data structured data is the most common type of data that we use it is also known as relational data in a financial report for example numbers and names are arranged into columns and rows making it easier for analysis and processing by nature structured data is quantitative easily searchable sortable and analyzed using tools like Microsoft Excel spreadsheets or relational databases which can store large amounts of structured data sql or structured query language is a programming language used to manage relational databases it allows users to manipulate and query data stored in a database making it a valuable tool that’s used by data analysts and business users however the structure makes any addition or removal of data fields difficult since you must update each record to adjust to the new structure some applications where relational data is used are customer relationship management reservations and inventory management systems now let’s cover unstructured data unstructured data does not have a predefined structure or format it is best used for qualitative analysis and usually resides in non-reational databases or unprocessed file formats some examples of this type of data are text documents audio and video files social media posts and images these types of files can be stored in a centralized repository that ingests and stores large volumes of data in its original form then there is a third type of data it is called semistructured data because it is not as organized as structured data and it is not stored in relational databases this type of data uses tags for organization and hierarchy video files may have an overall structured and contain semistructured metadata but they are considered unstructured data since the data that forms the video itself is unstructured there is a process for converting semi-structured data into a specific format that can be easily transmitted stored or processed it is called data serialization it uses a method of formatting that will allow the data to be transmitted or stored in a way that is easily understood by both the sender and the receiver without the need to know all the specific details of the data this is useful when dealing with semi-structured data that doesn’t fit neatly into traditional databases or data structures if you want to learn more about serialization please visit the additional resources at the end of this lesson now you’ll learn how to classify your data in order to choose a suitable storage solution for structured or unstructured data the correct storage solution can deliver better performance improve manageability and save on database costs when selecting a storage solution it’s important to consider the type of data you’re working with what operations are needed to transform the data and what level of management and maintenance is required the business data used at adventure works for analysis on a year-to-year comparison is not updated frequently it is stored in multiple data sets and some latency can be accepted since it is mainly read only not all data analysts need write access but they can all read from all data sets this is a type of structured data that will most likely be queried by data analysts who use SQL more than any other query language therefore a suitable storage solution for this example is a SQL database or a cloud-based solution like Azure SQL database but it can also be bundled with another cloud-based solution Azure Analysis Services to model the data in Azure SQL database this model can be shared with business users who can connect to it through PowerBI for analysis and gain business insights in summary selecting the appropriate storage solution is vital for addressing the specific requirements of your data remember when we spoke about serialization and the formatting to allow the storage of unstructured or semistructured data one of those formats is a blob this is a binary large object where the data is stored in a binary ones and zeros format for Adventure Works online retail website Azure Blob Storage is an ideal option for storing unstructured data such as photos and videos it’s a scalable and cost-effective cloud storage service which is designed to store large amounts of unstructured data such as images videos or documents the website has a product page where a bicycle photo needs to be displayed at the same time as the specific bicycle model the photos will not be queried independently by including the photo ID or URL as a product property the photo can be retrieved by its ID without any time lag this demonstrates how unstructured data can be stored the right storage solution allows Adventure Works to achieve optimal performance and efficient data management in this video you learned that while structured data is easier to work with and analyze unstructured data is often more abundant and valuable businesses and organizations are increasingly focusing on harnessing unstructured data to gain insights into customer behavior emotions and other aspects that can shape their strategies choosing and implementing the correct storage solution can benefit companies and organizations by improving performance reducing costs and increasing efficiency adventure Works generates data from many different departments and stores this data in many different sources wouldn’t it be great if they could combine data from these different sources with PowerBI they can combine data sources using connectors in this video you’ll learn about the different kinds of connectors available in PowerBI their purpose how to choose a connector and securely connect to the cloud data source adventure Works needs to generate a report that compares the sale of bicycle models across the company’s different outlets web retail and individual sellers however the sales data is stored in different sources the company needs you to generate an integrated report that combines these different data sources you can combine these data sources using connectors in PowerBI you can use PowerBI as a single business intelligence solution to generate an integrated report by combining the company’s data sources through the use of connectors but before you begin let’s find out more about connectors connectors are links that transport data between a data source and an application they’re basically the bridges that connect PowerBI to different sources of data with connectors you can create a link or bridge between PowerBI in different data sources like databases files services SharePoint and more connectors make it easy to connect between data sources you can then transform clean and visualize the data into PowerBI for report and analysis to generate insights but before you start importing your data it’s important to understand what your business requirements are for the data source this includes things like whether the data is stored on your own computer and gets updated every so often or if the data is coming from an external source and needs to be updated in real time you also need to know who will be using the data and how it will be used these requirements are essential because they can affect the way you load the data into PowerBI so it’s important that you get them right microsoft frequently adds new data connectors to its desktop and services platforms it typically releases at least one or two new connectors every month as part of the regular PowerBI update this has resulted in PowerBI having a vast collection of over 100 data connectors available files databases and web services are the most used sources all PowerBI connectors are free to use but they might be marked as beta or preview depending on their development stage any data source marked as beta or preview has limited support and functionality so don’t make use of it in production environments now that you’re familiar with the data connectors available in PowerBI it’s time to help Adventure Works generate their report let’s examine the steps involved in setting up a connector to a SQL database first navigate to the home tab and locate the get data button you have two options to choose from here you can either select the get data button and then choose all or you can select the expand arrow next to the get data button and select more this lets you access a wide range of data connectors available in PowerBI to make sure your data is mapped correctly in PowerBI it’s crucial to identify the specific nature of the data for instance if you’re working with a document meant for an Azure SQL database using the Excel connector wouldn’t give you the desired outcome as a PowerBI user in the get data window navigate to the Azure SQL option and select it then select the connect button you can also use the search bar to filter the available connectors and quickly find what you’re looking for after selecting the data source you’ll be prompted to set up the connection depending on the type of data source you’ve chosen the specific details you need to provide will differ for example if you’re working with an Excel file you’ll need to specify the location of the file on the other hand if you’re dealing with a SQL server database you’ll need to enter the server name and the database connection details there are a few additional options you may want to consider in addition to specifying the server address and database name you can also choose between different connection modes such as import or direct query most of the time you’ll select import other advanced options are also available in the SQL Server database window but you can ignore them for now you’ll cover them at a later stage in the course after you’ve specified the server and database names you’ll be prompted to sign in with a username and password you’ll have three different sign-in options to choose from depending on your credentials the first option is to use your Windows account this is often the easiest option for users who are already logged into their computer the second option is to use your database credentials for instance SQL Server has its own signin and authentication credentials that are managed by the database administrator the third option is to use your Microsoft account credentials which require your Azure Active Directory credentials once you’ve selected the sign-in option that’s appropriate for your situation enter your username and password and then select connect this will allow you to securely connect to your data source once you’ve successfully connected your database to PowerBI desktop the available data in the navigator window appears this window displays all the tables or entities that are available in your data source such as the SQL database in this example to preview the contents of a table or entity simply select the check box next to the table to import data into your PowerBI model select all tables that you want to bring in finally once you’ve selected the tables you can choose to either load the data into your model in its current state or transform it before loading for now the focus is on the data loading process data transformation will be covered in more detail at a later stage by selecting the appropriate data and choosing the load option you can easily bring in the data you need to start building visualization and analyzing your data in PowerBI connectors are an essential component of PowerBI the wide range of available connectors lets you connect to lots of different data sources to bring them all together into one place you can then import or extract the data from these sources into reports and dashboards for analysis and visualization by leveraging the full range of connectors you can access valuable insights to make datadriven decisions for your business you should now understand that connectors are a powerful asset that can help you get the most out of your data analysis what if you could reorder products you buy frequently with a click of a button that would be really convenient right and what if other types of tasks could be automated by businesses well in today’s datadriven world organizations are constantly searching for ways to automate tasks to optimize productivity microsoft PowerBI is an integrated suite of software tools applications and connectors that can help you transform your data sources into clear and compelling visualizations connectors play an important role in connecting to various data sources and executing actions or triggering workflows based on specific events there are two types of operations available to create automated workflows triggers and actions in this video you will explore how actions are triggered to create efficient and effective scheduled actions so let’s get started with triggers and actions in PowerBI addio Quinn a data analyst at Adventure Works a bicycle manufacturer is responsible for analyzing daily sales reports and providing insights to the management team however the manual process of importing data from multiple sources and analyzing it can be laborious and timeconuming to streamline this process Adio asks your help to leverage PowerBI’s triggers and actions to automate the workflow with PowerBI you can schedule an action to refresh the data and email the latest sales report to the management team with this automated workflow in place you can now focus on analyzing the data and providing valuable insights to the management team without worrying about the manual process of importing and analyzing the data in PowerBI triggers and actions work together in configuring a workflow either based on time or specific actions a trigger is always required to initiate a workflow and prompt it to run additionally actions in PowerBI enable interaction with the data source through various functions automating tasks and processes with actions in your workflow can save time reduce manual effort and make your workflow more efficient moreover scheduled actions in PowerBI can automate tasks and actions based on specific time intervals by setting up a schedule reports and dashboards can be updated with the latest data regularly without manual intervention thereby improving data accuracy and streamlining workflows now we are going to explore how to set up a schedule data refresh when it comes to working with data in an organization having access to the latest and most relevant information is essential outdated data won’t be useful to the organization as it doesn’t reflect the current situation relying on old data can even hinder the organization’s growth since there could be more recent and applicable data readily available in this video we’ll explore the topic of automating tasks in PowerBI in PowerBI users have the option to create scheduled actions which enable them to automate tasks and actions at specified time intervals today you are going to help Adio a data analyst at Adventure Works and his job involves regularly updating sales report data sets according to a predetermined schedule by setting up a schedule data refresh Adio can now automate the process saving him valuable time and effort let’s begin by opening your browser and heading to https/app.powerbi.com/home powerbi.com/home to get to the scheduled refresh screen in the navigation pane on the left hand side of the screen select data hub next locate the data set you wish to work with in our case the sales report data set next select the ellipses and then select settings to expand the data set settings this will take you to a new screen where you can configure the trigger scheduled refresh section is where you define the frequency and time slots to refresh the data set let’s walk you through the steps to set up an online refresh schedule in PowerBI services here’s what you need to do step one turn the switch to on step two you can modify the schedule to fit your needs choose the frequency you want the data set to refresh such as daily select the time zone you want to use for example UTC London under time select add another time and enter a time for the refresh to occur repeat this step for additional refresh times as needed step three once you’re done simply select apply and you’re all set did you know that you can easily adjust the frequency time zone and time of your scheduled refreshes in PowerBI this allows you to ensure that your data is always up to-date and accurate plus you can even set up scheduled notifications to be sent to a specific email address how convenient is that beware if your data set hasn’t been active for 2 months the scheduled refresh will be automatically paused are you ready for a quick rundown on data refreshing in PowerBI great as a PowerBI user refreshing data typically means importing data from the original data sources into a data set you can choose to refresh data based on a predetermined schedule or on demand depending on your needs if your underlying source data changes frequently it may be necessary to perform multiple data set refreshes daily however it’s important to note that PowerBI limits data sets on shared capacity to a maximum of eight scheduled daily data set refreshes with these easy steps you can now create a refresh schedule that works perfectly for you in this video you explored the topic of automating tasks within PowerBI specifically using scheduled actions to automate tasks and actions at specified time intervals by automating processes such as data refreshing users can save valuable time and effort we walked through the steps to set up an online refresh schedule in PowerBI services and highlighted the importance of periodically checking the refresh status and history to ensure data sets are error-free good job congratulations on reaching the end of the first week in this course on how to extract transform and load data in PowerBI this week you explored how to work with basic and advanced data sources in PowerBI let’s now take a few minutes to recap what you learned this week this summary will help you review the concepts presented previously and clear up questions you might have you began the course by covering basic data sources you learned that for example by analyzing sales data alongside supplier data you can identify trends in customer demand you also learned that data from different parts of an organization may come from different sources and may be stored in different ways that’s when you identified the many different data sources supported by PowerBI like flat files relational data sources and NoSQL databases you also learned how to set up a flat data source after that you learned that local data sets provide data that is only available to a specific individual or organization and are typically stored locally local data sets are a good option for organizations or projects with few users that demand high security and need speed over quantity on the other hand shared data sets allow multiple individuals or organizations access to data and are usually stored on multiple locations or cloud-based platforms they are suitable for large enterprises or projects that require multiple users working at the same time then you had the opportunity to complete a practical exercise on how to set up an Excel data source in PowerBI after that you covered different storage modes in PowerBI you learned that you must think carefully about the benefits and limitations of each storage mode and select the one that best suits your needs import mode is a great option if you are working with small to medium-siz data sets and if the data is loaded into PowerBI data model in this model data must be refreshed manually on the other hand direct query mode connects directly to the data source and queries are sent to the source in real time so there’s no need to refresh the data manually however this mode might impact performance you also covered dual and hybrid modes as alternative storage modes after you explored these different storage modes you then learned how to configure them in PowerBI next you had the opportunity to apply your skills and configure storage modes in PowerBI you discovered that structured data also known as relational data is arranged into columns and rows by nature structured data is quantitative easily searchable sortable and analyzed using tools like Microsoft Excel spreadsheets or relational databases which can store large amounts of structured data on the other hand unstructured data does not have a predefined structure or format unstructured data is best used for qualitative analysis and usually resides in non-reational databases or unprocessed file formats some examples of this type of data are text documents audio and video files social media posts and images semistructured data is not as organized as structured data and it is not stored in relational databases this type of data uses tags for organization and hierarchy an example of semi-structured data is video files you then learned about connectors connectors are the bridges that connect PowerBI to different sources with connectors you can import data from databases files Outlook servers SharePoint and many other sources you also learned that before you start importing your data it’s important to understand what your business requirements are for the data source you then explored the two types of operations used for creating automatic workflows triggers and actions triggers are used to create efficient and effective scheduled actions for example Adventure Works can use triggers to automate parts of their PowerBI workflow like refreshing data and emailing reports next you undertook another practical exercise in this exercise you implemented triggers to automate your workflow in PowerBI you then tested your understanding of the concepts that you encountered in this lesson in the knowledge check finally you undertook a module quiz this quiz tested your understanding of all concepts that you explored in this module you should now be familiar with the fundamentals of data sources you should be capable of extracting data from basic and advanced data sources to work with in PowerBI great work i look forward to guiding you through the next week’s lessons in which you’ll learn about transforming data in PowerBI you’re making progress in your journey to become a data analyst you’ve learned how to extract data and now it’s time to learn how to transform it so you can make better use of it depending on your data sources data transformation can involve different activities such as cleaning merging and profiling in this video you’ll learn how to identify components of data transformation and understand why data transformation is required adventure Works CEO Jamie Lee has set a new goal for the company to increase sales she’s relying on company data to uncover trends and insights and make that goal achievable your manager Addio Quinn has asked you to create a PowerBI report that visualizes the data in a meaningful way but before you can start working with that data you need to clean and transform the raw data to ensure its accuracy and consistency in the first part of this course when you explored the extract stage of the extract transform load process you learned that data may come from different sources however the data from these sources may contain inconsistencies that make accurate analysis difficult data from different sources can be untidy incomplete and inconsistent making it difficult to draw meaningful insights that’s why data transformation is a crucial step it helps you prepare data for analysis now let’s examine some of the inconsistencies you may find in data by this point in the course you should know that data is classified into three main groups called structured semistructured and unstructured data each data group is suitable for analysis but may require different tools to ingest transform and store you can say that data coming from sources that you define as structured data is more ideal to work with and compliant with the rules since these sources are systems that have strict rules and prioritize data integrity data coming from conventional databases generally have a low probability of inconsistent or erroneous data however in semistructured data unstructured data and even in some types of structured data it is likely that there is data that needs to be transformed before starting the report design for example let’s say you are working on an analysis related to products in an e-commerce database for this task you need some relevant fields for your report however the table has hundreds of fields so you need to decide how to identify the relevant data to create your report an example of useful data transformation in this scenario is including certain columns from the data and excluding others before loading the data for analysis and reporting another transform example would be selecting fields and transforming by merging them such as in a customer table with fields for the first and last name but you want to display them as a single full name field by merging fields with a space between now let’s explore what data cleaning is data that is not structured is more flexible in terms of rules and therefore more likely to be disorganized and require cleaning you may not encounter as clean data as you would expect in Excel data or in data organized using delimiter symbols such as angle brackets or commas in such cases the data should have a preliminary examination to identify incorrect data or separate rows where content refers to the same values like where house written as two words and warehouse as in one word you can resolve these inconsistencies by passing them through filters with specific rules this examination is referred to as data cleaning another data issue you may encounter is the need to merge or append multiple data sources for example if Adventure Works has two data sources for sales one for online sales and another for in-person sales you’ll need the data from both to create a monthly sales report depending on the data formats you can use commands such as append or merge data transformations to combine the data for analysis in this video you learned that data transformation can help improve data quality by removing errors inconsistencies and inaccuracies this results in cleaner more reliable data for analysis it also allows you to standardize data when working with multiple sources with data transformation you can help organizations like Adventure Works use data that is more understandable organized and consistent to achieve goals like increased sales in this video you will explore some features of Power Query and learn to navigate the Power Query editor interface adio Quinn the data analyst at Adventure Works asks you to clean and transform the company’s sales data which is scattered across multiple sources in preparation for data analysis power Query can help you with this power Query is part of PowerBI desktop allowing for seamless data preparation within the PowerBI environment power Query is a data transformation and data preparation tool allowing you to connect clean and transform data from a wide range of sources it ensures that your data is ready for analysis enabling you to create insightful visualizations and reports let’s explore how Power Query helps you clean shape and organize data from various sources the first feature is data connectivity power Query connects to various data sources both on premises and the cloud directly within PowerBI desktop you can access data from traditional databases as well as file-based sources next there’s data extraction and transformation power Query’s interface allows you to extract and transform data with ease during the extraction process you can filter sort and apply custom transformations ensuring that you import only the required data then there’s the power query editor in PowerBI within PowerBI desktop which provides a graphical user interface or guey for designing and managing queries tabs such as home transform add column and view have data manipulation tools there’s also query reusability and applied steps power Query records each transformation as an applied step allowing you to review modify or delete any step this ensures that your data transformations are transparent and easily modifiable finally there’s performance and scalability power Query handles large data sets efficiently using various techniques that optimize performance and reduce memory usage let’s demonstrate these features in Power Query to achieve Jaime’s goal of increasing sales you must work with sales data from different regional teams stored in different file formats like Excel CSV and even a SQL database to get started you’ll need to import this data into PowerBI using Power Query to begin the import you must add a data source in the PowerBI desktop in the home tab select get data to choose a data source the Power Query editor opens in a separate PowerBI window where you can apply various data transformations such as removing columns changing data types and filtering data next you need to load the data select your data source and configure the connection settings if necessary select transform data to open the Power Query Editor now let’s discover how to navigate in Power Query the Power Query editor has several key areas let’s start with the ribbon the ribbon is the set of toolbars at the top of the window it helps you quickly find the commands that you need to complete your tasks the ribbon tabs such as home transform add column and view contain commands and tools for data transformation and manipulation the queries pane is located on the left side of the editor the queries pane displays a list of all the queries in your project select a query to view or edit its applied steps and data preview this pane is where you can manage and navigate between different queries in your project by selecting a query you can view the data and the applied steps associated with it helping you keep track of your work and maintain organization in your project then on the right pane below the ribbon there’s the applied steps section it displays the sequence of transformations applied to the selected query select a step to view the data state at that point or delete reorder or modify steps as needed the applied steps section provides a visual representation of the transformations applied to your data making it easier to understand the changes made by reviewing the applied steps you can identify errors redundancies or inefficiencies in your data transformations finally in the center of the Power Query window let’s explore data preview the data preview pane displays a preview of your data as it appears after the applied transformations you can interact with the data by sorting filtering or changing the data type of columns this pane enables you to review your data at different stages of the transformation process helping you to get your transformations accurate and effective before loading the data into the data model in this video you learned that Power Query is a versatile tool in PowerBI that streamlines data import cleaning and transformation from multiple sources its features such as data connectivity data extraction and transformation make it an integral part of PowerBI desktop it helps you prepare and transform data from different sources within Adventure Works to simplify analysis and create insightful visualizations and reports the Power Query Editor interface offers a userfriendly experience allowing you to perform various data transformations with ease thanks to the applied steps list in Power Query you can easily undo and reorder steps without losing progress in this video you’ll learn how to use the applied steps list to undo modify and reorder steps first let’s open the Power Query Editor in PowerBI to do this from the home tab select transform data after selecting your data source the Power Query Editor opens in a separate window next let’s locate the applied steps list in the Power Query editor you’ll find the applied steps list on the right pane below the ribbon it has all the steps you’ve performed on your data presented in the order of application the applied steps list is a visual representation of the transformations applied to your data by reviewing the applied steps you can identify errors redundancies or inefficiencies in your data transformations to view the data state at a specific point in the process select the corresponding step in the applied steps list the applied steps list makes it easy to correct a mistake or change your mind or undo a transformation to undo a step simply select the X icon next to the step to remove power Query will automatically revert the data to the state it was in before that step was applied please note that removing a step will also remove all subsequent steps in the list as they are dependent on the previous transformations what if you need to reorder the sequence of steps to reorder steps select and drag the step you’d like to move to a new position in the list power Query will update the data accordingly applying the transformations in the new sequence you should note that reordering steps might affect the results of subsequent transformations review your data and the applied steps list to check everything suppose you need to modify a step just select the gear icon next to the step this opens a settings window to edit the transformation parameters when changed select okay to apply the update as with reordering steps modifying a step might affect subsequent transformations always review your data and the applied steps list to ensure everything is as expected to add a new step use the Power Query Editor ribbon to choose a transformation such as filtering or sorting when you perform a new data transformation it’s added to the applied steps list with the Power Query Editor you can also add filters filtering is the process of narrowing down your data set by displaying only the rows that meet specific criteria it helps focus on a particular subset of data remove unwanted data that may affect your analysis or simplify your data set for better readability let’s check how to add a filter in the Power Query Editor select the column header for the column you want to filter this highlights the entire column with the column selected select the small down arrow next to the column header this opens a drop-own menu with filtering options such as text filters number filters or date filters depending on the data type in the column choose the type of filter and select okay notice the new filtering step has been added to the applied steps list you can also sort your data set sorting is the process of arranging your data in a specific order either ascending or descending sorting organizes data based on specific attributes such as alphabetical order numerical values or chronological order helping to identify the highest or lowest values in a data set select the column header for the column you want to sort in the home tab of the ribbon find the sort group choose sort ascending A to Z or sort descending Z to A to sort the selected column in ascending or descending order the data is sorted based on your chosen sorting order check the applied steps list to ensure the new sorting step is added finally for better organization and readability you can rename any step in the applied steps list just rightclick the step you’d like to rename and select rename enter a new descriptive name for the step and press enter renaming steps helps keep track of transformations making it easier to navigate and understand the data transformation process in this video you learned how to use the applied steps list in Power Query to undo modify and reorder steps it has a visual representation of the data transformation process making it easier to understand complex queries and track the impact of each action on the data set the applied steps list provides easy undo and redo functionality flexibility and reordering steps and efficient troubleshooting capabilities saving time and effort how do you efficiently remove and rename columns to focus on the data that matters you can do it with Microsoft Power Query in Microsoft PowerBI in this video you’ll learn how to remove and rename columns and promote header roles in Power Query in PowerBI as you continue to work on Adventure Works goal to increase sales your manager Adio Quinn asks you to prepare a report on sales and customer demographics you have a data set with numerous columns but you only need a few of those columns for your analysis you must get the data organized and streamlined but you’re not sure where to start that’s where Power Query comes in power Query is a powerful data transformation tool within PowerBI that allows you to connect to different data sources clean data and transform data with ease a common data manipulation you’ll encounter is working with columns working with columns in Power Query in PowerBI is an essential skill for data analysts and professionals who regularly deal with data one of the main benefits of learning to work with columns is efficient data preparation eliminating unimportant or repetitive columns allows you to concentrate on the most crucial data for your analysis minimizing the data set size and streamlining the data structure for easier manipulation and quicker processing another benefit of working with columns is improved data readability and interpretation removing unnecessary columns helps declutter your data set making it easier to read and understand renaming columns with more descriptive names helps you quickly identify the purpose and content of each column one other benefit of working with columns is that it allows for enhanced data analysis and reporting by focusing on the most relevant columns you can produce more accurate and meaningful analyses this allows you to deliver actionable insights to your team and organization leading to better decision making finally working with columns means time and resource savings efficiently removing and renaming columns in Power Query can save you a significant amount of time during the data preparation stage this means you can devote more time to analyzing the data and generating insights by streamlining your data preparation process you also reduce the computational resources required to process your data this can lead to faster analysis and in some cases cost savings particularly when working with cloud-based services that charge based on resource usage now let’s explore a step-by-step guide on how to remove and rename columns and promote header rows in Power Query let’s start by demonstrating how to remove columns the first step is to load your data into Power Query Editor open PowerBI on the ribbon select home select get data and choose your data source for example Excel or CSV once connected to your data the Power Query Editor opens displaying your data the next step in the Power Query Editor is to locate the columns you want to remove to select a single column select its header if you need to select multiple columns hold down the keyboard control key or the command key if you’re using a Mac and select multiple column headers to remove with the columns you want selected you’re ready to proceed right click on any of the selected column headers in the context menu that appears select remove columns the selected columns are removed from your data set you will notice a new step removed columns appears in the applied steps list on the right pane reflecting the updated data state now let’s cover how to rename columns first you select the column you want to rename in the Power Query editor select the header of the column to rename rightclick the header of the selected column in the context menu select rename a text box appears type in a new column name press enter to save the change again you’ll notice the new step in the applied steps list let’s check how to promote header rows the first thing is to identify which row in your data set contains the headers in most cases this is the first row if your data set has additional information or metadata above the headers you may need to scroll down to find the appropriate row now you can promote the header row once you’ve identified the header row on the ribbon use the home tab to locate the transform group select use first row as headers this promotes the first row to be used as column headers replacing the existing headers note if the header row isn’t the first row you’ll need to remove any rows above the header row before promoting it to do this select the rows you want to remove by selecting the row numbers on the left side of the editor then on the ribbon in the home tab select remove rows you will notice a new step removed rows in the applied steps list on the right pane reflecting the updated data state in this video you learned how to remove and rename columns in Power Query you also learned how to promote header rows these are important skills for you to master as an aspiring data analyst they empower you to transform raw data into valuable insights that drive smarter decision making and lead to a greater impact within your organization furthermore efficient data preparation saves time and computational resources when analyzing your data you need to ensure accuracy and reliability but data sets often contain errors that lead to inaccurate results using Power Query you can fix many common data set errors in this video you’ll learn how to identify common types of errors and discover how best to fix them using Power Query in PowerBI adventure Works is preparing to analyze its latest sales data worksheet however there are several errors in this data set like null values duplicate rows and inconsistent data types these errors must be resolved before analysis let’s take a few moments to help Adventure Works fix these errors using Power Query first you must import the data set to transform in this case it’s the Adventure Works sales data set on the home tab select get data and choose text CSV for the file type browse to the location of your data set and select open to import then select load to load the data next select transform data in PowerBI desktop the transform data button is in the home tab in the queries group of functions the button is positioned to the right of the recent sources button the sales data is loaded into Power Query it shows a list of bicycle products and key information about each product like name price weight category and description however several of these rows contain null or missing values these errors need to be resolved before the data can be analyzed to systematically identify missing or null values select the drop- down arrow in the column header for the variable you’re examining this opens a filter menu used to filter the data in the column based on specific criteria the filter menu contains options like empty or null available options depend on the data type of the column empty refers to blank cells in text columns null refers to missing values in numeric or date columns select the appropriate option to filter and display rows that contain missing or null values in the selected column inspect the data table in the editor and identify any rows with missing or null values in this data set two rows contain missing values row 16 and row 17 have a missing value in the product subcategory column now that you’ve identified the values you can resolve them there are three ways to resolve missing values you can replace them with default values replace them with values from another column or remove the rows containing missing values for adventure works the best approach is to replace its missing values with default values logical default values can represent the missing data without distorting the analysis or visualizations first in the ribbon at the top of the editor select the transform tab you use this tab to access the tools and functions for modifying and transforming the data next select the replace values button then select replace values from the drop- down menu you use this option to replace specific values in a column with a new value in this case you can replace all null or missing values a replace values dialogue box appears on screen it has a text box labeled value to find where you specify the value you want power query to identify and replace the aim is to find missing or null values in the product subcategory column so in the value to find box you can write null below the value to find box there’s another text box labeled replace with this is where you type the new value you want to replace the missing or no values with the new value should be consistent with the columns data type which is text so let’s replace the missing values in the product subcategory with the text value trail which represents the default category for trail bikes finally select okay to confirm and make the change when you select the okay button in the replace values dialogue box Power Query scans the sheet for the values you’ve instructed it to identify it then replaces each instance of these values based on the criteria you specified in the replace with box you can review a history of all data transformation operations you’ve applied to the data set by selecting the pane called applied steps on the right hand side of the power query editor window adventure Works has fixed the null values in its data set but there are still duplicate rows errors present the entries in rows 22 to 24 are duplicates of other records in the sheet and identical records also exist in rows 25 to 27 let’s help Adventure Works resolve these errors on the home tab access the data manipulation functions from these functions select the remove rows option and a drop-own menu appears select remove duplicates from the options power Query analyzes the data set and finds rows that have identical values in the selected columns it then removes all but one instance of each group of duplicates that’s good progress just one final error left in the data set inconsistent data types in the form of order dates let’s fix this final error the inconsistent data is in the column order date select the column header to select and apply changes to the entire column next select the transform tab to access the data modification options select the data type button then select the date data type from the drop- down menu this converts all values in the column to the select to data type meaning all data types in the column are now consistent thanks to your help Adventure Works has removed all errors from its data set it can now perform data analysis without the risk of producing inaccurate results you should now understand how to identify common errors in data sets like missing or no values duplicate rows and inconsistent data types you should also be able to resolve these issues using the tools available in Power Query identifying and resolving these errors is essential for making sure your analysis runs on accurate reliable and highquality data you are a data analyst at Adventure Works tasked with analyzing sales data across different product categories and regions using PowerBI understanding the importance of reshaping the data to uncover valuable insights you know you’ll need to transform the data so far in your introduction to transforming data in PowerBI in this course you’ve learned about Power Query data types columns and preparing a data set in this video you’ll gain further insight into PowerBI’s powerful data transformation capabilities by discovering unpivoting and pivoting in Microsoft Power Query unpivot and pivot operations are data transformation techniques that you can use to reshape and restructure data in PowerBI let’s explore each operation in turn the unpivot operation refers to the transformation of data from a wide format with multiple columns to a narrow format with fewer columns by reshaping the data structure it involves converting column headers into row values resulting in a more structured and standardized representation of the data the unpivot operation is useful in data analysis supporting data normalization by organizing data in a tabular format this facilitates analysis variable comparison and data aggregation and summary as related information is consolidated into a single column transforming data from a wide to a narrow structure can also enable data compatibility and integration with other systems or tools that require a narrow format for example in the case of the adventure works sales analysis you can perform the unpivot operation to convert the sales data which is organized in a wide format with separate columns for each region into a long format where the region specific data is stacked vertically in a single column this makes it easier to compare sales across different regions and gain a holistic view of the overall performance on the other hand the pivot operation refers to the transformation of data from a narrow format with fewer columns to a wide format with multiple columns by reorganizing the data structure it enables data analysts to convert rows into columns based on specific criteria or values this operation is often used to summarize and aggregate data create cross tabulations and represent data in a more structured easy to understand way for analysis and reporting to illustrate say you want to analyze the sales data based on different product categories as part of the Adventure Works sales analysis using PowerBI’s pivot functionality you can transform the rows containing individual product categories into separate columns this pivot operation enables you to present the sales data in a more concise and structured manner making it easier to identify trends top selling products and performance within each category you’ve been introduced to PowerBI’s unpivot and pivot operations to transform and structure your data as with other data transformation techniques reshaping the data can help your team gain deeper insights and support business success through datadriven strategies decisions and actions now let’s take a moment to work through a practical application of the unpivot and pivot operations to the Adventure Works sales data using Power Query in PowerBI desktop suppose Adventure Works uses two separate Excel files to assess their quarterly sales and product and category distributions the first Excel file contains the sales target data consisting of three columns month 2022 and 2023 within this file there are 12 rows representing each month and each row displays the target sales amount for the corresponding month and year to enhance the table structure for easier readability your manager asks you to perform an unpivot operation to create a table with columns for month year and target which will also increase the number of rows the second Excel file includes category and subcategory data showcasing the category and subcategory data as columns without the product names you are tasked with performing a pivot operation on this file to present the product count per category in a tabular format to address the tasks given to you by your manager you can start by downloading and importing the two Excel files into Power Query with each data source selected select the transform data option to open the Power Query editor where you can apply various transformations including the unpivoting and pivoting operations for the first Excel file containing the sales target data you need to perform an unpivot operation to unpivot the table columns select target query on the left menu highlight the 2022 and 2023 columns select the transform ribbon tab in Power Query and then select unpivot rename the attribute column to year and the value column to target amount you now have an unpivoted table where the columns are converted to rows to accomplish the second task and pivot the table columns in the Excel file with the product categories and subcategories select the product categories query on the left menu on the transform ribbon tab select pivot column then on the pivot column window that displays select the column subcategory from the values column list expand the advanced options and select the option count all from the aggregate value function list lastly select okay with the pivot column feature applied you change the way that the data is organized subcategory names are converted to columns and row count for each subcategory is added as a row value for each column in this video you explored unpivot and pivot operations in PowerBI and the application of both in practice by building your technical expertise and learning about effective data transformation techniques like unpivoting and pivoting you can maximize the potential of PowerBI to unlock valuable insights from business data ultimately contributing to growth and success of organizations like Adventure Works you’re making good progress in your journey to becoming a data analyst you’ve learned how to transform data by using Power Query and have worked on data sets now it’s time to learn how to combine different data sources so you can use it more effectively the capability to combine queries is valuable as it empowers you to combine and merge diverse tables or queries enhancing your data analysis capabilities in the next few minutes you will be introduced to why combining data may be necessary and how you can combine tables or queries adventure Works have recently acquired another bicycle business adventure Works CEO Jamie Lee has assigned a task to the sales department to ensure that sales data from this business is incorporated in the Adventure Works sales reports your manager Adio Quinn has tasked you with creating a PowerBI query that merges the data but before you start working on the data you first need to understand the reasons why it is important to combine data the first reason for combining data is that it allows you to consolidate information from various sources or tables into a single table this consolidation can provide a unified view of the data making it easier to analyze and gain insights the next reason why you would combine tables is to create relationships combining tables is crucial for establishing relationships between related data in PowerBI relationships between tables are used to create meaningful visualizations and enable interactive analysis by combining tables you can link data points across different tables based on common fields or keys combining tables also enables you to enrich your data by adding additional information for example you may have a table with client details and another table with product information by combining these tables you can create a comprehensive data set that includes both client and product details allowing for a more comprehensive analysis another reason to combine data is that it provides a broader scope for analysis by merging multiple tables you gain deeper insights by analyzing data from different angles and lastly combining tables helps simplify data management in PowerBI instead of working with multiple separate tables having a single consolidated table reduces complexity and makes it easier to handle data updates refreshes and maintenance tasks now that you understand the reasons why it is important to combine data let’s look at the ways to do it in PowerBI there are two ways to combine data append and merge when you append queries you are adding rows of one table or query to another table or query by adding multiple lists one below the other you will see an increase in the number of rows say for instance you have two separate classes class A and class B that need to take an exam together to do this you have to combine the 20 students in class A with the 20 students in class B resulting in a combined class list of 40 students on the other hand when merging queries you consolidate data from multiple tables into a single entity by leveraging a shared column between the tables for example data with specific content such as gender category and city is stored in different independent tables and referenced by main tables that require this information this allows you to use this information within a specific context enables easy data classification and ensures data integrity you will learn more about both of these operations over the coming lessons in this video you learned about data combination techniques and the reasons for using it combining data in PowerBI is essential for creating accurate comprehensive and interactive reports and visualizations it allows you to leverage the full potential of your data by consolidating relevant information from multiple sources establishing relationships and enabling more insightful analysis good job adventure Works has recently acquired an additional bicycle business your manager Adio Quinn tasked you with creating a PowerBI query that merges the current sales data of Adventure Works with the sales data from the newly acquired business and he needs the query by the end of the day but you do not panic you know that PowerBI can help you combine different tables and queries to consolidate information create relationships enrich data enhance analysis and simplify data management in the next few minutes you will learn why appending tables or queries may be required at the end of this video you will also be able to describe the operation of appending one table to another by now you know that there are two ways to combine data in PowerBI append and merge when merging queries you consolidate data from multiple tables into a single entity by leveraging a shared column between the tables you will learn more about merging in the coming lessons when you append queries or tables you add rows from one or more tables to another query or table in this video you will focus on append before I demonstrate how the append operation is done let me share a very important tip with you say your manager has asked you to list the Adventure Works products that have fewer than 100 units sold for the current year the products that have not been sold do not appear in the sales table so you have to identify them by subtracting the sold products from all the products as a result you have two data sets to be merged products with 100 or fewer sales and products that have never been sold if you only list the products with sales data of less than 100 you won’t include the products that haven’t been sold at all to overcome this problem you have to merge the products with total sales below 100 and the ones that haven’t been sold at all to present the complete picture back to the task audio set you before you append the adventure works sales.xlsx and the other sales.xlsx XLSX files you have to format the data of both files to ensure they have an equal number of columns and that the columns have the same names and data types if you don’t have an equal number of columns or different column names the extra columns will be added to the most right of the query by preserving their values in the originating query and setting null values for the matching new query in this example columns A and B are common columns in both data sets columns C and D are unique and added to the right of the merged list since the D column does not have any data in the first data set the row values will be null after the merge similarly in the second data set null values will be added for the previously non-existent C column this may be confusing so try to have an equal number of columns with the same column titles let’s explore how this is done to format tables select other sales query in the query pane at the left menu of the power query window rename the quantity column to order QTY name to product name and total to line total by selecting the column names once you have completed the reformatting process you can merge the queries on the Power Query Editor ribbon navigate to the home ribbon tab and select the append queries drop-down menu you can select append queries as new to create a new query or table from the appended output or select append queries to merge the rows from an existing table into another if you select append queries as new you will create a new master table this selection displays the append window where you can select the tables you want to combine from the available tables section and add them to the tables to append section when you select okay a master table is created that contains the sales data of both Adventure Works and the newly acquired company in this video you learned how to combine data by appending tables and queries by appending different sales data you can create a master sales table this will help you to consolidate and enrich data from multiple tables and queries and simplify data management combining or joining data from different sources is like putting puzzle pieces together to form a big picture the big picture can help you discover details you could have missed when examining the individual pieces in this video you will discover what a join is and explore the purpose of joining data and its importance in data analysis before we explore the power of joining data to unlock new perspectives you need to understand what a join is when you have data in two tables and the columns of those tables are exactly the same appending the data from one table to another is straightforward however to combine the data of two tables with different column structures you need to specify the method in which the two tables should be combined this is known as a join join is when you merge or combine data from different places to create a bigger and a more complete data set it helps you view all the information in one place like putting puzzle pieces together to understand the whole picture let’s look at an example your manager Adio Quinn has tasked you to list all products with their category names and indicate which category has the most products during your investigation you notice that category data is referenced to a table called categories it is also being used by the common columns named category key on closer inspection you notice the row with a category key of one has a category name of bikes and the row with a category key of two has a category name of accessories your conclusion is that any row with a value of one in the category key column has bikes as the products category one of the key usage areas of joins is merging the two tables in this manner and matching related data by using the relationship one of the key usage areas of joins is merging two or more tables and matching related data by using the relationship joining data is essential for PowerBI data analysts because it enables you to combine information from different sources giving you a complete picture of the data joining data can help you validate data accuracy make informed decisions and perform advanced analysis joining data also empowers you to gain a holistic understanding uncover valuable insights and make datadriven conclusions overall join is a powerful technique that enhances your data analysis capabilities and allows you to unlock the full potential of your data in a previous video you learned that there are two ways to combine data in PowerBI append and merge in both merge and append operations the use of join is essential for combining tables effectively let’s explore merge with join in more detail when you merge queries you’re combining the data from multiple tables into one based on a column that is common between the tables merge with join allows you to match related data integrate data and explore relationships when you append queries you are adding rows of data to another table or query append with join helps you to ensure consistency and allow you to expand your existing data set whether it’s a merge or append operation the use of join is essential for aligning integrating and combining data from different tables it ensures that the relevant information is properly matched and merged enabling you to analyze and understand the data in a meaningful way in this video you learned what a join is as well as the purpose of joining data and its importance in data analysis by now you are aware that combining data and using join keys can save you hours of searching through vast amounts of data for a specific product item but did you know that you can simplify your query even further by specifying how the data should be combined in this video you will learn about join types specifically the difference between left outer right outer full outer and inner joins a join type in Microsoft PowerBI refers to how tables of data are related to each other in the software the joins are important because they determine how data is consolidated from multiple sources into a single view understanding joint types and their implications is crucial to building accurate efficient and meaningful data models in PowerBI over the next few minutes you’ll be introduced to four different join types left outer right outer full outer and inner join let’s explore each join type and the way it combines data from multiple tables based on matching criteria let’s say we have two tables one on the left for sales and one on the right for countries the sales table has three columns date country ID and units the countries table has two columns ID and country the sales table country ID column can be used as a join key with the ID column of the countries table now let’s explore each join type and how they combine data first let’s start with a left outer join if a left outer join is used all rows in the left table are kept and the matching rows from the right table are merged in if the left table is missing columns that the right table has the columns are included as part of the merge it is important to note that if there is no match for a row between the tables default or null values will be used for columns where matching data is unavailable in this scenario the resulting table will have the columns from the left table date country ID and units along with a country name column since the right table did not have a country ID of four the country name is null a right outer join works similarly to the left outer join except that all rows in the right table are kept and the matching rows from the left table are merged in again if the right table is missing columns that the left table has the columns are included as part of the merge similarly if there is no match for a row between the tables default or null values will be used for columns where no matching data is available in our scenario the resulting table will have date country ID units and country name the full outer join is used when you want to retrieve all records from both tables regardless of whether they have matching values in the join condition in this scenario since the right table has an ID of four and the left table does not have a corresponding entry with a country ID of four a row is created with a country name for ID 4 and with null values in all other columns in the previous video what is a join you used full outer joins and appended with joins by matching related data for inner join only matching rows from both left and right tables are merged together this join type is helpful when you want to focus only on the sales that have corresponding data in another table and exclude any sales data that don’t match as a data analyst you often come across the requirement to combine data from different tables or data sets related to sales and product tables this is where merging operations specifically join types become crucial keep in mind that you should choose the combination types based on how you choose them taking into account the specific needs of the analysis the choice of join type will impact the inclusiveness of the data in your analysis it’s important to consider your analysis objectives and the specific requirements of your project each join type serves a different purpose and selecting the appropriate one ensures that you obtain the desired result set for your analysis of order and order details data as you start working with more and more data sources keeping all the different data in different tables will become quickly unmanageable identifying similar and related data that can be merged is an important skill for a data analyst over the next few minutes you will learn how to identify and merge tables using joins in PowerBI in relational data fields such as category or status are often kept in a separate table for instance when a new product is added the category information is associated with an entry in a different table instead of being manually repeated in multiple rows in the product table as you have previously learned data from two different tables can be linked by join keys this works for tables from individual and multiple data sources however sometimes you’ll be working with a single data source such as a database where these relationships are already established in these scenarios merging the data using a join is a straightforward operation a column in one table will act as a key to the column of another table in databases this is known as a foreign key relationship and the foreign key is used as the join key this is almost impossible for databases that have a large number of products for example an e-commerce business selling books or adventure works who sell a large number of product variants selecting from defined categories or any other parametric data ensures easy classification of data and enables us to work within a consistent and comprehensive data set consider a scenario where you are working in the sales department of Adventure Works a multinational bicycle store and you have been given a task by your manager Adio Quinn to consolidate orders and their corresponding details currently in two tables into a single table there is a typical foreign key relationship between the order and order details tables which is order ID adventure Works provides the following details to deal with situations such as this the orders table is created to store information such as the name of the store the date of the purchase the cashier’s name and so forth since there can be multiple individual products associated with a single order Adventure Works database has created a separate but related table to store these variable numbers of associated product purchases it allows you to add new products to your current purchase by opening as many rows as needed in this way you’ll develop a structure that is dynamic and flexible saving space and time by only storing the necessary information to truly understand the join operation or in PowerBI terms the combine with merge operation it is important to first understand the relationship between tables the merging operation arises from the need to separate tables avoid forcibly distributing data that can be stored in a single table into separate tables visualize relationships such as product category transaction status person city where the definition table and its rows need to be separated in the order example the order details can connect unique data with repeating data in a more efficient manner now you can complete your task to combine the two tables orders and orders details with merge go to home on the power query editor ribbon and select combine then merge queries drop-down menu and select merge queries as new this selection opens a new window where you can select the tables that you want to merge from the drop- down list next select the column that matches between the tables which in this case is order ID select left outer join in the join kind drop-down which displays all rows from the first table and only the matching rows from the second after you select okay you are directed to a new window where you can view your new merged query now let’s take a look at doing this in more detail in Microsoft PowerBI in this scenario you are working in the sales department of Adventure Works which is a multinational bicycle manufacturer and you have been given a task by your manager Adio Quinn to consolidate orders and their corresponding details which are currently in two tables into a single table in PowerBI you select the Excel workbook option in the data group of the home tab select order.xlsx and order details.xls XLSX there is a typical foreign key relationship between the orders and order details tables let’s try to understand this with an example from our own social life we have all probably shopped at a market at least a few times at the end of the shopping we go to the cashier scan our items make the payment and receive a receipt the receipt contains information such as the name of the store the date of the purchase the cashier’s name and various other details at the bottom of the receipt there is a section that lists the quantity unit price and total amount for each item purchased followed by a grand total or the amount paid now let’s explore how we can structure these commonly encountered pieces of information into a table format adventure Works provides the following details to deal with these situations the order table is created to store information such as the name of the store the date of the purchase and other details found on the receipt in our earlier market scenario since there can be multiple individual products associated with a single order Adventure Works database have created a separate but related table to store these variable numbers of associated product purchases it allows you to add new products to your current purchase by opening as many rows as needed in this way you develop a structure that is dynamic and flexible saving space and time by only storing the necessary information to truly understand the join operation or in PowerBI terms the combine with merge operation it is important to first understand the relationship between tables if there is a need to separate tables the merging operation arises from that need avoid forcibly distributing data that can be stored in a single table into separate tables visualize relationships such as product category transaction status person city where the definition table and its rows needed to be separated now in the example of order order details that we have learned you have connected unique data with repeating data in a more efficient manner now you complete your task to combine the two tables order order details with merge go to home on the power query editor ribbon and select combine then the merge queries drop-down menu where you can select merge queries as new this selection will open a new window where you can choose the tables that you want to merge from the drop- down list and then select the column that is matching between the tables which in this case is order ID you will choose to use a left outer join in the join kind dropdown which displays all rows from the first table and only the matching rows from the second after you click okay you will be routed to a new window where you can view your new merged query and that concludes how to combine tables with merge in PowerBI in this video you learned how to combine data by merging tables and queries it can help you to consolidate information from multiple tables and queries by using related fields with foreign keys good job adventure Works is looking to expand its business by identifying new product lines that it can market to its customers it hopes that the results of data analysis will identify potential new product lines meet Daniel he’s a talented data analyst with Adventure Works they’re in-house expert on configuring and transforming data in PowerBI including merging data in Power Query adventure Works has noticed that a lot of customers have been returning bicycles to their stores for repair and maintenance these are often very simple repair and maintenance tasks like replacing tires or tightening loose bolts and screws the company suggests that Daniel analyzes the customer and sales data related to these transactions perhaps these customers might be willing to purchase a service plan for their bicycles first Daniel identifies the relevant data sources he begins with an Excel sheet named sales data this worksheet contains data on each bicycle Adventure Works has recently sold including the categories they belong to a description of each bike the prices they sold for and the staff who sold them the worksheet also includes data on the repairs carried out on each bike like the names of the parts that were replaced there are other relevant data sets available on a sheet named customer data this worksheet provides information on all customers including their names contact details age the bikes they have purchased and the repairs they have requested daniel uploads these data sources to PowerBI where he configures them for data analysis by transforming the data sets in Power Query once the data has been configured and transformed Daniel then uses joins to merge these worksheets together to identify what kind of bicycles customers are buying which customers are sending their bicycles to the store for repair and what kind of repairs are required he uses the results of his analysis to segment customers into profiles that focus on data such as age groups location and purchases he then identifies related search engine queries for individuals who match these profiles through combining and analyzing this data Daniel discovers that many of the customers seeking repairs are adults between the ages of 18 and 35 who live in rural areas this demographic mostly purchases mountain bikes which they use for weekend biking excursions he presents his data insights to Adventure Works the company realizes that he can offer these customers a service plan or bicycle health check in addition existing store staff can carry out these repairs so no new staff are needed to deliver this product it also helps the business to retain and generate a new revenue stream from existing customers this scenario emphasizes the importance of combining or merging data sources in Microsoft PowerBI by combining data sets you can deliver new insights on topics in the case of Adventure Works Daniel was able to create a customer profile and identify the needs of that profile adventure Works then provided a new product to this customer profile when it comes to generating data insights the benefits of merging data sources can’t be overstated the more data you have on your topic the greater an understanding you can develop and all of this can be achieved with Microsoft PowerBI and a strong data analytics skill set congratulations on reaching the end of the third week in this course on extracting transforming and loading data in PowerBI you’ve now reached the end of this module let’s take a few minutes to recap what you’ve learned you began this module by exploring the process of transforming data in PowerBI you first examined why data needs to be transformed you learned that raw data is not always gathered or sourced in a condition that’s suitable to work with it might be incomplete inconsistent or have other errors so it’s important that you transform and clean your data you can clean data by setting up filters in PowerBI that identify and resolve errors this way the filter data is accurate consistent structured and easier to analyze you then reviewed Power Query and its interface you learned how to navigate this interface and locate useful tools and features for connecting cleaning and transforming data from a wide range of sources and you explored the steps for these actions by helping Adventure Works connect to its data sources and then clean and transform the data they contained an important part of this cleaning process includes the applied steps list an editable list of all transformations applied to a selected query you can use this list to undo and reorder steps in the process next you explored the different data types in PowerBI the data types you explored included number types data and time type text or true or false and binary you learned that these different data types are used to classify values to help you better organize and structure your data sets you also learned that when working with data sets you might need to remove and rename columns you were presented with many of the benefits of reworking columns like more efficient readable and enhanced data and analysis or significant time and resource savings you continue to explore Power Query by reviewing steps for dealing with common errors power Query can fix errors like null values duplicate rows and inconsistent data types it’s important to resolve these errors before analyzing your data in Power Query you then made use of your new knowledge by helping Adventure Works to prepare a data set by cleaning the data and resolving its errors you then undertook a knowledge check in this item you proved your understanding of the concepts you encountered by answering a series of questions finally you explored a list of additional resources designed to help you improve your knowledge of the topics that you covered this week in the second week of this module you explored advanced data transformation methods in PowerBI you began this week by learning about the importance of data combination combine information create relationships between tables improve data and analysis and simplify data management you then reviewed the two main methods for combining data in PowerBI which are append and merge append means to add one table row or query to another merge means consolidating data from multiple data sources into a single table and you examined the process for combining tables with append and power query editor you then put your new skills to use by assisting Adventure Works with appending tables in their database next you completed a knowledge check which tested your understanding of these concepts through a series of questions and you were presented with a list of additional resources that you could review to learn more about advanced data transformation in week three you learned about methods for combining data that you could use for data transformation you discovered that one method of combining data is to use a join a join is a useful way of combining data from different sources you also learned that join keys are the values used to link rows between tables you also learned that there are different types of joins these different types include the left outer join right outer join full outer join and inner join which of these join types you choose to use depends on your data transformation needs you then looked at how to combine tables using a merge operation in Power Query Editor by identifying the relevant keys and require join operations you can merge two or more tables to deliver new insights into your data next you demonstrated your competence with these new skills by helping Adventure Works to merge two of their data sources to deliver new insights into their business finally you undertook a knowledge check which tested your understanding of the concepts that you encountered this week and you completed a module quiz in which you demonstrated your understanding of all concepts you encountered throughout the entire module you’ve learned a lot about transforming data in PowerBI and as you approach the next module consider going through some of the learning material again to reinforce your understanding looking ahead you will expand your knowledge of the ETL process by diving into advanced ETL in PowerBI where you will learn all about loading and profiling data and advanced queries best of luck you have gained detailed knowledge about the extract and transform steps in the ETL process so far and you have applied this knowledge by considering scenarios and tasks in this video you will learn about the final step of the ETL process load the load operation in summary enables the transformed data obtained by reading from a data source to become available for reporting purposes considering that the ultimate goal of PowerBI is to provide data visualization through reports and dashboards the importance of making the data available for this purpose becomes evident up until the load stage you have completed tasks such as accessing data sources establishing connections extracting data and performing transform operations the purpose of all these operations was to bring meaningful and cohesive data into the reporting interface filtered based on specific criteria the load process ensures the visualization of all the extracted and transformed data there are two main ways to load data in the PowerBI user interface load and transform data let’s look at each option a bit closer starting with load with the load option data is loaded directly into the data pane in PowerBI if you choose to load data directly you can still transform the data at a later stage the second option transform data allows you to transform the data before loading it the changes to the data are applied to the data model and the data pane is refreshed in PowerBI visualizations can now use the applied changes whether you choose to load the data directly with the load option or transform the data before loading with the transform data option loading time can vary depending on the size of your data set optimizing performance and reflecting updated data from the source in reporting are of great importance in the data loading process in the upcoming sections you will gain detailed information about these topics in some cases you might have some source tables which are used during the ETL process that will not be used directly in the reporting area and some of these tables may not meet the production demands of your data warehouse in such cases you will need an intermediate state between the data source and the data warehouse called the data staging area a staging area serves as an intermediate storage location for raw or unprocessed data allowing it to be temporarily stored and prepared for further processing in a data pipeline the existence of a data staging area is not obligatory for your ETL jobs so you can execute ETL jobs without creating staging areas however it is recommended to simplify the process of data cleansing and consolidating data coming from multiple sources by now you know that the data loading process is the final step of the ETL operation and that it is the most crucial step for making the data available in the reporting environment to achieve this the data is loaded into Power Query either directly from the data source or after performing transformation operations additionally a staging area is often used as an intermediate step to store the data in a more organized manner aiming to facilitate maintenance and management tasks by completing the load stage you are now ready to explore the data create compelling visualizations and gain valuable insights to support decision-making for your organization data staging is one of the key concepts in data loading over the next few minutes you will learn the basics of data staging the reasons for its necessity and the advantages of using it in the overall ETL processes to better understand the concept of staging let’s use an everyday life example imagine you’ve invited friends over for dinner and you’ve bought ingredients from the grocery store to prepare the meal however you don’t serve the ingredients as they are you might marinate the meat in a pot cut the vegetables and place them in a bowl for washing and prepare other dishes like making a salad or putting appetizers on a plate in this example all the ingredients represent raw data while the processes of marinating washing cutting and waiting correspond to ETL operations the pots bowls and other utensils used before serving can be thought of as the staging area now let’s apply this everyday life example to data staging a staging area serves as an intermediate storage location for raw or unprocessed data allowing it to be temporarily stored and prepared for further processing the staging area typically acts as a bridge between the data sources and the data warehouse a staging area simplifies the process of data cleansing and consolidation of operational data originating from multiple source systems particularly for enterprise data warehouses that centralize an organization’s critical data remember a data staging area is not required for your ETL jobs you can still execute ETL jobs without creating one however based on your need to consolidate data coming from multiple sources it is recommended over at Adventure Works the company receives feedback about its products from various channels such as social media platforms and corporate websites your manager Adio Quinn has tasked you to prepare a data set by using these resources to consolidate and to prepare the data for use in reports and dashboards none of the feedback can be used in its raw form as they have different formats you must transform the data and then consolidate it in a unified list since you will only use this data in the ETL process it is appropriate to use a staging area let’s take a few moments to complete this task using Power Query the first step is to import the two data sets Adventure Works social media feedbacks one and Adventure Works Social Media Feedbacks 2 to transform and consolidate in the staging area to do this navigate to the home ribbon tab at the top of the PowerBI window select the Excel workbook button inside the data group in the middle of the toolbar select your data sets and select open then select your data sets and select transform data in the window that opened now you have two queries Adventure Works social media feedbacks one and Adventure Works social media feedbacks 2 in the queries pane at the left menu of Power Query to successfully complete your task you have to consolidate these two queries into a single query and add an extra column to indicate where the feedback came from to do this you have to use these queries and integrate the data into a more defined and optimized model to do this you need a staging area as you have to consolidate these two tables into one but also keep them separately you have to create a new group called the staging area in the queries pane at the left menu of power query select new group type staging area in the name text box and select okay now move both the data sets adventure work social media feedbacks one and adventure work social media feedbacks 2 to the staging queries group your tables are now organized according to your need select the Adventure Works Social Media Feedbacks one and Adventure Works Social Media Feedbacks 2 tables respectively and disable the load by clearing the checkbox enable load you will keep the include and report refresh option this way both tables will still be used in queries but will not be part of the data model you are now familiar with a concept of a staging area and how it is implemented in PowerBI imagine you have just started working at Adventure Works as a data analyst you have a lot of data to analyze to determine which products are preferred by which client and why to perform successful analysis on these many items it is necessary to have data that includes fields suitable for analysis with an adequate amount of data and a variety of data ranges representing the overall data over the next few minutes you will be introduced to data profiling and statistical analysis and why it is important when reviewing data sets by the end of this video you will have been introduced to a high-level understanding of data profiling and statistical analysis when reviewing data sets you will also learn about the distribution anomalies and outliers in the context of data profiling let’s first cover an introduction to data profiling before analyzing any data set it is important to examine and evaluate the data you are working with analyzing the data without evaluating its accuracy completeness and alignment with your objectives can lead to misleading results when examining a data set for the first time there are several aspects you should look at especially for numerical fields you should check these characteristics for each numerical field minimum or min maximum or max average or mean frequently occurring values or mode and standard deviation the best way to start assessing data is with data you can immediately troubleshoot imagine you are reviewing a data set that has an age field for instance there could be someone in the data set with an age of 200 which would be extremely unlikely to be true if so there may be an outlier in the data look at the minimum and maximum values such as appearing between 21 and 77 these are realistic ages unlike 200 the concept of distribution of data refers to how the data points are spread or arranged within a data set it describes the pattern or shape of the data when plotted on a graph understanding the distribution of data is crucial in data analysis because it helps you gain insights into the central tendency variability and overall characteristics of the data next let’s consider outliers the formal definition of an outlier in statistics is a data point that significantly deviates from other observations outlier data can be handled by applying a technique called min max scaling or normalization the aim is to adjust the mean and standard deviation of the data proportionally while preserving the ratio of the distance between outlier data and other data points analyzing the distribution allows you to make informed decisions identify outliers and choose appropriate statistical techniques for further analysis there are situations where there may be values in the data set that skew the average for example there may be examples close in age let’s say there are three individuals aged 80 and above if you solely rely on the average to evaluate the distribution these outliers can mislead you by increasing the average in this case it would be appropriate to examine the distribution more closely when taking a closer look at the data you may find that the distribution is normal but the three records mentioned in the example are outliers next let’s look at standard deviation standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a data set it provides a way to understand how individual data points differ from the mean or average of the data set the main objective here is to prevent outliers from causing deviations in your analysis results minimizing their impact finally let’s return to the point of distribution of data the balanced distribution of data points that fall outside the outliers is another factor that affects data quality and your analysis results it is important for descriptive variables such as age gender income status occupation city and neighborhood to represent as many diverse groups as possible and be evenly distributed among others if not a cluster of records that closely resemble each other will lead to narrow intervals when defining norms which will mislead your analysis profiling and statistically analyzing data including examining its distribution min max mean and mode values detecting outliers if any and normalizing outliers ensuring that the data represents the entirety of the data set are the key elements that demonstrate data quality considering these factors will enhance the accuracy and quality of analysis and predictions made with this data by now you should have a good understanding of the concepts of profiling data and possible situations where you will need to apply the profiling techniques in this video you will learn about data profiling and statistical analysis and how to use them in PowerBI as well as this you will cover how to use profiling tools to inspect the data adventure Works recently conducted a field survey to increase sales and collected potential customer data this resulted in an Excel file containing information such as age gender occupation income level address and phone number of prospective customers since the survey data was collected manually it was not subjected to any validation therefore before analyzing the data it is necessary to confirm that the data is valid within the desired ranges and quantities and exhibits a good distribution before starting analysis on any data set it is important to examine the data by examining various aspects such as completeness accuracy uniqueness and consistency data profiling enables the identification of potential issues and anomalies within the data set this proactive approach allows you to make informed decisions about data cleaning transformation and enrichment ultimately leading to improved data quality additionally data profiling facilitates effective data exploration and visualization by providing insights into data patterns relationships and trends it empowers users to discover hidden insights uncover data inconsistencies and make datadriven decisions with confidence before delving into data profiling tools let’s first consider two important factors in data profiling unique and distinct in PowerBI unique is known as total number of values that only appear once distinct is known as total number of different values regardless of how many of each you have microsoft PowerBI offers the following two profiling tools in the Power Query editor column quality and column distribution let’s begin with column quality column quality focuses on valid error and empty rows on each column allowing you to validate your row values the column quality feature labels values in rows in five categories valid shown in green error shown in red empty shown in dark gray unknown shown in dashed green indicates when there are errors in a column the quality of the remaining data is unknown unexpected error shown in dashed red these indicators are displayed directly underneath the name of the column as part of a small bar chart the number of records in each column quality category is also displayed as a percentage by hovering over any of the columns you are presented with a numerical distribution of the quality of values throughout the column additionally selecting the ellipses button opens some quick action buttons for operations on the values column distribution provides a set of visuals underneath the names of the columns that showcase the frequency and distribution of the values in each of the columns the data in these visualizations is sorted in descending order from the value with the highest frequency by hovering over the distribution data in any of the columns you get information about the overall data in the column with distinct count and unique values you can also select the ellipses button and choose from a menu of available operations let’s consider column distribution specifically relating to distribution of distinct and unique amounts imagine that you have a selection of bike accessories that are supplied by four different suppliers supplier A supplier B supplier C and supplier D in this case there are four distinct suppliers now imagine you have two bikes each with a unique supplier to any other bikes you currently stock these would be considered two unique suppliers another type of profiling in PowerBI is column profile column profile provides column statistics such as minimum maximum average frequently occurring values and standard deviation and in addition value distribution on the selected column this is very important when assessing data to detect anomalies and outliers now that you’ve covered the basics of data profiling tools let’s apply this in PowerBI and inspect some data adventure Works conducted a field survey to increase sales and collected potential customer data this survey resulted in an Excel file containing information such as age gender occupation income level address and phone number of prospective customers the survey data was collected manually it was not subjected to any validation therefore before analyzing the data it is necessary to confirm that the data is valid within the desired ranges and quantities and exhibits a good distribution navigate to home at the top of the PowerBI window select Excel workbook inside the data group in the middle of the tab select potential customers.xlsx and select transform data in the opened window check the column quality check box inside the data preview group of view to assess column quality in the age column 89% of the values are valid 0% of the values are air and 11% of the values are empty rows to assess column distribution for the occupation column on the view tab from inside the data preview group check column distribution note that there are nine distinct values and two unique values computer programmer and accountant are the occupations which appear only once for each column note that if all the row values are distinct then unique and distinct amounts will be equal for example you can see that there are 19 distinct and 19 unique values for the surname column select the age column and then check the column profile checkbox note that maximum value for age column is 132 which is not acceptable examine the minimum maximum average and other column statistics and review the value distribution chart in this video you learned how to profile data by assessing column quality distribution and profile data profiling in PowerBI offers several advantages in the process of data analysis it helps you gain a comprehensive understanding of the data quality structure and distribution with its ability to assess data quality and provide valuable insights data profiling in PowerBI plays a crucial role in enhancing data reliability accuracy and overall analytical outcomes in the world of technology even the most meticulously designed software can harbor hidden bugs waiting to unleash chaos upon unsuspecting users imagine a scenario where a simple bug managed to infiltrate a company’s database threatening to compromise the accuracy of critical reports and potentially sending shock waves through senior management however thanks to the miraculous powers of data profiling with the aid of PowerBI disaster was averted and the company emerged victorious buckle up as we take you on a thrilling journey through the realm of software mishaps triumphs and the heroes who saved the day it all began innocently enough deep within the complex coding of a company’s flagship software a tiny bug had nestled its way into the system this bug had an uncanny ability to transform innocent data into deceptive monsters causing them to wreak havoc when unleashed into the wild the bug was sly and patient biting its time until the perfect moment to strike as the software went about its daily operations the bug began silently distorting the data it touched unbeknownst to the users inaccuracies were creeping into the system lurking beneath the surface reports that were once reliable now became unreliable leading to questionable decisions and raised eyebrows among senior management fortunately the company had an ace up its sleeve a team of brilliant data profilers armed with the mighty PowerBI with its robust data profiling capabilities PowerBI became the ultimate weapon against the deceptive bug and its corrupted data the team rallied together ready to utilize PowerBI’s analytical prowess and visualizations to uncover the truth hidden within the tainted database armed with PowerBI the heroic team embarked on a quest to hunt down and eradicate the corrupted data they connected PowerBI to the company’s database leveraging its intuitive interface and advanced algorithms to identify the anomalies lurking within the system powerbi’s data profiling features allowed the team to analyze and scrutinize every nook and cranny of the company’s data unearthing the bugs footprints one by one after days of tireless work the data profilers empowered by PowerBI emerged triumphant they successfully identified and isolated the distorted data ensuring its exclusion from future reports powerbi’s rich visualizations and interactive dashboards enabled the team to present their findings to senior management in a clear and concise manner further solidifying their victory as the dust settled the company took a moment to reflect on the incident they recognized the transformative power of PowerBI’s data profiling capabilities and the critical role it played in safeguarding their data integrity the bug had served as a wake-up call reminding them of the importance of incorporating robust data profiling tools like PowerBI into their systems helping them catch potential issues before they cascade into crisis in this thrilling tale of software mishaps and heroic data profilers we’ve witnessed how a simple bug had the potential to plunge a company into chaos however thanks to the power of data profiling with the aid of PowerBI accuracy was restored the diligent efforts of the data profiling team did not go unnoticed as senior management praised them for their exceptional work and dedication in resolving the crisis the successful outcome served as a reminder of the invaluable role data profiling plays in maintaining the integrity of systems it showcased the power of collaboration expertise and the remarkable capabilities of tools like PowerBI in conquering challenges and emerging triumphant as a data analyst at Adventure Works your team is responsible for analyzing vast amounts of data to gain insights into customer behavior and improve business operations microsoft Power Query is an essential tool in data analysis workflow enabling you to transform and integrate data from various sources you heavily rely on Microsoft PowerBI for your daily tasks preparing reports for business units by connecting to data sources and performing extract transform and load operations since adventure works strive for optimal efficiency and results your manager Adio Quinn has assigned you the task to research best practices for specific configurations performance preferences security and other related topics to ensure the most optimal use of PowerBI in your work over the next few minutes you’ll be introduced to best practices when working with data sources in PowerBI and also understand why these practices are important to implement let’s start by exploring how you and your team can apply best practices to enhance your Power Query workflows and improve data quality and analysis your first step is to plan and document your data transformation requirements you define the desired output identify the relevant data sources and outline the transformations needed you also ensure that data source credentials are properly documented and securely stored by maintaining an organized and consistent approach your team can streamline your Power Query process and avoid confusion next you carefully select the appropriate connector to connect to your data sources you consider factors such as the type and location of the data source the volume of data and the available connectivity options with PowerBI’s wide range of connectors you can seamlessly connect to databases cloud services files and APIs it is important that you evaluate the performance capabilities and scalability of the connectors to ensure optimal performance for your data requirements considering the performance and optimization of your data transformations and calculations your team follows the principle of do expensive operations last you prioritize and schedule resource intensive operations towards the end of the data transformation process this approach ensures that complex calculations merging large data sets and applying multiple transformations on a significant number of rows are executed efficiently leading to faster data loading and more responsive reports your team also pays attention to data type selection for columns aiming to improve performance and data accuracy you review and adjust the inferred data types manually preventing incorrect data interpretations and reducing memory consumption data profiling plays a crucial role in your team’s data analysis process you leverage PowerBI’s data profiling capabilities to gain a comprehensive understanding of data quality structure and distribution by examining aspects such as completeness accuracy uniqueness and consistency you identify potential issues and anomalies within the data set this proactive approach enables you to make informed decisions about data cleaning transformation and enrichment ultimately improving data quality to ensure smooth data processing your team implements error handling techniques such as conditional logic and custom error messages you also incorporate data validation checks to identify and handle unexpected data inconsistencies effectively the next best practice is to consider your merge strategy when merging or joining multiple queries you consider the most efficient merge strategy selecting inner joys whenever applicable you remove redundant fields to avoid unnecessary duplicate columns in the resulting merge query to maintain an organized work environment your team utilizes groups as containers for your queries you create nested groups when needed and easily move queries between groups by dragging and dropping them regularly reviewing and removing unnecessary steps in the Power Query editor is another practice you follow removing unused or redundant transformations helps improve processing time and simplifies query maintenance monitoring the performance of your Power Query workflows is an ongoing task for your team you evaluate the refresh speed resource consumption and overall efficiency by fine-tuning query settings such as parallel loading or data load options you optimize performance based on your specific requirements following these best practices when working with Power Query will enable you to effectively shape and transform your data while maintaining data integrity improving performance and streamlining your workflows remember consistent documentation efficient data filtering error handling and optimization techniques are key to achieving reliable and efficient data transformations with Power Query embrace these practices adapt them to your specific requirements and continue exploring new features and capabilities to become a Power Query expert in the world of Microsoft PowerBI data is the foundation of meaningful insights and informed decisionmaking however managing and preparing data for analysis can be a complex and timeconsuming process this is where data flows can help in this video you will explore what data flows are and why they are used in PowerBI you’ll learn the subscription level required to use them and engage with a fictional scenario showcasing their application and the advantages and limitations they offer adventure Works is a company operating in multiple regions each with its own set of data sources and reporting requirements to manage these multiple data sources Adventure Works wants to use the PowerBI data flows feature data flows allow you to connect to data sources perform data transformations and create business logic to build data entities that can be shared across different reports and dashboards they can also be published to the PowerBI service and in shared reports and dashboards data flows simplify the process of data preparation allowing users to cleanse transform and shape their data with ease you can apply business rules clean untidy data and create calculated columns through Microsoft Power Query a powerful data transformation tool within PowerBI data flows offer a visual interface for building data transformation logic making it accessible to users lacking coding skills you can use data flows in Microsoft PowerBI Desktop and Microsoft PowerBI service in PowerBI desktop you can create and manage data flows using the Power Query Editor this allows you to connect to various data sources perform transformations and define the structure of your data entities you can then publish these data flows to the PowerBI service for further use once published to the PowerBI service data flows can be accessed and managed through the PowerBI web interface you can schedule data flow refreshes configure data connectors and establish relationships between data flows and other data sets in your workspace additionally you can use the capabilities of Power Query online a cloud-based version of Power Query to perform data transformations directly in the PowerBI service by supporting data flows in both PowerBI desktop and PowerBI service powerbi enables a seamless experience for users to create share and collaborate on data flows throughout the entire data preparation and analysis process this flexibility allows users to work with data flows using their preferred environment while ensuring consistent and efficient data management across both desktop and cloud-based environments a PowerBI Pro license is required to use data flows in PowerBI however a PowerBI premium subscription is necessary for advanced features and capabilities such as incremental refresh compute engine selection and larger data capacity powerbi premium unlocks additional functionalities and performance optimizations that enhance the data flow experience advantages of data flows include reusability data flows enable the reuse of query logic and transformations saving time and effort in data preparation tasks data centralization data flows provide a centralized and consistent data source ensuring data integrity and reducing duplication collaboration users can collaborate on data flows making sharing and working on data preparation processes easier scalability data flows use cloud-based processing capabilities enabling efficient handling of large data sets and complex transformations limitations of data flows include data refresh data flows have specific refresh limitations such as the frequency and dependencies on data source availability data flow management currently data flows are managed individually and there is limited visibility into dependencies between data flows advanced transformations while data flows offer a wide range of transformations certain complex scenarios may require advanced coding or alternative solutions data flows in PowerBI help users streamline and enhance their self-service data preparation workflows by providing a scalable and collaborative approach to data integration and transformation data flows enable organizations to unlock the true potential of their data while data flows offer numerous advantages such as reusability centralization collaboration and scalability you must be aware of their limitations and consider alternative approaches for advanced transformations by effectively using data flows you can accelerate data preparation ensure data consistency and make informed decisions based on reliable and well-prepared data power Query is a powerful data transformation and manipulation tool within PowerBI that allows users to shape and transform data from various sources but performing repetitive steps on multiple queries can be a tedious task especially when the queries involve similar but separate sets of data one of the key features to solve this issue is through reference queries which provide flexibility reusability and efficiency in your data transformation process in this video you will learn about reference queries in Power Query and its importance in streamlining data workflows you’ll also explore best use cases for reference queries and data flows by establishing a query reference you can establish a connection between an existing query and a new query enabling data flow across sequential models any modifications made to the original query will automatically apply to the referenced query ensuring consistency and up-to-date information instead of modifying transformations individually in multiple queries you can make updates in the master query and those changes will be automatically applied to all reference queries this provides cohesion and makes it easier to maintain and update your data transformations so what are the benefits of query referencing let’s explore some examples first there is reusability by referencing queries you can reuse common data transformations across multiple queries this promotes consistency in your data processing and reduces the risk of errors that can occur when duplicating complex transformations next there is efficiency reference queries eliminate the need to repeat time-consuming data transformation steps instead you can leverage the results of a previously defined query significantly improving the performance of your data workflows lastly you have scalability as your data analysis requirements grow reference queries allows you to build modular and scalable data transformation workflows you can create separate queries for different data sources or transformation steps and combine them as needed providing flexibility and adaptability to changing business needs in Power Query you can reference a query by using the reference option by right-clicking any query in the queries pane reference will create a new query a copy of the original query but containing one single step you can rename the new query as you need and then start to use it in this way you establish a connection between the queries enabling data flow and transformation continuity let’s delve into this further through a scenario you are working as a data analyst at Adventure Works which recently acquired another bicycle business your manager Adio Quinn has assigned you the task of appending the product data from the newly acquired company to Adventure Works’s existing products prior to appending the new products you need to perform several transformation tasks such as changing column types and removing unnecessary columns however your manager has asked you not to modify the existing queries to preserve their original form and use them as a source for other operations to accomplish this you need to create references from the original queries rename the new queries apply necessary transformations and then append the data any changes made to the base queries will impact on the new queries this approach allows you to keep the original queries update the reference queries and ensure that any changes made to the base queries are reflected in the referenced ones query referencing creates many opportunities for advanced data transformation techniques you can apply conditional logic merge referenced queries or perform calculations based on reference data these advanced techniques further enhance the flexibility and power of your data workflows referencing queries in Power Query is a fundamental concept that allows you to streamline and optimize your data transformation process by leveraging query references you can improve reusability efficiency and scalability ultimately enhancing the overall productivity and effectiveness of your data analysis in PowerBI as data volume continues to grow so does the challenge of transforming that data into well-formed actionable information we want data that’s ready for analytics to populate visuals reports and dashboards so we can quickly turn our volumes of data into actionable insights however managing and preparing data for analysis can be a complex and timeconsuming process it’s important to consider the best approach for your data transformations and analysis in this video you will explore how to reference other queries and why a data flow may be more suitable choosing between referencing queries and data flows depends on the specific requirements of your scenario it’s important to evaluate factors such as data volume complexity of transformations user expertise and maintenance requirements to determine the best fit for your use case there are some performance considerations you need to bear in mind with regards to reference queries especially reference queries can contribute to slow data refreshes due to the nature of their referencing when a reference query is refreshed it needs to ensure that all the referenced queries are also refreshed to maintain data consistency this can result in longer refresh times especially if there are multiple layers of referencing involved furthermore reference queries can overburden data sources particularly when working with large data sets as reference queries rely on the data from other queries they need to fetch and process the data from the original sources this becomes more noticeable when dealing with complex transformations or frequent refreshes to mitigate these issues it’s important to optimize the design and usage of reference queries consider limiting the number of reference layers and optimizing the queries transformations to reduce unnecessary data processing additionally carefully manage the refresh schedule to avoid excessive load on data sources during peak usage times by implementing these best practices you can help minimize the impact of reference queries on data refreshes and prevent overburdening your data sources now let’s review data flows data flows offer a centralized and scalable approach for data preparation data flows are designed specifically for data integration and transformation tasks providing a self-service environment for business users to create and manage extract transform and load processes referred to as ETL processes with data flows you can connect to various data sources perform transformations using a visual interface and store the prepared data in the PowerBI service data flows are a feature available in both PowerBI desktop and PowerBI service data flows provide a cloud-based data preparation experience where you can build manage and share reusable data entities in summary understanding the differences and best use cases between reference queries and data flows is essential for optimizing your data processing workflows in Power Query reference queries in Power Query is a fundamental concept that allows you to streamline and optimize your data transformation process by leveraging query references you can improve reusability efficiency and scalability ultimately enhancing the overall productivity and effectiveness of your data analysis in PowerBI remember practice makes perfect experiment with reference queries in Power Query to gain hands-on experience and discover the immense value it brings to your data analysis endeavors at Adventure Works you have a task that needs separate analysis for three main bike product categories you soon realize that to complete the task you’re creating the same query three times the only difference being the change to the bike category it’s inefficient to completely rewrite queries whenever there’s a minor change in the data or a slightly different question from management what if there was a way to create adaptable reusable queries there is the query parameters feature in Microsoft PowerBI allows you to define one query that can be easily adjusted to handle different categories or variables this video will help you understand the concept of query parameters in PowerBI it explains how to effectively implement and manage query parameters let’s learn how query parameters can make your data analysis tasks more efficient and adaptable query parameters in PowerBI is a powerful feature that allows users to input a value which is then used in the data retrieval process from a data source essentially it’s a placeholder for information that can change the query parameter can be used in various operations such as filters transformations or creating new columns and tables let’s explore some possible uses of query parameters at Adventure Works adventure Works can use query parameters when connecting to its database to retrieve specific information rather than importing the entire data set for instance Adventure Works can establish a query parameter for a sales date range by inputting the dates PowerBI will only fetch data for that period saving resources and time parameters can also be used in Adventure Works data transformations if there’s a need to frequently adjust a specific value in the transformations using a parameter avoids manual changes each time the value only needs to be updated in the parameter parameters can control filters on Adventure Works data if the company wants viewers of a report to concentrate on a particular product category they could create a parameter for the product category this allows the viewer to select the category they’re interested in and PowerBI will adjust the report accordingly now let’s explore creating query parameters in Microsoft PowerBI first you’ll need to open the Power Query editor in PowerBI to do this go to the top left corner of the PowerBI desktop interface there is a set of tabs in a ribbon layout one of these tabs is home select this home tab once you are in the home tab select transform data this action will open the Power Query editor in the Power Query editor go to the Home tab select the manage parameters option this opens the manage parameters dialogue box where you can create parameters to create a new parameter select new now you are able to name your parameter and define its properties for instance you might name it product category filter under type from the drop-own menu select text as the data type next specify what values this parameter can take from the suggested values drop-down menu choose list of values in the input field that appears create your list by entering the different product categories from your data set therefore the values here are such items as mountain bikes road bikes and touring bikes once you’ve filled in these details select okay then okay again in the manage parameters dialogue to return to the Power Query editor query parameters can significantly enhance your PowerBI reports making them more flexible and interactive parameters enable efficient data retrieval and transformation by allowing for dynamic changes helping you cater to evolving business needs without having to rewrite entire queries the more adaptable your data analysis tools are the more capable you become in meeting your organization’s everchanging demands this makes your work more efficient and enables you to provide valuable insights that can guide your company’s decision-making processes keep exploring keep learning and embrace the power of query parameters in PowerBI to improve your analysis in previous videos in this course you learned about advanced query capabilities data flows and the differences between reference queries as mentioned before every instance of data transformation performed in Microsoft Power Query adds a step to the Power Query process these steps can be rearranged removed or modified as needed to optimize the data shaping process whenever you use the Power Query interface M language code is executed to perform each operation behind the scenes the M language is available for you to read and modify directly in the Power Query Advanced Editor in this video you’ll learn how to use this advanced editor to update an M query a core capability of Power Query is to filter and combine data from one or more supported data sources any such data mashup is expressed using the Power Query formula language Mquery although you don’t have to know M language to use Power Query being familiar with the language used behind the user interface as well as being able to update it when necessary is valuable for anyone using the tool for example you may need to perform custom transformations that cannot be easily accomplished using the Power Query user interface alone this is where knowledge of the M language and its syntax can be helpful using the M language you can perform advanced data manipulation tasks such as conditional filtering custom column creation data type conversions and merging multiple data sources the language is designed to be expressive and efficient enabling you to handle large data sets with ease when you access the M
language code there are certain group names and meanings that are called M syntax let’s explore the syntax using an M language code snippet this snippet showcases how to handle various CSV file operations in Power Query including setting up the initial data source and performing data transformations loading the file specifying the delimiter and encoding for the CSV document calculating the number of columns and assigning a value to a variable you can find more information on M syntax in the additional resources of this lesson it can also serve as template code for further data transformations using the Power Query M language in PowerBI which you can customize based on your needs the advanced editor provides syntax highlighting autocomp completion and error checking features making it easier to write and debug your AM code it also offers functions and operators that allow you to perform various data transformations calculations and aggregations now let’s explore how you can use the advanced editor tool in Power Query and modify steps by updating M language code using a practical scenario a report designer informs Adio Quinn your manager at Adventure Works about an error being received in the Power Query window he assigns you the task of identifying the cause of this error and resolving it you investigate the issue by examining the steps in Power Query and analyzing the problem using the M language discovering that the error is a result of a change in the source files location let’s outline the steps to resolve this issue using the advanced editor tool let’s start with the source file and adventure work sales spreadsheet in Excel if you navigate to the home tab at the top of the PowerBI window select Excel workbook in the data group followed by the Adventure Works sales file and lastly select transform data in the opened window you’ll successfully access the Power Query editor however suppose the location of the source file is unintentionally changed by another person for example the Excel file is moved to another folder this will cause an error in the Power Query window to explore what happens as a result of this error let’s navigate to refresh preview in the query group on the home tab and select refresh preview from the drop- down menu when you refresh the preview you now get an error message indicating that the source file is no longer reachable as the location has changed you can resolve this issue by using the advanced editor to do this you need to select advanced editor in the query group on the home tab next you’ll need to read the error message and code carefully to determine the necessary action in this case you need to correct the file path in this scenario I’ll change the path from C data C3 M3 L3 Adventure Works Sales.Xlsx to C data adventureworks sales.xlsx your file path will differ from this as it will specify the location of the file on your computer after you’ve completed your correction you can select done in the opened window with this edit you’ve modified the code using advanced editor correcting the file path and resolving the issue by using the advanced editor and familiarizing yourself with the M language you can unlock the full potential of Power Query whether for error checking or creating sophisticated data transformations that meet your specific requirements the advanced editor empowers you to manipulate and shape your data precisely congratulations on reaching the end of the third week in this course on extracting transforming and loading data in PowerBI you’ve now reached the end of this module let’s take a few minutes to recap what you’ve learned you began this module by exploring the final step of the ETL process load you learned that the load operation enables the transformed data obtained by reading from a data source to become available for reporting purposes you then explored the two main ways to load data in the PowerBI user interface load this option directly loads data into the data pane in PowerBI and you can still transform the data at a later stage and transform data the option allows you to transform the data before loading it with changes being applied to the data model next you discovered that in some cases you might have some source tables which are used during the ETL process that will not be used directly in the reporting area in some of these tables may not meet the production demands of your data warehouse in such cases you will need an intermediate state between the data source and the data warehouse called the data staging area a staging area serves as an intermediate storage location for raw or unprocessed data allowing it to be temporarily stored and prepared for further processing in a data pipeline you then made use of your new knowledge by helping Adventure Works transform and consolidate data by using a staging area next you undertook a knowledge check in this item you proved your understanding of the concepts you encountered by answering a series of questions in the second week of this module you were introduced to data profiling in PowerBI you began this week by learning about the importance of data profiling and statistical analysis when reviewing data sets you also learned about distribution anomalies and outliers in the context of data profiling and you learned about standard deviation next you explored the two profiling tools in the Power Query editor column quality and column distribution you then put your new skills to use by assisting Adventure Works with data profiling and statistical analysis using the profiling tools in PowerBI to inspect data next you completed a knowledge check which tested your understanding of these concepts through a series of questions in week three you discovered the best practices when working with data sources and why these practices are important to implement then you had the opportunity to complete a practical exercise importing a data set while considering the best practices you were then introduced to data flows you explored what data flows are and why they are used in PowerBI you learned about the subscription level required to use them and engaged with a fictional scenario showcasing their application and the advantages and limitations they offer next you explored reference queries and their importance in streamlining data flows reference queries in Power Query refer to the practice of using the output of one query as a data source or transformation step in another query you then explored the performance considerations you need to bear in mind when using reference queries next you demonstrated your competence with these new skills by helping Adventure Works to merge two of their data sources using reference queries to deliver new insights into their business next you explored the query parameters feature in Microsoft PowerBI you learned that this feature allows you to define one query that can be easily adjusted to handle different categories or variables and you examined the process for disabling helper queries in PowerBI after that you were introduced to the advanced editor and learned how to modify code you learned that whenever you use the Power Query interface M language code is executed to perform each operation behind the scenes and you learned that although you don’t have to know M language to use Power Query being familiar with the language used behind the user interface as well as being able to update it when necessary is valuable for anyone using the tool you then explored the various global options PowerBI offers that allow you to customize and optimize your experience when working with files you learned that these options provide flexibility and control over file settings ensuring a seamless workflow and enhancing your overall productivity finally you undertook a knowledge check which tested your understanding of the concepts that you encountered this week and you completed a module quiz in which you demonstrated your understanding of all concepts you encountered throughout the entire module you should now be familiar with the advanced ETL processes in PowerBI you should be capable of loading data with PowerBI profiling this data and using advanced queries in PowerBI great work you have almost reached the end of this course in this video you’ll consolidate key concepts you learned throughout you’ll revisit essential learnings related to the data analysis process for businesses and transforming data into valuable insights using PowerBI through your continuous effort you’ve gained a solid foundation in collecting data from and configuring multiple data sources in PowerBI preparing and cleaning data using Microsoft Power Query and inspecting and analyzing data to ensure data integrity you have demonstrated tremendous dedication to this course through your engagement with the videos readings exercises and quizzes what’s left now is to demonstrate the skills you’ve learned in the final course project this recap will serve as valuable preparation for your final course assessment and graded quiz in the final course assessment you’ll apply what you’ve learned by completing tasks that simulate a real world data analysis scenario to consolidate your learning you’ll then take a final graded quiz to assess the knowledge and skills you gained throughout this course let’s get started by revisiting your first week of learning in the first week you learned about data sources local and shared data sets working with Excel data types storage modes triggers and actions you primarily focused on data sources in the process you covered the skills to connect data sources choose the correct query modes either import or direct and setting up triggers and actions to stay updated with the frequently changing data week two began with analyzing the need behind the data transformation and getting familiar with the Power Query interface which will be used throughout the ETL operations you continued your journey with learning about columns data types applied step lists and common data errors and then you prepared a data set you also learned how and why to pivot and unpivot tables which are very popular operations finally you applied combining table operations which are appending merging and joining tables these week two contents are fundamentals for ETL operations week three began with loading data and staging area concepts you applied an end-to-end ETL operation then learned about data profiling which is very important for understanding data quality and distribution this helps you detect a potential anomaly in a data set before you start to analyze it you then explored how to use M language and advanced editor to apply detailed operations in Power Query finally you learned data flows and reference queries which are used to increase efficiency and productivity this course equipped you to use PowerBI and Power Query to construct end to end ETL solutions starting from understanding data sources then advanced transformation techniques and ended by loading data in PowerBI as you embark on the final course project and assessment you can approach it with confidence knowing that you’ve built a strong foundation of knowledge and skills by committing to your learning journey throughout the course however if you feel the need to review any of the concepts summarized for you in this video or require additional preparation remember that you have the flexibility to revisit any of the course items this might only be the start of your journey toward a career as a data analyst but you can be very proud of yourself for how much you’ve already learned and accomplished now you’re ready to tackle the course project and graded assessment quiz good luck you’ve got this well done on completing this course you should be proud of the progress you’ve made in your data analysis learning journey with Microsoft PowerBI throughout the course you explored how to extract transform and load data using PowerBI in depth gaining expertise in building ETL solutions using PowerBI and Power Query you explored collecting data from and configuring multiple data sources in PowerBI preparing and cleaning data using Microsoft Power Query and inspecting and analyzing data to ensure data integrity you learned about data sources and setting them up in PowerBI as well as some of PowerBI’s ETL capabilities including connectors storage modes and setting up triggers plus you discovered more about transforming data using Power Query whether you’re cleaning and preparing data sets in Power Query to deal with errors and inconsistencies or performing advanced transformations to combine data you are now better equipped to transform data using PowerBI and don’t forget that you now have more insight into loading and profiling data in PowerBI as well as performing advanced queries in Power Query you even practice transforming multiple data sources a key real world skill for a data analyst congratulations on the expertise you’ve gained in extracting transforming and loading data in PowerBI this insight marks a valuable milestone in your journey to comprehensively using PowerBI to unlock valuable insights from data completing this course contributes towards gaining the PowerBI analyst professional certificate from Corsera these professional certificates are designed to equip you with the necessary skills to become job ready for in- demand career fields the Microsoft PowerBI Analyst Professional Certificate in particular not only offers you the opportunity to enhance your data analysis skills but also gain a qualification that can lay the groundwork for a career as a PowerBI data analyst plus the professional certificate will help you prepare for the exam PL300 Microsoft PowerBI data analyst by passing the PL300 exam you’ll earn the Microsoft certified PowerBI data analyst certification this globally recognized certification is industry endorsed evidence of your technical skills and knowledge the exam measures your ability to prepare data model data visualize and analyze data and deploy and maintain assets to complete the exam you should be familiar with Power Query and the process of writing expressions using data analysis expressions or DAX you can visit the Microsoft certifications page at http://www.learn.microsoft.com/certifications learn.microsoft.com/certifications to learn more about the PowerBI data analyst certification and exam this course enhanced your knowledge and skills in the ETL process in PowerBI but what comes next well there’s more to learn so it’s recommended you move on to the following course in the program whether you’re new to the field of data analysis or already have some expertise and experience completing the whole program demonstrates your knowledge of and proficiency in analyzing data using PowerBI you’ve done a great job so far and should be proud of your progress the experience you’ve gained will showcase your willingness to learn motivation and capability to potential employers it’s been a joy to take part in your learning journey keep up the excellent efforts and best wishes for all your future endeavors have you ever been confronted with large amounts of information at once it can be an overwhelming experience how do you make sense of everything with PowerBI you can create data models that act as visual representations of your records however this requires familiarity with the process and mastery of many different techniques so we’ve designed this course to equip you with the skills you need data modeling is creating visual representations of your data in PowerBI you can use these representations to identify or create relationships between data elements by exploring these relationships you can generate new insights into your data to improve your business microsoft PowerBI is a fantastic tool for creating data models and generating insights and you don’t need an IT related qualification to begin using it this course is designed for anyone interested in learning about building data models it also establishes a strong foundation for those pursuing a career in data analytics by exploring PowerBI you’ll learn how to create data models using schemas and relationships analyze your models using DAX also known as data analysis expressions and optimize a model for performance in PowerBI in the first week of this course you’ll explore the key concepts related to data modeling you’ll learn to identify different types of data schemas like flat star and snowflake you’ll create and maintain relationships in a data model using cardality and cross- filter direction and you learn to form a model using a star schema the second week of this course focuses on DAX or data analysis expressions this syntax is used to create elements and perform analysis in PowerBI you’ll start by writing calculations in DAX to create elements and analysis in PowerBI you’ll explore the formula and functions used in DAX and use DAX to create and clone calculated tables you’ll then be introduced to the concept of measures you’ll learn where measures are used and what types are available you’ll work with measures to create calculated columns and measures in a mode and you’ll learn about the importance of context and DAX measures finally you’ll perform useful time intelligence calculations in DAX for summarization and comparison and learn how to use these techniques to set up a common date table in the third week of this course you’ll learn how to optimize a model for performance in PowerBI you’ll begin by learning how to identify the need for performance optimization this means analyzing your data models to determine how they can perform more efficiently you’ll then learn how to optimize your PowerBI models for performance you’ll explore different techniques and methods for ensuring that you’re running efficient models and you’ll also learn how to optimize performance using DAX queries in the final week of this course you’ll undertake a project and graded assessment in the project you’ll build and optimize a data model for Adventure Works you’ll have to build this model from scratch and optimize it to run efficiently finally you’ll have a chance to recap what you’ve learned and focus on areas you can improve upon throughout the course you’ll engage with videos designed to help you build a solid understanding of data modeling in PowerBI watch pause rewind and re-watch the videos until you are confident in your skills then consolidate your knowledge by consulting the course readings and measure your understanding of key topics by completing the different knowledge checks and quizzes this will set you on your way towards a career in data analytics and form part of your preparation to take the PL300 Microsoft PowerBI data analyst exam by the end of the course you’ll be equipped with the necessary skills to work effectively with data models in PowerBI good luck as you start this exciting learning journey as a data analyst you’ll often manage thousands hundreds of thousands or even millions of records but how can you generate insights from all this raw data you can convert it into data models in this video you’ll explore the basics of data models and learn how to create them over at Adventure Works the company needs to generate insights and increase sales from different data sources these data sources include customer sales and marketing data but these data sources are all in separate locations and the only way to generate insights is to combine them that’s where the data model comes in adventure Works can integrate its data sources as a data model in Microsoft PowerBI then generate insights in the form of visualizations let’s find out more about data modeling and learn how Adventure Works can make use of it at its core data modeling is creating a structured representation of data this representation can then be used to support different business aims in other words a data model shows how different data elements interact and it also outlines the rules that influence these interactions data models can be built in Microsoft PowerBI microsoft PowerBI is software that provides data analysts with a user-friendly interface for building data models other benefits of a PowerBI data model are that it can be used to define relationships between tables and assign data types you can also create calculated columns and measures and update your model as your business requirements change in PowerBI the foundation of creating reports and dashboards lies within the data model it’s important to understand how to design a data model that effectively aligns with the visual elements within your reports and dashboards there are several steps involved in building a data model in PowerBI connect to your data sources prepare and transform your data and configure table and column properties then create model relationships and finally create measures and calculated columns using DAX or data analysis expressions once your data model is in place you can analyze the data to generate insights to help you achieve your business objectives let’s explore some examples of how data models can be applied to business data by optimizing the data model you can significantly improve the performance of your PowerBI reports and dashboards it’s also easier to aggregate structured data in a data model thanks to the clear relationships and hierarchies with an effective data model you can perform more advanced analytical capabilities like complex measures and predictive analysis when your underlying data is structured organized and aligned your insights and reports are more likely to be accurate and reliable now that you understand more about data models let’s briefly explore how Adventure Works can build one with PowerBI to generate the sales insights they need first Adventure Works needs to connect to its data sources by executing a query in Power Query Editor the result is then loaded into the PowerBI data model as a table using Power Query in PowerBI Adventure Works can finish importing and cleaning their data sources this creates a data model that contains cleaned customer date employee and marketing data as separate tables each table in the model represents a specific business entity and each table also has its own related attributes the next step is to define the relationships between the tables in PowerBI’s model view the company can link its customers and sales tables using the customer ID column which is common to both tables with this relationship the company can now view each customer’s transactions adventure Works could also link its sales and marketing tables to understand which campaigns were most effective for boosting sales finally the company needs to create measures and calculated columns using DAX or data analysis expressions dax is a syntax used in PowerBI to analyze data you’ll learn more about it later in the course for now just know that Adventure Works can use DAX to create aggregations and custom calculations to generate insights on important aspects of their data like sales totals a strong understanding of data models will help you maximize your data’s full potential building sophisticated data models creates a robust foundation for data analysis and generating insights remember that your data model is the foundation of everything else generating business insights often means working through large amounts of data and it’s important that this data is stored and structured meaningfully with PowerBI you can structure your data using a schema in this video you’ll learn about different types of schemas and their advantages and disadvantages adventure Works wants to optimize its inventory and rework its sales strategy to sell more bicycles but first it needs to analyze the relevant data to determine the best way to approach this task these data sources include customer product and sales data along with information on other aspects of the business adventure Works can use a schema in PowerBI to organize and build relationships between these different data sources this way the company can generate its required insights let’s find out more about schemas and how Adventure Works can use one a schema refers to a structure that defines the organization and relationships of tables within a data set it represents the logical framework of how the data is organized and connected there are many benefits to using a schema in PowerBI which you’ll explore over the course of this lesson a schema plays a crucial role in defining the data structure it also enables efficient data analysis helps with the creation of visualizations and assists with generating meaningful insights from your data there are three different types of schema that can be used to organize and structure data a flat schema a star schema and a snowflake schema let’s review each of these schema types and find out how Adventure Works can use them a flat schema is the simplest form of a data model all attributes and fields related to the entity are stored in a single table as you discovered in earlier courses a table is a set of rows containing data with each row divided into columns each column represents a piece of information with a specified data type the required attributes and entities are stored in the rows and can be extracted as required from the columns there are several advantages to a flat schema it’s easy to retrieve data from it’s less complex to analyze flat schema data and it’s a simpler way to visualize data however even though it’s an easy approach to understand the flat schema still has a few disadvantages it requires large data sets which are difficult to maintain and slow to query it leads to data redundancy and inconsistency so is more suited to smaller data sets and it doesn’t allow for complex data sets which require more flexibility and detail next let’s explore the star schema data model a star schema is a more advanced approach to structuring and organizing quantitive or measurable data in PowerBI it allows for multiple tables to be connected through one central table in a star schema a central fact table connects to multiple dimension tables you’ll explore these concepts in a later lesson these connections look like a star shape so it’s called a star schema adventure Works can build a star schema using a central fact table that contains sales transactions the company can then link the fact table to dimension tables that contain records for customers employees dates and marketing campaigns let’s break down the components of the star schema using the example from Adventure Works database first there’s the fact and dimension tables you’ll explore these further in a later lesson and there are the table relationships there are many different types of relationships which you’ll also explore in a later lesson a star schema offers many advantages over a flat schema by storing data in separate tables star schemas help to reduce data redundancy and boost query performance it also provides a clear logical data model which makes it easier to understand the data structure however it’s also less flexible than other schema types adding or modifying tables can require extensive changes to the schema and the star schema can struggle to manage complex relationships next is the third and final model the snowflake schema a snowflake schema is an extension of the star schema it breaks down the dimension tables into multiple related tables existing tables in a star schema can be further denormalized into other tables which creates a hierarchy yet these tables maintain a relationship with the dimension and central facts tables for example Adventure Works can further normalize its product data into supplier and category data tables don’t worry about the terms normalize and denormalize for now you’ll learn more about these concepts later in the course extending a star schema into a snowflake schema offers several advantages it provides more efficient data storage and retrieval it improves data integrity and consistency and it reduces data redundancy it also offers scalability and flexibility by integrating new data tables as required yet there’s also disadvantages to a snowflake schema it’s more difficult to perform data analysis because of the extra relationships these new relationships also make the schema more challenging to understand and manage and they result in slower queries finally it’s important to validate your schemas to make sure they’re accurate when validating a schema you need to check for the following make sure each table column has been assigned the correct data type like text and numeric check that each column has the correct formatting applied confirm that all columns have clear descriptions with relevant context and make sure all table and column properties are correctly configured you should now be familiar with the different types of schemas in PowerBI and their advantages and disadvantages you can build on this knowledge to develop robust data models in PowerBI this way you’ll ensure that your data retains its integrity and simplicity and can be used to generate insights making datadriven decisions involves working with large complex data sets fortunately you can easily manage these data sets with a flat schema in this video you’ll learn how to create a flat schema in PowerBI and configure your table and column properties over at Adventure Works the company has received complaints from customers about incorrect and delayed orders let’s help Adventure Works build a flat schema to organize its data more efficiently the first step is to connect PowerBI to the data sources to connect to a data source in PowerBI desktop select the home tab then select the get data drop-down menu select the appropriate data source from this menu in this instance you need to select the Excel workbook option then navigate to the folder containing the Adventure Works spreadsheet and select open once you select the Excel data source PowerBI displays the available tables in the navigator menu for Adventure Works there is only one table in the Excel spreadsheet available to load adventure Works data select the table from the navigator menu a preview appears on the right hand side the preview shows the Excel sheet has one table which contains sales data for Adventure Works there are also other columns related to the data like product name category subcategory quantity and more you can perform transformations from this menu but in this instance you just need to load the data so select load to add the selected data table to your PowerBI data model next select the data set from the data pane on the right hand side of the PowerBI desktop interface then select data view from the left sidebar to view the data set you can now configure your table and column properties using the power query editor to access the editor select the home tab and then the transform data option for example you can select the properties feature to alter the spreadsheet name or add a description add some spacing to the spreadsheet name then add the following description Adventure Works sales data this makes it easier to identify the spreadsheet it’s particularly useful when working in a team now you can begin applying transformations to shape the data as a flat schema first you need to remove duplicate data from the order ID column select and rightclick on the order ID column in the drop-own menu select the remove duplicates option alternatively you can access the home tab and select the remove rows option in the drop-own menu select the remove duplicates option either action removes all duplicate values from the selected column you can also format the product weight column by changing the data to a decimal type select the column then select the transform tab select the data type option and select decimal number from the list of available options confirm your selection to change the column type when you’ve completed your transformations select the home tab and then select close and apply you’re then returned to the PowerBI desktop interface you can make further changes here using the table tools and column tools tabs for example from the column tools tab you can select the format option and change the product price column data type to currency the next step is to edit the model select model view from the lefthand sidebar to view the schema of the loaded data the model view shows that there is currently one table of data this shows that we are working with a flat schema since there are no other tables there’s no need to build any relationships however you can still make further changes to the table’s properties select the table in model view to open the properties pane you can make more changes here by selecting individual columns from the table you should now be familiar with creating a flat schema in PowerBI from your data sources and you should also know how to configure your table and column properties using PowerBI and Power Query creating a schema in Microsoft PowerBI is an essential skill for entry-level data analysts as you progress in your data analysis career you’ll explore even more complex schema structures to handle more intricate data scenarios as you discovered in an earlier lesson you can use schemas for data organization and two central components of all schemas are fact and dimension tables in this video you’ll explore these tables in more detail and learn how they can be used to build schemas adventure Works is dealing with an increase in delivery errors to help fix this issue the company needs to explore its data and discover the underlying cause it can use fact and dimension tables to find a resolution as you learned earlier a schema is a logical and visual representation of how your fact and dimension tables relate they’re the backbone of schemas in PowerBI fact tables are called fact tables because they consist of the measurements metrics or facts of a business process in other words they hold quantifiable measurable data let’s take the example of an adventure works fact table it sits at the center of a sample adventure works star schema it’s called sales orders and includes transaction details like order ID product ID customer ID quantity and total price these are core facts about transactions like the customer who made the purchase the price of the product they purchased and so on and this fact table is related to dimension tables dimension tables are typically textual fields and provide descriptive attributes related to fact data they offer the context surrounding a business process event in the Adventure Works star schema the dimension tables are linked to the fact table and include date customer sales and product data these are descriptive details that can be used to identify individual customers these two examples should help you understand how fact and dimension tables inform the building of a schema in the star schema model the fact table sits at the center the dimension tables radiate out like the points of a star each dimension table is directly connected to the fact table for example the sales order table is the central fact table in the adventure works star schema the dimension tables like date customer and product are connected directly to it this structure simplifies queries because you only need to navigate through two tables to answer questions like what were the total sales on a particular date and these fact and dimension tables can also be used to extend a star schema into a snowflake schema a snowflake schema makes use of dimension tables by normalizing them normalization means that existing tables within a schema are divided into additional related tables this technique creates a structure that resembles a snowflake this is where we get the name snowflake schema from for instance in addition to a central fact table Adventure Works product dimension table could be split into a product table connected to subcategory and category tables this schema reduces data redundancy but adds complexity to queries you can help Adventure Works use these schema designs to discover the cause of the delivery errors you can import the required data sources represent the data sets as a snowflake schema and perform data analysis your analysis might reveal that the errors are linked to inventory management issues or incorrect addresses on record with these insights Adventure Works can fix its delivery processes and avoid future errors you should now understand the importance of fact and dimension tables when building a database schema with these tables you can create different schemas that help to organize and make sense of your data and generate insights you’ll often have to untangle large data sets and make sense of the relationships between tables an understanding of cardality and table relationships can be useful in these situations in this video you’ll explore the concept of cardality and review the different relationships that can be created between tables in a database to help with its business planning Adventure Works asks questions of its data like what bicycle sells best in each region or what is the revenue of each store however the data required to answer these questions is stored across several tables posing a complex data analytics challenge adventure Works can solve this challenge using cardality and by identifying the table relationships before we find out how Adventure Works can solve its data issues let’s take a few moments to explore the concept of cardality in the context of data analytics cardality refers to the nature of relationships between two data sets in other words how tables in your database relate to each other it’s important that your cardality settings are correct incorrect settings can lead to inaccurate data analysis and flawed business decisions there are three types of cardalities or relationships between tables in PowerBI the first is a onetoone relationship in this instance a record in one column of table A corresponds to a unique record in one column of table B onetoone relationships are less common in data modeling but they are useful when dealing with specific scenarios for example a single business entity can be loaded as two or more model tables because the data might come from different sources this scenario is common for dimension tables for example in Adventure Works data set each bicycle model has a unique model ID listed in the product ID column and a separate table lists specific features for each model ID in a product features column together these columns form a onetoone relationship between the two tables next is the one to many relationship each record in a column of table A corresponds to multiple records in a column of table B but not the other way around adventure Works lists its stores in table A and it lists the employees of each store in table B the relationships between the stores and their employees establish a one to many relationship this is because each employee works for one store but each store has many employees this is the most common type of relationship in data modeling where one table acts as the primary table and the other tables act as related tables finally there’s the many to many relationship this is where multiple records in a column of table A are related to multiple records in a column of table B in both directions many to many relationships are often used to establish a relationship between two fact tables or two dimension tables in the case of Adventure Works a customer can purchase many different bicycle models logged in table B and each bicycle model can be purchased by multiple customers recorded in table A this creates a many to many relationship understanding these relationships and configuring your settings appropriately helps your queries and calculations flow correctly and generate accurate insights another important aspect when considering the cardality of your data is granularity granularity refers to the level of detail or depth of a data set the granularity of your data should align with the business questions you need to answer for example Adventure Works wants to view customer purchase histories over the past year with data granularity you can explore individual transactions to analyze individual customer behavior and identify purchase patterns however if you want to understand which specific bicycle models are performing well in a region you need sales data with high granularity high granularity data is the data set that captures detailed information about the transactions for example geographical sales of products can be captured as a continent country state city and all the way down to individual stores but for a more general analysis like total sales per store a lower level of granularity suffices low granularity data refers to the data set that captures a highle summary or an aggregated level over broader categories an example of this is monthly sales of a product category the sales data is summarized at the category level but only on a monthly basis understanding the granularity of your data is crucial for establishing correct cardality it also influences how you set up your cross filter direction in PowerBI which you will learn more about in a future lesson but be careful when judging the required level of granularity misjudging the level of granularity can lead to misrepresented data and incorrect business insights and excessive granularity can lead to too much data and slow down your queries by developing a keen understanding of cardality and granularity you can untangle complex data scenarios like the one at Adventure Works with confidence and ease understanding the relationships between multiple data sets requires an advanced tool and Microsoft PowerBI’s cross filters are the perfect fit in this video you’ll explore the concept of cross filter direction and learn how to identify different types of cross filters adventure Works needs to calculate which members of its sales team have sold the most product types and should be awarded a bonus however the data required to generate this insight is spread across multiple tables with fixed cross filter directions you can help Adventure Works analyze this data by changing the cross filter directions of its tables but first let’s find out what data analysts mean by cross filter direction in PowerBI cross filter direction refers to the pathway or the direction through which filtering happens between two tables in a data model it dictates how data from one table influences the data in another table this enables relational analysis without resorting to complex queries or manual data consolidation powerbi relationships are directional in nature unlike other database management systems the direction significantly impacts how filtering operates having a clear understanding of relationship direction is a crucial aspect of data modeling in PowerBI let’s look at how direction plays an important role the Adventure Works data set contains three tables product sales and salesperson the product dimension table is connected to the sales fact table using a one to many relationship based on the product ID column common to both tables and a oneto many relationship also connects the salesperson dimension table to the sales fact table based on their common rep ID columns there are two types of cross filter direction the first is single cross filter direction this is the default setting in PowerBI the filter propagates from one table to another but not vice versa a good example of single cross filter direction is the scenario you just explored adventure Works product and salesperson dimension tables are connected to the company’s sales fact table via a one to many relationship each arrow points in a single direction indicating that the relationships direction is single this means that sales data can be filtered by both product and salesperson so when the product table is filtered for product one the sales table is automatically filtered for all sales of product one the next type of filtering is birectional filtering birectional filtering is filtering against the direction of a relationship sometimes you’ll need to do this to answer a particular question for example as you learned earlier Adventure Works requires a report on employee performance the report must show the number of products sold by each salesperson you can generate this report using birectional cross- filtering to generate the required results you must filter from the sales fact table to the salesperson and products dimension tables so you need to change the direction of the filter to both let’s look at the process steps for this action you can apply a filter in the salesperson table for a specific sales team member this filters the sales table for all sales by that person the filter propagates to the product table as the direction is birectional we have now determined how many unique products the salesperson has sold however there are a few important points to note when using birectional filtering birectional cross- filter relationships can negatively impact performance and configuring a birectional relationship can also result in ambiguous filter propagation paths you can disable filter propagation within a relationship in PowerBI using the cross filter DAX function this setting can be particularly useful in certain advanced scenarios where you must isolate data for independent analysis you’ll learn more about DAX in the next module the direction of the relationships plays a very important role in data modeling in PowerBI properly applying these cross- filter directions can drastically enhance data analysis leading to more insightful and actionable conclusions different data sets are explored at different levels of detail depending on the questions to be asked answering these questions requires working with different levels of data granularity over the next few minutes you’ll explore the concept of data granularity and discover how it can help inform your data analysis over at Adventure Works the company needs sales data to help make strategic decisions about what products to stock it must identify the highest and least performing products using annual and daily sales data you can help the company generate these insights by using data granularity to analyze its sales records let’s begin by recapping what is meant by the term data granularity as you might recall data granularity refers to the level of detail or depth captured in a certain data set or data field granular data provides deeper and more precise insights this delivers more nuanced and valuable findings remember data granularity isn’t about always having the highest level of detail it’s about having the appropriate level of detail before you begin your analysis ask yourself do you require high granularity or low granularity the decision should depend on the specific requirements and objectives of the analysis it’s about striking the right balance between detail manageability precision and simplicity high granularity data is the data set that records very detailed information about each transaction this level of granularity provides a comprehensive overview of each transaction including specific attributes and metrics associated with the transaction let’s look at an example from Adventure Works database for instance in Adventure Works data analysis product related data can be captured as product ID category subcategory name price size and weight some benefits of high granularity include in-depth exploration of trends patterns and relationships within data sets to identify specific behaviors and anomalies the flexibility to aggregate and summarize data at various levels of detail and the ability to facilitate accurate decision making by drilling down into specific data points next let’s look at low granularity in low granularity data information is captured and analyzed at a high-level summary or an aggregated level the data is not broken down into individual records instead data is summarized over broader categories or periods here’s an example from the Adventure Works database for example Adventure Works can explore its sales quarter by business quarter or month the benefits of low granularity include a simplified view that’s easier to understand and allows for analysis without an overwhelming level of detail improved performance and reduced data volume which leads to faster query execution and a quick identification of trends and patterns for informed decision-m let’s take a closer look at data granularity and its role in data analysis in the context of data analysis high granularity data is often more desirable it offers a finer level of detail so it provides greater precision and potential for deeper insights for instance tracking sales hourly high granularity instead of monthly low granularity could reveal patterns like peak shopping hours during the day however working with high granularity data comes with its challenges the more granular your data the larger your data sets will be potentially slowing down data processing and analysis on the other hand low granularity data while offering less detail can provide a broader view of your data it’s also easier to manage because of the smaller data sets in Adventure Works the monthly sales data low granularity could help identify broader trends such as seasonal sales fluctuations of certain product lines for example bicycle repair equipment sells more during the spring and summer months this is because customers are more active on their bicycles you can ensure the relationships are accurate and produce consistent aggregations by matching the granularity levels it also helps with correct filtering and supports drill down analysis data granularity also has a significant impact on building relationships between tables in PowerBI for example to determine the highest and lowest selling products in the Adventure Works inventory you must produce reports of total sales and budget over time using the sales and budget data the sales data is in the sales table and has daily level granularity on the other hand the budget data is stored in the budget table and is monthly to establish the relationship between tables and produce accurate results you need to format the date table in both tables and then build a relationship based on a commonly formatted date column understanding and manipulating data granularity is a powerful skill that all data analysts must master the degree of granularity can impact the insights drawn and the ease with which data can be analyzed with a firm understanding of data granularity you can now approach your data analysis tasks with a refined perspective it’s time to discover the story that the right level of detail in your data can tell untangling complex intricate data is often too large a task for one individual thankfully a PowerBI star schema can simplify complex data over the next few minutes you’ll learn how to configure a star schema in PowerBI including differentiating between fact and dimension tables and configuring cardality and cross filter direction adventure Works needs to organize its data to understand what products have been ordered and where they need to be shipped you can help them to organize the data using a star schema but first let’s review the steps for setting up a star schema in PowerBI the first step is to disable autodetect powerbi auto detects relationships when you load multiple tables you need to disable the function so you can set your own relationships the next step is to load your fact and dimension tables into PowerBI select the required tables from your Excel spreadsheet or other relevant location and load them into the application once you’ve loaded the tables you must create relationships between them you can join tables by dragging relationships between key columns or from the manage relationship section of PowerBI desktop finally you need to set cardality and cross filter direction you must set cardality to determine how your database tables relate and you need to set the cross filter direction to determine the pathway through which filtering occurs between your tables now that you’re familiar with the steps for setting up a star schema in PowerBI let’s help out Adventure Works as you’ve just discovered the first step is to disable the auto detect function launch PowerBI desktop go to file and select options and settings then select options within the settings menu to open the options dialogue box on the left bar of the dialogue box select data load then deselect autodetect new relationships after data is loaded and select okay next you need to load your fact and dimension tables into PowerBI select home then get data select Excel workbook from the list of options in the get data drop-own menu navigate to the Adventure Works company data spreadsheet and select open the navigator menu appears on screen this menu displays a list of available tables within your spreadsheet you can select which tables you need from this menu you can also use the search bar to locate a table when working with larger spreadsheets a preview of each table appears in the preview pane when selected in this instance you require the product region sales and salesperson tables select these tables then select load the tables are now visible in the model view your next step is to create the relationships between the tables you must build a one to many relationship between the sales table and the product region and salesperson tables in this instance you can create a relationship between the product table and the sales table based on the product key column which is common in both tables similarly you need to relate the sales table to the region and salesperson tables based on the sales territory key column and employee key column respectively alternatively you can also create and configure relationships from the manage relationship section of PowerBI desktop from the model view select manage relationship select new to open a dialogue box called create relationship from here you can build and configure relationships select the sales table from the drop-own menu then select the product key column from the available options then select the product table and its product key column next you need to set up the cardality and cross filter directions to set up cardality select the cardality drop-own menu then select the appropriate relationship type in this case it is many to one finally under the cross filter direction drop-own menu select the filter direction powerbi’s default direction is single so leave this as it is for the current scenario however before you select a birectional cross filter make sure that you fully understand its implications select okay when finished you can repeat this process to create relationships between the other tables select new then work through the same steps again to create more relationships select okay from the create relationship dialogue box when finished then select close from the manage relationships dialogue box to return to the model view the star schema is now ready to use the sales table is the fact table it sits in the middle of the model and connects to the salesperson region and product dimension tables you should now be able to configure a star schema in PowerBI differentiate between fact and dimension tables and configure cardality and cross filter direction keep the data analysis needs of your organization in mind as you build and refine your star schemas with practice this powerful data modeling technique will become a vital tool in your data analysis toolkit data is not always structured in a way that provides quick insights but by leveraging the Snowflake design schema you can unlock your data’s full potential in this video you’ll explore the snowflake schema learn how to build your own and discover how to transition to one from a star schema adventure Works data is stored in a complex format it’s having difficulty retrieving the necessary information you can help Adventure Works build a Snowflake schema to enable more efficient data storage and make it easier to generate insights let’s begin with an overview of the Snowflake schema the snowflake schema is a type of database schema design that optimizes data storage and retrieval by normalizing the data into multiple related tables unlike the star schema which uses denormalized data with fewer tables the snowflake schema consists of a central fact table connected to one or more dimension tables the dimension tables are further connected to other related tables to create a hierarchy for example the Adventure Works sales data sets product dimension table has a product category and a product subcategory in a star schema all three fields exist in one dimension table however in a snowflake schema you can split this single table into three different tables and all these tables are related to one another via one to many relationships now when you filter a specific product category the filter is propagated through the tables from product category to subcategory product and then sales as the adventure works example has just shown the snowflake schema offers many benefits so it’s an ideal choice for complex data structures in PowerBI here’s a quick overview of some of these benefits it simplifies dimension tables by splitting them into separate tables simplifying dimension tables also improves data integrity because hierarchical relationships more accurately represent the data and splitting data sets into separate tables also helps to reduce data redundancy because each attribute is only stored once it also enhances data analysis because a more efficient structure means more accurate insights and finally a snowflake schema leads to better management of data using hierarchies now that you’ve explored the basics of the snowflake schema and its benefits let’s help Adventure Works build one before uploading the data set you first need to turn off PowerBI’s autodetect feature this feature automatically creates relationships between the tables but you need to do this manually to disable this feature open PowerBI desktop select file options and settings and then options within settings this opens the options dialogue box select the data load option to the left of the dialogue box then deselect autodetect new relationships after data is loaded then select okay now you can load the adventure works data set from the home tab select get data then select Excel workbook from the options in the drop-own menu navigate to the data set and select open the navigator menu presents a list of available tables from the data set select the following tables category product region sales salesperson and subcategory then select load the tables are loaded into PowerBI and presented in the model view you can now establish the relationships between the fact and dimension tables you can do this by dragging the primary key from the dimension table to the foreign key in the fact table for example drag the product key column from the products dimension table to the product key column in the select fact table you can then repeat this process for all related tables in the snowflake schema next you must create hierarchies in the dimension tables to enable greater data analysis create relationships between the product table and the category and subcategory tables based on the category ID and subcategory ID respectively via a oneto many relationship this creates a hierarchy of product dimensions but what if Adventure Works has already created a star schema let’s review the process for transitioning from a star to a snowflake schema open the PowerBI project that contains the star schema your first step is to normalize the dimension tables identify the tables in the star schema to be further normalized into related tables create separate tables and then link them using foreign and primary keys to create these tables you’ll need to use DAX you’ll explore DAX in greater detail in a later module for now let’s just use some basic DAX code select the table tools tab then select new table add the required DAX code to the formula bar to create a new category table repeat the same process with the required DAX code to create a subcategory table once you’ve created the new tables PowerBI attempts to detect the relationships between them remove any new relationships that it establishes between the tables next you need to update the product hierarchy in the dimension tables to reflect the new Snowflake schema structure build a relationship between the category and subcategory tables based on the subcategory ID then build new relationships between the product and category tables based on the category ID you can now use this hierarchy to interrogate data on individual products product categories and product subcategories configuring the Snowflake schema in PowerBI is a valuable skill by mastering these skills you can play a critical role in helping organizations make datadriven decisions optimize operations and drive growth choosing the right schema generates valuable data insights choosing the wrong schema generates incorrect and misleading insights so how do you select a schema in this video you’ll discover why the Snowflake schema is often the most suitable schema for your data sets adventure Works wants to use its data to generate business insights into its sales and marketing practices so it needs to structure its data in a way that enables efficient querying and analysis it considers using a star schema however the last star schema it used resulted in an overly simplified and denormalized data set so you suggest a snowflake schema to more accurately represent and analyze the complex relationships between its data components as you discovered in earlier lessons a star schema organizes data into a central fact table this central fact table is surrounded by dimension tables containing descriptive attributes this structure is suitable for certain kinds of analysis for example it’s useful for analyzing smaller data sets however it becomes problematic when dealing with more complex hierarchical relationships this is particularly true for the Adventure Works data set by using the star schema’s denormalized approach Adventure Works risks generating results that contain redundant data and a loss of data integrity this would make it difficult to perform an accurate analysis of the data on the other hand a snowflake schema would provide a much better approach as you discovered previously the snowflake schema optimizes data storage and retrieval by normalizing the data into multiple related tables this structure provides more flexibility in defining complex dimension hierarchies and it allows for the creation of subdimensions within these hierarchies this lets analysts explore data at much deeper levels of granularity however the downside is that increased table sizes result in slower query performance this impacts the team’s ability to derive insights and make datadriven decisions quickly the best approach for adventure works is to build a snowflake schema this schema uses a more normalized approach which is more beneficial for dealing with intricate data relationships it can be used to build out multiple levels of related tables in the form of a hierarchy this is much more efficient than a star schema which flattens a hierarchy into a single table you can normalize several of the tables in the Adventure Works data set for example the product dimension table can be split into two separate tables category and subcategory this structure makes it much easier to analyze the performance of individual products and their related categories through deeper granularity customer data can also be organized in a hierarchy the team can explore customers and their purchases by country state and city this level of granularity reveals insights into regional sales patterns and marketing campaigns another benefit of this hierarchical structure is that it helps the team to identify patterns and relationships between data sets a snowflake schema also eliminates data redundancy each attribute is stored only once in its respective table and a unique identifier ensures consistent and accurate data finally the normalization of dimension tables also helps to reduce the data model storage requirements this makes the snowflake schema a much more efficient approach choosing the right schema is crucial for data analysis especially when dealing with complex data sets as the case of Adventure Works shows opting for a snowflake schema can help avoid the risks of using a star schema for hierarchical data relationships as an entry-level data analyst understanding the importance of using the correct schema for your data set is crucial by recognizing when a snowflake schema is more appropriate than a star schema you can optimize your data analysis process leading to more accurate insights and better informed decisionmaking you might often encounter a data model that’s unsuitable or not fit for purpose and leads to data analysis issues when this occurs you can take steps to rebuild the model and fix these issues over the next few minutes you’ll learn how to identify and resolve some common challenges arising from unsuitable data models adventure Works uses a star schema for its data model in PowerBI to analyze sales and customer data however this data model is not effectively meeting the company’s analytical requirements adventure Works has very large data sets and the company’s departments want to visualize this data according to their specific needs however this is difficult to achieve with the currently employed model adventure Works needs your help to resolve these issues and create a new more suitable data model the first step is to analyze the existing model and identify its issues some examples of common issues you could find in a data model include inferior performance issues with data consistency and limited scalability let’s begin with the issue of inferior performance the current data model might not be optimized for query performance resulting in slow report generation and analysis complex calculations based on larger data sets contribute to slow performance this makes it difficult for business users to draw real-time valuable insights from that data the sales table in the adventure works model contains columns like product descriptions these columns can be normalized into a dimension table for faster insights the next issue identified with the data model is inconsistent data disperate sources of data can be integrated without being properly validated for example duplicate data or incorrect data types this can lead to inaccurate reporting in your analysis adventure Works data model contains multiple examples of duplicate and inaccurate data across its tables if these tables aren’t fully normalized this redundant and inaccurate data will enter the company’s reports the final issue that was identified is that of limited scalability in other words the model cannot scale alongside a company to accommodate its increased data volume and associated evolving analytical needs adventure Works current model cannot integrate additional data sources emerging business requirements or analytical needs so now that you’ve completed your analysis and identified the issues you need to resolve the model’s challenges you can propose the following measures as a line of action to resolve these modeling and analytical problems the first step is to conduct a thorough assessment of the current data model and find any other issues that might exist once you’ve identified all the issues you can plan a redesign of the data model you must also understand the following data model components to support meaningful analysis and decision making the model specific data elements and their sources and the dimension and fact tables the relationships that exist between the model’s tables and the model’s calculations and measures another important step is to collaborate with stakeholders and business users to define the analytical requirements and objectives to be achieved for example Adventure Works sales department wants to identify the top performing product categories for each region and the marketing team wants to understand the impact of marketing campaigns within specific territories understand these analytical requirements and objectives so you can redesign a data model that implements all these requirements from the stakeholders and management team based on your assessment you’ve decided to redesign the data model as a snowflake schema you can complete this process by performing the following actions normalize the dimension tables create new tables where necessary establish proper relationships and cardality and create hierarchies compute custom calculations and measures using DAX test and validate and document all changes these actions will bring the following benefits to the data model they’ll improve model performance and enhance data integrity they’ll also remove data redundancies and boost the scalability of data analysis you then need to carry out the final few steps transform and validate the data while also implementing data quality checks you can also optimize the model then test it to ensure it functions as required finally deploy the new data model and train users to make sure everyone is familiar with how it works by implementing these steps you can help Adventure Works resolve challenges posed by the not fitfor-purpose data model the newly optimized data model will meet Adventure Works’s analytical requirements improve its data integrity and guarantee adaptability to changing business needs congratulations on reaching the end of this first week in this course on data modeling in PowerBI this week you’ve explored concepts for data modeling let’s take a few minutes to recap what you’ve learned in this week’s lessons you began the week with an introduction to data models you learned how to identify the initial steps involved in data modeling like defining relationships between tables assigning data types and creating calculated columns and measures you then explored the process steps for building a data model in PowerBI this involves connecting your data sources preparing and transforming your data and configuring the table properties you also learned how to create model relationships and create measures and calculated columns with DAX and you reviewed the benefits of data models you discovered that data models can be used to enhance the performance of reports improve calculations improve analysis and insights and deliver more accurate reports you then explored schemas a schema is a structure that defines the organization and relationships of tables within a data set three types of schema can be used to organize and structure data the first is a flat schema this is the simplest data model form it’s a set of rows and columns containing data then there’s the star schema it’s a central fact table that links to multiple dimension tables these tables are connected through relationships and finally there’s the snowflake schema this is an extension of the star schema it breaks down dimension tables into multiple related tables you first learned how to set up a flat schema this involves removing duplicate data formatting columns and editing the tables properties in the lesson exercise you configured a flat schema for Adventure Works you also completed an activity configuring a flat schema with multiple sources finally you completed a knowledge check to test your understanding of data models and you reviewed links to materials for further learning in the additional resources item the next lesson focused on cardality and crossfilter direction this lesson began with an introduction to fact and dimension tables fact tables hold quantifiable measurable data on a business process it sits at the center of a star schema then there’s dimension tables dimension tables provide descriptive attributes related to fact data they radiate out from the central fact table a snowflake schema extends this design it normalizes the dimension tables by breaking them down into additional related tables next you explored the concept of cardality cardality refers to how your database tables relate to one another your cardality settings must be correct to ensure your insights are accurate there are three types of cardality in PowerBI the first is a onetoone relationship in this instance a record in one column of table A corresponds to a unique record in one column of table B next is the one to many relationship each record in a column of table A corresponds to multiple records in table B but not vice versa this is the most common relationship finally there’s the manny to many relationship this is where multiple records in a column of table A are related to multiple records in a column of table B in both directions you can understand these relationships using cross filters powerbi offers single cross filter direction and birectional filtering single cross filter direction is the default setting it propagates from one table to another as in table A to table B but not the other way birectional filtering is filtering against the direction of a relationship this means changing the direction of the filter to both so you can propagate the filter in the reverse direction another important aspect of cardality is granularity granularity refers to the level of detail or depth of a data set the granularity of your data should align with the business questions you need to answer do you need high granularity data in the form of a data set that captures detailed information about the transactions or low granularity data in the form of a data set that captures highle summary or at an aggregated level over broader categories you then tested your understanding of these concepts you completed a knowledge check to test your understanding of data models and you reviewed links to materials for further learning in the additional resources item in the fourth and final lesson you learned how to work with advanced data models the lesson began with an introduction to setting up a star schema in PowerBI the key steps in this process involve loading the required tables creating the relationships between the tables based on common keys and setting up cardality and cross filter direction you then completed an exercise configuring a star schema for adventure works in PowerBI and you compared your result against an exempller next you learned how to set up a snowflake schema in PowerBI the process steps are like those for setting up a star schema the key difference is that you must create hierarchies in the dimension tables to enable greater analysis you can also convert a star schema into a snowflake schema using DAX queries you then put this knowledge into practice by changing an Adventure Works star schema into a snowflake schema you continued your exploration of advanced data models with snowflake schemas you reviewed the importance of snowflake schemas including their key benefits and you explored the process for resolving challenges in data models finally you completed a knowledge check and module quiz to test your knowledge of the concepts you encountered you’ve now reached the end of this module summary it’s time to move on to the discussion prompt where you can discuss what you’ve learned with your peers you’ll then be invited to explore additional resources to help you develop a deeper understanding of the topics in this lesson best of luck we’ll meet again during next week’s lessons what if you’re analyzing a data model and the data you need isn’t in the original model if it’s possible to derive the data from the original model you can use DAX data analysis expressions to create custom calculations to generate the data in this video you’ll learn about DAX and explore the basic syntax of DAX formulas adventure Works needs to identify its top selling products and calculate its revenue but these insights are beyond the scope of the original data model they can only be generated by calculating the existing data so Adventure Works must use DAX or data analysis expressions to complete this task let’s begin with an overview of DAX dax is a programming language used in Microsoft SQL Server analysis services Power Pivot in Excel and PowerBI it is a library of functions operators and constants used in formulas or expressions to create additional information about the data not present in the original data model with DAX expressions you can create custom calculations on data models to extract maximum information from your data to solve real world problems to master DAX you need to understand its syntax different data types the operators and how to refer to columns and tables using functions let’s begin with the syntax dax usually computes values over columns in a table so you need to know how to reference a column in a table first write the name of your new calculation then add the equal sign operator next write the name of your DAX function then parenthesis that contain the logic of your formula write a table name enclosed in single quotes followed by the column name enclosed in square brackets omit the table name if the reference column is on the same table let’s demonstrate this using an example from Adventure Works the Adventure Works sales table doesn’t include any data that denotes the total number of products sold the company could generate this data using DAX in the DAX expression sales is the table name followed by the column name quantity to be referenced and sum is the DAX aggregation function total product sold is the name of the new calculated column that holds the results of the calculation when executed this DAX formula adds a new column to the existing table that contains the required data next let’s review operators dax formulas rely on operators there are many different types of operators they can be used to perform arithmetic calculations compare values work with strings or test conditions some commonly used operators in DAX include parenthesis for grouping arguments arithmetic operators for performing basic functions like addition and subtraction and comparison operators for comparing values dax also uses logical operators to return true false values and concatenation operators to combine two or more values into a single string adventure Works can use operators in a DAX formula to calculate its total revenue in this example the multiplication operator multiplies the unit price by the quantity to compute the total revenue the parenthesis group the arguments of the expression and the sumx DAX function adds the arguments values to calculate the total revenue finally let’s move on to DAX functions dax functions perform various calculations manipulate data and create custom expressions as you discovered in an earlier example Adventure Works need to calculate their total revenue and they can perform this calculation using the sumx DAX function for now you just need to be familiar with the concept of functions you’ll explore functions in more detail later in this lesson it’s also important to understand that DAX is not just about formulas and functions it involves understanding the data model the relationships between tables and the context in which calculations are made for instance understanding how the tables relate to one another in Adventure Works data model is crucial for creating meaningful calculations there are several important aspects of a relationship that will help you to understand DAX tables connected via a relationship are not the same they are either on one or many sides of the relationship columns used to build the relationship are the keys of the relationship the column on one side of the relationship needs to have unique values and tables relationships can be either single or birectional the direction of the relationship determines the direction of automatic filtering remember mastering DAX requires practice start with simple formulas and gradually incorporate more complex functions and operators and ensure you understand your data model and the relationships within it as your comfort with DAX grows so will your ability to turn data into meaningful insights eventually you’ll be able to unleash the full potential of your data using DAX and gain valuable insights for decision-making dax is a useful language for generating business insights using formulas however data analysts need to understand that DAX generates insights from data based on the context of that data in this video you’ll explore the concepts of row and filter context and discover how they impact data evaluation in DAX adventure Works needs to answer business specific questions like what are the total sales for each product and what are the top selling items by category it can generate these insights using DAX dax formulas answer these questions by evaluating the relevant data according to its row and filter context let’s find out more about the relationships between DAX and context dax computes formulas within a context the evaluation context of a DAX formula is the surrounding area of the cell in which DAX evaluates and computes the formula this surrounding area is determined by the set of rows and filters to be evaluated in a DAX expression it determines which subset of data is used to perform calculations dax expressions adapt or refer to the context for evaluating dynamic and contextaware results let’s begin with an overview of row context row context refers to the table’s current row being evaluated within a calculation when a DAX expression is evaluated for a specific row it considers the values of the columns in that row as the context of the calculation this allows for calculations to be performed at row level and it’s especially useful for iterating through rows within a table for instance if you create a formula for a calculated column the row context for your formula includes the values from all the columns in the current row let’s demonstrate the concept using Adventure Works sales table the table contains sales data for multiple products over one month stored within the following columns date product category quantity and price adventure Works wants to create a total sales calculated column that shows the total sales data for each product in the table the company can use a DAX formula to multiply the quantity data in the quantity column by the price data in the price column for each item the formula iterates through the relevant quantity and price column values at the row level and returns the results in the total sales calculator column in other words the formula calculates the new values via row context next let’s review filter context as the name suggests filter context refers to the filter constraints applied to the data before it’s evaluated by the DAX expression in the previous example a different result was produced in each cell because the same DAX expression was evaluated against different subsets of data however with filter context you can determine which rows or subsets should be included or excluded from the calculation let’s demonstrate filter context using the Adventure Works sales table adventure Works must calculate the total sales for all items in category X the company can create a DAX formula containing filters that target all sales recorded against category X once the formula is executed it iterates through each row and retrieves only the data with the value of X row and filter context also interact with each other to produce results when a DAX expression is evaluated it first considers the filter context then the row context takes effect let’s demonstrate how this occurs with Adventure Works the company can use the filter context to narrow its sales data to the selected region the row context then iterates each row in the filtered results and calculates the sales totals as you’ve just discovered a filter applied on a table column affects all table rows filtering rows that satisfy that filter if you apply two or more filters to columns in the same table they are executed under a logical end condition this means only the rows satisfying all the filters are processed by the DAX expression in that filter context be careful when applying a filter in a large data model with multiple tables a filter context automatically propagates through the relationships between the tables in the data model based on the selected cross- filter direction of the relationships in this example this means that when data is filtered in the sales order table then data in the related tables is also filtered you can disconnect the tables to prevent propagation a row context on the other hand doesn’t automatically propagate through a data model’s relationships if you have a row context in a table you can iterate the rows of a table on the many side of a one to many or many to many relationship using the related table function you can also access the rows of the parent table using the related function of DAX understanding the context of DAX expressions at the row and filter level is important as you continue to build data models for reporting and visualization context affects how DAX interprets and analyzes your data so always consider the context when creating and executing your DAX formulas as a data analyst you’ll often have to perform complex calculations on large data sets beyond the scope of spreadsheet software like Microsoft Excel in these instances you need to utilize formulas and functions in DAX in this video you’ll review some commonly used DAX functions and examples of formulas that use these functions adventure Works has experienced steady growth in recent months however this growth has led to data management issues so Adventure Works needs a better way to generate insights into its data fortunately DAX formulas and functions are the perfect solution for generating these insights let’s find out more about DAX formulas and functions and then discover how Adventure Works can make use of them you previously learned about operators the building blocks for creating a DAX formula however there are also many common formulas and calculations performed on data these are part of DAX’s extensive library of functions functions are reusable pieces of logic that can be used in a DAX formula these functions can perform various tasks including aggregations conditional logic and time intelligence calculations data analysts can use these functions to handle complex data challenges and drive meaningful insights to create a function you must be familiar with the syntax a function begins with the function name followed by parentheses containing the functions parameters dax function names are typically expressed using capital letters to help differentiate them from table and column names for example Adventure Works could use a function to get the distinct count of rows in the custom key column in a table named sales dax expressions can be difficult to write particularly complex calculations which require nested functions so you can use variables in your DAX formulas to simplify calculation results and store them for reuse you can use variables to store intermediate results in a temporary location they’re like a storage box that you can put information into to be retrieved later this improves reliability and readability and reduces the complexity of your expressions you can define a variable in DAX by placing var before your variable or expression follow the variables with return where the expression’s result is provided adventure Works can create a simple formula that defines two variables to generate insights into its sales and customer data sales amount and customer number are variables defined to determine the total sales and number of customers respectively the return statement divides one variable by the other the entire expression’s result is in the DAX query’s return statement although DAX functions can be classified into many broad categories there are some commonly used functions let’s review these and discover how Adventure Works could leverage them to resolve their business problems the calculate function evaluates an expression in a context modified by the specified filters adventure Works can use the function to analyze total sales for a product category based on the color of the products the company just filters the products based on a specified color like blue the calculate function evaluates the sum of the sales table sales amount column in a modified filtered context a new filter is added to the product table color column another useful function is average X the average function returns the average of an expression evaluated for each row in a table adventure Works can use this function to calculate the collective average for freight and tax the function calculates the average freight and tax on each order in the sales table it first sums freight plus tax amount in each row and then averages those sums you also need to be familiar with the summarize function the summarize function creates a summary table by grouping data based on one or more columns adventure Works can use the summarize function to generate a sales summary report displaying annual sales for each product category this function returns the summary of sales grouped around the calendar year and the product category the resulting table allows you to analyze the sales by year and product category dax is a powerful language for advanced data modeling and analysis its wide range of functions can be combined with formulas to generate deep insight and remember that DAX functions can be combined to create complex calculations that perform multiple operations this versatility and flexibility makes DAX an essential tool for data analysts you might not always be able to answer business questions using an existing data model it could lack the required data or be too complex in these instances you can use calculated and cloned tables to enhance your data sets and improve your analysis over the next few minutes you’ll explore calculated and cloned tables and learn how to create them from different sources using DAX functions adventure Works needs answers to business specific questions about its sales and marketing but its current data model isn’t up to the task however by creating calculated tables the company can compare and analyze its data to generate the required insights you can learn more about calculated and clone tables by discovering how Adventure Works can create them using DAX functions let’s begin with cloning a table cloning a table can be extremely useful for manipulating or augmenting data without affecting the original table this is especially true when working with tables that are refreshed periodically and any changes you made to the original table might be overwritten for example Adventure Works must augment its sales table to generate insights but it doesn’t want to alter the original data so the company can create and work from a cloned version of the table while leaving the original intact a table can be cloned using a simple DAX formula type the new table’s name an equals operator and the original table name in parenthesis add the word all to instruct PowerBI to clone all data from the target table this formula states that the clone table is equal to the original table adventure works can use this syntax to create a clone of their sales table called sales data you can also use DAX to create a calculated table based on data from various sources for example Adventure Works must combine customer data from a database with sales data from an Excel spreadsheet to analyze the relationship between its sales and customers the company can use DAX to merge these sources and enable its analysis calculated tables can also be used to normalize dimension tables adventure Works can use DAX to split their product dimension table into category and subcategory tables this creates a hierarchy that enables more efficient data exploration and reporting now that you’re familiar with creating and cloning calculated tables let’s help Adventure Works before we begin let’s quickly review the data model within our model the sales table is the fact table it’s connected to all other tables via one to many relationships and the cross filter direction is set to single for all relationships we’re now ready to start the first step is to create a new calculated table using DAX in the data view of PowerBI select new table from the table tools tab to expand the DAX formula bar select the formula bar and write an all DAX function that extracts all data from the sales table to create a new cloned version of the table press enter to execute the function and generate an exact copy of the sales table the new cloned table is listed as cloned sales next you need to create a calculated table based on different data sets this must be an annual sales summary table that references the sales and product tables from the imported data set select new table once again then access the formula bar and write a DAX expression that uses the add columns summarize and calculate functions to calculate and summarize the required data within the annual sales summary calculated table press enter to execute the formula and generate a new table called annual sales summary finally ensure you have the proper relationships set between the tables for the proper functioning of DAX review the new calculated tables and the relationships in the data pane and the PowerBI desktop model view adventure Works can now begin analyzing its sales data and answering specific business questions by creating visualizations and reports using the newly calculated tables and existing data calculated tables are useful in DAX and PowerBI for simplifying and enhancing data analysis you can deploy DAX functions to perform analysis without impacting the original data sets study these tools carefully and make them a central part of your skill set you might often encounter tables that don’t have the data you need you can generate this data by combining existing columns to create a new calculated column in this video you’ll explore the basics of calculated columns in PowerBI learn how to create them using DAX and evaluate their effectiveness in contributing to meaningful analysis adventure Works is analyzing the data in its sales table and realizes there’s no data for the profit margins on its product categories in the original data source calculated columns are the perfect solution to this problem adventure Works can add data on its profit margins using DAX expressions to create new calculated columns within the original data source before you begin helping Adventure Works let’s find out more about calculated columns a calculated column is a new column added to an existing data table in PowerBI data analysts can use calculated columns to derive new data from existing columns and add it to the data model once added these columns can be used in any part of a report or visual just like any other column traditional columns are filled with data imported from a data source a calculated column is created by defining a DAX expression you can create a DAX expression that calculates the data from two or more columns the result of this calculation is then added to the table as the newly calculated column write the name of your calculated column and an equals operator then write the names of the tables to be referenced in single quotation marks and their respective column names in square brackets include a relevant arithmetic operator depending on the operation required for example Adventure Works can create a total sales calculated column by multiplying the quantity and unit price columns in its sales table now that you’ve explored the purpose of calculated columns in PowerBI let’s help Adventure Works to calculate its profit margin from its sales data in its sales table by creating calculated columns launch PowerBI desktop and load the Adventure Works data set the workbook contains one table called sales the table tracks Adventure Works recent sales data access PowerBI’s data view to view the sales table adventure Works need to calculate its profit margin but to do this it must first calculate its total sales for the quantity of each item sold however the table is missing this data you can add this data to the table by creating a new total sales column you just need to multiply the quantity and unitpriced columns select the sales table from the data pane on the right hand side of PowerBI desktop in the table tools tab select the new column from the calculations group this opens the DAX formula bar write DAX code in the formula bar that multiplies the quantity column by the unit price column and adds the result as a new total sales column press enter to execute the code a new total sales calculated column appears under the sales table in the data view on the right hand side of the PowerBI interface you can use this new column in any report or visualization like any other table column now that you’ve identified the total sales data you can create a profit column to determine how much profit has been made on each item write another DAX formula that subtracts the cost from the total sales and generates the data as a new profit column press enter to execute the formula the new profit calculated column is added to the sales table now that you’ve identified the profits you can create the profit margin column select new column again then write another DAX formula in the formula bar that divides the profit and total sales columns and generates the result in a profit margin calculated column press enter to execute the formula the profit margin column is added to the data finally you need to format the calculated columns select the profit column and format it as currency then format the profit margin column as a percentage you should now understand the basics of calculated columns and be able to create them using DAX and evaluate their effectiveness measures uncover the information hidden in your data and help you to tap into its real potential over the next few minutes you’ll explore measures and their importance for data analysis you’ll also explore how calculated tables are built from pre-calculated measures adventure Works needs to calculate its sales data for all the products it has sold this month it also needs to ensure that this calculation can be updated monthly against new sales data the company can generate these insights using measures you can discover more about measures and how they function by exploring how Adventure Works uses them let’s begin with an overview of measures measures in PowerBI are used to perform calculations on data model fields measures play a pivotal role in data analysis and interpretation measures are used in PowerBI to perform aggregations calculations or evaluations on data that provide meaningful insights measures are typically used in data visualization elements examples of these elements include charts tables and cards by using measures you can compute aggregated values such as sums averages minima maxima counts or more complex statistical calculations measures in PowerBI offer several benefits in data analytics and reporting let’s explore some of the benefits measures are calculated in the context of the visualization a report they are used in this means they are dynamically updated based on filtering and other interactions within the report in other words if the context changes then so does the measure this dynamic calculation allows you to dive deeper into data and gain insights from different angles and perspectives measures are also reusable once created you can continue to recall them in your code this reduces the repetitive work of creating the same calculations and ensures data consistency across all reports another benefit is performance measures can be used to track the performance of different aspects of a business measures are commonly used to create key performance indicators or KPIs essential to monitor business performance kpis provide a quick snapshot of performance against predefined targets or benchmarks and finally measures also help to maintain consistency measures help maintain consistency in metrics across different visualizations and reports consistency ensures the same results show regardless of filtering or grouping in your measures your calculations must be standardized and uniformly applied throughout the analysis this ensures accurate and reliable reporting across various visualizations and dashboards measures can also be used to create calculated tables in PowerBI a calculated table is a table that you add to a model derived from existing tables by using a DAX formula adventure Works has created a measure called total sales this measure is the sum of all sales across all products now the company needs a new product table that lists each product alongside its respective total sales this can be done using a DAX formula in this DAX formula sales is the original table sales product is the product column in the original table and total sales is the measure Adventure Works created let’s take a moment to explore a sample of the syntax used to create such a formula begin with the name of your new measure followed by an equals operator then add the required expression that contains the logic of your measure for example Adventure Works can create a new measure called total sales that calculates the total sales amount from the sales table when executed this DAX formula will list each product and its total sales creating calculated tables from pre-calculated measures is particularly useful for creating a summary table from large data sets or for creating a table with data that does not exist in the original tables this can enhance data analysis visualization capabilities in PowerBI in this video you have learned about measures and their importance in data analysis you are also able to explain how calculated tables are built from pre-calculated measures measures in Microsoft PowerBI are essential to data analysis and interpretation they offer dynamic reusable and complex calculation capabilities enabling businesses to gain insights from their data and make datadriven decisions effectively and efficiently as a data analyst you want to be able to provide your business with answers and solutions to the questions they are asking using measures you can gain valuable insights into your data drive strategic decisions and enhance your business’s performance over the next couple of minutes you’ll explore the different types of measures in PowerBI adventure Works is using different types of measures to prepare its annual sales report to compile this report it must analyze its sales data across different regions and generate insights into specific products and sales team members let’s explore the different types of measures Adventure Works can use to prepare its report before we explore measures let’s quickly review the concept of additivity additivity refers to how measures behave when aggregated across different dimensions for example summing or averaging values however not all measures behave the same way so understanding the behavior and categorization of measures is crucial for accurate data analysis and visualization in PowerBI measures are essential for performing quantitative analysis and deriving meaningful insights from the data they provide a way to summarize calculate and compare data across various dimensions based on specific criteria and business requirements measures can be categorized into three types additive semi-additive and non-additive let’s explore these types of measures in more detail additive measures facilitate data aggregation across any business dimension like time geography or product categories the basic mathematical operations applied to these measures are addition and subtraction these types of measures provide consistent results regardless of how you group data additive measures also use the sum DAX function to aggregate over any attribute for example Adventure Works monthly sales analysis report shows revenue and quantities sold by product category and region this data is for a specific unit of time in this case per month you can use additive measures to aggregate revenue and quantity sold by summing them across all dimensions this allows you to view the total revenue and total quantities sold while analyzing the performance of various products regions and months of the year next is non-additive measures non-additive measures cannot be meaningfully aggregated across any dimension these measures involve calculations like ratios averages and percentages the result of aggregating a non-additive measure can be skewed or misleading and should be handled with caution for example at Adventure Works the average sales per customer is a non-additive measure the average sales per customer in January is $300 and in February it’s $350 however it doesn’t make sense to add these averages and state that the average sales per customer for the two months is $650 instead calculate the total sales and total numbers of customers for the two months combined then divide the total sales by the total number of customers to obtain the correct average sales per customer for the period finally let’s explore semi-additive measures semi-additive measures can be aggregated over some but not all dimensions they’re mostly used in situations where the data represents a state at a particular point in time they’ve meaningful aggregation for certain dimensions but not for all like with additive measures semi-additive measures use some to aggregate over some dimensions and a different aggregation over other dimensions examples of semi-additive measures that Adventure Works use include inventory balance and current account balance adventure Works has created a measure called inventory at hand it uses this measure to add inventory across different product categories or store locations but the measure can’t be used to add up the inventory across time like the change in inventory over a two-month period this is because it’s semi-additive for example Adventure Works had 50 bicycles in stock at the end of January and 60 at the end of February but it would not be accurate to say that it had 110 bicycles in stock for the two months the stock level changed over this period it wasn’t a fixed unit or measurement you should now be able to identify and distinguish between the different types of measures in PowerBI each of these measures plays a unique role in generating insights and guiding decision making as always with data analysis it is vital to remember that the value lies not just in the numbers but in their correct and thoughtful interpretation as a data analyst you’ll often have to identify trends from raw data supported by empirical evidence this sounds like a complicated task but you can make it easier by using statistical functions in this video you’ll explore the most common statistical functions used in measures and explore examples of each one adventure Works needs to identify trends in its business from raw data the company can use several basic statistical functions to generate these insights exploring Adventure Works use of these functions is a great way to understand how they work but first let’s begin by understanding what data analysts mean by statistical functions statistical functions calculate values related to statistical distributions and probability they also allow you to perform calculations and comparisons that reveal meaningful information about the data when it comes to quantitive data analysis statistical functions are the lifeblood of the process these functions enable in-depth analysis by providing insights into your data trends patterns and relationships some common statistical functions you’ll make use of include average median and count there’s also distinct count min which calculates the minimum and max which calculates the maximum let’s start with the average function also known as the mean this function sums up all the numbers in a data set and divides the result by the total count of numbers this function is frequently used to identify a central tendency in a data set it is beneficial when you need to find the middle ground or commonality within data for example Adventure Works can use the average function to identify its average sales amount the company can create a calculation to generate this data using the average function sales is the name of the table that contains the sales data and sales amount is the column that contains the numbers for which it wants the average the next statistical function is the median function this function calculates the middle value in a set of numbers it sorts the numbers in ascending order and then selects the middle number the median is the average of the two middle numbers for data sets with an even number of observations unlike the average the median is less affected by outliers and extreme values this makes it useful for data sets with skewed distributions for example Adventure Works needs to compute average response times for its customers service team with this data the company can measure the team’s performance and identify areas of improvement the data set contains the support table with the response time adventure Works can apply the median function to compute the median value support is the table name response time is the column containing the numbers for which the company requires the median which is response time in this case only numeric data types are supported in this function dates logical values and text columns are not supported next let’s explore the count function this function counts the number of rows in a column or a table it is often used to measure the size of a data set you can use it to count all rows or only rows that meet specific criteria the only argument in the function is column when the function finds no rows to count it returns a blank for example Adventure Works needs a report containing sales of product categories to generate this report it needs to analyze the count of sales for each product category it can use the count formula to calculate this category is a column name that contains values to be counted next let’s look at the distinct count function this function counts the number of distinct values in a data set this function is helpful when you need to understand the count of unique values or categories the only argument allowed for this function is a column you can use columns containing any type of data when the function finds no rows to count it returns a blank otherwise it returns the count of distinct values adventure Works needs to analyze the number of unique daily visitors to its website this data is stored in a website table containing a visitor ID column adventure Works can use distinct count to compute the number of unique visitors website is the table name for reference visitor ID is the column name that contains the values to be counted lastly let’s examine the min and max functions the min function is used to identify the smallest value in a column or between two scalar expressions the max function is used to identify the largest value in a column or the larger value between two scalar expressions both min and max functions can provide an overview of the range of your data adventure works can use these functions to analyze its store inventory the min and max functions identify the minimum and maximum product quantity from the inventory table using the quantity column inventory is the name of the table quantity is the name of the column that contains the values to be evaluated you should now be familiar with the most common statistical functions used in measures and be able to make use of them mastering these functions will undoubtedly elevate your data analysis skills do you want to create custom calculations for tables columns and measures you can create custom calculations by using DAX over the next couple of minutes you’ll learn about context and how it impacts DAX measures you will also examine different scenarios where measures are presented in various ways adventure Works wants to analyze its sales data determine which customers make the largest purchases and compute stock in hand across all stores in an inventory management scenario at this stage of the course you should be familiar with the concepts of DAX measures and contexts you’ll often create measures in the form of custom calculations but these custom calculations are contextsensitive it’s important to understand the influence of context because it can result in variations in your calculations these variations are based on the level of data you are evaluating the model structure and the visual you are using to represent it an understanding of context and variation helps deliver accurate data analysis and provides business intelligence to key stakeholders let’s recap the basics of context context in DAX comes in two primary forms row context and filter context row context is the current row being evaluated in an expression like racing bikes in the Adventure Works data set in contrast when you build reports in PowerBI you can filter the report data which results in DAX using the filter context this is the subset of data the calculation operates upon influenced by visuals or reports filters for adventure works it could be all cross-country bicycles sold in North America now let’s explore the impact of context on DAX to understand how the use of context in DAX measures can influence business decisions adventure Works wants to analyze and present a report on annual total revenue the company can use the sum xdax formula to compute the sum of all the quantity values multiplied by the unit price in the sales table by applying this measure to the sales table the formula computes the sum of all sales amounts but this measure utilizes only the row context adventure Works needs more insights to drive key decisions through data for example it must understand which products are selling the best to improve warehouse stock management and impact marketing decisions to identify the best performing product categories Adventure Works can filter the data set using a DAX query this query determines the total sales for products under the bikes category it incorporates filter context created by the category column from the product table in addition to the row context it incorporates filter context created by the category column from the product table in addition to the row context adventure works also needs to determine which customers make the largest purchases first the company must determine the average purchase amount using the average DAX function it can calculate the average sales amount per customer by applying this measure to the sales data set to compute the measure for the customer with the highest purchases you need to define a logic based on customer ID customer ID corresponds to the total sales amount of $2,000 and above as high purchase customers and those who spend less than $2,000 are average purchase customers in this case the customer ID is now acting as a filter context to compute the measure this instructs the sales and marketing team which customers to target in their campaigns you should now be familiar with the impact of context on DAX the contextsensitive nature of DAX is a powerful feature of PowerBI it enables dynamic calculations based on the context in which the DAX computes the formula understanding how context impacts DAX allows users to create more accurate insightful and dynamic reports that can be tailored to specific business scenarios powerbi is very effective for generating insights but writing DAX code to analyze data takes time fortunately you can create calculations and measures faster using PowerBI’s quick measures feature over the next few minutes you’ll explore the concept of quick measures learn about the different types available and review the process for creating them in PowerBI adventure Works wants to quickly analyze and monitor the performance of its sales team against several key performance indicators but constantly rewriting the same DAX code for each performance review is time consuming adventure Works can speed up the process using PowerBI’s quick measures feature let’s learn more about how quick measures work in PowerBI so you can help Adventure Works as you’ve just learned quick measures are a useful technique for performing commonly used calculations quickly and easily a quick measure runs a set of DAX commands behind the scenes then presents the results as a new measure you can use in your reports and visualizations in other words you don’t have to spend time writing DAX code the measure does it for you based on the inputs you provide there’ll still be times when you need to write DAX expressions for specific business case scenarios but quick measures can still act as a good foundation many different categories of DAX calculations are available to work with and you can modify these calculations to meet your specific analytical needs when creating quick measures in PowerBI you can choose calculation types depending on the nature of the analysis you want to perform types of quick measures include aggregate per category filters and time intelligence there are also totals mathematical operations and text quick measures in PowerBI offer several benefits for data analytics and reporting you can use quick measures to generate commonly used calculations with just a few clicks this eliminates the need to write DAX expressions making the process more efficient another benefit is accessibility you can create quick measures using PowerBI’s userfriendly interface this accessible UI means even users with limited DAX knowledge can create calculations quick measures also help with data they empower business users to take ownership of their data analysis and reporting this simple and accessible tool for creating calculations reduces dependency on data experts and quick measures also offer flexibility to iterate and refine calculations if you need to adjust a calculation or explore alternative metrics you can easily modify your quick measures without affecting the underlying data now that you’re familiar with the basics of quick measures let’s help Adventure Works use them to track the performance of its sales team before we begin let’s quickly review the model you’ve launched PowerBI connected to your data sources and loaded transformed and configured the following tables for your model products region sales and salesperson now you can begin creating measures in PowerBI the first step is to select the report view or data view to access the calculations group within this group select quick measure the quick measures window appears on the screen choose the required calculation type and fields to run the calculations alternatively you can select the ellipses next to the table name on the data pane then select new quick measure from the drop-own menu remember that the measure is created by default in the table you have selected from the data pane on the right side of the window choose select calculation this action opens a list of available calculation types in PowerBI adventure Works must calculate what quantity of each product each team member has sold so choose total for category filters applied next you must select the required fields from the right pane to perform calculations select the sales column from the sales table and assign it as the base value then select the category column from the product table and assign it to the category section then select add to add these elements to the measure the new quick measure appears in the fields pane and the underlying DAX formula appears in the formula bar adventure Works also needs to know how much revenue each team member has generated this year you can calculate this using a year-to-ate sales measure to create this sales measure you can repeat the same process as before select quick measure from the measure tools tab then select the year-to-ate total calculation type then select the sales column from the sales table as the base value and the order date column from the product table in the date section finally select add a new measure called sales YTD appears in the fields in the data pane thanks to your help Adventure Works can now quickly track the performance of its sales team using quick measures and you should now understand the importance of quick measures be familiar with the different types available and be able to create them in PowerBI measures are PowerBI features that let you explore your data to create meaningful reports and visualizations in this video you’ll learn how to create custom measures with DAX adventure Works needs to analyze its sales data to calculate its total sales and identify the top two best-selling products in each category and region you can use DAX calculations to create custom measures to help Adventure Works generate these insights custom measures refers to userdefined calculations or metrics created using DAX like traditional measures custom measures also generate insights about data let’s create custom measures to help Adventure Works generate insights into its sales data before we begin let’s quickly review the company’s data model you’ve launched PowerBI connected to your data sources and loaded transformed and configured the following tables in the model: products region sales and salesperson so within our model the sales table is the fact table it’s connected to all other tables via a series of active one to many relationships and the cross filter direction is set to single for all relationships we’re now ready to start creating measures the first step is to create a new measure called total sales using DAX in the data view of PowerBI select new measure from the table tools tab to expand the DAX formula bar type total sales as the name of your new measure be aware that any new measure added to the DAX formula bar is named measure by default if you don’t rename the measure all new measures are named measure one measure two and so on give your measures unique names to be easily identifiable particularly when creating several measures write the total sales measure using the sumx function to multiply the unit price and quantity columns from the sales table when you enter your formula a list of suggested functions appears after you type the equals operator you’ll need to ensure that you understand the functions on this list and that you select the relevant one for your calculation and once you reference a table or column name PowerBI displays a drop-own list of available tables and columns within your data model select the correct field when choosing a reference from the drop-own list to ensure your chosen measure functions as required press enter to execute the function and generate the new total sales measure you can view the new measure within the table you selected under the data pane on the right hand side of the PowerBI desktop interface next you must create a measure that identifies the number one and number two top selling products in each category you can use the total sales measure to create another new custom measure select new measure to expand the formula bar and write a measure called top two products the measure begins with a variable that defines the ranking of products using the DAX values function the return section returns the value with the required calculation the calculate function filters the results of the total sales measure based on the top two products the top function defines the top products based on their respective sales it uses the number two to represent the top two products this is a dynamic measure that you can use to present the number one and number two top selling products by product category color or region press enter to execute the function when executed the function displays the results of the measure in a matrix or table that shows the total sales amount for the top two performing products in each category you can dig deeper into the data by working through different business years thanks to your help Adventure Works now have the insights they require and you should now be able to create custom measures with DAX this is a valuable new skill you’ve learned when used correctly you can deploy dynamic calculations to generate insights quicker there may be times when you encounter a data model with a cardality and cross filter direction configured making it impossible to perform the necessary filters with the cross filter function you can change the cross filter direction for a specific measure while maintaining the original settings in this video you’ll develop an understanding of the cross filter function its syntax and its relationship to measures adventure Works needs to analyze its sales performance for the previous few years along with the performance of its sales team however its data model tables are connected via one to many relationships and single cross filter direction this prevents the company from filtering the data as required and changing the cross filter direction to both results in a permanent change fortunately Adventure Works can use the cross filter function to alter the direction while maintaining the original settings let’s explore how this works as you’ve just discovered the cross filter function changes the cross filter direction between two tables for a specific measure while maintaining the original settings in other words it specifies the cross filtering direction to calculate a relationship between two columns so how do you create a cross filter function a cross filter function can only be used within a DAX function that accepts a filter as an argument like the calculate function for example this means that the function receives two arguments the name of the table you want to filter along with the required column and the direction in which you want to filter let’s explore an example the syntax begins with the cross filter function the argument is then placed in parenthesis the argument is the name of each table followed by the names of the required columns in square brackets the first column name is typically the Manny side of the relationship and the second is the one side finally add the filter direction the first column name is typically the Manny side of the relationship and the second is the one side for example Adventure Works could filter between both sides of the relationship on its sales and products table using the product key columns common to both you might be familiar with cross filter directions from earlier in this course here’s a quick recap of the possible directions in which you can filter the relationships in your model you could use none which means that no cross filter occurs within the relationship there’s also the one-way direction filters applied on one side of a relationship propagate to the other however you can’t use the one-way option with a one:one relationship next is oneway right filters left in this instance filter propagation occurs from the right side to the left side of the relationship and finally there’s one way left filters right in which filter propagation occurs from the left side to the right side of the relationship let’s review an example of how adventure works can make use of cross filter function in the adventure works data model the sales fact table is related to the dimension tables via one to many relationships and single cross filter direction this means that filters propagate from the product table to the sales table but not in the other direction so when Adventure Works analyze products sold by year the results aren’t accurate because the model can’t filter the results correctly you could try to resolve this issue by changing the cross filter direction between the tables to both but this also changes how the filters work for all data between these tables instead you can create a cross filter function using DAX to change the filter only for the current measure create a new productby-year measure that computes the total number of products sold the distinct count function calculates the number of distinct values in the product key columns between the sales and products tables and the cross filter function alters the cross filter direction from single to both based on this column once Adventure Works analyzes the measure based on the year column from the date table the results are accurate according to the business analytical needs you should now be familiar with the cross filter function and how it works cross filter is a useful function to change the direction of a relationship without changing the relationship itself this function creates visualizations with custom filtering depending on the business needs you’ll often create measures that generate answers to specific data questions but what if you need your measure to answer another question you can use the calculate function to refocus your measure in this video you learn how the calculate function can alter the filter context for measures adventure Works needs to analyze its total sales for all its products it also needs to generate more granular data including sales of bikes blue colored products and sales within the US region it can calculate the total sales for all products using a standard measure but insights into the other data will require more specific filters adventure Works can use the calculate function to change the filter context and generate these insights let’s learn more about how quick measures work in PowerBI so you can help Adventure Works changing the context of a filter means changing the data that the filter must analyze for example Adventure Works needs to create a calculation or measure that analyzes its total sales for all its products this is the original filter context once this calculation is completed the company needs to explore its data in more granular detail by identifying how many bicycles it has sold it can combine the original total sales measure with a new bike sales measure that generates insights into how many bicycles have been sold so the filter context changes from all products to all bikes before you review some examples let’s review the syntax of the calculate function the calculate function can be invoked with an expression as its first argument a set of filters in square brackets then follows the expression these filters are defined or modified by expressions to find out more about how this works let’s explore how Adventure Works make use of the function adventure Works first needs to calculate its total sales the company can create the total sales measure using the sumx function the measure must multiply the sales table quantity and unit price columns this measure uses row context and iterates over each row of the sales table to compute the total sales of products for Adventure Works adventure Works can continue to use this measure in all the other calculations it needs to complete now that Adventure Works has a generic measure of total sales it can refocus its filters to generate insights into bike sales adventure Works can create a new measure called bike sales that uses calculate to analyze the sales of products in the bikes category when the category bikes is executed the formula calls the total sales measure again however this time it adds the bikes product category as an additional filter in the filter context in other words the filter context changes from all products to all bikes next Adventure Works needs to analyze all blue colored products in each category the company can write a new measure called sales of blue products when executed the expression incorporates the blue color from the product color column as an additional context for this calculation it calculates the total sales of blue color products from the entire data set you can also specify multiple filters in the same calculate function all the filters intersect regardless of the order in which they appear for example Adventure Works can create a measure called sales of blue products in USA that computes the total sales of blue products in the USA region this measure calculates the total number of blue products sold only in the United States by adding the country column from the region table in the overall filter context of the calculation but what if you’ve already created filters on these columns any existing filters will be overridden by those in your calculate function so how do you retain both sets of filters you can use calculate modifiers to keep the behaviors that already exist in your columns an example of a calculate modifier is keep filters you can add keep filters before your argument while placing the argument in parenthesis this ensures that existing active filters on your columns are not overridden or merged with new filters other examples of calculate modifiers include cross filter all and use relationship you’ll explore these modifiers in more detail later in this lesson you should now be able to use the calculate function to alter the filter context of your measures so you can create measures to generate insights into your data and modify your measures filters to ask and answer other questions about your data as a data analyst unlocking fresh insights requires exploring data from multiple angles with role-playing dimensions you can explore your data from different perspectives and eliminate the need for redundant data structures through active and inactive relationships in this video you’ll explore the concept of role-playing dimensions and active and inactive relationships adventure Works receives thousands of orders from all over the world and it’s important that the company continually analyzes its orders to avoid delayed or mistaken deliveries it can use multiple dimensions to explore its order related data from multiple angles let’s find out more about role- playinging dimensions by exploring how Adventure Works makes use of it in the context of PowerBI dimensions represent the various attributes or business entities used to organize data role-play dimensions are instances of the same dimension used multiple times in a data model each instance plays a unique role by representing different aspects of the data this provides the flexibility to analyze data from different viewpoints without duplicating data tables let’s demonstrate this with an example from the Adventure Works database adventure Works sales and shipping departments operate in sequence first new sales are recorded in the sales data set as order date then the orders shipping date is recorded in the sales data set finally the system automatically generates a delivery date when the customer receives the product so in Adventure Works sales data set the date dimension is used three times for new sales shipping dates and receipt dates adventure Works can analyze sales performance by order and shipping date without needing separate tables optimizing delivery time by delivery date analysis this helps the business to analyze sales performance based on order date and shipping date without creating separate tables for each date type when Adventure Works queries its data the role of the date dimension is based on the fact column used to join the tables for example the table join relates to the sales order date column when analyzing sales by order date an important part of role-playing dimensions are active and inactive relationships an active relationship is a relationship between two tables used for analysis reporting and visualization an inactive relationship is a valid relationship not being actively used in the current analysis to differentiate between active and inactive relationships PowerBI marks active relationships with a solid line and inactive relationships with a dotted line let’s examine an example from Adventure Works in the Adventure Works table the date and the sales tables have three relationships however there can only be one active relationship between two PowerBI model tables all remaining relationships must be set to inactive a single active relationship means there is a default filter propagation from the date to the sales table the active relationship is set to the most common filter used by the company’s reports which is the order date relationship you can utilize the inactive relationship for specific analytical needs using the DAX use relationship formula so how do active inactive relationships relate to role- playinging dimensions here’s a quick demonstration of how these concepts function in the Adventure Works database let’s begin with creating a role- playinging dimension after importing sales and date tables you can create two relationships between them one for order date and another for shipping date by default the first relationship is active and the second is inactive the date table serves as a role- playinging dimension for both order and shipping date any analysis reporting and visualization you require can make use of this active relationship occasionally you’ll need to analyze data from a unique perspective for example Adventure Works needs to calculate its total sales based on the shipping date however the shipping date is an inactive relationship so using this calculation requires a measure to create such a measure an inactive relationship needs to be employed this is where the DAX function use relationship comes in to use the shipping date the inactive relationship create a measure using use relationship for instance to calculate the total sales based on the shipping date you can create a DAX formula calculate is used here to alter the filter context of the entire measure sum is summing up the sales amount column of the sales table as the sales table is connected to the date table via order date column by default each DAX calculation is based on the relationship between the tables user relationship function in DAX overrides the relationship and establishes a temporary relationship based on the shipping date column of the sales table or inactive relationship the relationship becomes active only for the current calculation this formula forces PowerBI to use the inactive shipping date relationship for the calculation role-playing dimensions and active inactive relationships in PowerBI create an efficient data model for comprehensive analysis although it might take some time to get used to these concepts they will prove invaluable as you navigate your PowerBI journey as a data analyst you’ll often encounter table relationships that are difficult to perform analysis with fortunately you can alter or manipulate table relationships to facilitate more efficient analysis using the use relationship function over the next few minutes you’ll explore the use relationship function its syntax and its application adventure Works needs to analyze its sales data based on the shipping date it could create a calculated table for the shipping date and relate it to the sales table this might work well for a smaller data set but Adventure Works has millions of shipping records a more effective approach is for Adventure Works to use the use relationship function to create a measure that utilizes the inactive relationships between the tables before we explore how Adventure Works can analyze its sales data let’s find out more about the use relationship function the use relationship function is used within the calculate function it forces the inactive relationship between the tables for the considered calculation to be used this lets you switch contexts within your data model without changing the default relationship between the tables it’s most useful when there are multiple relationships between two tables the function allows you to create contextaware calculations that can analyze data based on different date dimensions or adjust analysis based on a different category of products the advantage of use relationship is that it enables you to perform analyses using different relationships available between the related tables without affecting the overall structure of the data model now that you’ve explored how the use relationship function works let’s review the syntax begin with the function and then place your argument in parenthesis the argument is the names of the required tables and their respective columns that define the relationship the order of the columns doesn’t matter for the accurate calculation this function doesn’t return a value but modifies the context of a calculation this changes the table relationships meaning that there is no scalar value or table returned as a function is executed instead it changes the context by overriding the relationship between tables let’s return to Adventure Works data model to explore the syntax in action as you discovered earlier Adventure Works data model has a sales fact table and a date dimension table the data model’s current active relationship is from the sales tables order date column and the date table’s date column as no shipping date dimension table exists in the data model Adventure Works needs to create an additional relationship between the sales fact table and the date dimension table using the sales table’s shipping date column by default the active relationship for any analysis and visualization is utilized however there may be a requirement to calculate the total sales using the shipping date to do this it can use the use relationship function within the calculate functions first Adventure Works creates a sales by shipping date measure then it inputs the calculate function followed by the required argument in parenthesis in this argument the sum expression calculates the total of the sales amount column from the sales table the use relationship function changes the context of this calculation by switching the active relationship from the sales tables order date column and date tables date column to the sales tables shipping and date date to sales shipping date and date date when executed this calculation results in multiple relationships between these tables an active relationship with the order date and an inactive relationship with the shipping date this affects only the calculate function where it’s used it won’t permanently alter the active relationship let’s review some important points to remember when working with use relationship use relationship only works within the calculate and calculate table functions if you try to use it elsewhere you will receive an error use relationship functions can be used multiple times within a single calculate function to switch multiple relationships the use relationship must exist in the data model but it doesn’t have to be active the use relationship function provides flexibility to derive insights from different perspectives within a data model this provides a layer of flexibility to PowerBI making it an essential function for data analysts to master it can be challenging for a data model to handle various roles for a single dimension so analysts deploy the use relationship function in their calculations to configure role-playing dimensions in this video you’ll learn how to configure a role-playing dimension in PowerBI using calculate and use relationship adventure Works wants to analyze its sales data based on the shipping date instead of creating a separate date dimension table it can use the use relationship function in DAX to roleplay dimensions helped the company achieve this by launching PowerBI desktop and loading the Adventure Works data set the data model contains two tables called sales and date the sales table tracks Adventure Works recent sales data access PowerBI’s model view to view the sales and date tables however after loading data the model is missing the relationships you can establish the relationships between the sales and date table in the model view of PowerBI select and drag the order date column from the sales table to the date table this is the active relationship between these two tables next select and drag the shipping date field from the sales table to the date column of the date table this is an inactive relationship represented by dashed line you can validate the relationship by selecting the connector line between the tables and doubleclicking it opens the edit relationship dialogue box you can observe the checkbox make this relationship active is unchecked next you need to create the measure total sales by shipping date in the home tab of data view select the new measure from the calculations group this opens the DAX formula bar write DAX code in the formula bar that uses use relationship function to create a custom relationship between the date column of the date table and the shipping date column from the sales table press enter to execute the code a new total sales by shipping date measure appears under the sales table in the data pane on the right hand side of the PowerBI interface you can use this new measure in any report or visualization to analyze monthly sales data based on the shipping date you should now be familiar with the process for configuring a role-playing dimension in PowerBI using calculate and use relationship by now you should be familiar with methods for generating insights into your data but the most powerful and effective data insights you can generate are timebased in this video you’ll explore the concept of time intelligence and discover its importance by reviewing some scenarios where it can be applied over at Adventure Works the company is preparing its sales strategies and marketing campaigns for the year ahead as part of its preparation it needs to generate insights into time related data like seasonal trends annual growth and specific sales periods adventure Works can generate insights into these timerelated aspects of its business by using time intelligence functions as the Adventure Works scenario suggests time intelligence functions refers to methods and processes that aggregate and compare data over time data analysts can deploy time intelligence functions to analyze data based on time related dimensions time related dimensions include dates weeks and months and quarters and years you can also generate comparisons of time related data over annual periods and yearto date or YTD so why do data analysts view time intelligence as important time intelligence provides the ability to analyze data within the context of time this enables a more in-depth understanding of trends and patterns as the earlier Adventure Works example demonstrates this data plays a significant role in a business’s ability to generate insights to help with its planning forecasting and decision-making processes let’s explore a few other benefits of time intelligence time intelligence is useful for trend analysis identifying trends in past business performance is crucial for future decisions for example Adventure Works can use time intelligence data to examine historical sales trends and recognize if certain products sell better at specific times of the year identifying trends in past business performance is crucial for future decisions insights derived from time intelligence also help with forecasting and predictive analysis adventure Works can forecast future trends and plan activity based on historical trends it can make informed predictions about sales and demands which helps with resource planning budgeting and risk management for instance if the data shows a consistent increase in mountain bike sales every spring the company can ensure adequate inventory before the season starts time intelligence also enables real-time performance monitoring this is possible by creating dynamic measures like year-to-ate or YTD and month-to-ate or MTD adventure Works can use these measures to monitor real-time performance against key performance indicators the company can then use these insights to respond quickly to changing conditions time intelligence calculations facilitate comparative analysis an example of this is year-over-year R Y functions adventure Works can compare its current growth rate sales performance and other metrics against data from previous years to analyze its progress time intelligence also facilitates the optimization of sales and marketing strategies adventure Works can analyze its sales trends and the impact of its marketing efforts over time it can then use the results of these analyses to fine-tune its marketing strategies and sales tactics to improve its results now that you know its benefits your next question might be how do I use time intelligence implementing time intelligence involves creating calculated fields and measures to analyze data over time you can use PowerBI’s automatic time intelligence features or deploy DAX formulas to create quick measures powerbi offers an auto date time feature that allows easy data analysis by year quarter month and date this is useful for smaller data models powerbi automatically creates one date table for each date column in the date model to analyze data by different date attributes this table is hidden from the user because PowerBI handles it automatically you can also use custom DAX calculations to shape your data model and implement time intelligence calculations with more complex and non-standard requirements time intelligence is essential for understanding and visualizing time related trends and patterns in data as a PowerBI developer mastery of time intelligence calculations is key to generating meaningful information from your data summarizing data over a specific period is a key skill for data analysts timebased data can generate temporal insights and trends within data in this video you’ll review the importance of using DAX based time intelligence functions to summarize data over time over at Adventure Works the company needs to generate insights into its recent sales trends the insights it requires includes revenue growth seasonal sales patterns and the impact of marketing campaigns adventure Works can generate these insights using time intelligence functions index to summarize its data over time so what does it mean to summarize data over time at its core summarizing data over time is identifying trends patterns and anomalies in business performance over a specific period like sales per quarter or annual growth you can generate these insights by using timebased data summarization functions some frequently used examples of these functions include total year-to- date year-to- date and dates between each function generates insights into different aspects of your data the functions are written by stating the function name and the required arguments in parenthesis this basic structure is similar across all functions but the syntax for the arguments varies r must be combined with calculate and other functions let’s begin with the year-to-ate calculation the year-to-ate calculation or YTD aggregates values from the beginning of the year to the specified date for example all sales from January 1st of that year to the specified date the year-to- date requires two mandatory and two optional arguments expression is the first mandatory argument it calculates the total sales from the sales table dates is the date column we use PowerBI default date dimension in the current lesson filter and year-end date are optional parameters for example Adventure Works wants to evaluate its realtime sales performance call the expression sales year-toate and add the total year-to-ate function after the equals operator in your first parameter reference the total sales column from the sales table and aggregate the values using sum in the second parameter reference the order date column from the sales table then add another date field in square brackets when you type the date field PowerBI allows you to select a field from the table next let’s review the date year-to- date function this function returns a running total in the form of a single column table containing year-to-ate or YTD dates in the current filter context this function is part of a group that also includes the dates MTD and dates QTD DAX functions for monthto date also called MTD and quarter to date or QTD you can pass these functions as filters into the calculate DAX function the syntax contains two arguments the first is dates the column containing the required dates and the second is the year end date an optional parameter while the total YTD function is simple it limits the filter expression to only one filter if you need to apply multiple filter expressions within year-to-ate values use the calculate function then pass the dates YTD function as one of the filter expressions for example Adventure Works needs a running total that calculates its year-to-ate sales on a month-by-month basis based on the order date column from the sales table it can calculate this by creating an expression called sales yearto date method 2 the expression does not refer to any separate date table instead the dates YTD function is combined with the calculate function so Adventure Works can incorporate further filters when executed the expression returns a calculated table with the required running monthly total the next function is dates between this function returns a table that contains all dates between a specified start date and an end date the syntax contains three arguments dates is the column containing dates start date is the date expression for the start of the calculation end date is the date expression for the last date for the calculation adventure Works wants to evaluate its total sales over the summer season so it must create a measure using the dates between function in DAX the DAX code computes the total sales between June 1st and August 31st 2023 the calculate function computes the values of the total sales column of the sales table and dates between defines the period for which the sales values are to be computed when executed the expression returns a calculated table with the required total sales figures as these examples have shown your data model requires a date table or dimension before you can use time intelligence functions however you can use PowerBI’s auto date time intelligence if you’re missing the date dimension or you can create a date dimension in PowerBI using Power Query or DAX as you’ve just discovered DAX-based time intelligence functions provide valuable flexibility in summarizing and analyzing timebased data you can use these functions with other DAX functions to build powerful and insightful data models as a data analyst it’s important to be able to compare data sets particularly those from different periods like previous years or months in this video you’ll learn how to use DAX for comparison over time using time comparison functions like date ad parallel period and same period last year adventure Works is preparing its marketing campaign for the holiday season as part of its preparations it needs to analyze and evaluate campaigns from previous years adventure Works can implement DAX time intelligence comparison functions to identify trends and patterns from marketing campaigns from previous years it can then use these insights to inform its current campaign before you can help Adventure Works let’s find out more about comparison over time comparison over time means as the term suggests comparing sets of data over specific periods for example comparing sales from this month to last month these comparisons are generated using time intelligence functions in DAX like same period last year date add and parallel period the basic syntax for each function is to state the function name followed by the required arguments in parenthesis however the rest of your syntax can vary according to the functions requirements and your analytical needs when executed the functions return insights in the form of a table let’s explore an example of each function from the Adventure Works database to learn more about how they work the same period last year function returns a table that contains a column of dates these dates are shifted one year back in time from the dates in the specified dates column in the current context in other words it compares the current period against the same period from last year the syntax for this function requires one argument in the form of specific dates adventure Works can use this function to evaluate its sales from the previous year to compare them against the sales team’s performance from this year it first creates a measure called revenue previous year then it defines var as the variable for the previous year’s revenue calculate computes the total revenue based on the same period last year function which takes the date column from the sales table as its parameter in this instance we are using PowerBI’s autogenerated date dimension finally the return function displays the value of the entire expression next Adventure Works wants to evaluate its year-over-year change in sales it can modify the measure it just created to calculate the change ratio it first creates a new measure called revenue year-on-year percentage variables used in the expression enhance the code readability and query performance and in addition to the previous calculation the divide function computes the change ratio of sales amount by dividing the difference by the previous year’s revenue the results of both measures can be visualized in table format the following table extract compares revenue for July and August over a three-year period next let’s look at the date add function the date add function returns a table containing a column of dates added either forward or backward in time by the specified number of intervals from the dates in the current context the syntax contains three arguments dates is the column containing the required dates the number of intervals is the integer value that defines the number of intervals to add or subtract from the date interval is the unit of time by which to shift the date the unit can be a year quarter or month for example Adventure Works can use the date add function to compare this month’s sales with the previous month’s sales the calculate function computes the total revenue based on the filter arguments previously computed in the revenue measure date add function takes the order date column from the sales table as a date reference one represents the unit of time and the negative sign indicates that the intervals are back in time month represents the unit of time you can also use day quarter or year the results of this measure can be visualized in table format the following table extract compares sales revenue for August to October over a 2-year period comparing data over time is a powerful method for deciphering business trends and growth patterns mastering this skill will enable you to provide valuable insights for your organization to help it strategize and grow when working with time oriented values your date table must be correctly formatted and configured to avoid issues with your analysis in this video you’ll explore the process for setting up and the benefits of a common date table adventure Works data model has multiple fact tables tracking different aspects of its business like sales products and resellers but the data model doesn’t contain a date table this means there’s a risk that the different fact tables might represent dates differently without a common date table this makes it difficult to compare or relate data from diverse sources let’s find out more about the role of a common date table then help Adventure Works to add one to its data model a common date table or date dimension is a prerequisite for time intelligence calculations you can’t execute them without a date dimension the date dimension must meet the following requirements there must be one record per day there must be no missing or blank dates and it must start from the minimum date and end at the maximum date corresponding to the fields in your parameters but what if your data model is missing a date dimension in this instance you can use PowerBI’s autodate time intelligence you can also create a date dimension in PowerBI using either Power Query or DAX this is useful when working on large data sets with complex calculations you can create a date dimension with DAX using the calendar and calendar auto functions both functions return a calculated table with a single date column and a list of date values when executed adventure Works could use the calendar function to create its date dimension the company can use the calendar function as a calculated table called date it can then include its required periods start and end dates as its arguments it can also use calendar auto the calendar auto function scans the data model for the date column it takes the start and end date from the order date column from the adventure works sales table fiscal year and month is an optional parameter if defined for a different end of the year month for example if you specify three the year starts on April 1st and ends on March 31st if not specified PowerBI takes the default year-end month which is December now that you’ve explored the basics of a common date table let’s help Adventure Works build one in its data model begin by launching PowerBI desktop and loading the Adventure Works data set the data model contains five tables: sales salesperson products reseller and region the sales table tracks Adventure Works sales data the data model has no date dimension table so you’ll need to create one navigate to the home tab and select new table in the formula bar that appears on screen write the DAX code using the calendar function to create the date dimension table this table must calculate all date values between the 1st of January 2017 and the 31st of December 2021 when executed the DAX code creates a table with a single column containing the dates specified in your code the date values in the column also have timestamps format the column as date format to remove the timestamps select an appropriate format from the drop- down list of the format section navigate to the home tab and select new table to populate the common date table you need to write more DAX code using the date related functions like year month week number and weekday these functions extract the relevant information from the date columns of the other tables next you need to mark the common date table as the date table navigate to the date pane select the ellipses to the right of the date table and select mark as date table from the drop-own list of options this opens the mark as date table dialogue box select the date option from the date column drop-own menu if these steps are completed successfully a validation message appears select okay this action overrides the PowerBI’s autogenerated date dimension for all time intelligence and datebased calculations in DAX within the data model finally access the model view of PowerBI and establish the new one to many relationship with single cross- filter direction between the date table and the sales fact table drag the date column from the date table to the order date column in the sales table the model is now configured for time intelligence calculations adventure Works can use the model to generate its timebased reports and visualizations you should now be familiar with configuring and formatting a common date table in your data model a common date table makes the data analysis process more accurate and efficient it’s an essential part of every data analyst’s toolkit to execute time intelligence functions your data model must contain a common date table in this video you’ll explore the process for setting up a common date table using IM language in Power Query adventure Works must execute time intelligence functions but its data model lacks a common date table let’s help Adventure Works by creating a date table using M language in Power Query m is a PowerBI developmental language used in Power Query to create new dimensions and tables within a data model it provides a much more visual approach to creating dimension tables to assist Adventure Works load the data tables into the PowerBI data model select transform data in PowerBI desktop to open the Power Query Editor access the Home tab and select new source select blank query from the drop-own list of options add the required IM language code to create the date dimension table in the editor the list dates function lists the dates in this code based on the provided date range in this instance you’re creating a 5-year table from January 1st 2017 to January 1st 2021 the syntax 365×5 represents all the possible dates within this 5-year range and duration specifies the duration of the period with one equaling one day once you execute the code PowerBI generates a list of dates these dates must be converted to a common date table navigate to the top left side of the Power Query editor in the transform tab and select to table this action converts the list of dates to a table with a column named list by default rename the column as date next you must change the columns data type to the date data type right click to open the drop-own list and select change type select the date option from the list now you need to populate the table with the related columns select the table’s date column and navigate to the add column tab of Power Query Editor select the date section to expand the drop-own list of options select the following columns to add to the table from the drop-own list year month name of month name of day and week of year access the properties name field in the query settings and rename the query as date then select close and apply to return to the PowerBI interface finally select the ellipses next to the date table from the data pane and mark the table as a date table select the date column from the dialogue box then select okay to confirm finally establish the required relationships between the data models date table and other tables the model is now configured for creating time intelligence measures using DAX and for creating reports and visualizations in this video you learned how to set up a common date table using IM language in Power Query this video is a short introduction to IM language and Power Query you’ll learn more about IM language as you continue your PowerBI studies meet Tina Adventure Works in-house expert on using time intelligence calculations in DAX adventure Works is looking to optimize all aspects of its business from sales and deliveries to financial planning using time intelligence calculations in DAX the company suggests that Tina analyze its data in these areas and generate insights that reveal where improvements could be made to the business first Tina focuses on sales she performs timebased trend analyses using year-to-ate functions to analyze trends and patterns in sales over time her analyses reveal seasonal spikes and downward trends in sales of certain products over different months and quarters adventure Works can use these insights to forecast demand for its products this means the company better understands what products customers purchase and when they will most likely buy them it can design and implement marketing strategies targeting consumers during the months they’re most likely to purchase specific products tina’s insights into sales trends also help Adventure Works to manage its inventory better by identifying what kinds of bicycles customers are likely to buy and when adventure Works can then ensure that these products are in stock in time for busy sales periods tina can also use time intelligence functions to track sales team performance she can compare current and past performance data to prepare for the upcoming sales period the insights generated from her comparisons are then used to set realistic targets for the team and identify the high performers the upcoming sales period also requires large investments in inventory and marketing fortunately time intelligence is also a useful budgeting and financial planning tool tina can compare actual financial data with budgeted values over different periods assess financial performance and track spending the company’s finance team can use these insights to make budget adjustments time intelligence functions can also identify issues and their root cause for example Adventure Works anticipated a high volume in sales of mountain bikes over the holiday sales period but sales declined over the season tina can use time intelligence functions to drill into the related data and isolate these sales anomalies to analyze the root cause of the slowdown in sales for example the decline in sales might indicate a shift in customer behavior that needs to be addressed time intelligence in PowerBI is an important tool that businesses can use to use the power of time dimensions in data analysis through the insights generated by time intelligence businesses like Adventure Works can generate valuable insights that drive informed decisionmaking and help resolve issues congratulations on reaching the end of the second week in this course on data modeling in PowerBI this week you’ve explored how to use data analysis expressions or DAX in PowerBI let’s take a few minutes to recap what you’ve learned in this week’s lessons you began the first lesson by learning about DAX dax is a programming language that adds new information about existing data it consists of a library of functions operators and constants these are used in formulas or expressions to add information missing from the original data model a key element of formulas is functions functions are reusable logic used in a DAX formula to perform tasks like aggregation or calculations commonly used DAX formulas and functions include calculate sum and average you then explored the syntax of a formula a formula begins with the name of your new calculated column or table followed by an operator typically an equal sign you then write the name of your DAX function and parenthesis that contain the logic of your formula you then learned about row and filter context dax computes formulas within a context the evaluation context of a DAX formula is the surrounding area of the cell in which DAX evaluates and computes the formula row context refers to the table’s current row being evaluated within a calculation while filter context refers to the filter constraints applied to the data this determines which rows or subsets should be included or excluded from the calculation you are then introduced to calculated tables and columns a calculated table is a new table created within a data model based on data from different sources a calculated column is a new column added to an existing table that presents the results of a calculation you then completed the lesson by putting your new skills to the test by assisting Adventure Works with its use of DAX in the exercise and completing a knowledge check in the second lesson you received an introduction to measures you learned that a measure is a calculation or metric that generates meaningful insights from data measures are an important aspect of data analysis and play a lead role in creating calculated tables and columns there are three different types of measures additive semi-additive and non-additive which type of measure is used depends on the needs of your data and its dimensions a key element of measures is statistical functions statistical functions calculate values related to statistical distributions and probability to reveal information about your data several common statistical functions are used in measures like average median and count you learned how to build statistical functions into your syntax and explored how to use common functions like using the average function to calculate the average of a data set you then discovered how context impacts DAX measures you reviewed Adventure Works business scenarios in which the context of measures influenced the company’s business decisions finally you tested your new skills with a knowledge check and explored additional learning material in the additional resources in the third lesson you expanded your understanding of measures you began by learning how to create quick measures in PowerBI using common calculations instead of DAX codes you then explored techniques for creating more complex custom measures with DAX next you learned how to use the cross filter function you can use the cross filter function to change the cross filter direction between tables for a specific measure while maintaining the original table settings a cross filter function can only be used with a DAX function that accepts a filter as an argument like calculate you can use calculate and its related modifiers to combine filters and generate more granular insights into your data you then tested your new skills by adding a measure to an adventure works data set in the exercise and you tested your understanding of the topics in a knowledge check in the fourth lesson you explored how DAX is used with table relationships you began the lesson by learning about role-playing dimensions instances of the same dimension used multiple times in a data model each instance plays a unique role by representing different aspects of the data this allows analysts to analyze data from different viewpoints without duplicating data tables in a data model relationships between tables are either active or inactive you can configure these relationships using the use relationship function alongside the calculate function to force the use of the inactive relationship you completed this lesson by helping Adventure Works to add a role-playing dimension between two tables in its data model you then tested your understanding of the topics in a knowledge check and explored further learning material in the additional resources in this week’s final lesson you explored time intelligence in DAX you learned that time intelligence functions refer to methods and processes that aggregate and compare data over time these functions can be used in PowerBI through the auto date time feature or DAX dax can summarize data over time by identifying trends patterns and anomalies over a specific period or it can be used for comparison over time by comparing data sets over specific periods these insights are generated using summarization and comparison functions that return the required insights there are also more complex functions that can be used with time intelligence a prerequisite for using time intelligence functions is a common date table or date dimension if this isn’t present in your data model you can build one using the calendar function or the calendar auto function both functions return a calculated table with a single date column and list of date values you also learned how to generate a calculated date table using language in Power Query you then explored a realworld scenario where time intelligence played an important part in a business’s decision-making process during this lesson you helped Adventure Works use time intelligence calculations in DAX during an exercise and activity you’ve now reached the end of this module summary it’s time to move on to the discussion prompt where you can discuss what you learned with your peers you’ll then be invited to explore additional resources to help you develop a deeper understanding of the topics in this lesson best of luck we’ll meet again during next week’s lessons imagine you’re a data analyst at Adventure Works a thriving multinational bike manufacturing company your role is significant it involves digging deep into the vast array of data sifting through it and translating it into meaningful actionable insights decision makers in Adventure Works rely heavily on your PowerBI dashboards which provide a window into the world of Adventure Works vast data landscape these dashboards through your analysis guide the company and reveal its successes challenges and opportunities however over time you start noticing an issue as the data volume is growing the reports are slowing down simple queries that used to take seconds now take many minutes even hours this bottleneck is frustrating staff delaying decisions and even starting to undermine the value of datadriven solutions there is an urgency to fix the situation and you must act before the issue escalates further that’s when you realize the need for performance optimization this video covers the importance of performance optimization in PowerBI and how it affects the overall performance of data models reports and dashboards by the end of this video you’ll understand the benefits of PowerBI performance optimization such as enhanced speed and efficiency informed decision-making improved user experience resource efficiency and timely report generation over the next few minutes you’ll learn about the challenges Adventure Works face due to growing data volume and how performance optimization in PowerBI can address these issues in the context of PowerBI optimization refers to the process of modifying tuning or streamlining your data models reports and dashboards to achieve the best possible performance at its core it’s all about making sure your reports and dashboards run as smoothly and quickly as possible when you’re dealing with small volumes of data performance isn’t typically a concern but as your data grows the performance of your PowerBI solutions can start to deteriorate this might manifest as slow report loading times sluggish response times when interacting with dashboards or even timeouts and errors performance issues can arise due to a variety of factors including inefficient data models complex DAX calculations and inappropriate visuals however regardless of the cause performance issues can have a significant negative impact on the user experience and the usefulness of your PowerBI solutions that’s where performance optimization comes in by understanding and applying optimization techniques you can improve the performance of your PowerBI solutions ensuring they continue to deliver value as your data grows now let’s dive into some of the benefits provided by performance optimization first enhanced speed and efficiency adventure Works manages enormous volumes of data from sales records production statistics customer interactions to employee information this data holds valuable insights that guide strategic decision-making by optimizing your PowerBI report and data model you can significantly cut down the loading and processing time of large data sets allowing you to execute queries faster this means the different teams at Adventure Works from sales to production to management can quickly access the data they need reducing weight times and enhancing overall productivity the next benefit of performance optimization is informed decisionmaking the ability to make timely and informed decisions at Adventure Works is critical to its success if there’s a sudden drop in sales of a specific bike model or if a new bike accessory becomes a hot seller company decision makers must know about it as soon as possible to adjust its strategies accordingly with an optimized PowerBI data model reports load swiftly enabling faster analysis of trends and thereby leading to more prompt informed decisions next let’s look at the improved user experience of optimizing performance in PowerBI at Adventure Works numerous team members rely on PowerBI reports for their tasks slow loading reports can lead to frustration loss of time and lower productivity in contrast an optimized PowerBI system can dramatically improve the user experience by ensuring reports load smoothly and swiftly this way team members can focus on deriving insights instead of waiting for reports to load as Adventure Works continues to expand the data it manages grows as well requiring more computing resources in this situation they need more efficient use of resources an optimized PowerBI data model can make more efficient use of the resources handling larger volumes of data without a noticeable drop in performance this is crucial as it allows Adventure Works to handle its growth and the accompany increase in data without requiring excessive increases in computing resources lastly there is timely report generation different teams at Adventure Works may require regular reports to function efficiently the sales team might need weekly sales reports while the manufacturing team might require daily production reports with an optimized PowerBI data model these reports can be generated and distributed in a timely manner facilitating smooth operations across the company and ensuring each team has the data it needs when it needs it by embracing the power of performance optimization in PowerBI you’re not just enhancing the speed and efficiency of reports and dashboards you’re helping Adventure Works to make better decisions faster remember every second saved in loading a report every query executed faster every frustration eliminated by a smoothly loading dashboard these are victories in your quest to unlock the full potential of data so continue to explore optimize and innovate for it’s through these actions that you make a difference in organizations industries and the world you are the data pioneer and the future is in your hands imagine it’s your first day at Adventure Works a multinational manufacturing company renowned for its premium bicycles as a newly hired data analyst you have an enormous challenge to analyze the constant stream of data generated by the company’s diverse operations every sale in North America every accessory produced in Asia and every customer interaction in Europe sends ripples through the vast ocean of data that Adventure Works amasses every day this data is a disorganized treasure trove filled with critical insights that can drive strategic decision-making and fuel the company’s continued growth but how do you extract these precious insights from an unoptimized data set that’s where your secret weapon comes in the effective combination of optimization techniques and PowerBI this video aims to assist you in understanding the fundamental concept of optimization in PowerBI using a relatable scenario set in the context of Adventure Works by the end of this video you’ll understand the various optimization techniques such as sorting filtering indexing and data transformation and how they contribute to enhancing the efficiency and accuracy of data analysis over the next few minutes you’ll learn the importance of optimization in decisionmaking and strategy formulation to recap optimization in the context of PowerBI is the process of transforming cleaning and organizing your data sets to achieve the best possible data performance optimization involves techniques like filtering sorting and indexing which can make your data more manageable and your searches faster improving overall efficiency adventure Works operates in a data inensive environment this includes sales data from diverse markets manufacturing data from various plants product management data on hundreds of items human resource data on employees from different regions and much more to help understand this let’s put ourselves in the shoes of Lucas Pereira an assistant data analyst at Adventure Works lucas is tasked with understanding the sales performance of their different bike models across North America the sales data in front of Lucas is vast filled with information about bike models sales dates customer details and regions this is where optimization becomes a vital tool in Lucas’s arsenal there are four tools that will help Lucas with his task: sorting filtering indexing and data transformation in PowerBI sorting is an optimization technique that allows Lucas to organize his data alphabetically by bike model this seemingly straightforward step is like putting on a pair of glasses it sharpens the focus on the sales patterns and performance of each bike model making the data set much easier to read and interpret the benefits of sorting go beyond simplicity and aesthetics it sets the stage for faster and more efficient data processing by grouping similar data the search operation is enhanced thereby saving time it allows Lucas to identify trends patterns and outliers more quickly leading to quicker insights and decision-m in the competitive environment that Adventure Works operates this speed can translate into significant business advantages lucas then moves on to filtering his data to focus on his area of interest North America filtering data enhances clarity and relevance it eliminates unnecessary noise making the data more manageable lucas removes all irrelevant data related to other regions filtering leaves him with a data set that focuses exclusively on North American sales and by doing so Lucas can conduct more precise and targeted analyses leading to more relevant insights and strategies it also reduces the processing time and computational load making the overall process more efficient if filtering takes place during the transformation stage it also reduces the amount of data stored within PowerBI like using a well-laidout map to reach a destination faster indexing enhances the data analysis process by providing faster access to specific data points lucas creates an index on bike models and regions this allows him to quickly locate the data for a particular bike model in a specific region without having to sift through the entire data set it saves time and makes the analysis process more efficient enabling Lucas to respond faster to queries or generate reports more quickly thereby enhancing the decision-making process finally Lucas applies data transformation to standardize the sales dates which are in multiple formats the key benefit of data transformation is the improvement in data consistency which facilitates more accurate and meaningful analyses standardizing the dates allows Lucas to conduct a proper date related analysis enabling him to track and forecast sales patterns accurately it helps eliminate potential errors in the analysis due to inconsistent data the cumulative effect of these optimization techniques turns data sets into a powerful instrument of insight lucas’s journey through the data set of Adventure Works demonstrates that by streamlining and simplifying the data set optimization makes the data more accessible and manageable by applying optimization techniques businesses like Adventure Works can harness the true power of their data turning information into actionable business strategies as you’ve seen through Lucas’s journey data is more than just numbers on a screen it’s a mosaic a narrative a path that can lead you to new insights strategies and victories but to interpret data effectively you must refine it shape it and most importantly understand it that’s what optimization techniques do they’re the compass the map and the light that guide you through the maze of data so step up to the challenge use the power of optimization in PowerBI to create your own stories of success imagine it’s a Monday morning at Adventure Works headquarters and sales data from the previous quarter has just arrived as a newly appointed data analyst you’re eager to dive in and extract meaningful insights from the data pouring in from several stores and customer orders worldwide in addition there’s data from various suppliers and manufacturers who deliver essential parts for Adventure Works diverse bicycle product line for this report you are tasked to trace the journey of a specific component from the Adventure Works suppliers data set to the products data set as you start loading the data into PowerBI things begin to slow down queries that should take seconds are taking minutes and some aren’t loading at all you notice that the performance issues intensify when dealing with relationships between the different tables in your data model specifically many to many relationships this video helps you to understand how to identify data model performance issues in relationships and how to resolve them by adjusting the cross filter direction by the end of this video you’ll understand how to edit the relationships and optimize the performance of your data model using PowerBI over the next few minutes you’ll learn how to balance accuracy and performance in your data model by applying birectional filters only where necessary to understand the issue let’s first dive into what a manyto-y relationship entails in a data model relationships in data models represent how data tables connect and interact with each other the simplest form is a onetoone relationship where one row in a table corresponds to one row in another however real world data isn’t always that simple often one record can correspond to multiple records in another data set and vice versa this is where you can encounter the many to many relationships in the context of Adventure Works consider the relationship between the products and suppliers tables each product at Adventure Works is made up of various components from multiple suppliers and each supplier can provide components for multiple products this mutual relationship where each entity can relate to multiple entities on the other side is what we call a many to many relationship now let’s dive into the cause of many to many performance issues and how you can resolve it your focus is on the model view so select the bottom icon in the model view your tables are represented as boxes with field lists lines connecting these boxes represent the relationships between these tables find and select the specific relationship you wish to edit in this case you are interested in the relationship between the products and suppliers tables if your model has many tables and relationships you might need to drag the tables around or zoom in and out using the scroll wheel or the zoom slider at the bottom right of the screen now that you’ve located the relationship it’s time to edit it double click on the line connecting the products and suppliers tables this action opens a new dialogue box titled edit relationship the cross-filter direction between the products and suppliers table is causing performance issues in the data model since you wanted to trace the journey of a specific component from the adventure works suppliers table to the products a one-way filter would be appropriate for this limiting the products data to only those that involve the chosen component in the edit relationship dialogue box locate the option labeled cross filter direction the current setting is both meaning filters can flow from the products table to the suppliers table and vice versa to change the cross filter direction to reduce this complexity select the drop-own menu for cross filter direction and select single or suppliers filters products now that you’ve made the desired changes it’s time to save them at the bottom right of the manage relationships dialogue box select the okay button this action will close the dialogue box and apply your changes to the data model by changing the direction of its filter you’ve simplified the data model this simplicity has made it more efficient and resolved the performance issues you’re a newly hired data analyst at Adventure Works your first task is to source prepare and analyze data to aid the marketing initiatives as you’re delving into the data you start to encounter an issue you notice that your PowerBI reports usually swift and reliable have started to slow down you discovered that this is due to high levels of cardality in this video you’ll explore the impact of cardality on performance and how high cardality affects your data analysis tasks by the end of this video you’ll have the practical knowledge to reduce cardality to improve the performance of your PowerBI reports over the next few minutes you’ll learn how to identify high cardality explore strategies to reduce cardality decimals and consider the implications of these changes on your data as you might already be aware cardality in the context of PowerBI refers to the number of distinct values in a column for example imagine analyzing a data set containing a column called product category within this column you might find several different categories each of these unique categories represent a distinct value and the total count of these unique items determines the cardality of the product category column a column with a high number of distinct values has high cardality when you have high cardality it can increase the size of your data model and the time taken to process queries slowing down your PowerBI reports imagine trying to find a specific book in a library that doesn’t have a categorization or indexing system that’s essentially what happens when cardality is high the PowerBI engine must sift through more unique values slowing down the process while high cardality can slow down the performance of your PowerBI reports identifying high cardality columns and modifying them appropriately can enhance your report’s performance powerbi itself is a high-erformance system that can handle large volumes of data with high cardality however there are always trade-offs in system design and reducing cardality can help when dealing with truly large data sets let’s explore some methods for reducing high cardality one strategy to reduce cardality is through summarization during transformation this step is similar to moving from a detailed view to a summary view of your data instead of looking at individual transaction data you can group them by categories such as product category order date or delivery date in Adventure Works instead of analyzing every unique bike sale you could aggregate sales data on a product category basis however that’s not the only method to reduce high cardality a second strategy is to reduce cardality by changing decimal columns to fixed decimals high precision decimal values can significantly increase cardality for instance consider the product weight column in Adventure Works sales table responsible for tracking the weight of each bike to the microgram the variation in bike weights is very large leading to high cardality by rounding these weights to a fixed decimal point you can significantly reduce cardality now that you’ve learned how to identify high cardality let’s look at how you can reduce it as you just discovered you can reduce the cardality of Adventure Works data model through summarization once you have located the column you want to summarize in this case product category select the columns header to select the entire column then go to the transform tab on the top menu bar in the transform toolbar select group by a new group by window will appear in this window you can specify the column you want to group by and the aggregation function you want to apply like sum count average etc based on the nature of your data after specifying these settings select okay this form of summarization lowers the cardality leading to improved performance and as the second strategy demonstrated you can also reduce cardality using fixed decimals to do this locate and select the decimal columns header you want to modify in this case the product weight column then select the transform tab on the top menu bar in the transform toolbar select data type a drop-own menu will appear with a list of different data types from this list select fixed decimal number after this the column’s data type will be changed and it should now contain fewer unique values effectively reducing its cardality by following these steps you can reduce the cardality of your data thereby improving the performance of your PowerBI reports however remember that reducing cardality might also result in less granular data so always take into consideration the requirements of your analysis before you decide to reduce cardality as you continue exploring the world of data always remember that it’s not about having less data or more data it’s about having the right data and when you master this you can turn raw numbers into insightful stories make informed decisions and create impactful change data enthusiasts are often required to look for real-time insights and dynamic visualizations to make informed decisions direct query in PowerBI enables you to dive into vast amounts of data with auto refresh functionalities though direct query connectivity has several benefits it comes with its own set of behaviors and limitations let’s walk through these elements of direct query as data connectivity options in PowerBI adventure Works has expanded its operations in recent years to various regions across the world the company wants to build a real-time sales dashboard to monitor sales performance across various regions categories and products adventure Works has a massive transactional database that records sales data in real time the company also wants to implement data security to ensure data access permissions are defined within the database and users only have access to the data they are authorized to view to meet the requirements of Adventure Works you need to establish a direct query connection in PowerBI to retrieve and analyze the data let’s explore what direct query is and how it can help you to connect to your data sources direct query is a data connectivity option in PowerBI that allows analysts to connect directly to the data sources without importing data into PowerBI model instead of loading data into the memory direct query sends queries directly to retrieve data from the sources for real time analysis although it is best practice to import data into PowerBI model there are times when using direct query is inevitable let’s review some of the benefits that Direct Query offers direct Query allows you to execute queries in real time for example in a multinational retail corporation new sales transactions are added every hour to the database this ensures that the sales dashboard always displays the latest data large data set imports to PowerBI models can cause performance problems and high memory consumption by using direct query PowerBI avoids loading an entire data set to the model optimizing memory usage direct query respects the data source level security ensuring that only the authorized users have access to the data the data access permissions defined in the underlying database are enforced providing a secure and controlled data access environment let’s examine the behavior of direct query connections when you establish a connection in PowerBI desktop via direct query if the connection is made to a relational database like SQL you can select a set of tables from the database that will return a set of data for example at Adventure Works you can select data from the central SQL data warehouse via direct query connection to perform realtime sales analysis data loading in PowerBI only loads the schema not the actual data reports and visuals send queries to the underlying database to retrieve the necessary data the visual refresh time depends on the performance of the underlying data source the tables you selected for Adventure Works are not imported to PowerBI model only the schema is therefore the data refresh cycle sends the query to the central database once added information is recorded to the source database the reports and visuals do not reflect the updated data immediately you will need to refresh the report to display the latest data for instance each new sale record of Adventure Works saved on the database will be reflected on the dashboard after you refresh the report if you publish a PowerBI report to a PowerBI service it displays the same behavior as with imported data except there is no data imported all the report elements can be used in creating a dashboard the dashboard titles are automatically refreshed as per refresh frequency that you can configure dashboard visuals will show data from the latest refresh when opened for example if your manager asks you to present the most recent dashboard every morning then you can set up refresh time an hour before the presentation time the use of direct query can have negative implications the limitations vary depending on the specific data source that is being used it is always faster to query data from memory import data rather than querying it from the server direct query the performance depends on the size of the data the database server specifications the network connection speed and optimizations to the data source you must understand these performance implications before deciding to use the direct query for your data analysis in PowerBI with direct query you can apply some data transformation in the query editor of PowerBI however not all the transformations are supported this also depends on the data source for example a SQL server supports some transformations while SAP business warehouse doesn’t support any transformation in the query editor in the latter case you need to apply transformation in the underlying data source data modeling and DAX are also limited in direct query mode for example PowerBI default date hierarchy is not available in direct query and some of the DAX functions such as parent child functions are also not available complex DAX measures also cause performance issues so it is advisable to start building simple aggregation measures and test the performance before moving to more complex calculations in DAX when using direct query mode almost all the reporting capabilities that you have with imported data are also supported for direct query models provided that the underlying source offers a suitable level of performance however when you publish your PowerBI report to a PowerBI service the quick insights and Q&A features of the service are not supported in direct query mode dax measures filters can cause performance implications in reports of direct query models direct query offers an alternative way to connect PowerBI to the data sources but it has some limitations data analysts must understand the behavior benefits and limitations of direct query before deciding to use it for their analytical and business needs direct query models demand consistent performance across all layers of the solution fortunately there are several optimization and query reduction strategies that you can use to help you along the way over the next few minutes you will learn how to optimize the underlying data source for better query performance adventure Works is experiencing poor report performance it is taking too long for pages to load in the reports table and matrix visuals are not refreshing quickly enough when certain elements of the report are selected while reviewing the data model you discover that the model is using direct query to connect PowerBI to the source data resulting in the poor report performance you will need to act in order to optimize the performance of the direct query model in direct query mode performance optimization is needed at each layer of the solution the first layer of the solution to be optimized is the data source you’ll need to tune the source database any optimization done to the underlying source database will enhance the direct query connection which will improve your PowerBI reports the following standard database practices apply to most situations avoid using complex calculated columns because the calculation expression will be embedded into the source queries review the indexes and verify that the current indexing is correct if you need to create new indexes ensure that they are appropriate powerbi desktop provides you with the option to reduce the number of queries sent to the database in direct query mode in PowerBI the default behavior of a filter or slicer is that when you select an item in that slicer or filter the other visuals of the report will be filtered automatically in direct query mode this will send multiple queries to the database for every selection within a filter or slicer these multiple queries will reduce the performance of your report for example you want to select multiple items but when you select the first item five queries are sent to the underlying database on selecting the second item another five queries are sent to the database this will result in a further slowdown of speed this is especially true when you have a multis select slicer or filter you can optimize the number of queries sent to the database in PowerBI desktop the optimization of performance through query reduction requires effective strategies and techniques aggregations allow for pre-calculated summary values that can be imported and stored in the memory engine of PowerBI an optimized data model can lead to efficient query processing simplifying relationships eliminating unnecessary columns and avoiding complex DAX expressions wherever possible can enhance query optimization by reducing the number of queries sent to the underlying data source you can limit the number of visuals and filters in a PowerBI report while working with direct query connectivity for example you can reduce the number of visuals on the report page or reduce the number of fields that are used in a visual in direct query mode performance optimization is vital to deliver a smooth and responsive user experience implementing query reduction strategies and focusing on query performance enhancements allows you to maximize the benefits of real-time data connectivity in PowerBI as a data analyst you’ll often need to optimize the query performance of direct query connectivity fortunately configuring the table storage will improve data retrieval speed and reduce the query workload on the data source over the next few minutes you’ll learn direct query performance optimization with table storage adventure Works is experiencing slow data retrieval speeds while trying to build its reports upon further investigation you discover that the cause of the slow retrieval speed is due to the query workload on the data source you will need to use table storage to reduce the query workload and improve the retrieval speed let’s explore what storage modes are and how they can be used to optimize the performance of your direct query data sets storage modes in PowerBI determine where the data of that table is stored and how queries will be sent to the data sources you can specify the storage mode of the table individually within your data model the storage mode lets you control whether PowerBI desktop catches table data in memory for reports storage modes in PowerBI offer the following benefits as users interact with visuals in PowerBI reports DAX queries are submitted to the underlying data set caching data into memory by properly setting the storage mode can boost the query performance and interactivity of your reports tables that are not cached don’t consume memory for caching you can enable interactive analysis over large data sets that are too large or expensive to completely cache into memory you can choose which tables are worth caching and which aren’t you can reduce the refresh time by only importing the tables that are necessary to meet your business and analytical requirements this will optimize the data refresh time and frequency now that you’re familiar with what storage modes are let’s examine the three storage modes that PowerBI supports if a table is using the import storage mode it means that the data of that table will be stored in the in-memory storage of PowerBI every query to the data would be a query to the in-memory structure and not to the data source for instance Adventure Works sourced a sales table from a SQL server but is using the import storage mode a copy of the data will be stored in the memory engine of PowerBI whenever you refresh a report in PowerBI desktop it will query the in-memory structure instead of sending queries to the SQL server data source tables using the direct query storage mode will keep the data in the data source for example if adventurework sales data is stored in a SQL server and a report is created within the storage mode PowerBI will send SQL queries to the data source and to retrieve the results because the table is using the direct query storage mode you can use SQL profiler at the same time to view manage and optimize the queries when using dual storage mode one table can act either as direct query or import with respect to the relationship to the other tables in some cases you will fill in queries from imported data while in other cases you will fulfill queries by executing an ondemand query to the data source for example to a SQL server let’s find out how various storage modes work in PowerBI desktop while connecting to direct query mode launch PowerBI desktop and connect to SQL Server via direct query navigate to get data and select a SQL Server from the drop-own list of options you’ll be presented with a SQL Server database dialogue box enter the server name and database name by default import mode is selected select direct query and select okay this action directs you to the SQL server containing an Adventure Works database named Adventure Works DW2022 here you can select the number of tables you want to load to PowerBI model select the following tables from the database the internet sales fact table and the product customer and sales territory dimension tables navigate to model view and expand the properties pane select the sales table scroll down to the properties pane and expand advanced access the storage mode drop-own menu to view the three storage modes select the import storage mode for the internet sales fact table once you have selected the import mode a dialogue box appears on screen this dialogue box warns that setting storage mode to import is irreversible you will not be able to switch back to direct query select okay you have now successfully optimized the storage mode of the fact table in the adventure works database you can further leverage this feature to decide which tables of the schema you need to import and which you can keep in direct query connectivity depending on the analytical requirements in direct query mode performance optimization is vital to deliver a smooth and responsive user experience by implementing query reduction strategies to optimize the number of queries sent to the underlying database and focusing on query performance enhancements you can maximize the benefits of real-time data connectivity in PowerBI aggregations in PowerBI are a great method of generating fast query performance and interactivity in your reports and visuals aggregations in PowerBI enable you to dive deeper into your data without compromising the speed and performance of your query in direct query connections powerbi not only provides a potential solution for small data sets but it also has the potential to handle large data sets by switching to direct query as direct query does not store data into the memory PowerBI sends queries to the underlying data source for every page of the report however direct query mode can be slow depending on the number of visuals in a report and the number of users interacting with the data at a given time for example imagine your report contains four visuals and every time you apply a filter to the data PowerBI sends queries to the data source sending queries to the data source with each interaction makes the direct query quite slow fortunately PowerBI has a solution to handle the slow response of direct query called composite mode composite mode allows you to use part of the model as a direct query which for larger tables is typically a fact table and use part of the model to import data for smaller tables usually dimension tables this approach allows you to achieve better performance when you work with smaller tables as they are just querying the in-memory storage of data however the tables that are part of direct query connection are still slow in response this is where a useful feature within composite mode called aggregations can come into play in PowerBI aggregation refers to summarizing or consolidating large volumes of data into more manageable summary tables to improve query performance by condensing detailed information into simpler high-level values aggregations are the solution to speed up the direct query connected tables within a composite mode with the help of aggregations you can create layers of pre-agregated values which are stored in memory storage of PowerBI for faster performance let’s consider these concepts in a scenario adventure Works wants to analyze data for the last 5 years of sales across all its products and regions the fact table might contain tens of millions of rows making it a huge data set for PowerBI’s import limit of file size in this example the objective of performing the analysis is to query the sales values by the year region product or customers category in short you are querying the fact table by aggregations of the dimension tables therefore creating and managing aggregations of the fact table will help you to reduce the file size of the sales table and optimize query performance for Adventure Works for example suppose you are aggregating sales data by calendar year the aggregated table can pre-calculate the sum of the sales amount for every calendar year in this case you only have five rows of data one for each year and that is smaller than the original fact table this pre-calculated aggregation can be imported to the memory of PowerBI and will be efficient in querying daily analysis furthermore if you want to analyze data at a higher level of granularity at a daily level the total number of data rows is still tiny in comparison to the millions of rows in the fact table as dimension tables are typically smaller than the fact table aggregated tables are always smaller than the fact table before you create aggregations in PowerBI you need to decide the granularity of analysis you want to perform on the aggregations for example evaluating sales amount at day level once you decide on the grain the next step is to create aggregations you can create aggregations in one of three ways you can create a table with aggregations at the database level for instance SQL Server database if you have access to the data source and then import the table to PowerBI you can create a view of the aggregation for example in SQL Server database and import the view to PowerBI if you have access to the data source you can use Power Query editor in PowerBI to create aggregations aggregations in direct query have several benefits let’s explore three specifically in case you are handling a large data set aggregations provide a faster and optimized query performance and assist you in analyzing the data they also reveal insights without querying the underlying data source that is slower in response and in worst case scenario the query times out if users at Adventure Works are experiencing slower refresh time of the reports in PowerBI you can create aggregations which help you to speed up the refresh process the smaller size of aggregated tables imported to memory reduces the refresh time enabling a better user experience adventure Works is anticipating a growth in sales volume for the upcoming year you can leverage aggregations to create and manage aggregations as a proactive measure to futureproof the solution thereby enabling a smooth scaleup of the company aggregations are the game-changing feature of PowerBI in optimizing the speed and performance when dealing with huge volumes of data with the help of aggregations you can have layers of pre-calculated tables stored in the memory of PowerBI always ready to respond to queries when users interact with the data in reports powerbi’s aggregation feature is useful for creating a seamless bridge between raw data and meaningful analytics in this video you’ll learn how to create and manage aggregations in Power Query Editor of PowerBI first you need to load the required tables launch the PowerBI desktop and connect to SQL Server via direct query navigate to get data and select SQL server from the drop-own list of options this opens a dialogue box called SQL Server database enter the server name and database name by default import mode is selected select direct query and then okay the action directs you to the SQL server containing the Adventure Works database powerbi opens the navigator window with the list of tables select the following tables to load the internet sales fact table and the customer and date dimension tables once the tables are loaded PowerBI autoestablishes the relationships between the tables in this instance you only need to review the relationship between the date and internet sales tables delete any inactive relationships between these tables next you need to create aggregations using Power Query Editor from the home tab select transform data to open the editor create an aggregated table based on the internet sales fact table note that this action converts the existing table to an aggregated table to keep the original table intact the first step is to reference the fact table select the internet sales table from the queries pane right click and select reference from the drop-own list this action duplicates the internet sales table rename the query as a sales or aggregated table next from the home tab of the query editor select choose columns this opens a select columns dialogue box for the current aggregations create an aggregation using the order date key and customer key columns from the list of columns first unselect all columns then select the following columns order date key customer key unit price and sales amount select okay next select group by from the transform tab to open the group by dialogue box by default basic is selected choose advanced the first section presented is grouping this is because you’ve selected two columns for grouping select add grouping to add another field select order date key and customer key from the first and second grouping columns respectively the second section is aggregations to find the new column name then the mathematical operation for the aggregation like sum count average and so on finally select which column the calculation should be based on for the current lesson add the following aggregations sum sales amount based on the sales amount column sum unit price based on unit price column and order count which will take the count rows operation and does not require a column reference select okay after adding and defining aggregations the action will add an aggregated table to the data model the new aggregated table is much smaller than the original table now you have created an aggregation based on fact internet sales keeping the original table intact the table is added to the data model next you need to establish the relationship between a sales table and the customer and date dimension tables build the relationship between the a sales table and the dimension tables customer key and date key columns finally you need to set the storage mode of the aggregated table as import navigate to the model view and expand the properties pane select the a sales table in the properties pane expand advanced select import from the storage mode drop-own list of options the action opens the dialogue box indicating the warning message setting storage mode to import is an irreversible operation this means that you will not be able to switch back to direct query there is another recommendation on the dialogue box the number of weak relationships can be reduced by setting the customer and date dimension tables to jewel the checkbox set affected tables to jewel was checked by default leave this checked and select okay this action imports the a sales table to PowerBI’s memory and converts the storage mode of the dimension tables to Juul the reason is that both dimension tables are connected to the original fact table that is direct query sourced and to the a sales table that uses import mode this means the dimension tables are set to dual storage mode so they can act both ways depending on the situation select the dimension tables and check the storage mode option in the bottom right hand corner of the visualization pane to confirm that dual storage mode is selected in this video you learned how to create and manage aggregations in Power Query Editor of PowerBI congratulations on reaching the end of the third week in this course on data modeling in PowerBI this week you’ve explored optimizing a model for performance in PowerBI let’s take a few minutes to recap what you’ve learned in this week’s lessons you began the week with an introduction to what optimization is and why it is necessary you learned about PowerBI dashboards you learned how dashboards can provide access to large volumes of data that can be used to generate insights on successes challenges and opportunities you then explored query lag and how simple queries that used to take seconds could begin to take many minutes even hours you investigated the challenges that growing data volumes can bring as well as how performance optimization can address those issues and you reviewed the benefits of performance optimization in PowerBI and how it affects the overall performance of data models reports and dashboards you then further examined optimization and what it is and how performance issues can arise due to a variety of factors including inefficient data models complex DAX calculations and inappropriate visuals you explored how optimizing your PowerBI report and data model can significantly cut down the loading and processing time of large data sets allowing you to execute queries faster next you examined how the benefit of performance optimization informs decision-making and how the ability to make timely and informed decisions is critical to its success and how with an optimized PowerBI data model reports load swiftly enabling faster analysis of trends and thereby leading to more prompt informed decisions you then explored the user experience and the benefits that an optimized PowerBI system can have dramatically improving the user experience by ensuring reports load smoothly and swiftly next you learned about resource efficiency and how an optimized PowerBI data model can make more efficient use of resources handling larger volumes of data without a noticeable drop in performance you explored optimization by example and how to analyze the constant stream of data next you examined optimization techniques such as filtering sorting and indexing which can make your data more manageable and your searches faster improving overall efficiency you are introduced to four tools that will help you to understand vast amounts of data: sorting filtering indexing and data transformation you learned how sorting made data sets much easier to read and interpret how filtering reduces the processing time and computational load making the overall process more efficient how indexing allows you to quickly locate the data for a specific region without having to sift through the entire data set and how data transformation facilitates more accurate and meaningful analyses next you moved on to resolving performance issues in data models which had you explore the different types of relationships such as onetoone and manny to manny you then learned how to identify and reduce cardality levels and how identifying high cardality columns and modifying them appropriately can enhance your reports performance you learned about the behavior and limitations of direct query connections you learned that direct query is a data connectivity option in PowerBI that allows analysts to connect directly to the data sources without importing data into PowerBI model you explored the benefits of direct query which are real-time updates reduced memory usage and data security you then investigated the negative implications of direct query which are its impact on performance its limited support for data transformation its limitations in modeling in DAX and its reporting limitations you explored optimizing direct query performance with query reductions you learned that in direct query mode performance optimization is needed at each layer of the solution and how PowerBI desktop provides you with the option to reduce the number of queries sent to the database in direct query mode you learned some effective query reduction strategies and techniques including aggregations optimizing the data model and report optimization you then explored optimizing direct query performance with table storage and how storage modes in PowerBI determine where the data of that table is stored and how queries will be sent to the data sources and that you can specify the storage mode of the table individually within your data model you examined the benefits of storage mode which are query performance larger tables and data refresh optimization you then learned about import mode and that if a table is using the import storage mode it means that the data of that table will be stored in the in-memory storage of PowerBI you also explored direct query mode and that tables using the direct query storage mode will keep the data in the data source you then learned about dual mode and that when using dual storage mode one table can act either as direct query or import with respect to the relationship to the other tables you then moved on to aggregations in PowerBI and how aggregations in PowerBI enable you to dive deeper into your data without compromising the speed and performance of your query in direct query connections you explored composite mode you learned that composite mode allows you to achieve better performance when you work with smaller tables as they are just querying the in-memory storage of data and how in PowerBI aggregation refers to summarizing or consolidating large volumes of data into more manageable summary tables to improve query performance by condensing detailed information into simpler higher level values you identified the three ways to create aggregations which are you can create a table with aggregations at the database level for instance SQL server database if you have access to the data source and then import the table to PowerBI you can create a view of the aggregation for example in SQL Server database and import the view to PowerBI if you have access to the data source you can use Power Query Editor in PowerBI to create aggregations finally you learned about the benefits of aggregations that in the case you are handling a large data set aggregations provide a faster and optimized query performance and assist you in analyzing the data they also reveal insights without querying the underlying data source that is slower in response and in worst case scenario the query times out and if users are experiencing slower refresh time of the reports in PowerBI you can create aggregations which help you to speed up the refresh process the smaller size of aggregated tables imported to memory reduces the refresh time enabling a better user experience as well as that you can leverage aggregations to create and manage aggregations as a proactive measure to futureproof the solution thereby enabling a smooth scaleup of the company you’ve now reached the end of this module summary it’s time to move on to the discussion prompt where you can discuss what you’ve learned with your peers you’ll then be invited to explore additional resources to help you develop a deeper understanding of the topics in this lesson best of luck we’ll meet again during next week’s lessons you’re nearing the end of this course on data modeling in PowerBI you’ve put great effort into this course by completing the videos readings quizzes and exercises and you should now have a stronger grasp of the foundations of data modeling these include basic concepts of data modeling using DAX for analysis and optimizing a model for performance you’re now ready to apply your knowledge in the exercise and the final course assessment in the exercise you’ll build and optimize a data model putting everything you’ve learned into practice this is followed by the course assessment or graded quiz that consists of 30 questions related to topics you covered throughout the course but before you start let’s recap what you’ve learned in the first week of this course you discovered that data modeling is the process of creating visual representations of your data in PowerBI you can use these representations to identify or create relationships between data elements by exploring these relationships you can generate new insights into your data to improve your business microsoft PowerBI is a fantastic tool for creating data models and generating insights and you don’t need an IT related qualification to begin using it during your exploration of PowerBI you learned how to create data models using schemas and relationships analyze your models using DAX also known as data analysis expressions and optimize a model for performance in PowerBI you also explored key concepts related to data modeling you learned to identify different types of data schemas like flat star and snowflake create and maintain relationships in a data model using cardality and cross filter direction and form a model using a star schema in the second week of this course you focused on DAX or data analysis expressions this syntax is used to create elements and perform analysis in PowerBI you began by writing calculations in DAX to create elements and analyses in PowerBI you then explored the formula and functions used in DAX and used DAX to create and clone calculated tables you were then introduced to the concept of measures you learned where measures are used and what types are available you worked with measures to create calculated columns and measures in a mode and you learned about the importance of context and DAX measures finally you performed useful time intelligence calculations in DAX for summarization and comparison and learned how to use these techniques to set up a common date table in the third week of this course you learned how to optimize a model for performance in PowerBI you began by learning how to identify the need for performance optimization this means analyzing your data models to determine how they can perform more efficiently you then learned how to optimize your PowerBI models for performance you explored different techniques and methods for ensuring that you’re running efficient models and you also learned how to optimize performance using DAX queries now that you’ve built a solid understanding of the fundamentals of data modeling you’re ready to test your knowledge by undertaking the exercise and the final course assessment best of luck congratulations you’ve made it to the end of the data modeling in PowerBI course your hard work and dedication has paid off you’re making great progress on your data analysis learning journey and you should now have a thorough understanding of basic concepts of data modeling using DAX for analysis and optimizing a model for performance you should now have a firm knowledge of data modeling in PowerBI think about everything you can do with this new knowledge well done for taking the first steps towards your future data analysis career by successfully completing all the courses in this program you’ll receive a Corsera certification this program is a great way to expand your understanding of data analysis and gain a qualification that will allow you to apply for entry-level jobs in the field this program will help you prepare for the PL300 exam by passing the exam you’ll become a Microsoft certified PowerBI data analyst it will also help you to start or expand a career in this role this globally recognized certification is industry endorsed evidence of your technical skills and knowledge the exam measures your ability to perform the following tasks prepare data for analysis model data visualize and analyze data and deploy and maintain assets to complete the exam you should be familiar with Power Query and the process of writing expressions using data analysis expressions or DAX these are two concepts that you’ve explored in detail in this course and will continue to learn more about in future courses you can visit the Microsoft certifications page at http://www.learn.microsoft.com/certifications to learn more about the PowerBI data analyst associate certification and exam this course has enhanced your knowledge and skills in the fundamentals of data modeling in PowerBI but what comes next there’s more to learn so it’s a good idea to register for the next course whether you’re just starting out as a novice or you’re a technical professional completing this program demonstrates your knowledge of data modeling in PowerBI you’ve done a great job so far and you should be proud of your progress the experience you’ve gained will show potential employers that you are motivated capable and not afraid to learn new things it’s been a pleasure to embark on this journey of discovery with you best of luck in the future welcome to data analysis and visualization in PowerBI in this course you’ll discover the power of visualization in Microsoft PowerBI to create datadriven stories and solve realworld business problems data analysis and visualization are not only essential skills for data analysts to uncover and communicate data insights they are vital for organizations across different industries to flourish in today’s datadriven world from healthcare to finance data analysis and visualization play a critical role in informing decisionmaking and driving success with its extraordinary visuals PowerBI is a data analytics and visualization tool that you can use to transform data into intuitive visualizations it empowers you to present data in a visually appealing way that stakeholders can understand facilitating datadriven decisions you are currently on a path of discovery centered on data analysis in PowerBI exploring the skills tasks and processes that enable data analysts to create compelling data stories with PowerBI so what can you expect for this part of your learning journey you’ll start by diving into creating reports in PowerBI and exploring the various visualizations available to you and their potential to solve different business problems you’ll learn how to format these visuals and add them to reports and dashboards the powerful mediums through which you can provide stakeholders with insights in PowerBI you’ll master the art of designing reports and dashboards that are not just visually appealing but accessible userfriendly and interactive you’ll discover how to share your carefully crafted reports with stakeholders ensuring your hard work reaches the right audience and the journey doesn’t end there you can look forward to learning how to use visualizations and other features like AI to perform data analysis you’ll closely examine the data in your PowerBI reports discovering how to extract meaningful insights and value by using PowerBI’s analytical tools and performing advanced analytics by the end of this course you’ll learn how to recognize different types of visualizations in PowerBI add visualizations to reports and dashboards apply formatting choices to visuals incorporate useful navigation techniques into PowerBI reports design accessible reports and dashboards and use visualizations to perform data analysis to complete the course successfully you’ll need to apply the skills and knowledge you gain to a practical graded assignment in this assignment you’ll build reports and dashboards based on a realworld business scenario involving Adventure Works a fictional bicycle manufacturing company you may have encountered before in this program you’ll also need to complete a final graded quiz demonstrating your understanding of the key concepts in data analysis and visualization but no need to worry the videos readings exercises and quizzes in this course will gradually guide you through the learning material preparing you thoroughly for your assessment you have the flexibility to recap and revisit items as you need so watch pause rewind and re-watch the videos until you are confident in your skills the readings knowledge checks and quizzes will help you consolidate your knowledge and measure your progress ultimately this course is about more than just gaining knowledge and skills in data analysis and visualization in PowerBI it’s about setting yourself up for a career in data analysis by completing all the courses in this program you’ll earn a Corsera certificate to showcase your job readiness to your professional network plus the program prepares you for exam PL300 which leads to a Microsoft PowerBI data analyst certification globally recognized evidence of your realworld skills so are you ready to add data analysis and visualization skills to your data analyst toolbox well this course will equip you to recognize use and format different visualizations strategically design accessible and beautiful reports and dashboards and extract more value from your data using visualizations and advanced analytics best of luck as you embark on this learning journey renee Gonzalez the marketing director at Adventure Works walks into our office and finds a report on her desk the report is packed with data sales figures marketing campaign results regional statistics customer feedback and more but as she flips through the pages the strings of numbers and texts seem to blend failing to convey any meaningful story it’s like trying to decipher an alien language can she make informed decisions based on this data probably not data on its own is often meaningless but here’s the game changer when you apply the tools of data visualization and analysis the data starts to weave a story patterns emerge from the chaos trends become evident and the confusing jumble of numbers transforms into insights that can guide business decisions this is the power of business intelligence in this video you’ll explore the basics of business intelligence or BI specifically focusing on data visualization and analysis and the role it plays in making complex data accessible and understandable you’ll discover how business intelligence and data analysis go beyond data visualization providing deeper insights and forming the backbone of informed decision-making in its simplest terms business intelligence or BI is a technological approach to convert raw unprocessed data into meaningful actionable information for business analysis the heart of business intelligence is to create an environment where data informs strategic business decisions it’s about leveraging data to improve operations increase efficiency and boost financial performance bi uses several tools and methodologies to achieve these objectives including data mining analytical processing querying and reporting but two of the most critical tools in this toolbox are data visualization and data analysis data visualization is a graphical representation of information and data think charts graphs maps or any other visual format that makes complex data more understandable accessible and usable to grasp the power of data visualization let’s revisit the scenario at Adventure Works say the marketing director is examining the sales figures for different products in the last month the spreadsheet is dense with rows and columns of information you’d be hardpressed to discover any significant insights just by glancing at the raw data but imagine if you could take these numbers and transform them into a visually compelling line graph suddenly the sales trends are immediately visible it’s easier and quicker to identify high-erforming and underperforming products which can inform strategic planning and datadriven decision-making it may also provide insights into seasonality and the effect of marketing initiatives on income visualization is a powerful transformative tool used to spot patterns and anomalies identify trends and grasp complex data sets at a glance in addition to visualization another critical aspect of BI is data analysis while data visualization provides a graphical representation of your data data analysis dives deeper into these visualizations to undercover the reasons behind the trends and patterns data analysis is like the detective work of BI it sifts through data asks critical questions and uncovers the truth to illustrate the importance of data analysis let’s explore another term from BI profit margins the profit margin is a critical financial metric that provides insights into a company’s profitability you can calculate this by subtracting the cost of goods sold from sales revenue and dividing the result by the sales revenue but just knowing this profit margin figure isn’t enough let’s say for example that Adventure Works has a profit margin of 20% what does this figure tell you on its own not much but when you analyze this figure in relation to other factors the story begins to unfold for example to determine whether the margin is good or bad you can compare it across different periods or to the company average historical data or industry benchmarks you may also want to analyze the contribution of different products to profitability likewise you can also analyze the profit margin in relation to other financial metrics like sales revenue and expenses or external factors like market trends for a more comprehensive view of the financial health of Adventure Works data analysis helps you understand not just what is happening but also why it’s happening it allows you to diagnose problems spot opportunities and make informed decisions data analysis can also be pivotal in predictive analytics an aspect of BI that uses current and historical data to forecast future events behaviors and trends let’s imagine Adventure Works is planning to launch a new product line by analyzing past sales data customer behaviors and market trends you can predict how well customers might receive this new product its potential sales and even what type of marketing might be most effective this type of predictive insight can be instrumental in crafting successful business strategies as you embark on your own journey in the world of business intelligence remember that you’re not just a data analyst you’re a storyteller each strand of data is a part of your narrative and it’s up to you to assemble these strands into a narrative that guides a business to success remember data is just data it’s what you do with it that counts with data analysis and visualization you can transform data into actionable intelligence imagine a stakeholder at Adventure Works is handed a spreadsheet with numbers representing sales production and human resources data trying to draw conclusions or make decisions using these rows and columns is as challenging as navigating a dense forest with a paper map although the map may have all the information you need it isn’t easy to understand and interpret but what if there was a way to examine this data that’s immediately understandable and meaningful data visualizations can act like a navigation system with a clear interactive display that demonstrates how to navigate the forest of vast and complex data in this video you’ll learn about data visualization including its role in business intelligence and how data flows and is represented in visualizations in Microsoft PowerBI at its most basic a visualization is a graphical representation of data however visualizations are much more than just common graphical depictions converting raw data into a visual format using PowerBI can help you identify patterns trends and insights that might not be apparent in textbased data for example suppose Adventure Works wants to track the performance of its different bike types across various regions the data comes from several sources ranging from sales and regional reports to customer feedback in a spreadsheet this data would be complex and hard to digest however you can use PowerBI with its many ways to visualize data which you’ll learn about later to transform the data into a compelling interactive and easily digestible format visualizing data for business intelligence is crucial particularly in complex and dynamic business environments like Adventure Works let’s explore how data visualization in PowerBI can enhance business intelligence at an organization like Adventure Works the data generated from its operations is vast and complex visualizing this data simplifies the complexity transforming large intricate data sets into intuitive easy to understand graphical representations data visualizations can reveal patterns trends and correlations hidden in raw data for example Adventure Works could use a bar chart to visualize sales data demonstrating geographic regions where sales are the highest they could also use a scatter plot to identify correlations between marketing spend and sales performance powerbi’s interactive visualizations allow companies to dive deep into their data they can drill down into specific areas of interest such as analyzing sales trends for a particular product in a specific market over a given period leading to more precise datadriven decisionmaking visualizations make data more accessible to a broader audience not everyone at organizations like Adventure Works will be comfortable interpreting raw data but most stakeholders can understand a well-designed chart or graph as a result more stakeholders can engage with the data and contribute to datadriven decisionm visualizations are a powerful communication tool and can tell a compelling story with data making the insights more memorable and persuasive to demonstrate the success of a new product line to stakeholders at Adventure Works you could use visualizations to highlight key performance metrics in a visually engaging way now that you know more about the importance of visualizing data for business intelligence let’s explore how creating visualizations works in PowerBI creating visualizations in PowerBI begins with connecting to your desired data sources these can range from Excel spreadsheets to SQL databases once connected you can use Power Query to extract transform and load the data into PowerBI these transformations include renaming columns changing data types filtering rows and combining data from multiple sources you can then load this refined data into PowerBI’s data model for further manipulation using data analysis expressions or DAX a formula language for creating custom calculations the next stage of the workflow involves representing this processed data in visualizations powerbi provides a wide variety of visualization types such as bar charts scatter plots pie charts and even geographical maps after selecting a visualization type you map the data elements to different aspects of the visualization from adding values to the axes or fields to the color scheme PowerBI allows you to add slicers which are visual filters that allow viewers to segment and filter the data in real time to enhance the usefulness and interactivity of these visualizations the final step in the workflow involves arranging the visualizations on a report page and then sharing the report with other stakeholders the PowerBI service allows you to publish these reports enabling a broader audience to interact with them online even on mobile devices visualizations don’t only present data in a more understandable form they also enable realtime data analysis for example as sales figures at Adventure Works are updated the visualizations in PowerBI will update automatically this provides companies like Adventure Works with up-to-date accurate insights and enables them to react more quickly to changes in their business environment the next stage of the workflow involves representing this process data in visualizations data analysts must carefully craft them to communicate the right insights effectively this includes ensuring you select the correct type of visualization for the data you want to represent for example while pie charts are appropriate for displaying parts of a whole line graphs are more suitable for displaying trends over time an inappropriate choice of visualization can lead to misunderstandings or even misinformation visualizations are not only advantageous but essential in today’s datarich business environments rather than simple graphical representations of data used correctly visualizations are like keys to insights transforming the way stakeholders understand and engage with data and journey through the complex world of business intelligence with PowerBI you can guide stakeholders to strategic decisionmaking uncovering valuable insights and knowledge as a new data analyst at Adventure Works you’re overwhelmed with the vast amount of sales customer and manufacturing data you know the data contains invaluable insights about commerce customer behavior production efficiency and more but how do you translate it into meaningful information that stakeholders can understand and act upon you have a powerful solution PowerBI visualizations in this video you’ll learn about commonly used visualizations in Microsoft PowerBI you’ll discover their purpose and versatility in relation to data representation and interpretation you learned that data visualization is the graphical representation of data a method to uncover patterns trends and insights that may not be apparent in raw data visualizations communicate complex data sets in an intuitive and accessible way creating an approachable narrative that encourages datadriven decision-making let’s explore some of the common visualization types available in PowerBI and their practical uses in the context of Adventure Works the first visualization type is the column chart column charts are a clear straightforward way to compare different categories in a vertical orientation they can demonstrate data changes over time or illustrate comparisons among items column charts are generally used when there are fewer than 10 categories on the x-axis the horizontal axis at the bottom of the chart adventure Works could use a column chart to compare the sales of different bicycle models over the past year each column would represent a different product category and the height of the columns would indicate the sales figures allowing stakeholders to compare and contrast sales performance across models quickly bar charts are another powerful visualization for comparing different categories unlike column charts however bar charts are a horizontal representation of data the length of each bar corresponds to the quantity of the data it represents bar charts are useful for comparing larger quantities or categories with lengthy labels long labels are inappropriate for column charts as their vertical orientation means the labels appear sideways which can be challenging to read you can also use bar charts to display comparisons among discrete categories or non-ontinuous distinctly separate groups of data such as different payment methods for example Adventure Works could use a bar chart to compare the number of order transactions per payment category this clear and straightforward visual would make it easy for stakeholders to compare the performance of the different payment methods identify opportunities for payment option optimization and gain insight into customer behavior and preferences a further common visualization type in PowerBI is the line chart line charts are best suited for showing trends over time they connect individual numeric data points forming a line this visual is useful when you have a large data set and are interested in visualizing trends patterns or fluctuations in your data over time it’s particularly effective when used to represent many data points adventure Works could use line charts to track sales trends over time they might compare the monthly sales figures of different bicycles for the past five years to identify when sales peak and when they are slow helping inform strategic decisions about promotions and inventory powerbi also offers area charts which are in essence line charts except color or texture fills the area beneath the line these charts help compare two or more quantities and show part to whole relationships over time or across categories representing how individual segments contribute to an entire data set for example in an area chart for adventure works based on sales data each product type like mountain bikes or road bikes would be in an area on the chart showing its sales as a portion of the total sales this can help stakeholders understand how each product contributes to total sales and how this relationship changes over time now let’s explore pie charts pie charts are circular graphics divided into slices to illustrate numerical proportions this visualization type is ideal when you want to show a data set as a proportion of a whole each slice of the pie represents a category of data and the size of each piece is proportional to the quantity it represents from the whole adventure Works might use a pie chart to illustrate the proportion of sales made up by each product category each slice would represent a different product category and the size of each slice would be proportional to the revenue generated by that category this visual would enable stakeholders to understand which products contribute most to overall sales at a glance keep in mind that pie charts become less effective when there are too many categories to compare resulting in a high number of small slices in this case a bar chart might be better for clear visualization the last visualization type you’ll learn about in this video is the table tables in PowerBI are a way to view raw detailed data and exact numbers they display information in columns and rows providing a comprehensive numerical view of your data while they don’t offer the same visual impact as other chart types tables can display additional details that might be critical to stakeholder understanding of your data adventure Works could use a table to display a detailed monthly sales breakdown for each product category by region this would allow the relevant stakeholders to examine exact sales figures and make precise comparisons supporting detailed nuanced analysis in this video you discovered a range of common visualizations available to you in PowerBI each visualization type plays a unique role in data storytelling by understanding and effectively using the visuals in PowerBI you can transform raw data into a masterpiece that conveys insightful actionable information driving more thoughtful decision-making and improving business outcomes in a complex organization like Adventure Works sales reports are indispensable in coordinating sales efforts across regions and product lines let’s explore how to apply visualization items to a basic sales report once you’ve imported your data using get data on the home ribbon and cleaned and transformed it using the power query editor you can start adding visualizations to your report canvas first let’s add a column chart to visualize how sales are distributed among various product categories helping Adventure Works gain insight into the performance of different products from the visualizations pane select the clustered column chart button this will create an empty chart on your report page now that you have an empty clustered column chart it’s time to fill it with data you can find your data fields in the fields pane also referred to as the data pane or data section typically located on the far right side of the PowerBI interface these fields correspond to the columns in your data source find and select the product category field on your sales data source while holding the field drag it over to the Xaxis box under the visualizations pane releasing it will drop the field into the box by placing the product category field in the Xaxis well or input box you’re telling PowerBI to use the unique values from this field to create individual columns on the chart the next field you need to add to your chart is the order total field select and drag the order total field to the yaxis box as you did with the product category field and the x-axis when you drop a field into the y-axis box PowerBI will perform a calculation on that field for each category in this case it will calculate the sum of the order total for each product category and display this data in the respective column with this column chart stakeholders at Adventure Works can identify trends opportunities and challenges in product performance that can guide product development marketing campaigns and pricing strategies next let’s create a pie chart to represent sales distribution by different payment methods visually a pie chart will make it possible for stakeholders to determine how much of the total each payment method represents to start creating your chart select the pie chart button in the visualizations pane this will add an empty pie chart to your report page to start populating the chart with data find the payment method field in the fields pane and drag it into the legend well in the visualizations pane by putting the payment method field in the legend well you’re telling PowerBI to create a different slice of the pi for each payment method in your data after that find the order total field in the fields pane and drag it into the values well when you drop a field into the values well PowerBI performs a calculation on that field for each category by default PowerBI calculates the sum so it will calculate the sum of the order total for each payment method this pie chart can help Adventure Works understand key revenue streams and customer payment preferences and even guide decisions around payment processing partnerships finally let’s add a line chart visualization to the report line charts are effective for showcasing trends or changes over time for example this chart can help stakeholders recognize and understand the patterns and cycles in their sales data and identify any anomalies to create the line chart identify the line chart button from the visualization pane and select it this will generate an empty line chart on your new page to fill your empty line chart with data locate the order date field representing time and drag it into the xaxis field well located in the visualizations pane by doing this you’re instructing PowerBI to use time as the xaxis of your line chart which forms the basis for the trend analysis then locate the order total field and drag this field into the yaxis field well by default PowerBI will calculate the sales sum for each date and plot it as a data point on the line chart this offers stakeholders a practical way to visualize and understand sales trends over time stakeholders can use the line chart to inform strategic decisionmaking and drive business growth remember that PowerBI may make certain assumptions about your date data when creating line charts for example if your order date field includes specific times PowerBI might plot every unique timestamp to ensure PowerBI aggregates data according to your preferences select the drop-own arrow next to order date and choose your desired level of detail for example by year quarter month or day after creating your visualizations the next step is to save your report to ensure you don’t lose any of your work to save your report select the file option located in the upper left corner of the PowerBI interface a drop-own menu will appear from this menu select save a window will open asking you to name your report name it something descriptive to help you and others understand what the report is about such as Adventure Works Sales Analysis Report in this window select save again to finalize the process and there you have it you’ve learned how to apply visualization items to a basic report in PowerBI the sales analysis report complete with visualizations holds valuable insights for Adventure Works and will support datainformed decisionmaking imagine you are a data analyst at Adventure Works working with vast amounts of information daily while innovative and interactive charts can be flashy and captivating there are moments when your audience wants simplicity a straightforward no frrills presentation microsoft PowerBI’s table visualization is useful when you want to employ the classic clear-cut style of tables to ensure your audience can grasp the essence of the data quickly it elegantly presents refined data allowing viewers to immediately consume critical information and insights in this video you will learn more about the table visualization in PowerBI and how to configure it when you load a raw data set into PowerBI like an Adventure Works sales report with data from February March and April it is tough to pinpoint details quickly for instance figuring out the monthly sales for each region becomes a challenge and if you are trying to dive even deeper aiming to identify specifics like the number of orders that were either cancelled or shipped extracting this information from this raw format is a difficult task the table visualization in PowerBI can summarize all these insights and still present them in tabular format the same sales data is now presented using a table visualization the table displays summarized insights which is much more userfriendly to work with you can even customize the table visualization to improve its aesthetic appeal or aid engagement and comprehension now that you know more about the table visualization in PowerBI let’s learn how to configure this visualization once you load your data in PowerBI using a table visualization is quite straightforward open your report view and select the table visual from the visualizations pane this will instantly place this visual in the report area you can resize this visual by dragging the corners or sides while keeping this visual selected select as many data fields as you want for example you can select month and order total on the data pane this will give you an insight into monthly total sales if you want to break down the sales by different regions simply add the product region field from the data pane and the table visual will display monthly sales for each region adding another field order quantity to this visual gives you more insight into how many items were shipped cancelled or still under processing the visual even calculates the totals automatically displaying them at the bottom of the visual what if you want to see the order status in this table just select the order status field from the data pane notice how the table visual summarizes valuable information like order quantity and order total for each row you can sort any of these columns by selecting the column header for example selecting the product region column header sorts it in ascending order another click on the same header will sort it in descending order you can change the sequence of these columns by dragging the fields up or down on the visualizations pane let’s drag the order status after the product region notice how the visual changed the way it’s displaying the data it now shows the order status column right after the product region column you have the option to format this table visual and change its appearance by customizing various options available in the format tab expand the style presets option and select any preset from the available dropdown the appearance of your table will change instantly you can also further customize the table by expanding other sections for example you can display horizontal grid lines by expanding the grid section and selecting your desired color and width you can also change the table header font size color and other options by expanding the column headers section there are many other options to format the appearance and feel of the table whether to reflect your brand colors or to increase its visual appeal for your audience using raw data can feel like looking for a needle in a haystack it can be overwhelming messy and confusing but using table visuals in PowerBI is like sorting that haystack into neat manageable piles making it easier to find what your audience is looking for with data neatly laid out rowby row and column by column table visualizations present insights clearly and are an invaluable tool for bridging raw data and actionable intelligence your manager asks you to present a sales report to key stakeholders during a business meeting later in the week imagine you receive an Excel file containing all the adventure work sales data for the current year the sales department wants an appealing report that offers a comprehensive view of the company’s monthly sales volume and the number of processed orders and cancellations so what is your strategy for completing this task this is where Microsoft PowerBI’s bar and column charts can make you shine in this video you’ll discover the different bar and column charts in PowerBI that can help you efficiently represent your data you will also learn about the four field wells you can use to customize these charts axis legend values and tool tips previously you learned that bar and column charts are popular types of visualizations to display data in a clear and organized way they are beneficial for showcasing categorical data or data that can be organized into distinct groups bar charts display data horizontally whereas column charts display data vertically the simplicity and intuitive nature of bar and column charts make them effective tools for presenting data and identifying patterns or trends over time with six different types of bar and column charts in PowerBI you can convert raw data into visually appealing and meaningful insights let’s explore each of these chart options their features and how to add and configure them to PowerBI it can be difficult to identify patterns or insights when working with raw data sets containing text and numbers in this data set sales volume across different regions and the order status such as shipped or cancelled are organized into various columns let’s examine how to visualize this data using the different bar and column charts available in PowerBI to create a bar or column chart that demonstrates the number of orders by status and month select the month order quantity and order status data fields from the data pane with the relevant data fields selected let’s start by placing a bar chart on the report area you can do this by selecting the stacked bar chart icon on the visualizations pane you can resize it as needed by dragging its edges with this chart stakeholders can quickly compare and gain insight into the number of orders shipped cancelled or processed during February and March this is much easier to interpret compared to working with the raw data set you have the option to visualize this data using the variety of bar and column charts available to you to change the chart type select the chart you placed and then select the relevant icon from the visualizations pane such as the stacked column chart a stacked column chart is like a stacked bar chart but data is displayed as columns instead of horizontal bars another option for visualizing the data is a clustered bar chart in a clustered bar chart the values are displayed in individual bars instead of a group in the next option the clustered column chart the data is shown in individual columns the last two options are the 100% stacked bar chart and the 100% stacked column chart in both charts important insights are displayed on the tool tips for example if you hover your mouse over any of these bars or columns PowerBI displays the percentage and value of any grouped item such as the order quantity in PowerBI you can select any of the charts individual bars or columns to highlight them the other items fade making the selected items more prominent this is useful for highlighting specific areas or insights of interest now let’s explore four essential field wells in these charts the legend X and Yaxis and tool tips the field wells represent different sections of your chart that you can customize according to your requirements the first field well is called a legend it displays under the title or on the side of a chart the legend field controls the color coding or grouping of the bars or columns in your chart it helps to differentiate between different categories or subgroups within the data the legend makes it easier to understand which color in the chart represents which item you can hide the legend by turning it off in the format tab on the visualizations pane you can hover your mouse over the bar or column to display the data if the legend is not shown the next field wells are the X and Y axis each axis represents the data points you want to compare or analyze for bar charts the X-axis shows the values like order quantity and total sales and the Yaxis shows the categories like month or product regions for column charts this is reversed the x-axis shows the category and the y-axis shows the values like order quantity or total sales the final field well is called tool tips a tool tip displays data or extra information when you hover over the data points of a chart understanding the different types of bar and column charts in PowerBI such as stacked clustered and 100% stacked charts allows you to present your data in visually engaging and meaningful ways by using the four field wells axis legend values and tool tips you can create customized visualizations that are informative and insightful adventure Works is preparing for their annual sales conference your team leader has tasked you with presenting a report that portrays the direction of sales trends the report must also incorporate monthly information regarding delivered pending and canceled orders this is where Microsoft PowerBI’s line and area charts become instrumental in this video you’ll explore line and area charts when to use them and how to add them to your reports learning to use these charts is essential for creating attractive reports that empower stakeholders to make informed and effective decisions a line chart uses a line to connect individual data points it is the perfect tool for illustrating a sequence of values or displaying trends over a time period for example a line chart can help Adventure Works understand how sales are progressing monthtomonth or year to year a line chart with multiple lines can show sales across different regions over time and help the stakeholders understand the trend or sales performance while a line chart focuses on trends an area chart emphasizes the magnitude of changes it can display the part to whole relationships among your data making it easier to compare quantities for example regional sales represented by an area chart can help stakeholders intuitively understand and compare the degree each product region contributed to total sales for each month there’s a variant of the area chart called a stacked area chart where the data points from multiple categories are stacked on top of one another this can be useful when emphasizing the total across several categories for example you could use a stacked area chart to illustrate the total orders over a period and demonstrate how each product region contributes to the total so how do you decide when to use bar or column charts which you learned about previously or line and area charts when presenting a few items bar and column charts can be visually appealing and effective however when dealing with many data points these charts can become cluttered and difficult to read each bar or column takes up a certain amount of space and the chart can become overcrowded if there are too many to plot unlike bar and column charts area charts are effective for visualizing changes in multiple values over time both line and area charts are effective in visualizing the changes in values of multiple categories particularly over time while line charts are useful for identifying trends area charts offer a further benefit they help us interpret the magnitude of the values they also effectively illustrate the cumulative impact of the data points over the selected time providing an overall picture of the data trends now that you’ve been introduced to line and area charts let’s take a moment to explore how you can create them in PowerBI start by importing the Adventure Works quarterly sales data set file to a new PowerBI project in PowerBI the line chart area chart and stacked area chart icons are available in the visualizations pane to create a line chart select the line chart icon from the visualizations pane and place it on the report section open the data pane and select two fields month and order quantity the x-axis of the visualization is sorted by descending order quantity to modify it to ascending order navigate to the visual settings and select sort access and sort ascending a line chart is handy for illustrating trends for example this line chart displays the
total sales from February to April it clearly demonstrates an upward trend in sales for the quarter the sales team at Adventure Works may also want to compare the performance and trends of different regions across the quarter to do this select the line chart open the data pane and select the product region the line chart now indicates that although there appears to be a general upward trend in sales in all regions the European region outperformed both Asia and North America in February March and April as you discovered earlier you can display your data another way using area charts and stacked area charts to create a new area chart select the area chart icon from the visualizations pane place it on the report section and select the month and order quantity fields from the data pane using the visualization settings change the ascending order quantity to descending order in the x-axis to highlight the increase again for a more nuanced understanding of the number of orders for the quarter you may want to display the data by individual regions to do this select the product region field from the data pane while keeping this chart selected the sales team can get a better idea of how the regions contributed to the order quantity in February March and April you can also display the values in a stacked manner you can do this by selecting the visual and then selecting the stacked area icon on the visualizations pane this allows you to display the individual values as well as the total on a single chart in all these charts you can hover over the data points to display the values in a tool tip for example a tool tip could display the exact sales figure for a specific month this tool tip is one of the four essential field wells available in many visualizations in PowerBI the other three important field wells are the legend the X and the Y-axis you can configure the titles of these axes colors and other details by selecting the paintbrush icon on the visualizations pane this will open the format tab where you can make any necessary changes line area and stacked area charts are potent tools in PowerBI that can convert complex data into easily understandable visuals learning to use these visualizations and their essential field wells can equip you to deliver effective PowerBI reports that present clear and compelling comparisons of data over time and across different categories the sales manager at Adventure Works wants a comprehensive overview of how order quantity relates to overall sales performance for the past few months while bar charts can easily display the sales or the order quantity juggling these metrics on one chart could be a visual challenge likewise line charts offer an excellent way to track changes over time but won’t show the difference between sales and order quantities by visualizing the order quantity and total sales metrics for the past few months simultaneously the sales manager can quickly identify any patterns or trends and make strategic decisions to boost sales performance this is where combination charts referred to as combo charts in Microsoft PowerBI can help in this video you’ll learn more about these charts including how to create and format them in PowerBI a combo chart is a dynamic combination of a line and a column chart allowing you to visually represent two different yet interconnected data points powerbi offers two types of combo charts a line and a stacked column chart and a line and a clustered column chart a line and stacked column chart is helpful for displaying a total across the series of data and how each individual part contributes to the total for example you could create a line and stacked column chart for the sales team using columns to visualize total monthly sales each stacked by different product regions the line represents a different but related factor order quantity on the other hand line and clustered column charts are excellent for comparing several sets of data side by side this can be useful to track and compare different metrics over the same period for instance you might have columns representing the sales of each product region by month with a line indicating the average order quantity across all regions as a PowerBI analyst combo charts are one of the many essential visualization tools in your toolbox so let’s delve into the process of adding and setting up a combo chart in PowerBI suppose you need to create a combo chart in PowerBI using an Adventure Works data set containing sales data the purpose of the chart is to provide the sales team with insights into orders for February March and April including the overall performance of each month and each sales region to create this combo chart you’ll need four data fields: month order quantity order total and product region let’s start by placing a line and stacked column chart on the report area from the visualizations pane you can resize the visualization by dragging its edges select the chart while keeping it selected open the data pane on the visualizations pane and select the month order quantity and order total fields in the column yaxis field in the visualizations pane order quantity and order total appear together select the order quantity field and drag it to the line yaxis field both the line and column visuals now appear on the inserted chart now let’s add one more field from the data pane product region the chart now has a stacked look with each colored segment representing the contribution of each product region to the order total stakeholders can now not only compare the sales performance over the quarter but also compare the performance of each region monthtomonth you can also sort the chart in ascending order to do this select the three dots on the top right corner of the chart followed by sort axis from the drop-down menu and sort ascending you can change this chart to a line and clustered column chart by selecting the chart and then selecting the line and clustered column chart icon on the visualizations pane let’s briefly explore some of the key field wells for the chart the x-axis or shared access for the line and columns displays the categories in this chart month is used as the category the line y-axis is where you place the data to be displayed as a line like sum of order quantity the column yaxis is where you place the data to show as columns like order total and finally the legend is used to add categorical fields to the chart for example the product regions when you hover over a data point with your mouse some default values for the data point display if you’d like to add additional information to this displayed data select the appropriate fields from the data pane and drag them to the tool tip area combo charts in PowerBI are yet another tool in your data analytics toolbox with your knowledge and understanding of these charts and their functionalities you can present complex and related data points seamlessly and in a visually compelling way at Adventure Works your recent report made quite an impact your manager asks you to create another Microsoft PowerBI report adding visualizations other than the area charts you used previously your team suggests using pie and doughut charts which can offer similar critical insights to area charts but are clearer when many items have the same data range as it can be difficult to identify these items correctly in an area chart this is where pie and donut charts can be helpful in this video you will learn about these charts and how to use them in your PowerBI reports pie and donut charts are two types of visualizations available in PowerBI these charts which are circular and cut into slices provide a way to represent data proportionally while pie and doughut charts are useful for comparing different categories they become less effective when comparing large amounts of categories as the slices can become too small and difficult to distinguish between choosing between a pie and a donut chart depends on the specifics of your data and your report requirements let’s explore each type of chart starting with a pie chart in a pie chart each slice of the pie corresponds to a unique category from your data set the size of each slice is directly proportional to the quantity it represents suppose you have a quarterly sales data set with a pie chart you can visually compare the contribution of each month to the total sales the larger the slice the higher the sales for that month providing your audience with an immediate and intuitive understanding of the distribution of sales like a pie chart a doughut chart segments are proportional to the data they represent the difference between a pie and a doughut chart is that the doughut chart is ringing shaped with a circular central space you can use this space to provide context for the surrounding segments returning to the sales data example you could use the donuts chart center to highlight total sales average sales or any other key metric you’ll learn more about this later in the course when choosing between a pie and a doughut chart to represent parts of a hole the doughut chart may be a better choice if you’d like to display additional information in the space in the center having explored pi and doughnut charts let’s uncover the steps for adding and configuring them in PowerBI imagine you need to create a pie chart using a quarterly sales data set from Adventure Works for the pie chart you need to specify at least two data fields let’s start by placing a pie chart on the report area from the visualizations pane and resizing it by dragging its edges select the pie chart and while keeping it selected open the data pane and select two fields month and order quantity ensure that month goes to the legend field and the order quantity goes to the values field you can add more data to create a more detailed pie chart or illustrate additional insights for example you may want to examine the total order quantity by region to do this select the product region field from the data pane and ensure that it goes to the details field now the pie chart slices display the total order quantity sold in February March and April for Asia Europe and North America you can sort this chart by order quantity to display the slices in size order to do this select the three dots in the top right corner of the chart select sort axis and then sort ascending you can also visualize this data using a donut chart which also shows the relationship of parts to a whole to convert the pie chart to a donut chart select the pie chart while it is still selected select the doughut chart icon on the visualizations pane unlike a pie chart the center of the doughut chart is blank this allows space for additional information that can provide context for the surrounding segments to make your charts more interactive and display more data when presenting them to your audience you can enable drill mode for example select product category from the data pane and then select the drill down icon to turn on the drill mode ensure that product category goes to the legend field there is no visual change if you add the product category field when the drill mode is off once you turn on drill mode you can display the additional details by selecting each slice for example if you select the slice that displays the total sales in April more information is displayed to return to the main chart select the drill up icon in the dynamic world of data analytics the correct visualization can make all the difference pi and donut charts offer clean effective ways to visualize and compare proportions to illustrate the relationships within your data by using these visualizations in PowerBI you can present clear and engaging presentations you’ve been exploring the range of visualizations that Microsoft PowerBI offers one of these is a tree map chart like a pie or donut chart tree maps are another helpful tool in PowerBI for illustrating your proportional data however instead of circles tree maps use rectangles to display your data you might be wondering why do I need another chart if they serve a similar purpose using different chart types can enable you to make the best use of space in your reports and add variety by displaying data in new and exciting ways in this video you’ll become familiar with tree map charts understand their applications and how to craft them in PowerBI to create insightful presentations a tree map is a unique visual used to display hierarchical data or data that’s organized in a treelike structure as nested rectangles the entire chart represents the total data set or tree and each rectangle or branch represents a portion of the whole tree each rectangle’s size corresponds to the value or size of the data it represents while pi and doughut charts are familiar and widely used to represent data proportionally they have limitations for example pie and donut charts can become cluttered and difficult to read when dealing with many categories or variables or when the differences between data points are small however the design of a tree map chart allows for easier visualization and interpretation of larger data sets its rectangular nested structure means it can handle more data points without becoming overly complex to illustrate this pie chart represents sales at Adventure Works across Asia Europe and North America for one quarter when you convert the same chart to a tree map it becomes less cluttered and the information is presented in a more readable way now let’s create a tree map chart using a quarterly sales data set from Adventure Works let’s start by placing a tree map chart from the visualizations pane on the report area you can resize it as required by dragging the edges to create a tree map chart you need three fields to add data fields select the chart while keeping it selected open the visualizations pane and select month order total and product region from the data pane this visual automatically directs the selected data fields to the appropriate field wells month to the category well product region to the details well and the sum of the order total to the values well if you are not satisfied with this automatic selection of the field wells you can manually drag the data fields to the appropriate field well let’s compare this tree map chart to a pie chart created using the same data there is a legend in the pie chart which is absent in the tree map chart because the month names are already displayed in each branch inside the tree a separate legend is not required also the pie chart displays the data values by default which are missing from the tree map chart you can enable the data values in a tree map chart to do this select the chart and open the format tab on the visualizations pane select data labels to turn on the data values now the tree map chart displays the values beside the month and the region name similar to a pie and donut chart you can add more fields to the tree map chart and enable drill mode to add more data fields select the data field order status from the data pane while keeping the tree map chart selected a drill down arrow icon appears on the top right hand corner of the chart select the drill down icon to enable the drill mode then select any branch to display the detailed information making it interactive if you’d like to return to the main less detailed visual you can select the drill up arrow icon you can also customize your tree map by changing the font size of the category and data labels and colors of the categories to do this open the format tab on the visualizations pane then open the data and category labels section here you have the option to change colors and the font sizes of your chart as needed tree mapap charts offer a unique approach to displaying hierarchical data allowing for efficient use of space clear comparisons and effective handling of larger data sets while pi and donut charts are popular knowledge of tree map charts provides an added layer of flexibility and depth to your reports you now know what a tree map is and how it can elevate your data storytelling and presentation skills well done imagine you are in a sales meeting presenting a chart focusing on employee turnover rates at Adventure Works while this chart may help management understand why employees are leaving the company or make resourcing decisions it is not useful in the context of the sales department that’s because the chart is not representing a key performance indicator relevant to the sales department such as total sales revenue previously you discovered the importance of creating targeted charts to help stakeholders make informed decisions these charts are tailored based on the key performance indicators or KPIs relevant to different departments in this video you’ll learn more about visualizing KPIs by exploring the elements available in PowerBI to display KPIs in an engaging way kpis differ from regular charts and metrics because they align directly with strategic business objectives instead of simply presenting raw data KPIs offer insight into how that data impacts overall business goals and progress a well-designed KPI visual helps stakeholders clearly understand organizational or departmental goals and the metrics that signify progress by providing a concise summary of complex data KPI visuals make it easier and more efficient for stakeholders to comprehend a business’s overall performance progress and key metrics this empowers stakeholders to make informed decisions and implement datadriven strategies to promote successful business performance microsoft PowerBI offers a range of visualizations to display KPIs including cards multirow cards gauges and the KPI visual let’s explore each of these visuals and their uses the card visualization displays one value or a single data point this type of visualization is ideal for representing essential statistics you want to track on your PowerBI dashboard or report for example you could use a card visual in a sales dashboard to provide a snapshot of the total sales revenue enabling stakeholders to gain instant insight into overall financial performance next is the multirow card visualization that displays one or more data points with one data point for each row another visualization you can use is the radial gauge this visual is a circular arc that displays a single value measuring progress toward a goal or target or indicates the health of a single measure although radio gauges can highlight critical insights in a visually appealing engaging way they take up a lot of space compared to the insights they provide let’s examine the structure of this visual powerbi spreads all the data values evenly along the arc from the minimum leftmost value to the maximum rightmost value the default maximum value is double the actual value you should specify the target minimum and maximum values using the corresponding field wells in the visualizations pane to create a realistic gauge chart that represents your data the shading in the ark represents the progress towards your target and the value underneath the ark represents the progress value lastly the KPI visual in PowerBI is a powerful tool for tracking the performance of a metric against a target the KPI visual also includes a trend line or chart to show the data’s trajectory over time in this case the chart is showing the daily sales trend against the target of $10,000 it displays an indicator that shows whether the performance is above or below the target for example this KPI visual clearly indicates that the total sales amount on the last day is falling behind the target the KPI visual usually has three field wells indicator which is the primary measure you are tracing trend axis which shows how the indicator is performing over time and target goals which represents the benchmarks you are trying to achieve you’ll place the relevant measures or fields into these field wells to represent your data accurately and comprehensively with the chart key performance indicators act as a health checkup for a business providing stakeholders with insights into their progress toward reaching business goals by using PowerBI’s card multiro card gauge and KPI visuals you can make KPIs quick and easy to understand that means stakeholders can make informed decisions and reach their goals faster suppose you’re a data analyst at Adventure Works as the financial year ends you need to provide management with a report analyzing sales trends and financial performance across regions throughout the year ribbon and waterfall charts in Microsoft PowerBI can help you achieve this goal in this video you will learn about these specialist charts and how to use them in your PowerBI projects a ribbon chart is a form of stacked chart for visualizing data that changes over time and has a clear ranking order these charts stack the highest ranked series at the top of the chart making it easy to track shifts in the rankings over time they are also helpful for comparing the performance of different categories across distinct time intervals in the adventure work scenario management wants to understand the sales ranking of various regions throughout the year this ribbon chart effectively conveys how the different sales regions performed compared to each other and how their sales rankings varied from February to April waterfall charts show a running total as PowerBI adds and subtracts values these charts are useful for understanding cumulative effects in data analysis and visualization cumulative effects refer to how an initial value is affected by a series of positive or negative sequential factors events or changes over time for example a waterfall chart can be used in financial analysis to visualize how a company’s net income results from a cumulative effect of various financial elements including revenue costs and other factors like taxes this waterfall chart depicts how adventure work sales total changed from February to April for the different product regions showing a general upward trend with this visual stakeholders can intuitively grasp the overall sales performance as well as easily compare and contrast the contributions of each month and the regions to the sales total over time now let’s take some time to explore how to configure ribbon and waterfall charts in PowerBI you can start with a blank PowerBI file this data set contains sales data for Adventure Works across different regions over time let’s place a ribbon chart from the visualizations pane on the report area you can resize it as needed the aim of the ribbon chart is to demonstrate the change in sales value and ranking changes in categorical data like product regions and month so you’ll need to include three data fields to display the data properly while keeping the chart selected open the data pane and select the relevant fields month product region and order total ensure that month goes to the xaxis field product region to the legend field and order total to the y-axis field none of these fields is optional when creating a ribbon chart you can sort the category fields by selecting the three dots on the top right corner of the chart followed by sort axis let’s select sort ascending to ensure the months are sorted in the correct order note that each month has two distinct areas on this chart first is the actual sales value for each region the other shaded area shows how that region performed compared to the previous month’s data for example by hovering over this shaded area for Europe in April the tool tip reveals that Europe’s sales rank changed from second in March to first in April you can create a waterfall chart using the same process as you followed with the ribbon chart alternatively you can convert the ribbon chart you created by selecting it and then selecting the waterfall chart icon from the visualizations pane there are four field welds in this waterfall chart category breakdown order total and tool tips ensure that month goes to the category field which defines the x-axis and shows the individual positive and negative values then ensure the product region goes to the breakdown field which represents different segments in the category however unlike ribbon charts this field is optional in waterfall charts lastly ensure the order total goes to the yaxis field this field denotes the yaxis values to calculate the running total if there is a decrease in the sales total the waterfall chart displays red areas to observe this you can sort the chart in descending order by selecting the three dots in the top right corner then selecting sort axis and sort descending each month shows the total sales and how these regions are performing compared to the previous month’s data you can find out additional information about this performance using the tool tips field by hovering over any of the red or green areas you learned about two specialized charts in PowerBI ribbon and waterfall charts ribbon charts help represent rankings and their shifts over time which is ideal for sales performance analysis across categories waterfall charts on the other hand are perfect for breaking down the cumulative effects of various factors providing clear insights into financial performance these charts are impactful visualizations for complex data sets the sales manager at Adventure Works has noticed a recent decline in online sales despite continued marketing efforts and website traffic concerned that marketing strategies may not be converting leads into sales the marketing team asks you to create a visualization that represents the customer journey from lead or interest in the product to actual sales they’d like to gain insight into dropoff rates between the stages and identify areas they can improve their marketing strategies to improve sales performance funnel charts in PowerBI are one type of visualization you can use to represent the progression of data through different stages like a sales workflow in this video you’ll learn about funnel charts and how to implement them in PowerBI the funnel visualization displays a linear process that has sequential connected stages where items flow sequentially from one stage to the next funnel charts are commonly used in business or sales contexts they are well suited to visualizing data that’s sequential and moves through at least four stages where you expect a greater number of items in the first stage than in the final stage the charts can help reveal bottlenecks such as where a significant number of items are being lost are not moving forward in linear processes in addition you can use them to calculate a potential outcome by stages such as revenue sales or deals and track conversion and retention rates these rates relate to how many potential customers move through each stage of the sales process and stay in the process similarly you can use them to track the progress and success of click-through advertising campaigns now let’s take a moment to examine an example funnel chart representing the stages of a sales workflow each bar in the chart represents a stage the customer goes through during the sales process it begins with the lead stage at the top of the funnel representing customers interested in a product or service the qualify solution and proposal stages follow where these leads are evaluated for their potential presented tailored solutions and then sent formal sales proposals lastly the finalized stage is where the lead agrees to the proposal closing the sales deal each stage in the chart decreases as the lead conversion process progresses creating a funnel shape the narrowest part of the funnel represents the leads that resulted in actual sales now that you know more about funnel charts and their uses let’s explore how to create and configure a salesfunnel chart in PowerBI for the sales team at Adventure Works you’ll start with a blank PowerBI file the data set contains sales data including information about the lead conversion stages let’s start by placing a funnel chart on the report area from the visualizations pane you can resize it as needed keeping the chart selected open the data pane and select two fields sales ID and conversion stage ensure that conversion stage goes to the category field well and sales ID to the values field well category defines the stages of the process and values assigns the numeric data to each stage notice the shape of the funnel the highest value is displayed on the top gradually displaying the lower values each of the horizontal bars in a funnel chart is called a stage as mentioned before this is the typical pattern of the sales conversion process many people are identified as potential leads in the first stage but the number gradually decreases as they finally become the customer if you hover your mouse over each stage it displays information that compares to its previous stage and the highest or the first stage you can use the tool tips field well for providing this additional information when hovering over a specific stage you can format the colors of each stage whether to reflect your brand colors or improve readability and aesthetic appeal to do that go to the format tab on the visualizations pane and open the colors section then turn on show all and select the color for each stage you can also sort funnel charts in reverse order where the lowest value shows at the top and the highest value at the bottom you can do that by selecting the three dots icon at the top right corner of the chart then sort a access and sort ascending funnel charts are an invaluable tool for presenting sequential or staged data these charts provide a clear and concise visualization of various stages of a process such as a sales pipeline or customer journey enabling you to identify trends bottlenecks and opportunities by incorporating funnel charts into your PowerBI reports you can provide stakeholders with a comprehensive view of essential data supporting more informed and strategic decisionmaking suppose Adventure Works has been facing a steady decline in its profitability for some months marketing has invested heavily in advertising across multiple platforms and has run several promotional campaigns to boost sales the company is struggling to understand the relationship between its advertising spend and its sales revenue in this video you will learn about scatter charts their purpose and configuring them in PowerBI scatter charts are a powerful tool in data visualization they use dots to represent values obtained for two variables in a data set plotting these two numeric variables along two axes scatter plots help illustrate how one factor is affected by another representing correlations between the variables the relationship between the variables can be linear follows a straight line nonlinear follows a curved line or random scatter charts can help you identify trends patterns and perhaps most importantly anomalies like outliers in your data anomalies refer to deviations from the general pattern of the data outliers are a type of anomaly where valid data points significantly differ from other observations deviating from the general data trend they tend to lie far away from other data points in a scatter chart for example in a scatter chart representing the relationship between sales revenue and advertising spend at Adventure Works you might expect the data points to show a positive correlation where higher advertising spend is associated with more sales an outlier would be a data point representing unusually high sales revenue and low marketing spend this data point is worth investigating as it may indicate an effective marketing strategy able to generate revenue beyond what is expected based on the amount of money spent on marketing a keen eye for outliers is essential because they can dramatically skew statistical measures and data distributions though they might seem problematic at first outliers often carry vital information about the process under investigation or the data gathering mechanism they can help businesses gain valuable insight into potential issues or areas for improvement and optimization let’s help Adventure Works investigate the relationship between their advertising spend and sales revenue by creating a scatter chart the company can also explore any outliers using this chart enabling them to quickly identify issues areas for improvement and exceptional successes let’s use an imported data set containing Adventure Works sales and advertising expenditure data for this task to understand how various advertising media are performing with their advertising budget against the sales revenue you need to compare two fields the sales revenue and profit margin you need to identify each of these items via their campaign ID and platform type start by opening the report view place a scatter chart in the report area by selecting the scatter chart icon from the visualizations pane and resize accordingly while keeping the chart selected open the data pane and select these four fields campaign ID profit margin sales revenue and platform the campaign ID should go to the values field these represent your individual data points the profit margin goes to the xaxis field the sales revenue goes to the yaxis field and the platform goes to the legend field the x and yaxis field wells contain the data fields to compare against each other to display more data when hovering over a data point drag the advertising spend field from the data pane to the tool tips field now hover over any data point to see the updated tool tip this scatter chart is visualizing the correlation between marketing spend and sales the data points or markers are shown as dots you can manually change the size of these markers if needed by opening the format tab and the markers section the data points behaving as expected are closely gathered in the chart creating a cluster there are three outliers instantly evident this makes it easy to investigate these data points and gain insight into what caused the deviations from the expected pattern the data point in the leftmost corner represents a campaign that has an unusually high advertising spend compared to its sales revenue this is not in line with the trend seen in the other campaigns where a lower advertising spend usually correlates with a higher sales revenue marketing can use this insight to make decisions around resourcing for example reallocating the advertising budget to campaigns that are not underperforming in contrast the data point in the middle represents a campaign demonstrating a substantial deviation from the expected trend with a low advertising spend yet an unusually high sales revenue likewise for the data point on the top right corner sales revenue is exceptionally high given its relatively low advertising spend this campaign outperforms all others in terms of sales despite the minimal investment in advertising stakeholders can investigate these outliers to gain insight into the successful strategies and optimize other campaigns two additional field wells for scatter charts in PowerBI are worth noting the size field enables you to change each marker size dynamically it provides insight into how additional factors are affecting the data points for example let’s drag the advertising spend data field to the size field on the visualizations pane notice how the size of the data points change with the dot in the leftmost corner being the largest and the dot in the top right corner being the smallest the size of these points is now representing the advertising expense you can also add animation to your chart by adding a data field to the play axis for example let’s drag the advertising spend field to this play axis the chart now displays as a video like a player with a play button when you play it will animate each data point and display advertising spend in the top right corner this is useful for engaging audiences during presentations in this video you discovered scattered charts in PowerBI a type of visualization you can use to represent the relationship between two variables scattered charts are a powerful data visualization tool for uncovering outliers providing insights into trends and patterns and assisting datadriven decision-making they are an essential part of any data analyst’s toolkit congratulations you’ve completed the first module of this course creating reports in Microsoft PowerBI this week you are introduced to the different types of visualizations in PowerBI and how to add them to reports and dashboards with an emphasis on the significance of visualizations in presenting valuable insights to stakeholders you started the week by exploring the course overview and structure as part of your course introduction you set up your PowerBI environment and online account preparing you for the course exercises you also explored the importance of visualization and analysis in the context of business intelligence using real world scenarios and terms to enrich your understanding next you were introduced to visualizations in PowerBI starting with an overview of their importance in business intelligence you discovered the power of visualizations to simplify vast and complex data uncover patterns and trends enable detailed investigations of data make data accessible to and engaging for all kinds of stakeholders and communicate your analysis insights effectively you also explored creating visualizations in PowerBI a process that involves connecting to your data sources extracting transforming and loading your data selecting your visualization types and mapping data elements to different aspects of the visuals arranging the visualizations on the report page and finally sharing your report you learned how to apply visualization items to a basic report and were introduced to some common business reports you then familiarized yourself with the visualizations pane in PowerBI gaining hands-on experience in creating your own business report a sales report for Adventure Works you also explored how to pin visualizations in PowerBI in order to empower stakeholders to access key insights quickly encourage collaboration and promote a datadriven culture in your third lesson you delved deeper into basic visualizations in PowerBI you explored bar and column charts line and area charts combo charts pie and donut charts and tree map charts you not only learned how to create these different charts but also when and how to use them for maximum impact and effective data representation you also had the opportunity to practice your new skills by completing various activities and tasks using different chart types plus you discovered how important it is to target your data visualizations based on the needs of your audience with the basic visualizations covered you moved on to some of the specialist visualizations in PowerBI you learned about key performance indicators which are measurable metrics linked to an organization’s objectives and their vital role in business you were introduced to cards multi-roll cards gauges and KPI visuals visualization types in PowerBI that you can use to represent KPIs in business reports kpi visualizations provide stakeholders with a snapshot insight into overall performance and progress towards goals you also learned about ribbon waterfall funnel and scatter charts including their different purposes and how to configure each of them in PowerBI you then had the opportunity to put your knowledge to good use by creating a performance report for the marketing team at Adventure Works configuring visualizations that showcased relevant KPIs and answering realworld questions about performance over time you are now equipped with essential data visualization techniques and report creation skills in PowerBI you will build on your learning thus far discovering how to enhance the user experience and accessibility of your reports keep up the momentum and ensure you use the quizzes and additional resources to further consolidate your learning you’re a data analyst at Adventure Works a company that relies heavily on data analytics for decision-making the company recently added some talented individuals to its sales team including Logan who is visually impaired and uses screen reading software to access digital content soon after joining the team Logan realizes that the Microsoft PowerBI reports he receives are not entirely compatible with his screen reader he finds it difficult to interpret the visuals and graphics and there are some components that he cannot access recognizing the potential impact on Logan’s performance and the ability of the sales team to make datadriven decisions his manager immediately alerts the data analytics team while their reports are comprehensive and visually appealing the team has neglected the critical aspect of accessibility in this video you’ll learn about accessibility in data and reporting its importance in the business context and designing PowerBI reports that are accessible and inclusive to all in the context of digital systems accessibility refers to products applications websites and tools designed to allow all users to use them effectively regardless of whether they have any disabilities accessibility practices cover a wide variety of elements to ensure the usability and inclusivity of digital content this includes enabling digital content compatibility with assist of technology or AT which is used to increase maintain or improve the functional capabilities of people with disabilities such as Logan’s screen reader powerbi supports many accessibility standards that help ensure your PowerBI experiences are accessible to as many people as possible among these standards are the web content accessibility guidelines commonly known as WUKAG that help ensure web content is accessible to people with disabilities according to key principles of these guidelines web content including information user interface components and navigation should be perceivable operable understandable and robust or interpretable by a wide range of user agents including assist of technology implementing accessibility features in PowerBI reports can enhance the audience’s experience and comprehension of your reports in several ways firstly accessible reports promote inclusivity by designing PowerBI reports with accessibility in mind you ensure everyone can interact with and understand the data regardless of any limitations this results in a more inclusive and equal environment accessible reports also improve usability the practices used in creating accessible reports such as providing clear and concise titles adding alternative text descriptions for visuals and implementing keyboard navigation typically results in a better user experience for everyone in addition you can cater to different user learning and processing preferences by using various channels or methods to present information like text visuals audio and tool tips multimodal presentation can enhance comprehension and engagement for a wider audience accessibility features can also promote a clear interpretation of the data presented using techniques such as tool tips or descriptive titles can provide more context and reduce the chances of misinterpretation of the data finally accessible reports ensure compliance with various jurisdictional laws and regulations regarding digital content accessibility this keeps your organization within the legal framework and builds trust with your audience to promote accessibility which is vital in data and reporting PowerBI offers a variety of features for designing accessible reports powerbi visuals are fully keyboard navigable and compatible with screen readers facilitating user interaction and navigation powerbi also supports high contrast themes ensuring better readability plus users can use focus mode to expand visuals improving visibility and view data in a screen reader friendly tabular format with the show data table option for users with difficulty with color like color blindness you can use markers to convey different series in visuals like line or area charts similarly PowerBI supports pattern fills in visuals like pie or bar charts which you can use in addition to or instead of solid colors it also has some built-in report themes that consider accessibility guidelines when choosing colors and themes you need to ensure that there is enough contrast between text and background colors and be aware of color combinations that are difficult to distinguish you can add alt text which refers to alternative text descriptions to the visuals in your reports to make them more accessible alt text conveys essential insights even if users cannot see your visuals adding descriptive titles and labels to your visuals also enhances their accessibility as well as their understandability and usability finally some users may have motor difficulties and rely on assistive technologies that for example use keyboard commands for reading and interacting with your report content you can set the tab order of reports to help keyboard users navigate them in an order that matches the way other users visually process the report visuals in this video you discovered the importance of making PowerBI reports easy to use for all users and how to design accessible PowerBI reports which you’ll explore in more detail as you progress through the course accessibility ensures you follow the rules about being fair and inclusive makes your reports easier to use and helps everyone understand your data the usability and understandability of your reports play a vital role in communicating analysis insights and ultimately for stakeholders like Logan to apply data insights to decisions in the business context knowing the importance of accessible reports you need to include features that make your Microsoft PowerBI reports accessible to everyone in this video you’ll learn how to configure and format visualizations to improve accessibility let’s start by adding alt text or an alternative text description to a pie chart visual in an existing report for Adventure Works this is especially useful for people with visual impairment because screen readers can read this text when they select a visual to provide alt text for any object in a PowerBI desktop report start by selecting the object in the visualizations pane select the format section expand general scroll to the bottom and fill in the description in the alt text text box this text box has a limit of 250 characters alt text should include information about the insight that you would like the report consumer to take away from a visual because screen readers read out the title and type of visual you only need to add a description related to the data and main point of the visual for example alt text for this pie chart could be sales figures for February March and April in Europe North America and Asia combined next let’s explore how to set up tab order to improve accessibility by ensuring easy keyboard navigation navigate to the tab order page of the report to set the tab order select the view tab in the top ribbon in the show panes panel select selection in the selection pane choose tab order to display the current tab sequence for your report you can select an object then use the up and down arrow buttons to move the object in the hierarchy you can also select an object with your mouse and drag it to the position you’d like in the list now let’s move on to working with titles and labels to increase accessibility for visuals in your reports make sure that any titles access labels legend values and data labels are easy to read and understand let’s navigate to the titles and labels page of the report and compare the two-line chart visuals the visual on the left has no legend or access labels this makes it difficult to comprehend the insights the chart is meant to convey by including a legend the report consumer now knows which line in the chart corresponds to which product region and including the axis labels of February March and April makes it easier to interpret the trends in the data over time you can also add data labels to your charts to do that select the visual select the format section and find the data labels toggle and turn it to on turning data labels on for this chart displays the order total amount for each month along the lines representing the product regions this makes it easier for the user to interpret the visual at a glance with data labels you can even choose to turn on or off the labels for each series in your visual as well as position them above or below a series while PowerBI does its best to place data labels above or below a line sometimes it isn’t clear for example in this visual the data labels are jumbled and not easy to read to change the default position expand the data labels menu and select above or under from the position drop- down list positioning your data labels above or below your series can help ensure clarity especially if you’re using a line chart with multiple lines with a few adjustments the data labels are now clearer you learned that markers can also help to convey information in visuals like line area combo scatter and bubble charts adding markers improves accessibility by not only relying on color for users to interpret your visual and distinguish between data points for example different series in a line chart to turn markers on select the visual then the format section in the visualizations pane next expand the shape section scroll down to find the show markers toggle and turn it to on the line chart is now displaying markers to change the shape of the markers for each line separately select the format tab and expand markers from there select any series from the series dropdown and change the shape and size of the markers from the shape section lastly let’s explore the focus mode and show data option in PowerBI when a report consumer is examining a visual in a dashboard they can expand it to fill up more of their screen by selecting the focus mode icon in the context menu of the visual this displays only the selected visual allowing for better presentation and focus to return to the main report area select the back to report button to view the data in a visual in a tabular format select the three dots icon on the top right corner of the visual followed by the show data table in the visual context menu this displays the data in a table that is screen reader friendly you can also switch the layout to vertical or horizontal by selecting the layout button on the top right corner of the visual in this video you learned how to format visuals to improve accessibility and use various accessibility features in PowerBI integrating accessibility features improves inclusivity by ensuring users can access and interact with your content and can enhance the overall comprehension and usability of your reports your manager Adio asked you to design a report highlighting critical data within a table visual he wanted you to display data bars with sales figures for immediate recognition and to differentiate specific rows based on their data values for increased readability to implement this request you discovered PowerBI’s useful feature conditional formatting this feature enables the customization of charts based on diverse data criteria enhancing report readability and user engagement in this video you’ll learn about the conditional formatting feature in PowerBI and how to apply it to visualizations conditional formatting is a feature that allows you to apply specific formatting to cells or rows in a table or matrix based on specific conditions this feature is significant when you have vast amounts of data and want to highlight certain elements that meet specific criteria for example if the total profit displayed in a table was a negative value indicating a loss you could highlight this by using conditional formatting to change the value to a red color other visuals also support conditional formatting for example you can format a bar chart so that if the sales target for a specific product category goes beyond a certain threshold that category’s bar will change color conditional formatting offers many benefits it provides immediate insights allowing users to quickly spot trends anomalies and focal points without going through a vast amount of data one by one a more visually appealing report particularly one with colored data or data bars in a table can enhance user engagement making the information more accessible and readable in addition relying solely on manual analysis can result in users missing crucial details however with conditional formatting vital data points are automatically highlighted significantly reducing the potential for errors now let’s explore how to add conditional formatting to a table visual which offers excellent support for conditional formatting select the table visual from the visualizations pane you can resize it as needed in the report view now select the month product region order status order quantity and order total fields from the data pane from the format tab expand style presets and select the alternating rows preset from the drop- down menu if you’d like to resize the columns you can drag the column corners as needed you can also change the column headers by doubleclicking the fields in the column well on the visualizations pane let’s rename sum of order quantity to order quantity and sum of order total to order total now let’s show data bars using conditional formatting data bars display on columns with numerical values like order total or order quantity in this table to show the data bars rightclick the order total field in the column well on the visualizations pane select conditional formatting and select data bars this will display the data bars dialogue box in this data bars dialogue box you can select a color for positive and negative bars positive bars will display when the value is positive and negative bars when the value is negative select the colors and select okay the data bars will display in the order total field with your selected colors you can also change the background color of a cell using conditional formatting let’s try this with the order status column say you want to change the background color when the values are shipped cancelled and processing respectively to do that rightclick the order status field in the columns well on the visualizations pane select conditional formatting then background color this will show the background color dialogue box where you can set the conditions to apply specific formatting type shipped in the value text field and change the background color then select the plus new rule button to add a new rule in this new rule type cancelled and change the background color add one more rule and type processing and change the background color select the okay button and the table will update with the new conditional formatting instantly remember that you can add as much conditional formatting to each field as you want in this video you discovered how to implement conditional formatting in a table visual conditional formatting in PowerBI is an effective feature that you can use to enhance the clarity and usability of your visualizations making your data easily accessible and increasing visual appeal and user engagement during a recent project review you presented a report you carefully designed to the Adventure Works marketing team the presentation went smoothly engaging the audience with crucial data insights however Renee the marketing director noticed that the visual elements of the report didn’t align with the company’s brand colors and style guide renee asked you to update the design elements of the report to reflect the company’s brand aesthetics as you started selecting each individual item and manually adjusting their colors it was clear that this would be a tedious time-consuming task luckily your manager stepped in demonstrating how themes in Microsoft PowerBI could simplify the task at hand and save you a lot of time and effort in this video you will learn more about themes in PowerBI and working with them in your reports themes in PowerBI are predefined sets of colors fonts and visual styles that you can apply to your reports easily and quickly they ensure visual consistency across different reports and can save significant time that would be otherwise spent customizing individual items you can customize themes to align with company color schemes and design guidelines this can help enforce a strong brand identity in your reports and create a more impactful and professional appearance using themes in PowerBI can enhance accessibility in a variety of ways powerbi offers theme customization options you can use to cater to specific accessibility needs such as high contrast themes for users with visual impairments you can also enhance readability by using themes that employ distinct and consistent colors assisting users in differentiating between various data points and categories plus PowerBI provides built-in themes to help make your report more accessible for example by offering themes with colors that are easy to distinguish and visible to colorblind users this can broaden the accessibility of your reports to a more diverse audience not to mention a well-designed theme ensures that reports are userfriendly and easier to interpret let’s take a moment to explore how you can apply these themes in PowerBI you can choose report themes by going to the view ribbon in the themes section select the drop- down arrow and then select the theme you want to apply to your report these themes are similar to themes seen in other Microsoft products such as Microsoft PowerPoint here you can also find accessible themes which you can utilize to create accessible reports select a theme to apply it to your report instantly if you would like to customize the appearance of your PowerBI reports in the future changing the theme allows you to update all your visuals at once for more options you can also browse the collection of themes created by members of the PowerBI community by selecting theme gallery from the themes drop- down menu this opens the themes gallery in your browser in the themes gallery you can select any theme then scroll down and download the JSON file for the theme to install the downloaded file select browse for themes from the themes drop-down menu go to the location where you downloaded the JSON file and select it to import the theme into PowerBI desktop as a new theme this theme will instantly apply to your current report you can customize a theme directly in PowerBI Desktop to do this select a theme that is close to what you’d like you can then customize the theme by making any necessary adjustments to customize a theme from the view ribbon select the themes drop-own button and select customize current theme a dialogue appears where you can make changes to the current theme you can then save your settings as a new theme there are customizable theme settings in various categories you can name your custom theme and define color settings customized text settings such as font family size and color and visual settings which cover background border header and tool tips and adjust page elements like wallpaper and background as well as filter pane settings including background color transparency font and icon color size and filter cards after you make your desired changes select apply to save your theme you can now use the theme in your current report it will also be available in the custom themes section in the themes drop-down menu in this video you learned about themes in PowerBI using themes can significantly enhance the efficiency consistency and accessibility of your reports enabling you to effortlessly maintain a uniform look that aligns with brand guidelines learning how to use and customize themes is an essential skill that’ll help you make visually appealing easy to understand and professional reports quickly you need to present this quarter’s sales data to Adventure Works management team the data you’re dealing with is multifaceted and includes information like product categories regions stores periods and various performance metrics like total sales average sales and profit margin you include various charts and graphs that visually represent the overall sales trends regional performance and product category performance in a dashboard for management however the team also wants more granular and contextual information like store specific performance and individual product performance within categories due to the dashboard’s highle design displaying all these detailed data points could clutter the dashboard and overwhelm users you can use PowerBI’s tool tip feature to deal with this in this video you will learn about how this feature can improve the accessibility of your PowerBI reports and how to add custom tool tips you learned that tool tips in PowerBI display additional information about the data being displayed in your visuals when users hover over different data points you can create custom tool tips by adding extra items to the tool tips field well for a visual tailoring the content to the needs of your report users tool tips can contribute to improved accessibility of PowerBI reports and dashboards in various ways tool tips allow you to provide an extra layer of detailed information without cluttering the dashboard for example hovering over a specific region in a regional performance chart could show the top performing and bottom performing stores within that region this can make complex charts and graphs more accessible to all users including those with cognitive disabilities you can customize tool tips to provide contextspecific details for instance when a user hovers over a bar representing a product category in a bar chart the tool tip can display the top three best-selling products within that category for visually impaired users descriptive tool tips can provide crucial information that might not be readily accessible from the visualization screen readers can read out tool tips making the data more understandable for those with visual impairments tool tips are included in the show data table option for every visual tool tips can also support users that find distinguishing between different segments or lines in a chart based on color challenging such as colorblind users detailed tool tips can help these users by providing the necessary information when they hover over parts of the visualization even if they cannot visually distinguish between the colors users can discover new insights and patterns with tool tips in turn they may facilitate users who need additional support to interpret the visualizations and ensure insight clarity you can also use tool tips to explain or define the metrics and measures used in the visualizations enhancing users understanding of the data a further benefit of interactive features like tool tips is that they can make the data exploration process more engaging increasing user engagement lastly tool tips can help maintain a clean minimalist design in the dashboard by minimizing visual distractions tool tips ensure you don’t overwhelm the dashboard with additional details this allows users to focus on highle trends and patterns and explore details when necessary aiding their overall comprehension of relevant insights now that you know more about tool tips and how they can support report accessibility let’s explore how to configure and customize them in PowerBI if you hover over this ribbon chart PowerBI displays a tool tip that contains contextual information useful for understanding the visual for example hovering over this faded area shows various performance indicators for the Europe sales region such as monthly order totals and rankings the tool tip can also display other information related to this data point if you hover over the solid color it provides the month region name and the sum of order total you can customize this tool tip say for example some stakeholders want additional information related to order quantity and product stock to add this information select the visual open the visualizations pane and scroll to the tool tips field well drag order quantity from the data pane to this well powerbi will automatically convert it to sum of order quantity you can further customize a tool tip by selecting an aggregation function select the arrow beside the field in the tool tips well then select from the available options like sum average minimum maximum and many others as per your requirement you can repeat this process for product stock once tool tips are added to the tool tips well hovering over the same data point on the visualization also displays values for the sum of order quantity and sum of product stock you can also change the position of these fields in the tool tip by dragging them in the tool tips field well in this video you discovered how to add tool tips in PowerBI and how they can make your reports more userfriendly and accessible ultimately tool tips help add extra details without cluttering your dashboards and reports this feature can improve clarity and data comprehension and ensure all users including those with cognitive disabilities or visual impairments can access vital information the sales team at Adventure Works wants a comprehensive overview of their bicycle sales performance from overall company performance down to specific product models and different sales representatives setting up a hierarchy in a Microsoft PowerBI data model is a neat way to organize and explore related data from a general view to specific details in this video you’ll discover more about hierarchies in reports and how to create well ststructured hierarchies in PowerBI so that users can easily explore data at various levels of detail in your reports data hierarchies are a way to organize and structure your report data and visuals in PowerBI hierarchies group related data items by hierarchical relationships while you do not need to organize your data in PowerBI using hierarchies it can make it easier for users to understand the data and the connections between different components hierarchies in PowerBI also support data exploration making it possible for users to navigate from high-level data overviews to more detailed information these hierarchies enable drill mode in your visuals empowering users to drill down into detail within the same visualization or report for example PowerBI automatically creates a date hierarchy when importing date columns from data arranging dates from more general to more specific such as year quarter month and day in a data set with timebased sales data a hierarchy like this enables users to explore the sales totals from a broader point of view such as yearly sales to a more detailed one such as sales on a particular day let’s explore hierarchies further by considering the example of an adventure works data set containing sales records you can create a hierarchy by organizing the data points into a structured framework that starts with bike as the main category and further breaks down into subcategories which you can break down further into specific product names this way stakeholders can understand the overall sales of bikes at a glance and explore the data at a more detailed level such as the sales performance of mountain bikes versus road bikes or the sales performance of individual products similarly for a data set containing geographical sales data you can structure the data according to the hierarchy of continent country city area this way report users can drill down into the data by geographic level from exploring global trends to examining local successes or difficulties so how can you create hierarchies like these in PowerBI let’s take a moment to explore the process you can start by importing your data set in this case the adventure works sales data set into a blank PowerBI report you don’t need to transform any data then select the sales table followed by the load button if you open the data pane you will notice that PowerBI has automatically created a hierarchy with all the date fields such as estimated delivery date and order date for example if you expand order date then date hierarchy it shows the dates organized according to year quarter month and day how can you create a hierarchy of your own let’s create a hierarchy for product related data using the product category product subcategory color and product name fields imagine how this hierarchy should be constructed the product category should be the overarching or main category at the top rightclick the product category field in the data pane and select create hierarchy from the context menu this will immediately create a new item in the data pane called product category hierarchy if you expand this item the product category field is nested inside it to add more fields to this hierarchy right click on a field for example the product subcategory and select add to hierarchy from the context menu then select the newly created product category hierarchy the product subcategory field will be added to the product category hierarchy following the same process let’s add product color and product name fields to this hierarchy you can remove any field from the hierarchy by right-clicking on it and selecting delete from model you can instantly add a table visual to your report area by checking the check box before the hierarchy on the data pane you can resize this visual as needed alternatively you can create a visual and then apply the hierarchy to it select the tree map visual from the visualizations pane and resize it as needed while keeping it selected mark the checkbox of the product category hierarchy in the data pane now select the order quantity field the tree map visual will be ready with drill down mode instantly and you can dig down into as many levels of data as you want you can turn the drill down mode on by selecting the down arrow on the top right corner of this visual and make the report interactive understanding report hierarchy enables you to organize data for yourself and the stakeholders working with the report you’re creating hierarchies facilitate an understanding of how different data fields relate making the data less confusing and more userfriendly with hierarchies users can start with the bigger picture and smoothly zoom into different levels of detail as needed empowering them to make a range of informed decisions imagine you are asked to design an interactive visual for a report that displays crucial information while allowing users to delve into any chart element and engage more deeply with the associated data points users should have the flexibility to navigate through multiple layers and return to the main report as needed while drill down only allows users to navigate from a broader to more detailed level within the same visualization with PowerBI’s drill through feature users can navigate from a visualization to a separate detailed report page focused on the selected data point in this video you’ll learn how to configure the drill through feature in a PowerBI report for Adventure Works let’s start with a pie chart displaying total sales figures by month this visual provides stakeholders with a way to compare monthly order totals at a glance suppose you want to direct users who require more detail about sales performance to a separate page that displays the sales data broken down by region and order status you can add a new page to your report by selecting the plus icon at the bottom to add a page title doubleclick on this new page title and type regional sales add a table visual to the page and resize it accordingly then select month from the order date hierarchy order quantity order status and product region the table is now displaying all of this data at once so how can you have users land on this new page because the pie chart displays total sales by month you can link the table to the chart using the shared month field while keeping the table selected drag the month field from the order date hierarchy to the drill through field well notice how a back button is added above the table visual you can now press the control key on the keyboard and select this button to return to the main report returning to page one in our report area when you right click on any slice of the pie chart for example April a new item in the context menu called drill through displays select regional sales and notice how the table is now showing only the sales records for April returning to the main report if you rightclick on the March slice and select drill through followed by regional sales you are shown the regional sales table for only March’s sales data suppose some stakeholders also want insights into the performance of different categories of bikes let’s create a new page that displays the data by bike categories sold in every month and link it to the main chart using the drill through feature add a new page and rename it bike categories select a card visual resize it as needed and select month from the order date hierarchy on the data pane dragging it into the fields well next select a multirow card and resize it as needed select the order quantity and product category fields on the data pane drag the month field to the drill through well to link the new page to the main chart now let’s return to the main page and explore the new addition if you select any slice for example March there are two items available under the drill through menu in the context menu if you select bike categories you will be taken to the bike categories page but now data is showing for only March you can add as many pages as you need and link them to other report pages using the drill through feature in PowerBI in this video you learned how to use the drill through feature in PowerBI this feature is essential for professional and real life business data visualization enabling you to create multi-page reports with easy navigation allowing users to dive deeper into the data as needed without sacrificing clarity in reporting and visualization sorting and filtering functions can help users better understand the data presented in reports highlight patterns and trends and focus on information that’s relevant to them in this video you’ll discover how to apply and manage sorting and filtering features in PowerBI with PowerBI you can sort or order the data in your report visuals based on different data fields like ascending or descending order for example in a report on sales performance sorting a column chart depicting sales performance by region in ascending order makes it easier for stakeholders to identify the lowest and highest performing sales regions an unsorted visual can create confusion and make the visual unreadable and difficult to understand consider this line chart showing sales trends for the quarter the chart is sorted by sales amount by default and the months are not presented in logical chronological order if you do not sort the visuals by month users might have difficulty understanding or misinterpret sales performance over time as at a glance it seems like sales are declining however when properly sorted by month it is clear that sales increased in all three regions over time there are also many filtering options available to you when creating your reports filtering enables you to select specific data points or subsets of data as needed to ensure the data presented is relevant and clear this is helpful for excluding certain values when representing your data with different visuals for example this report displays the combined total of orders from different sales regions it includes all types of orders including cancelled orders or those still being processed in this example you may want to use filtering to exclude these data fields if you add an order status filter to show only the numbers for orders that have been shipped the picture changes dramatically by filtering out canceled orders and orders still being processed stakeholders can focus on completed orders and gain a better overall picture of actual sales performance in the different regions now that you know more about the sorting and filtering features let’s explore how to use them in PowerBI you can sort any chart in PowerBI by data fields in a variety of orders depending on your needs to sort select the three dots on the top right corner of the visual followed by your preferred sorting method some visuals like this line chart give you the option to sort the legend as well arranging the different categories presented in the legend in a particular order other visuals like this pie chart offer only sort access which refers to sorting data points along the horizontal or vertical axes in a particular order from the axis you can select various data fields and then also select to sort them in ascending or descending order let’s sort the stacked column chart in the bottom left corner of the report by month currently it is sorted by order quantity in ascending order select the three dots on the top right corner of this chart select sort axis then month followed by sort ascending the chart is now sorted by month in ascending order beyond sorting PowerBI offers powerful filtering capabilities there is a filters pane that you can use to apply different filters to the whole report page as well as individual charts let’s filter the line chart in this report to show the order total for the shipped orders only notice the filters on this visual section in the filters pane let’s filter the line chart in this report to show the order total for the shipped orders only here you can select relevant fields and apply filtering for example you can exclude Asia from this line chart by selecting the product region and then checking every region excluding Asia the line chart will update instantly it now displays sales data for Europe and North America only you can also add other filters like order status here drag the order status field from the data pane to the add data fields here box now check shipped the line chart will update and display the order total for only shipped orders instead of individually applying filters you can apply filters on all chart items at once from the filters pane unselect any chart item by selecting a blank area on the page and open the filters pane if it’s not opened yet notice the section called filters on this page this is where you can drag the relevant data fields and set filters for all visuals on the report page let’s drag the order status field from the data pane to this section and check shipped notice how all visuals on this page reflect this change instantly if you have a multi-page report you can apply filters to all pages by dragging any field to the filters on all pages section in the filters pane and then by setting the filters you can also remove a filter anytime by selecting the field you want to remove in the filter pane followed by the cross or X icon in the top right corner in this video you explored sorting and filtering discovering how these can provide stakeholders with a clearer picture of their data these features are fundamental to data analysis and reporting in PowerBI applying sorting and filtering to your visualizations makes it possible for stakeholders to focus on the vital relevant data points enabling faster datadriven decision-making imagine you’re presenting a report to key decision makers at Adventure Works one visual displays sales across a quarter while another portrays product categories arranged in descending order based on the number of orders the stakeholders request more interactivity in the report for example by selecting a specific month on the sales chart they wish to see corresponding product categories emphasized in the other chart this provides clarity on which products sold the most during a particular month microsoft PowerBI’s cross filter and cross highlight functionalities make it possible for you to emphasize related data across multiple charts or remove unrelated data in this video you’ll learn about these exciting features and how to use them in your PowerBI reports cross filtering refers to the practice of selecting an item or data point on one visual which in turn filters out unrelated data in another visual it creates a relationship between two separate visuals such that a selection in one visual affects the data shown in another for example with cross filtering selecting the mountain bikes column in a report will filter the table visual to display only sales data related to this product category the other product categories are no longer shown with cross highlighting when you select a data point in one visual it highlights the related data in other visuals instead of filtering out unrelated data this is the default behavior for most visuals in PowerBI to illustrate with cross- highlighting selecting the mountain bikes column in one chart highlights the sales of mountain bikes in February March and April for each region in the stacked bar chart unlike cross- filtering it still displays unrelated data however it’s dimmed or faded let’s take a moment to explore these cross filter and cross highlight features in PowerBI in this report there are four different visuals displaying various sales data let’s start by examining how default cross highlighting works in PowerBI using the stacked bar chart in the top left corner if you select any region for example Europe it highlights the bar related to Europe and dims the other bars notice how all other charts instantly reflect your selection and highlight data that is related to your selection in the stacked bar chart the bright areas represent data related to Europe and the dim areas represent data from other regions you can press the shift key on the keyboard and select multiple regions or even multiple units in the stacked bar chart every time your selection changes the other charts respond automatically by highlighting the related data take note that the table visual behaves differently rather than fading the irrelevant data it hides them based on your selection this is called cross filtering to clear your selection you can select the selected item again to return to normal view if you select data points on any of the charts on this page the other charts will cross highlight based on your selection instantly for example if you select mountain bike on the stacked column chart in the top right corner the other charts respond just remember that cross- highlighting means irrelevant data will remain visible but dimmed and cross filtering means irrelevant data will be hidden you can change the default behavior of interaction in PowerBI reports from cross- highlighting to cross filtering to do that select the file menu options and settings and then options this opens the options dialogue box from here select the report settings from the left sidebar and then check change default visual interaction from cross highlighting to cross filtering in the visual options section and select okay now if you select mountain bike on the stacked column chart notice how the stacked bar chart on the left reacts it is not showing the dimmed areas anymore and is displaying data related to the mountain bikes only in other words cross filtering hides all sales data unrelated to mountain bikes based on your selection in the other visual cross filtering and cross- highlighting are powerful features in PowerBI that can enhance the clarity and effectiveness of your reports having the ability to enable one chart to influence another you offer a more interactive and intuitive experience for report users this approach not only makes your report more dynamic but also simplifies the data analysis process as you create more interactive reports for your audience filtering data becomes increasingly important at Adventure Works the CEO asks you to set up a sales report that she can use in a presentation with the company’s shareholders next week you want to make this report as useful as possible for the CEO but unfortunately her schedule is busy between now and the presentation you know she will be filtering data but cannot predict every filter she will apply however you know that she’ll most likely filter the data by region and product this is a perfect scenario to use a slicer in Microsoft PowerBI in this video you’ll learn what a slicer is how it works and how to apply slicers to your reports a slicer is a great way to apply common filters to a report page quickly when added to a report you can use the slicer to display a list of commonly used or most important filters the slicer can be displayed in multiple formats depending on the field on which the slicer is filtering for example if you apply the slicer to a field with text data type the slicer can display as a list of unique entries in that field similarly if you apply the slicer to a field with a date type the slicer can be displayed as a date range selector however no matter which format the slicer is displayed in the underlying behavior is the same the slicer provides a list of filters that users can apply to the visualizations in the report when a filter is selected the visualizations will immediately update to reflect the filtered data it is important to note that you do not need to connect every visualization in a report to the slicer as a PowerBI data analyst you can configure which visualizations are impacted by the slicer selected filters you can also synchronize multiple slicers so that when a slicer applies a filter other slicers on different pages are updated to reflect the selected filter this is useful when filtering through multiple layers of data for example if you had one slicer for regions on a sales page and another slicer for regions on a costs page when you select a specific region the region is selected on both slicers this helps improve the user experience as filtering remains consistent as you navigate multiple pages of the report now let’s explore how to configure a slicer in a PowerBI report let’s begin with an existing sales report for Adventure Works the report has two pages sales summary and sales detail on the sales summary page you need to apply two slicers one for region and one for products let’s start by adding the region slicer navigate to the visualizations pane and select the slicer icon then select the slicer in the report and navigate to the data pane in the data pane select the region field in the region table notice that the slicer now lists all of the sales regions of Adventure Works if you select the entry for France in the slicer this will apply a filter for sales data belonging to France notice that when you apply the filter the visualizations update immediately next let’s add the slicer for products again navigate to the visualizations pane and select the slicer icon select the slicer in the report and navigate to the data pane this time select the product field in the product table the slicer now displays the lists of all products now let’s confirm that each visualization is connected to the slicers to do this navigate to the format option in the ribbon menu and select edit interactions each visualization will show a filter icon indicating that filters are being applied if you want to disconnect the slicer select the none icon in the visualization remember that you can synchronize the slicers across pages to reflect the current filter context let’s configure two slicers to synchronize with each other first I’ll create the same region slicer in the second page of the report by adding the slicer visualization and again applying the region field from the data pane next navigate to the view menu and select sync slicers this opens the sync slicers view select the region slicer in the report it is now displayed in the sync slicers view expand the advanced options drop- down menu enter the name of a group you want this slicer to belong to for this scenario let’s name the group region there are two additional options here sync field changes to other slicers and sync filter changes to other slicers for this report you need to select both options as you want to sync the slicers with each other when the viewer interacts with them and also for maintainability purposes so that if you change the filtered field in the data pane both slicers will update now select the region slicer in the first page and navigate to advanced options again once again enter the group name region while you can enter any name for the group you must name it consistently if you misspell the group name on a slicer it won’t synchronize correctly again select sync field changes to other slicers and sync filter changes to other slicers now it’s time to test the report when applying a filter using the region slicer for example by selecting France the visualizations on the first page update now when you navigate to the second page the region slicer on this page is already set to France and the data is filtered you learned about adding slicers to PowerBI reports in this video slicers are a dynamic tool that you can use to enhance the interactivity of your reports while also improving the user experience as you design reports for different audiences it is essential to consider their filtering needs and identify common or important filters to apply the world of apps has rapidly expanded over the past decade from apps on your mobile phone to apps in the web browser on your desktop with people already familiar with the app experience what if you could make your reports more app-like this could improve the user experience for your target audience immensely and encourage them to interact with and use the reports you build microsoft PowerBI comes with a built-in set of buttons that you can add to your reports to increase interactivity from navigation between pages to quickly applying filters in this video you’ll discover more about buttons and how they’re invaluable in your toolkit for building interactive reports buttons in PowerBI come with many configurable options the two most common configurations you will work with are the visual style and the action you can change the visual style of buttons to different shapes such as rounded rectangles pillshaped and arrows you can also change the colors of the buttons and their text if the business you work for already has other applications these options help you align with potential existing app and user experience guidelines the action of the button is how it behaves when a user interacts with it let’s explore the different options available back returns the users to the previous page of the report this action is useful for drill through pages bookmark allows users to capture or bookmark a particular state in the report it presents the report page that’s associated with a bookmark that is defined for the current report you’ll learn more about this later drill through navigates the user to a drill through page filtered to their selection without using bookmarks page navigation also involves navigation without using bookmarks it navigates the user to a different page within the report q&a opens a Q&A explorer window when your report readers select a Q&A button the Q&A explorer opens and they can ask natural language questions about your data apply all slicers and clear all slicers buttons apply all the slicers or clear all the slicers on a page lastly web URL opens a web page in a browser these buttons provide different means through which users can engage with your reports let’s explore how to enhance the interactivity of a report by adding buttons this PowerBI sales report has two pages sales summary and sales detail on the sales summary page there are slicers available let’s start by configuring buttons for page navigation to add a button navigate to the insert tab in the ribbon select the buttons dropdown and choose right arrow position the arrow in the top right corner of the report select the button in the report to open the format pane the format pane allows you to configure the different options of the button for now let’s expand the action section in the format panel in the action section first select the off button so that it changes to on enabling the action next select page navigation as the type and then choose the sales details page as the destination now let’s navigate to the second page of the report again navigate to the insert tab in the ribbon select the buttons drop-down and choose left arrow in the action section select page navigation and then the sales summary page as the destination finally position the arrow in the top left corner of the report page you can test the buttons by holding the control key and selecting the buttons given that there are slicers on the sales summary page you can ensure a good user experience by allowing the report viewer to clear the slicers quickly to do this navigate to the insert tab in the ribbon select the buttons drop-down and choose clear all slicers let’s position the clear all slicers beside the slicers on the report page for ease of access now when the viewer applies a filter using the slicers they can select the clear all slicers button to reset the state of all the slicers these simple changes will help improve the user experience of the report buttons are a useful way to improve the user experience for your target audience when building your next report consider how you can use buttons to simplify navigation add filtering and provide access to the Q&A feature as you progress with your learning you’ll explore how this feature is particularly useful when building reports for mobile devices at the end of the last financial year Adventure Works conducted a customer survey to determine how happy customers were with the way the company handled product orders and deliveries unfortunately a common complaint was that it took too long for orders to arrive after being placed to investigate the possible causes of this delay you have created a report in Microsoft PowerBI that tracks data from different sources including storefront orders warehouse fulfillment and courier delivery because you plan on sharing this report with multiple departments you know each department will want to filter the data specifically to align with their responsibilities rather than expecting users to apply complex filters they are unfamiliar with to isolate the data they’re looking for your manager suggests using the bookmarks feature to make this data easily accessible to them in the next few minutes you’ll learn what bookmarks are and how to add them to your reports in PowerBI bookmarks in PowerBI are a way to capture the current state of the report you are viewing and share this state with other viewers for example if you apply filters to a report you can save the filtered state as a bookmark viewers can then select the bookmark and the report will change to the filtered state you established when adding a bookmark there are four state options that you can save data properties such as filters and slicers display properties such as visualization highlighting and visibility current page changes which present the page that was visible when you added the bookmark and selecting if the bookmark applies to all visuals or selected visuals in the adventure works example bookmarks will enable different users to focus on different parts of the data without setting up filters every time you can also highlight specific insights and create customized views relevant to the different departments by default all states are saved for all visuals if you modify a report after you create a bookmark any visualizations not present when you created the bookmark will appear in a default state so remember if you change a report you should make sure to update your bookmarks to reflect the changes given that bookmarks in PowerBI are excellent for creating tailored interactive reports that users can easily navigate and extract crucial insights from it’s essential to know how to create them let’s take a moment to find out let’s start by filtering data in an existing sales report in PowerBI with two pages sales summary and sales details let’s filter data related to the France sales region by selecting France in the region slicer next let’s filter further by selecting the Mountain 200 Black 38 model in the product slicer now that the report is in a filtered state let’s create a bookmark to do this select view in the ribbon menu and then bookmarks this opens the bookmarks panel to create the bookmark select the add button this saves the state and creates a new bookmark with a default name to rename the bookmark select the three dots beside its name and select rename for this bookmark let’s rename to France if you don’t want the bookmark to open the current page you can select the three dots beside the bookmark again note that current page has a check mark beside it indicating that it is enabled for the bookmark to disable it select current page now let’s test the bookmark clear all slicers so that the report is reset if you open the bookmark panel again and select the bookmark you can observe the filters reapplied to the report bookmarks in PowerBI empower you to streamline data exploration and customize and tailor reports based on user needs by capturing states of reports such as data and display properties bookmarks allow different users to filter and focus on specific aspects of the data easily bookmarks are also a valuable tool for enhancing interactivity and creating tailored user-friendly reports that can support datadriven decision-making adventure Works has embraced the datadriven decision-making unlocked by Microsoft PowerBI however as you’ve continued building and updating various reports you’ve identified a significant time cost to maintaining them and when you need to add new visualizations to the company’s many reports moving all the existing individual visualizations is very timeconuming the lead data analyst suggests grouping the visualizations to make maintenance easier this video will demonstrate how to group and layer visuals to improve maintainability let’s start with an existing Adventure Works sales report the report has four visualizations sales revenue by region sales revenue by month sales units by region and sales units by month to make maintenance more manageable let’s create two groups one for the sales revenue visualizations and one for sales units visualizations to do this first select the sales revenue visualizations by holding down the control key and selecting the two visualizations then navigate to the format tab in the ribbon menu and select group next select the two sales units visualizations by holding down the control key and selecting them again navigate to the format tab in the ribbon menu and select group notice that now when you select and move the sales revenue by product visualization the sales revenue by month visualization moves too this is because they are grouped you can view all existing visualizations and groups using the selection pane to open the selection pane navigate to the view tab in the ribbon menu and select the selection button the groups created in this video are listed under the layer order tab in the selection pane inside each group are the visualizations that belong to the group to improve maintainability let’s rename the groups let’s doubleclick the first group’s name and rename it sales revenue similarly doubleclick the second group’s name and rename it sales units the ordering of groups and visualizations is important in the pane as this determines how the elements are layered for example moving the sales revenue group to overlap the sales units group results in this group displaying under the sales units group visually to change the visual order you can select the revenue group in the selection pane and select the upward arrow so that it moves above the units group in the layer order now suppose after reviewing the groups with a colleague you conclude that managing the visualizations as a single group would be better in the selection pane you can select and drag both sales units visualizations in the units group to the revenue group notice that the units group is automatically removed as there are no more visualizations belonging to it let’s add a title to the report page which is now more maintainable through its grouped visualizations and descriptive group name select the insert tab in the ribbon followed by text box in the text box add the text sales detail then select all the text in the text box and change the font size to 24 now let’s organize the layout of the report select and drag one of the visualizations and the group will move move the group to the bottom of the report page then move the report title to the top of the report and adjust its sizing as more pages are added to a report and future updates are made time is saved by organizing visualizations into groups in this video you discovered how to group and layer visuals in PowerBI grouping visualizations is a crucial activity for improving the maintainability of reports make sure to consider the benefits of grouping visualizations and how to implement groups effectively when designing reports in PowerBI data analysis expressions or DAX is a powerful language for creating custom calculations however DAX is contextsensitive so it’s important to understand how context influences the reports you build with it in this video you’ll explore how visualizations impact DAX context adventure Works is analyzing its total annual revenue the company needs to identify its total revenue based on different product categories as part of its analysis once the analysis is completed the results must be delivered to management as a visual presentation adventure works can use DAX filter context in visualizations to perform its analysis and create its reports let’s begin with a recap of what we mean by the term context in Microsoft PowerBI in data analysis context comes in two primary forms row context and filter context row context refers to the table’s current row being evaluated within a calculation whereas filter context refers to the filter constraints applied to the data before it’s evaluated by the DAX expression in other words you can determine which of your reports rows or subsets should be included or excluded from the calculation the interaction between DAX evaluation context and visualization is crucial for creating dynamic and interactive reports and dashboards each time you interact with the data like selecting a portion of a chart or an item in a slicer you alter the filter context let’s consider an example to find out more about how this works adventure Works can create a DAX measure of profit margin and then create a visual in the report canvas from this measure the visualization displays the profit margin of the entire data set because that is the current context let’s learn more by exploring how Adventure Works make use of DAX filter context in its visualizations adventure Works begins its analysis of its product categories by creating a DAX formula that calculates the sum of the quantity of each product sold multiplied by the unit price in the sales table when executed the formula computes the sum of all sales amounts the result of this formula is that Adventure Works has sold $3.5 million worth of goods over the past year however when this measure is added to a PowerBI report as a visual like a bar chart for example it isn’t very engaging it offers limited insight into the sales data by displaying only the total revenue the visuals become more engaging and display meaningful insights when used with filter context for example Adventure Works could generate more useful insights by comparing or contrasting total sales revenue across product categories by comparing sales of bicycles to other categories Adventure Works discovers that bicycles outsell all other products by a considerable amount adventure Works can still view the total revenue but each of these revenue figures now has a meaning which is the total revenue for each product category powerbi is displaying the sum of all sales within a specific product category but now it’s computing different values for different cells because of the evaluation or filter context total sales by category adventure Works can enhance these visuals further by using the year category from the date table as another filtered context or attribute once this context is applied a new visualization is generated each table cell shows a different value even if the formula is always the same you can place multiple fields in both rows and columns this is because both the row and column sections of the table define the context as you discovered earlier the interaction between the DAX evaluation context and the visualization alters the filter context interaction affects DAX calculations and alters the results in the visualizations let’s explore this process using an adventure works data set now that Adventure Works has calculated its annual total sales it creates two slicers in its report one for the region and the second for the month when a specific region is selected the profit margin measure recalculates and the chart dynamically adjusts adventure Works can also select a month to implement month as an additional filter on top of region the measure now displays the profit margin value for a specific region in a specific month the contextsensitive nature of DAX is a powerful feature it enables dynamic calculations based on the context in which DAX computes the formula by understanding how context impacts DAX you can create more accurate insightful and dynamic reports to tailor to specific business scenarios congratulations on completing the navigation and accessibility module of the data analysis and visualization with PowerBI course this module taught you essential skills for creating accessible well ststructured and interactive reports let’s recap what you accomplished you started with how to design accessible reports you discovered the significance of accessibility and the many benefits of implementing accessibility features in PowerBI such as improving your reports inclusivity usability and understandability you learned about some of the PowerBI features that can support the accessibility of your reports including keyboard navigation and tab order screen reader compatibility accessible themes and high contrast support focus mode and displaying data in a screen reader friendly table format markers and pattern fills and alt text titles and labels you explored how to enhance accessibility by formatting and configuring your visualizations using these accessibility features learning how to design reports that cater to a diverse audience who can all access and comprehend the information you present conditional formatting was a key focus empowering you to apply dynamic rules to your visualizations that enhance their clarity and usability you also engaged with themes in PowerBI and the ways they can enhance the accessibility of your reports such as enhancing readability in addition to other benefits such as visual consistency and enhancing clarity and brand identity in the process you learned how to apply configure and customize themes in PowerBI to further guide your journey you were introduced to best practices for designing accessible reports you then put your newfound knowledge of accessibility into action by applying formatting themes and design best practices to create an accessible report for Adventure Works you went on to learn how to enhance the accessibility of your reports even further by adding custom tool tips to your visualizations you also explored the many ways tool tips can improve accessibility in your reports such as making the data more accessible to users with visual impairments as tool tips are screen reader compatible and making complex charts more understandable to users including those with cognitive disabilities next you focused on report navigation and filtering you began by comprehending the concept of report hierarchies and learned how to configure them effectively in your reports these hierarchies empower users to drill down into your data as needed encouraging user interaction and engagement and enhancing user understanding you also learned how to configure PowerBI’s drill through feature which empowers users to navigate from a visualization to a separate detailed report page focused on the data point they select another key area of exploration was sorting and filtering data which are fundamental to data analysis and reporting in PowerBI you gained proficiency in applying and managing these techniques in PowerBI reports to enhance data presentation and exploration and highlight relevant insights you were then introduced to the concept of cross filtering and cross highlighting providing you with the knowledge to configure interaction behaviors for visualizations improving the interactivity of your reports whereas cross highlighting highlights the related data in other visuals when a user selects a data point in one visual cross- filtering filters out or removes the unrelated data from the other visuals you applied your skills by sorting and filtering marketing data in a report emphasizing and contextualizing the importance of sorting and filtering in the real world after that you took your PowerBI reporting skills to the next level with an indepth exploration of creating highly interactive reports you discovered the dynamic nature of slicers and how they can contribute to enhanced report interactivity plus you explored using buttons to add more interactivity to your reports and learned how to customize them to suit your needs you learned how to improve user experience and storytelling in your reports by adding bookmarks as well as how to add URLs to enrich your PowerBI reports further grouping and layering visuals provided a way to efficiently manage the visuals in your reports making report maintenance more efficient you put your skills into action by creating an interactive report demonstrating your proficiency in using the drill through button slicer and bookmark features finally you recaped the importance of filter context in DAX measures and how it impacts visualizations throughout this module knowledge checks were strategically placed to assess your understanding of key concepts covered in relation to designing accessible reports navigating and filtering data effectively and creating interactive reports keep up the excellent work and get ready to explore designing accessible dashboards and data sharing bringing you closer to becoming a proficient PowerBI data analyst and visualization expert the marketing director at Adventure Works receives an overwhelming number of data reports monthly sales numbers customer demographics market trends and product performance metrics all need to be analyzed and interpreted and she needs your help doing this luckily you know about dashboards a tool in Microsoft PowerBI that can help transform this data into valuable insights but what is a dashboard and how does it differ from a report in this video you’ll explore the concept of dashboards in a business context you’ll discover their importance functionalities and how they serve as key tools in data analysis and decision-making processes let’s start by exploring what a dashboard is consider the dashboard of a car it presents critical data like speed fuel level and engine temperature in a consolidated visually understandable way this information allows you to make necessary decisions while driving similarly in the business context a dashboard visualizes the critical information required to accomplish specific objectives skillfully arranged and consolidated on one screen for example a sales dashboard for Adventure Works might display total sales sales by region top selling products and trends over time dashboards can present data from different sources in various forms making it easier for stakeholders to understand they are interactive and real time allowing users to in essence have a conversation with their data and drill down into specific details when needed say you notice an unusual sales spike in one region at Adventure Works with an interactive dashboard you can delve deeper into the data inspecting the specifics of the sales transactions identifying the products involved and even the key customer demographics contributing to this sudden surge dashboards play an important role in today’s competitive business world where informed decision-making is vital to success with dashboards you can transform raw data into actionable insights providing a comprehensive view of business performance at a glance dashboards can serve as an essential navigational tool for tracking various aspects of business performance for example for Adventure Works dashboards can bring the different threads of data on sales trends production efficiency customer behavior and market dynamics together presenting a comprehensive view of the overall health and trajectory of the business suppose there’s a sudden drop in sales in a specific sales region without a dashboard recognizing this issue would require sifting through vast amounts of sales data a time-consuming process with the potential for oversight however a well-designed dashboard can quickly highlight this anomaly triggering a timely investigation and corrective action dashboards also play a vital role in promoting a culture of transparency and accountability within an organization they act as unbiased databacked mirrors that reflect the true performance of different business units against set targets and benchmarks by doing so dashboards can foster a sense of ownership and accountability among team members encouraging continuous improvement dashboards make data accessible to everyone break down barriers and encourage data sharing between teams as well as promote a shared understanding of business performance across departments but what is the difference between a dashboard and a report though often used interchangeably dashboards and reports serve different purposes in Microsoft PowerBI a report in Microsoft PowerBI is highly interactive users can slice and dice the data drill down into details apply filters and explore various facets of the data within the report itself in essence a PowerBI report provides an indepth interactive multi-perspective view of a specific data set or topic it’s like an exploratory journey through your data a dashboard on the other hand is like a summary or highlight reel of one or more reports it’s a one-page overview of the most important metrics or KPIs selected from the various pages of one or more reports a useful way to consider the difference between a dashboard and a report is to compare it to a news bulletin versus an indepth news article the news bulletin or dashboard provides key highlights summarizing the most essential points if a particular news point catches your attention you can read the full news article or report for a more detailed understanding as you continue your data analysis journey remember that the true power of data lies not in its volume but in its usability both dashboards and reports are vital navigation tools in the sea of data they provide visibility drive accountability facilitate understanding and ultimately inform decision making addio your manager at Adventure Works asks you to create a dashboard in Microsoft PowerBI that highlights key performance indicators and insights from a sales analysis report you and your team created this screencast will explore how to create and configure a dashboard in Microsoft PowerBI as well as how to configure the mobile view for the dashboard and customized themes previously you learned that a dashboard is a consolidated display of multiple visualizations reports and other data in a single layout to create a dashboard open your Microsoft PowerBI service and navigate to your workspace in the left navigation pane then from your available workspaces select the adventure works workspace let’s create a new canvas where you can pin your visuals on the top left corner select new and then select dashboard a popup appears asking you to name your dashboard let’s name it Adventure Works Sales Dashboard after typing the name select create once you have created your dashboard you can start adding visuals return to your workspace and open the sales report you and your team created each visualization in your report has a pin icon in the top right corner select the pin icon for the total sales by product category bar chart this opens a dialogue box where you can choose where to pin this visual select your newly created Adventure Works sales dashboard from the drop-down menu the bar chart is a good starting point for your dashboard as it provides a broad overview of sales distribution by product category then pin the monthly sales trends line chart this chart shows the sales pattern over time which is critical for identifying seasonal trends or growth patterns in the modern business landscape having mobile accessible data is key with PowerBI’s mobile layout feature you can configure your Adventure Works sales dashboard to be mobile friendly ensuring stakeholders can access insights on the go to switch to mobile view go to the main navigation bar find and select the edit menu from the drop-down options select mobile layout to switch the view from desktop to mobile once you select the mobile layout your screen adjusts to replicate a mobile devices screen size now instead of a wide canvas it displays a vertical layout this canvas is blank but don’t worry all your visuals are safe and where you left them you just need to decide which visuals to show on the mobile layout and where to place them a list of all the visualizations in your dashboard is displayed on the right side of your screen each visualization has a pin icon next to it to select the visuals you’d like to appear in the mobile layout select the relevant pin icons selecting these pins indicates the visuals you’d like to appear in the mobile layout you can select and drag each visualization to move it around on the canvas you can also resize each visualization by dragging its edges finally let’s explore how to change the theme for the Adventure Works sales dashboard start by navigating to the Adventure Works Sales Dashboard you just created in the upper menu find and select the edit menu this opens a drop-own list of view options select dashboard theme another drop-own list appears select switch theme a popup window displays various pre-made themes you can apply to your dashboard choose a theme that you feel best visually represents the data and select it then select save the theme is now applied to your dashboard and you’ll immediately observe the changes in color and style applied across all your visualizations and there you have it you now know how to create a dashboard configure the mobile view and customize your dashboard theme foundational knowledge that is vital to using dashboards in PowerBI and conveying key insights from your reports with its large scale of operation Adventure Works generates immense data volumes daily as a data analyst your role involves harnessing this data making sense of it and transforming it into insights that inform strategic decision-making but with such a large mass of data where do you start microsoft PowerBI has the answer it’s quick insights and Q&A features over the next few minutes you’ll discover how to optimize the usability of your PowerBI dashboards by adding quick insights and utilizing the Q&A feature you’ll also learn how to set up quick insights and integrate the Q&A feature into your dashboards quick insights is a feature in PowerBI that automatically searches data sets to discover and visualize potential insights it identifies patterns trends outliers and other useful insights that may not be immediately obvious for example uncovering sales patterns to help the marketing team at Adventure Works target their campaigns more effectively quick Insights not only presents the insights in an easy to understand format but also explains how it arrived at these insights this way even if you’re new to data analysis you can follow along and gain a solid understanding of the data let’s explore the steps to set up and use the quick insights feature in PowerBI open your Microsoft PowerBI service and navigate to your workspace on the left hand side of the screen here different data sets and reports shared with you are displayed select the data set or report you want to analyze open or select the ellipsus menu and get quick insights to initiate the automated analysis powerbi starts an automatic scan of your data during this process the function applies various machine learning algorithms and statistical functions to your data set it searches for potential patterns trends correlations outliers and other interesting attributes this process can take a few minutes depending on the size and complexity of your data set after the scan you can access the insights by selecting view insights this will lead you to a new page filled with cards each insight card visually represents a particular pattern or trend in your data hover over the visuals or select them to display more details this is where your data interpretation skills come into play in this case you have to understand what each of these visuals represents and how it relates to the Adventure Works business context if you find any insight particularly useful or wish to share it with others in your team you can pin it to a dashboard to do this hover over the card and select the pin icon in the top right corner of the card then select the dashboard you want to pin it to or create a new one now let’s move on to the Q&A feature the Q&A feature is a natural language processing tool in PowerBI it allows you to ask questions about your data in plain English and provides answers in the form of charts graphs or simple numeric results this feature is invaluable in the business context because it allows users of all levels to interact with their data and find specific answers without requiring deep technical knowledge the key advantage of the Q&A feature is its flexibility you ask questions ranging from simple questions like “What was the total revenue last quarter?” to more complex ones such as “Which product had the highest sales growth rate last year?” The more you use the Q&A feature the more it learns and adapts to your question style offering even more relevant and precise answers over time let’s explore how to set up and use the Q&A feature in PowerBI at the top of your dashboard there’s a field ask a question about your data this is the Q&A box place your cursor in the box to ask your question type your question in normal conversational language as you type PowerBI Q&A will start offering suggestions and autocomplete options based on the data in your dashboard for instance if you’re interested in sales trends you could type “What were the total sales last month?” or “Show sales by product category.” As soon as you finish typing your question PowerBI Q&A generates an answer in the form of a data visual such as a bar chart line graph or table this visualization is based on the best interpretation the Q&A can make of your question if the interpretation is not what you intended you can rephrase or refine your question the PowerBI Q&A tool uses machine learning so it becomes smarter and more accurate the more you interact with it if the visual answer to your question is particularly useful and you want to keep it handy you can pin it to your dashboard to do this locate and select the pin icon at the top right of the visual choose the existing dashboard where you want to pin it or create a new one with quick insights and Q&A you are well equipped to bridge the gap between data and decision-making these features simplify complex data analysis enabling you to deliver actionable insights faster and more accurately imagine you’ve prepared stunning visuals in Microsoft PowerBI for Renee Gonzalez the marketing director at Adventure Works showcasing sales trends across different product categories you’ve pinned these visuals to your dashboard for easy reference but as you start digging deeper into the data exploring trends and cross- filtering data you come across a snag the pinned visuals are static snapshots they don’t interact or update you realize you’ve hit a roadblock that prevents you from extracting the full potential of your data analysis frustrating right you’re not alone as that’s a common issue with pinned visuals in PowerBI in this video you’ll explore the limitations of pinned visuals in PowerBI and how to overcome these limitations by setting up and pinning live reports to your PowerBI dashboard in PowerBI a pinned visual is a snapshot of a specific piece of data or chart from a report that is attached or pinned to a dashboard you can pin various things like a line chart showing sales trends over time a bar chart comparing the performance of different product lines a gauge displaying progress towards a goal or even a simple card displaying a single important number like total sales or total customers pinned visuals provide an at a glance overview of specific insights however they have certain limitations
the main limitation is their lack of interactivity you can’t cross filter or drill through data using pinned visuals which prevents you from exploring data trends in greater detail for example imagine Renee is studying a pinned visual showcasing sales trends for different bicycle product categories as she scans the data she wants to filter it by region to understand which categories are more popular in certain regions this could provide valuable insights for regional marketing strategies however the static nature of pinned visuals prevents her from cross-filtering or drilling through the data leading to incomplete insights and potentially missed opportunities for datadriven strategies so is there a way around these limitations absolutely the solution lies in pinning live reports to your dashboard instead pinning a live report means attaching an entire report page to your dashboard as a live tile unlike standard visuals pinned to a dashboard live report tiles are dynamic and maintain the interactivity of the original report this includes the ability to drill through data cross filter and view tool tips which provides a more immersive data exploration experience directly from the dashboard pinned live reports retain the original report layout and formatting making the visuals aesthetically consistent the interaction between visuals within live reports reveals relationships and patterns that isolated visuals cannot while pinned visuals offer a quick view of specific data points pinning live reports significantly enhances data exploration and analysis capabilities providing a comprehensive interactive view of your data now let’s explore how to set up and pin live reports the first step is to select the report you want to pin to your dashboard if you’re starting from scratch you will need to create a new report once you have opened your report select the reading view button on the ribbon directly above your report then select the ellipses on the far right of the ribbon followed by pin to dashboard from the drop-own menu the pin live page feature lets you pin an entire report page as a live tile on the dashboard this means the tile will continually update and allow interaction something a simple pinned visual cannot do a dialogue box asks you to choose a destination for your pinned live report you can select an existing dashboard or create a new one by typing a new name into the text box after you’ve selected the destination select the pin live button in the bottom right corner to pin your live report to the selected dashboard to view your newly pinned live report navigate to your chosen dashboard by selecting the workspaces button on the lefth hand navigation bar and selecting the dashboard where you pinned the live report now a live interactive report is directly accessible from your dashboard it retains all its interactive capabilities in the report view allowing you to filter and drill down into the data directly from the dashboard any changes you make to the original report will reflect in the live report on your dashboard ensuring real time data updates by using live reports you not only enrich your data storytelling but also create opportunities for more deeper more insightful analysis pinning live reports to your dashboard can help you turn static one-dimensional visuals into dynamic insightful narratives your manager Adio asked you to create a comprehensive report on the sales of Adventure Works product lines across different regions you have cleaned and analyzed the data and created a final report that is visually appealing and informative now you need to share the data and insights contained in the report with key decision makers in Adventure Works this is where Microsoft’s PowerBI publishing reports feature comes into play over the next few minutes you’ll discover the process of publishing reports in PowerBI let’s start by exploring what publishing reports in PowerBI means when you publish a report you move it from your local PowerBI desktop and upload it to the more accessible and collaborative online platform PowerBI service publishing a report connects you with decision makers allowing you to share your reports with colleagues your whole organization or external stakeholders who need to draw insights from the data in data analysis the purpose of creating reports is to assist with decision-making guide strategies and provide insights into business operations and for that to happen you need to publish and share the reports for example you can publish and share your report with the regional sales managers at Adventure Works this enables them to access the report through the PowerBI service where they can identify bestselling and underperforming products analyze sales patterns such as seasonal trends and then plan and focus marketing efforts accordingly furthermore a published report is not static you can set up automatic data refreshes so the report is always up to date with the latest data let’s explore how to publish reports in PowerBI publishing a report to PowerBI service from PowerBI desktop involves a series of steps let’s work through these steps the first step is to save the report since PowerBI will not allow you to publish unsaved reports select file in the top left corner of the PowerBI desktop interface and then save as to save the report choose a location on your computer and give it a descriptive name like Adventure Works product sales report select save once you’ve saved the report the publish option becomes available in the home tab of the ribbon of PowerBI desktop select publish and a new dialogue box pops up in this dialogue box indicate where you want to save the report in PowerBI service select Adventure Works as your workspace and then the select button for larger projects or collaborations you can create and select different workspaces once you’ve selected the destination PowerBI starts publishing the report a loading dialogue appears indicating that the report is being published depending on the size of the report and your internet connection this could take a few moments once your report is published a new window pops up to confirm it says success and gives you two options you can either open the report in PowerBI service or you can cancel and open it later in this case let’s select open selecting open launches the default web browser on your computer and takes you directly to your report in PowerBI service the report now displays as it will appear to other users while data analysis is about facts and numbers it’s also about communication publishing reports in PowerBI is a crucial part of the data analysis storytelling process as a data analyst your reports are pivotal in driving datainformed decisions and a vital link in the chain of business intelligence as a data analyst at Adventure Works you are tasked with reviewing and sharing sales data since Adventure Works is a multinational company the final report contains large amounts of information which you need to present in a format that is more manageable for stakeholders microsoft PowerBI allows you to pageionate and export reports as a result you can break down complex sets of results into smaller more digestible parts and share them easily in this video you will learn how to create multiple pages of content in a PowerBI report and navigate between them you will also learn how to export these pages to a PDF file in PowerBI you can organize and present your data across multiple pages within a single report which is known as pageionation a page in a PowerBI report is like a page in a book pages make it easier for the reader to navigate and understand the content for example if you have a large data set with numerous visuals presenting all of them on a single page can make the report difficult to read and interpret by dividing your report content into multiple pages you make your report more organized and easier to navigate let’s discover how to configure pageionation and export reports in PowerBI desktop with PowerBI desktop open navigate to the file menu located in the top left corner of the applications home screen once you select file a side menu appears select open report and then select browse reports to open a dialogue box navigate to the location on your computer where your PowerBI report file is stored select the file and then open to load the report now that your report is loaded you need to make sure you’re in the right view to pageionate your report a vertical pane on the left of the screen contains three views in PowerBI report data and model select report this choice is now highlighted on the bottom left of the report view screen is a tab with the name current page to add a new page select the plus sign which is the new page option to rename this page appropriately to represent the data it contains right click on the page name and select rename page you can then move visuals and report elements by cutting and pasting them from your main report to these newly created pages you can navigate between pages by selecting the tabs this allows you to organize the data in your report and makes it easier to review and understand if you need to present this report in a meeting or share it with colleagues who don’t use PowerBI you can export it to a PDF format select file in the top left corner of your PowerBI desktop screen on the menu that opens select the export option a side menu opens with the different export formats available select the to PDF option to begin the process of exporting your PowerBI report as a PDF document depending on the complexity and size of your report this may take a few seconds to a few minutes once the export is completed the PDF file will open automatically to display the result creating multiple pages and exporting to PDF can help you to produce effective PowerBI reports pageionation and exporting in PowerBI help you break down and categorize data clearly to enhance understanding and easily share insights that can drive informed decisions you’ve spent hours working on a sales report for the management team at Adventure Works and are confident that it will not only meet but exceed their expectations the feedback unfortunately is not about the insights your report offers it’s about the loading time your sales stats visuals load at a sluggish pace causing the stakeholders to become impatient despite your effort in creating the report its slow loading time overshadows its merits sounds like a nightmare right but it doesn’t have to be this is where Microsoft PowerBI’s performance analyzer comes into the picture over the next few minutes you’ll learn about the vital role of PowerBI’s performance analyzer in optimizing the performance of your reports by the end of this video you will understand why it’s important to measure current performance before implementing changes using the performance analyzer so let’s get started the performance analyzer a tool in PowerBI is designed to help you understand the load time for each visual element in your report this functionality is crucial in scenarios where a report has various visuals filters and calculations each of which can potentially impact the overall performance of the report it is critical to measure current performance before making changes to a report in data analysis just as you wouldn’t make business decisions without first analyzing relevant data you shouldn’t implement changes to your PowerBI report without understanding the current performance situation and identifying any problem areas with insights from the performance analyzer you can take targeted actions improve the performance of the lagging visuals and transform your report into a fast loading efficient tool the performance analyzer doesn’t just highlight what’s wrong it also shows you what’s right not all visuals or filters in your report will be problematic many of them might be well optimized and load swiftly recognizing these efficient components allows you to learn from them and apply those best practices to other reports or visuals now let’s dive into the interface and discover how to activate the performance analyzer in PowerBI desktop after your report is open and loaded select the view tab find and select the performance analyzer option at the top middle of the screen a new pane titled performance analyzer opens on the right side of your screen displaying buttons for starting and stopping recording refreshing visuals and exporting data the performance analyzer pane has a button labeled start recording to begin gathering performance data for your report select this button once activated the performance analyzer starts monitoring any actions taken on the report capturing useful performance metrics for each visual element on the page now that the recording has started you need to generate the actions you want to analyze this could involve refreshing a report page to load all the visuals or navigating through different report pages if it spans multiple pages you can manually refresh the page by selecting the refresh visuals button in the performance analyzer pane this action causes PowerBI to reload all visuals on the page and the performance analyzer records the performance data for each visual during this process the performance data displays in a list in the performance analyzer pane with each visual on a separate row this list contains information such as the name of the visual the duration of time it took for the visual to render the time it took to run the DAX query for the visual and more this information can help you understand how long it takes for each visual to load and render and identify any potential bottlenecks in your report expanding the row by selecting the plus icon reveals more granular details about the performance of that visual this includes a breakdown of the time it took for each operation such as the DAX query execution visual display rendering and any other operations the actual DAX query run and more the performance analyzer lists visuals in the order they were rendered on the page by default however this order may not always be the most useful when diagnosing performance issues you can reorder the list by selecting the duration column header this sorts the visuals by the time taken to render allowing you to quickly identify which visuals are taking the longest to render and could be potential targets for optimization once you’ve gathered the performance data you need you can stop the performance analyzer recording select the stop button in the performance analyzer pane to conclude the data capture you can always start a new recording session by clicking the start recording button again as a data analyst your task isn’t just to ensure that your reports are accurate or comprehensive but also that they’re efficient a well optimized report can mean the difference between insights that sit on a virtual shelf gathering dust and insights that spark change and propel a business forward in the world of data speed isn’t just a convenience it can enhance the impact of your reports lead to better decision-making and drive business success imagine you’re a data analyst in Adventure Works working through streams of data finding patterns making connections and uncovering insights that could improve business performance you’re in the middle of an exciting project where you’ve created a new complex DAX query to analyze sales performance and uncover trends but as you load your PowerBI report you’re not met with a rush of insights but rather a slow loading screen that seems to drag on forever this isn’t just frustrating it’s a barrier between you and the crucial insights needed to drive Adventure Works forward as these performance issues make your data exploration and analysis frustratingly slow you remember a helpful tool the performance analyzer in this video you’ll discover the role of the performance analyzer tool in diagnosing and resolving DAX performance issues you’ll become familiar with the process of identifying if a DAX query is causing a delay and learn how to optimize it for improved performance at the heart of PowerBI’s data modeling is DAX or data analysis expressions as you may recall DAX encompasses a wide range of functions operators and constants that you can combine to create different formulas and expressions the power of DAX lies in its flexibility with DAX you can build custom calculations within data models thereby allowing you to analyze data in unique and powerful ways however just like a powerful vehicle it requires skill and care to operate effectively and efficiently while DAX has immense analytical power it can sometimes run into performance issues these issues arise when the DAX queries that are created based on your formulas and visual configurations become complex making the engine work harder and longer to return the results for example suppose you are dealing with large adventure work sales tables that need to be sifted through your DAX formulas might be complex and inefficient or you might have a data model that’s been improperly structured regardless of the case these issues can lead to slow report loading times sluggish interactions and an overall frustrating user experience to help identify and resolve these performance issues PowerBI has a built-in tool called the performance analyzer this tool provides detailed timing breakdowns on all the various components and processes that occur when your report is refreshed it helps you spot which visuals fields or DAX calculations are taking up the most time and hence slowing your report down let’s explore how to identify and resolve DAX query performance issues using the performance analyzer once you’ve loaded your PowerBI sales report you first need to open the performance analyzer on the ribbon interface at the top of your PowerBI report locate and select the view tab within the view tab find and select the performance analyzer option in the performance analyzer pane locate and select the start recording button now it’s time to refresh your report you can accomplish this in two ways either by selecting the refresh button situated in the home tab of the ribbon interface or by directly interacting with the report interactions could be in the form of changing a filter selecting a slicer or simply navigating to a different page of the report as you interact with the report while the performance analyzer is recording it will track and document the time taken to load each individual visual item this data is crucial for diagnosing performance issues once the report has finished refreshing review the performance analyzer pane you’ll see a list of all the visual items in your report and their respective load times pay special attention to any visual items that take a significantly longer time to load compared to others for the visuals with slower load times you can drill down into the details by selecting the arrow beside the visuals names this will provide a detailed breakdown of the DAX query time and the visual rendering time helping you understand where the bottleneck lies if the DAX query time is high then your effort should be directed towards optimizing the DAX measures in this case it appears that the average sales by product category is slowing down the report performance as it has a considerably larger DAX loading time locate the average sales field from the data view on your right and select it to view the underlying DAX formula the filter and all functions used in this formula iterate over the entire data table to calculate the average sales for each product across all stores this operation becomes particularly slow when working with larger data sets to simplify the DAX formula eliminate the filter and all functions and instead use the average X function the average X is a function that evaluates an expression for each row of a table and then returns the average result however since it operates directly on the data context which is already filtered based on the report’s current context it avoids the need to iterate over the entire data table finally rerun the performance analyzer to test if the optimization was successful the advantage of applying an optimized formula is that it simplifies the calculations and reduces the computational load by avoiding the iteration over the whole data table it leads to a significant speed up in query execution you’ve now seen how seemingly simple tasks like generating a sales report at Adventure Works can become complex it’s in these complexities that you as the data analyst can create value by optimizing your DAX queries and delivering faster smoother reports you can empower stakeholders to make quick and informed decisions remember data analysis isn’t about delivering vast amounts of information it’s about delivering the right information in the right format at the right time each time your report loads a little faster or your DAX query runs a little smoother you’re not just improving a technical process you’re contributing to better faster and more informed business decisions you are now better equipped to find the hidden inefficiencies in your DAX queries confront them headon and turn them into opportunities for learning and growth adventure Works has a rich set of data from manufacturing to sales the data is vast and you are responsible for developing a comprehensive dashboard that compiles all these data sources into meaningful insights you start creating a report in Microsoft PowerBI and use DAX the formula language in PowerBI as you create complex DAX expressions you realize that the report starts to lag the calculations are getting more complex and timeconuming and you wonder if there’s a more efficient way to handle all this data without sacrificing performance in your search for solutions you discover DAX variables which are said to have the power to make PowerBI dashboards more efficient could using DAX variables be the answer to you improving your report performance in the next few minutes you’ll discover DAX variables and their importance in PowerBI you’ll also learn how to effectively implement DAX variables to optimize the performance readability and accuracy of your PowerBI reports dax or data analysis expressions is a formula language that includes functions operators and values you can combine to construct formulas and expressions in PowerBI and Power Pivot in Excel in programming and formula languages a variable acts as a storage container you can put something into it like a number or a string or even the result of a more complex expression once you’ve assigned a value to a variable you can reference that variable by its name elsewhere thus saving you the need to recomputee or refetch that stored value in DAX variables serve a similar role but with a twist catering to its analytical nature instead of thinking of them as simple storage containers think of them as computational snapshots when dealing with complex data sets like the multi-layered operations at Adventure Works recalculating the same values or expressions can be resource inensive especially if done multiple times in a single report or visualization this is where using variables in DAX for PowerBI is beneficial let’s explore the benefits of using DAX variables in more depth using variables allows for storing intermediate results complex calculations done multiple times can be stored in a variable and referenced thereafter saving computational effort and time this optimization leads to faster report rendering and performance enhancement especially in large data sets dax formulas can sometimes become quite lengthy and complex by breaking down these formulas and storing parts of them in variables the main formula becomes more streamlined and easier to read improving readability also once a value or a result is stored in a variable it remains consistent throughout the formula this ensures consistency and no variation due to repeated calculations leading to more accurate results in addition to ensuring consistency reusing variables in multiple expressions within a formula means you don’t have to recalculate or redefine commonly used values or results and provides flexibility in formula construction should there be an error or an unexpected result in your report having your formula broken down into variables makes it easier to pinpoint where things might have gone wrong instead of sifting through a long complex formula you can check variable values individually making debugging easier lastly breaking down complex expressions into smaller parts held within variables makes your formulas more transparent and easier to understand this reduced complexity can be immensely beneficial when working in teams where other data analysts or report developers might need to decipher or modify your DAX expressions for example if you were to calculate the total sales for Adventure Works in the last year and then use that figure in multiple parts of your DAX formula without variables the same total sales value might get recalculated every single time it’s referenced this redundancy isn’t just a waste of computational resources it’s a drain on performance by using a variable you compute the value once store it as a snapshot and then reference this snapshot wherever needed in your formula ensuring both clarity and improved performance now let’s examine how to use a variable in DAX to improve report performance in PowerBI let’s start by opening the existing Adventure Works sales PowerBI report once your report is open you’ll notice various panes on the screen on the right side you’ll find the data pane which lists all the tables that your report is connected to select the sales table that contains the empty sales measure upon selecting the sales measure the formula bar will open where you can start writing your DAX formulas begin the formula with the var keyword this is the starting point for declaring a variable after typing var add a space and then name your variable it’s a good practice to name your variable something meaningful for instance if you’re calculating total sales for the last 12 months you might name your variable sales_12 months next you’ll provide the DAX expression that calculates the value for the variable after the equal sign write out the DAX formula you want the variable to hold this expression calculates the sum of sales amounts over the last 12 months after defining all necessary variables the next step in your DAX measure is using the return keyword this keyword indicates the final output of your DAX measure after performing calculations using your variables once you’ve written out your measure press enter with the measure saved to your table you could use the variable you created to quickly compare the last year’s sales figures across different product categories or regional markets by leveraging the pre-calculated variable the report would render these comparative visualizations much more quickly using variables in DAX within PowerBI offers a streamlined approach to handling complex calculations and improving report performance as you get more accustomed to this feature you’ll find yourself employing variables more often to make your DAX measures both efficient and maintainable using variables to optimize your data models and make them efficient can ensure not only quick results but more accurate insights every line of DAX you write every measure you create and every insight you derive has the potential to influence decisions shape strategies and drive success adventure Works has seen soaring sales this year with mountain bikes especially flying off the racks like never before but as you sift through your PowerBI dashboard a nagging feeling settles in the mountain bike sales data for the past 12 months that you have been visualizing through a complex DAX formula isn’t tallying up with the raw sales numbers questions whirl through your mind is there a missing link an error in the formula maybe the weight of potential inaccuracies weighs on you mistakes mean mistrust in data and mistrust in data can lead to poor business decisions in this video you’ll learn how to use variables in DAX to troubleshoot issues like this one to recap a variable in DAX lets you store a value or a table to be used later in your formula think of them as placeholders or temporary storage units for your data by breaking down your DAX formula into smaller pieces and storing parts of the calculation in variables you can keep track of each step making the process more comprehensible and easier to debug returning to the earlier adventure works example suppose you’re faced with a formula representing the sales for the last 12 months given the vast amount of data and interconnectedness of the business processes ensuring accuracy in the formula is paramount so let’s help Adventure Works troubleshoot their mountain bike sales data for the past 12 months before you can do any troubleshooting understanding the overall structure and components of the formula is essential without a comprehensive grasp of what the formula consists of determining what might be causing an issue becomes like finding a needle in a haststack once you have opened your PowerBI report on the right side of the interface you’ll notice the fields pane within the fields pane scroll until you locate the DAX measure you wish to troubleshoot in this case the measure to troubleshoot is the sales_12 months upon selecting the measure a formula bar appears above the report canvas this bar allows you to view the DAX expression while carefully examining the expressions present you can identify components like the calculate function sum aggregation and dates in period function as each of these plays a role in the calculation once you identify each component of the measure it’s time to create variables for each part by breaking down the formula into smaller parts and assigning them to variables you can address each segment separately this modular approach aids in understanding which part of the formula might be behaving unexpectedly on the upper ribbon select the modeling tab and select the button named new measure this indicates you’re creating a new formula or metric that isn’t present in your data upon selecting new measure the formula bar becomes active for you to define the logic of your formula and break it down into variables start by typing var which stands for variable followed by a space then provide a name for your variable like current date using the equals sign assign the function today to this variable and return the result now let’s create a new measure and add a variable called last year sales for the dates in period section with variables holding specific parts of the formula analyzing them individually allows for isolated testing by evaluating each variable separately you can confirm its correctness ensuring that each foundational block of the formula is sound before the whole formula is put together finally let’s create variables for the product category and subcategory to return the result for each on the right hand side locate the visualizations pane select the card icon to place a blank card onto your report canvas a card visual is useful because it displays a single prominent value ideal for scrutinizing individual variables once the card is active you’ll notice areas named values and axis in the visualizations pane locate your variable named current date in the fields pane select hold and drag it to the values area of the card the card will now dynamically showcase the current date as you continue the troubleshooting process create new card visuals on the canvas and drag the sales filtered by category and sales filtered by subcategory measures to the cards to provide a snapshot of the isolated categories after assessing individual variables it’s crucial to observe how they interact together sometimes even if variables are correct when isolated they may not interact as expected when combined this step ensures that the overall logic of combining the variables is correct let’s create a new measure called mountain bike sales to weave these variables together with the calculate function calculate modifies or extends the context in which a calculation occurs so combining these variables essentially tells PowerBI to consider only sales amounts of mountain bikes in the cross country subcategory for the last 12 months to visualize the combined logic drag the newly made measure mountain bike sales onto a new card visual if everything is functioning correctly this should vividly illustrate the mountain bike sales restricted to the last 12 months for the cross country subcategory you notice that the sales filtered by subcategory card is significantly different in value from the mountain bike sales card based on your troubleshooting you uncover that while the technical logic of your DAX calculation is correct a pre-existing filter was applied onto the sales filtered by subcategory card that skewed your calculation showing sales for the past 6 months to resolve this select the sales filtered by subcategory card visual and clear the applied filter in this video you learned how to use variable for troubleshooting you discovered the importance of breaking down a DAX formula piece by piece understanding each element and its interaction and how this modular approach provides a systematic method for troubleshooting you also explored the process of defining DAX variables and combining them to ensure their interactions produce accurate results imagine you’re a captain navigating the seas of business data your compass is your understanding of key performance indicators your sales are your dashboards and your map is Microsoft PowerBI the winds of analytics fill your sales pushing you towards better informed decision-making this module bringing data to the user has equipped you with the navigational skills needed to sail through the waters of business analytics you’ve not only discovered the pivotal role of dashboards in steering organizational decisions but also ventured into report navigation and publishing configuring mobile views fine-tuning report performance and sharing leveraging features like quick insights and Q&A and optimizing reports using DAX variables let’s recap key concepts including dashboards in business decisionmaking including how to create and customize them sharing information with stakeholders such as PowerBI workspaces publishing reports and optimizing pageionation for better navigation and user experience and the usage of the analyze in Excel feature in PowerBI and optimizing reports using DAX variables thereby making your report easier to debug and more efficient you started with a deep dive into creating dashboards you explored the concept of dashboards in the business context their importance functionalities and how they serve as key tools in data analysis and decision-making processes much like a car’s dashboard that shows critical data like speed and fuel level you learned that a business dashboard provides a consolidated real-time visual display of key performance indicators or KPIs such as sales trends and customer behavior while they share similarities with reports dashboards differ in that they offer a one-page summary of the most important metrics in contrast reports provide a more indepth multi-perspective view you also recognize the need to understand the visual and interactive nature of dashboards their role in promoting transparency and accountability within organizations and how they aid in breaking down barriers to information sharing your exploration continued to how to build a simple dashboard configure the mobile view and change themes you started by creating a new report dragging and dropping various data fields to make visual charts like bar graphs and line charts once you had your visuals you combined them into a single dashboard for a comprehensive view of important metrics to elevate your data analysis capabilities you explored how to optimize the usability of your PowerBI dashboards by adding two key features its quick insights and Q&A features you also discovered the limitations of pinned visuals in PowerBI how their static nature can prevent deep data exploration and how to overcome these limitations by setting up and pinning live reports next you delved into sharing reports with stakeholders you learned about PowerBI workspaces and their importance alongside the stepby-step process of creating a simple workspace workspaces are essential as containers that hold various components such as dashboards reports workbooks and data sets you explored the step-by-step process of publishing reports in PowerBI as well as the concept of pageionation and why it’s beneficial for creating organized reports publishing reports serves as a bridge connecting you the data analyst with decision makers and team members who need to draw insights from the data pagionation affirmed that dividing your report content into multiple pages makes your report more organized and easier to navigate akin to chapters in a book your journey then led you to understand the different elements of report page properties including page information canvas settings canvas background and wallpaper report page properties let you customize your report pages giving you control over how your report is presented influencing aspects like page size view and background enhancing overall readability and effectiveness you also learned how to use the analyze in Excel feature in PowerBI to take your reports and further analyze them combining the visual capabilities of PowerBI with the analytical depth of Excel it provides a live connection from an Excel pivot table to the data in PowerBI so when data in PowerBI is updated you can simply refresh your Excel report to see the new data you also explored the practical aspects of tuning report performance you grasped the role and function of the PowerBI performance analyzer the process of activating it starting a recording refreshing visuals analyzing performance data and exporting data for further analysis the performance analyzer helped you identify the parts of your report slowing things down by providing a detailed breakdown of loading times for each visual you also identified if a DAX query was causing the delay and took the necessary actions to optimize it for improved performance the process of simplifying a DAX formula involves reducing the complexity of the formula which might include eliminating unnecessary calculations using more efficient functions are avoiding iterating over large tables this can make the formula more efficient and less demanding on the DAX engine reducing the computational load in the final part of our journey you explored the importance of DAX variables how to use variables to enhance the performance and accuracy of your PowerBI reports and the steps to effectively implement them for optimal performance using variables in DAX formulas enhances readability by breaking down complex and lengthy expressions into more digestible smaller parts variables act as named references for parts of these formulas making the main expression streamlined and easier to interpret throughout this module you journeyed from understanding the foundational significance of dashboards to the details of optimizing DAX formulas at every step you’ve gained skills and techniques that empower you to bring data to the user a fundamental aspect of data analysis and visualization these skills and techniques aren’t just tools they’re instruments of change that can drive organizations like Adventure Works towards innovation efficiency and success the marketing director at Adventure Works Renee was captivated by the Microsoft PowerBI reports you produced recognizing their value in the company’s decision-making process Renee wants to delve deeper into the data introduce statistical results categorize data patterns and make predictions about future trends although these tasks have been vital for businesses for decades immensely helping their decision-making they were traditionally complex and timeconuming however the analytics in PowerBI has changed this powerbi offers a versatile and userfriendly toolbox to tackle analytical tasks effortlessly making these processes much more efficient and accessible but how can you use the analytics in PowerBI in your reports over the next few minutes you’ll be introduced to the concept of analytics and explore the analytics capabilities offered by PowerBI analytics refers to systematically using data statistical and quantitative analysis and predictive modeling techniques to uncover meaningful patterns insights and trends within data sets although these tasks have been vital for businesses for decades immensely helping their decision-making an essential part of analytics involves interpreting and visualizing data to extract valuable information resulting in actionable insights for informed and strategic decisions powerbi empowers you to transform raw data into meaningful insights through its various advanced tools and functionalities analytics in PowerBI unlocks many ways to enrich your visualizations adding significant value to your reports as you progress through this course you’ll explore the many ways analytics in PowerBI can enhance and elevate your reports for now let’s explore some of the PowerBI features available for analytics leveraging the statistical summary tool you can easily add functions to your visualizations like calculating averages and middle and median values you will also learn how to use the topend analysis in a visualization to highlight critical data points saving you time from repetitive tasks and manual calculations another feature you’ll learn about is DAX measures which can enhance PowerBI’s visualizations to find unusual data points called outliers with grouping and bin data for analysis you can classify two or more associated data points into groups or separate them into equals-sized groups respectively mastering organizing your data into meaningful categories can reveal trends and patterns in your data helping you make smarter decisions applying clustering techniques empowers you to discover another way of associating similar data points in a subset of your data using the clustering algorithm using a straightforward feature that identifies similarities and dissimilarities in the attributes values your data gets divided into subsets called clusters unveiling valuable patterns in your data powerbi empowers you to conduct time series analysis timebased data analysis with the time series involves exploring trends and patterns occurring over a range of time as you explore this feature further you’ll learn how to predict future trends using time series forecasting and discover captivating visuals to support your timeass associated data like the play axis an advanced visual containing a dynamic playback of data over time powerbi also offers the analyze feature this powerful feature automatically detects relationships and connections in your data revealing valuable insights that might have gone unnoticed with the press of a button on any data point PowerBI runs a rapid analysis to provide users with automated generated insights you can leverage advanced analytics custom visuals to create exceptional reports there are a variety of custom visuals in PowerBI called advanced analytics custom visuals or AI visuals powerbi leverages machine learning algorithms to provide insights on the data you provide on the chart visuals like key influencers and decomposition tree will take your data reports to a new level another AI powered feature of PowerBI service quick insights generates valuable information from your data sets in the form of a dashboard with the press of a button this will save you time and help stakeholders make better decisions faster plus you can uncover predictive and prescriptive insights with PowerBI’s AI capabilities you can generate AI insights with functionalities like sentiment analysis which visualizes emotions or attitudes in data and key phrase extraction which identifies phrases in text data these AI capabilities empower you to forecast future trends and stakeholders to make datadriven decisions with confidence you’ve now been introduced to the PowerBI features available for analytics in upcoming videos you will delve deeper into each one of the features and witness their magic at work exploring the powerful tools of analytics in PowerBI unlocks a world of possibilities for you to drive datadriven decision making with your reports by harnessing the power of analytics in PowerBI you can help organizations optimize their strategies and stay ahead in today’s dynamic business landscape adio your manager at Adventure Works just imported the company’s sales data for quarter 1 into a Microsoft PowerBI report there is an air of anticipation as your team brainstorms ways to extract valuable insights from this information despite the raw nature of the data set only containing product details order dates and the total order amount the team sees immense potential to build upon the aim is to create a report that can answer crucial questions like what was the total order amount per product category what were the average and medium amounts per product category did the early March ad campaign have any impact on sales adio is confident that PowerBI’s statistical summary capabilities can easily transform these questions into an insightful report in this video you will learn about these capabilities exploring the process of integrating a statistical summary into a PowerBI report data and statistics are closely intertwined as statistics serve as the essential language to articulate and analyze your data powerbi captures the power of statistics offering a comprehensive range of statistical functions you may already be familiar with some of the functions commonly used in data analysis such as sum of totals average for mean calculations and medium minimum and maximum to find the middle smallest and largest values in a data set powerbi not only provides rich features to seamlessly incorporate these functions into your visualizations and reports but also utilizes the DAX language that encompasses all of these statistical capabilities this powerful combination is referred to as the statistical summary in PowerBI using Adventure Works sales data set let’s examine two different ways of adding the average statistical function to a visualization this will help the sales team identify which product category accumulates the highest average order amount in addition to identifying whether Adventure Works early March ad campaign impacted orders the marketing team also needs to retrieve the number of orders per day from the data set as you are learning to integrate a statistical summary in a report let’s extract and utilize just three columns of Adventure Works sales data product category order date and order total which is the total order amount to prepare for our statistical summary exploration let’s create a few simple graphs to work with first let’s create a clustered column chart and select product category first to represent it on the xaxis and order total second as its yaxis to visualize the total amount of orders for each product category adjust the visual to the screen and click on an empty space of the canvas to deselect the bar chart and create the second visualization a line graph right below the column chart which will contain the order date on its x-axis using just order date without the date hierarchy and then the order total again as its yaxis this visualization depicts the total order amount of each date lastly let’s create a table graph in the right corner of the screen add product category as its first column and order total as its second column this will provide a better view of the numerical data when adding a numeric column to a visual the default function displayed is the sum or total of the amount however there are numerous built-in functions that you can apply to your graph these functions display on the popup menu in the visualizations pane directly at the right of your column such as average median and deviation to better understand how this works let’s add the order total column again in the same graph and adjust the function to calculate the average order amount of each product category instead you can also create your own calculations using DAX expressions which include a rich set of statistical functions let’s produce a similar result using a straightforward DAX measure in the ribbons home tab select new measure assign the measure a name and use the median function specifying the order total column for the calculation lastly modify the column chart to a line and column chart add your measure to the y-axis and observe the result now let’s explore the time series data let’s add the number of orders for each day to the line graph to do this drop the order total column into the secondary yaxis and use the count statistical function this is a helpful function that counts table rows in the graph based on the filter context it is given in this case where each row represents a single order the count function counts the number of orders by using statistical summary in PowerBI you explored how you can effortlessly calculate statistical measures and add them to your visualizations all the critical questions were answered in the report as it displays the average and median value of each product category and even displays the impact of the ad campaign in March when the count of orders doubled with just three columns as your data source you unlocked the power of analytics in PowerBI with the aid of statistical summary many business requirements can be met and questions answered with ease thanks to the array of statistical features tailor made for data analysts by PowerBI renee the marketing manager at Adventure Works has just finished a critical meeting with other marketing team leads to discuss new approaches and strategies for attracting new customers after the meeting she promptly reached out to the data analytics team to discuss the implementation of these approaches in their reports during the meeting the marketing leads for North America and Europe decided to take different approaches for each continent’s market this requires grouping country orders by continent a task that hasn’t been implemented in the existing data set additionally the marketing team agreed on launching ad campaigns in 10day intervals microsoft PowerBI’s visualization options already include automatic monthly and weekly breakdowns but the challenge is to figure out how to assemble orders into 10-day groups the data analytics team quickly searches for a solution and discovers that you can address both these problems using analytics in PowerBI particularly the grouping and binning data features these features both associate data points with each other in their respective ways grouping in PowerBI gives you the ability to manually divide data points into separate groups of your choice on the other hand bin automatically separates data points into segments referred to as bins giving you two options to do so you provide the number of outcome bins with PowerBI splitting the data points between them or you provide the size of bins and PowerBI splits the data points into any number of bins required to fit your data into the specified sized bins now the question is how can they effectively implement these features in the customer report in this video you’ll be introduced to the concept of grouping and bin and you will learn how to differentiate between the two concepts you will also learn how they can be effectively implemented in a PowerBI report to clarify information and provide easy to understand deliverables let’s start by helping Adventure Works group the orders from each country by continent to visually highlight orders for Europe and North America you need to group them in the report first let’s select a stacked bar chart and set the country on the Y-axis and the sum of order total on the X-axis hold down the shift key and select in the visual all the countries that belong to North America including USA Mexico and Canada while still holding the shift button down right click on the visual and select group data from the drop-down menu this action automatically creates a group and assigns it to the legend field resulting in a different color for the countries that were grouped together now let’s explore how to edit the group created earlier the new group appears as a new column in the table with an icon on the left side indicating that it is a super group of another column right click on this new group and select edit groups from the menu to open a new window now you have the option to rename the existing group let’s change Canada Mexico and USA to North America similarly you can select all European countries while holding the control key select group and create a new group called Europe once you are done select okay in addition to highlighting categories of data you can also use the newly created groups as an axis in your visuals to do this create a doughut chart and add the sum of order total to the values field then add country groups to the details field this will help you visualize the distribution of the order amounts between North America Europe and the other regions the doughut chart clearly represents how the orders are distributed among these different groups making it easier to analyze the data at a glance to create bins based on the 10day campaign interval right click on the order date column and select new group select bin as the group type and size of bins as the bin type in the bin size select the 10day interval to align with the campaign requirement and select okay next create a line chart and use the new bin on the x-axis and the sum of order total on the y-axis this creates a visualization of the 10day ad campaign interval by using this technique the marketing team can effectively analyze the data based on the 10day intervals gaining valuable insights into the trends and patterns within the data set as you know by now grouping and binning data has always been crucial in data analysis as it organizes data points into similar meaningful categories uncovering patterns hidden within them powerbi introduces this capability in its engine allowing you to seamlessly group or bin columns in a simple manner without having the hassle over delivering the result in code language to fully grasp the power of this feature let’s compare them with the complexity of using DAX code to achieve the same bin technique with just a few clicks the data analytics team publishes the report quickly leaving Renee astonished by the powerful capabilities of groups and bins in PowerBI the marketing team can now easily identify trends within the groups of North America and Europe enabling them to make immediate comparisons with the rest of the countries moreover they can analyze and assess the 10-day campaigns effortlessly gaining insight into critical information on their performance well done the sales team at Adventure Works is so impressed by your Microsoft PowerBI report that they ask you to add more analytics to the data set the team wants to analyze if there is a trend in the order amount identify the largest order of each day by order amount and determine the top 10 best and worst sales days for the business you can accomplish this by including a histogram in the report and using the topend analysis feature but what is a histogram and how do you add topend analysis in the next few minutes you’ll learn how to identify and build histograms as well as filter data points into a topend analysis showcasing only the most significant data a histogram is a way to visualize a topend data query result while the topend function in PowerBI is a built-in DAX function that retrieves the topend records from a data set based on specific criteria it compares the parameters provided and returns the corresponding rows from the data source the n in top n refers to the number of values at the top or bottom data points are grouped into ranges or bins making the data more understandable a histogram is a great way to illustrate the frequency distribution of your data as you already know a typical chart visual relates to two data points a measure and a dimension incorporating them on its X and Yaxis respectively adventure Works has an existing bar chart to track the total order quantity for different product categories but they would like to know how often quantities occur to do this they would create a histogram of the quantities the x-axis contains the quantity groups and the yaxis contains the frequency that these groups occur the most used charts for histograms are bar charts and area charts sorting a field in ascending or descending order is a relatively common process in data analysis reporting but what happens when there are so many attributes that the columns completely cover the canvas area hiding the crucial information the top end analysis prevents this by sorting the data to display according to a category’s best or worst data points this enables stakeholders to quickly identify the top or bottom values in the data and make datadriven decisions efficiently now let’s explore how to create histograms to analyze sales data and visualize the top 10 dates and sales by implementing top-end analysis in a visualization for the adventure work sales team let’s start creating a histogram to analyze trends in order amounts the first step in creating a histogram is to create a bar chart and to add order total to the X and Y axes ensure you select the sum of order total and not the count resize the chart by dragging its edges so it’s clearly visible notice that having numerous data points on the X-axis may make it difficult for users to interpret the analysis histograms directly address this issue by grouping X-axis data points in groups to achieve this use the bin technique you learned about previously rightclick the order total column and select new group from the drop- down menu select bin as the group type and number of bins as the bin type for the bin count enter 20 and then select okay to create the new bin in the order total column now replace the new bin on the x-axis instead of the standard column in both charts congratulations you have now created your first histogram bar charts are one of the most common histogram charts with area charts being a close second while having the visualization selected select the area chart to modify it using histograms the distribution of order amounts per amount ranges is clearly visible with the most revenue being accumulated through orders that were just over the $2,250 mark now let’s explore how you can visualize the top end data points of a column to achieve this you need an attribute and a sorting column the sorting column will be used to create ascending or descending order on the attribute column before the attribute column is filtered to its top end values let’s observe a topend analysis implementation creating a chart to highlight the top 10 days by sales amount create a funnel chart which is one of the most popular top- end charts and add order date without hierarchy to the category and order total to the values to limit the chart to a top 10 analysis navigate to the filter pane select the arrow on order date and select top N as the filter type select top 10 to display the best days you would select bottom for the worst days and add the total amount to the buy value to sort by this amount you now have a better understanding of the capabilities and potential of histograms and top end analysis in PowerBI by working through this lesson you discovered how to construct histograms transforming data into visualizations that uncover distribution patterns furthermore you’ve practiced your topend analysis skills to isolate key data points to inform actionable insights during a recent strategy meeting at Adventure Works stakeholders discussed adjusting prices to align with the business strategy however the current sales data set seems disconnected and lacks cohesion making it difficult to use recognizing the importance of optimizing the company’s product offerings you’d like to apply advanced analytics to categorize products based on order details and pricing your goal is to establish meaningful connections between the products to enable datadriven pricing decisions having explored groups and bins in Microsoft PowerBI you’ve learned to organize data points hierarchically with groups or into equal-sized bins but what if you want to group data points based on similarities in their values that’s where the clustering technique in PowerBI comes into play this video aims to equip you with all the relevant knowledge needed to apply the clustering technique to a data set including how to cluster data in scatter charts and identify outliers with clustering clustering is a powerful feature that enables you to discover groups of similar data points within your data set efficiently it is enabled in scatter plot visualizations as they are the optimal charts for analyzing data dispersed and identifying outliers by analyzing your data the clustering technique identifies similarities and dissimilarities in attribute values and then separates the similar data into distinct subsets known as clusters these clusters provide valuable insights and aid in understanding patterns and relationships within your data it covers the valuable insights that clustering can offer using the earlier example as a practical demonstration let’s begin exploring patterns in the Adventure Works products based on their sales data launching a new PowerBI report with the sales data set imported select the scatter chart icon on the visualizations pane and resize it on the screen for better visibility add product name in the values field as this is the field you want to separate into clusters for the axes use product price as the x axis and order total as the y-axis ensure the sum function is correctly applied to both as the default aggregation with this setup you can now apply the clustering technique to gain valuable insights from the data with the dots scattered across the graph let’s apply analytics to identify similarities between these data points that would group them into categories select the ellipses in the top right corner of the chart to see the visualization options now select the automatically find clusters option a pop-up window on your screen provides various clustering options you can adjust name the cluster group product cluster and for the description use clusters for product name based on product price and order total then you have to choose how many clusters you want the data points separated into or even let PowerBI automatically choose the number for our example let’s input three as the number of clusters and select okay the clustering technique has divided the product data points into three clusters the first cluster comprises products with low prices leading to low order amounts the second cluster includes products with high prices but relatively lower order totals compared to cluster three where high product prices also resulted in high order totals continuing with the clustering analysis you can leverage the newly formed clusters as axes for additional visuals allowing you to gain further insights based on clustering patterns select a horizontal clustered bar chart and set product category as the yaxis and sum of order total as the xaxis adjust the chart size to cover the right part of the canvas from top to bottom to add the new data grouping into the analysis add product cluster as the small multiple to do this navigate to format in the visualizations pane then small multiples and select three rows and one column to compare these multiples easily lastly include the product name in the tool tips of the visualizations by analyzing the clusters in both graphs you can directly gain insights from your data set while most ebikes and road bikes appear to belong to the high-erforming cluster three there are some exceptions in the lowerforming cluster 2 hovering over these product categories allows you to display the product names that belong to this category providing valuable information for future business decisions by clustering the products you helped the pricing department make crucial decisions to improve the promotion of specific products and embrace datadriven strategies at Adventure Works by analyzing products belonging to the low performing categories they adjusted their prices strategically aiming to achieve better results and optimize the overall market performance in this video you have gained valuable skills in using the clustering algorithm in your scatter plots to group data points effectively by applying clustering you learned how to identify hidden relationships and patterns within your data making it possible to optimize various aspects of business such as product pricing promotions and overall strategies you received a new report requirement this morning your task is to build a customer demographic analysis leveraging the sales and customer data sets to derive valuable insights about the customers to fulfill the business needs for visualizations based on country customer age and order dates you will have to use both axes categories categorical and continuous axes but what are these categories and how do you decide which one to use in each visualization over the next few minutes you’ll be introduced to categorical and continuous axes and learn how to differentiate between them you’ll also explore how to configure these axes in Microsoft PowerBI let’s start by exploring categorical axes you can use a categorical axis to represent discrete non-numeric data points it organizes data into distinct categories such as names categories are groups with no inherent numerical order common examples of categorical data include product names geographic regions and employee roles when you use a categorical axis PowerBI automatically arranges data points in the order they appear in the data set categorical axes are best suited for displaying qualitative information and facilitating comparisons between distinct entities or categories bar charts stacked bar charts pie charts and categorical line charts are common visualizations that use categorical axes on the other hand a continuous axis is designed to represent numerical data points with an inherent order and can be measured along a continuous scale these data points are typically represented by real numbers and can be integers or decimal values examples of continuous data include sales revenue temperature time and age continuous axes are ideal for visualizing quantitative information allowing users to identify trends patterns and correlations within the data common visualizations that use continuous axes are line charts area charts scatter plots and histograms now let’s explore how to use these two axes in your reports using a realcase scenario let’s explore both axes to understand their use better open a new report with sales and customer data sets imported the first visualization you’re going to work on is sum of order total by order date add a clustered column chart and insert order date on the x-axis without date hierarchy and order total on the y-axis resize the visual by dragging the edges the visual displays spaces with no data for the dates that held no orders this is because PowerBI automatically selects the continuous access type when given a date column in its access field by selecting the categorical access the bar chart displays no space by removing the depiction of dates with zero order total keep in mind that there is no right or wrong way to visualize the data and there are no numeric differences between the two axes the choice of axis type should be the one that best addresses the business need to explore the categorical axis let’s create a second visualization using a sum of order total by location to do this insert a clustered bar chart and add location on its y-axis and sum of order total on the x-axis move the visualization to the right part of the screen and resize it so it fits the screen top to bottom location has no inherent order so PowerBI automatically implements a categorical axis and turns off the option of turning it into a continuous axis for the last graph let’s explore another possibility of a continuous axis customer age is a column with an inherent numerical order so when you add a line chart and insert age on the x-axis and order quantity on the y-axis PowerBI uses the continuous type of axis you can observe a major difference between the two axes if you try to access the visualization sorting method through the ellipsus you will notice that continuous access doesn’t allow you to use a different sorting other than the one inherited by the numeric column to change the default sorting you need to use a categorical axis understanding categorical and continuous axes and their roles in data visualization will enable you to select the correct axis based on the nature of the data you’re analyzing with this knowledge you can create more effective and informative visualizations making it easier to compare discrete categories or identify trends and patterns within numerical data renee the marketing manager at Adventure Works relies heavily on analytics using Microsoft PowerBI to equip herself for important executive meetings as part of her preparation for a high-level meeting with the company’s executives Renee has created several reports and presentations based on the results of the most recent marketing campaigns run by her department renee takes great care when preparing the analysis however she worries that there could be essential data insights that she and her team have overlooked seeking expert advice she turns to Lucas the data analyst for guidance lucas suggests using the analyze feature in PowerBI with this feature they can examine the data from different perspectives and ensure that no valuable aspects have been missed but what is the analyze feature and how can it be added to reports the analyze feature provides you with advanced analytics to automatically detect patterns trends and anomalies in your data in this video you’ll explore the analyze feature and how it can be used to identify trends and patterns now let’s help Renee to examine her data from different perspectives with the customer and sales data sets imported let’s create a new report and add visualizations first you’ll create a line chart and insert the order date on the x-axis without the date hierarchy and then the sum of order total on the y-axis you will also add an area chart next to it with the age field as the x-axis and the sum of the order total field as the y-axis finally on the bottom of the page you’ll add a clustered column chart with the product category as the x-axis the sum of order quantity as the y-axis and the order status as the legend then resize it to fit the screen now let’s start using the analyze feature on each of these visualizations to discover what insights it can add to your analysis starting with the line chart it is obvious that the biggest order was placed on the 7th of March to explore this further select this specific date rightclick and select analyze now you can select the explain the increase option once this is selected a variety of different visualizations appear these analyze the increased order figure on this day based on factors such as product size payment method product categories and others clusters that were created manually in the table will also be included in the analysis by scrolling through these automatically generated visuals you can gain a clear picture of the factors that caused the increase in the order amount now let’s run the analyze feature on the second visualization the area chart since using distinct ages isn’t very informative for analysis you’ll first create bins to group the age data to do this right click on the age column and choose the option new group apply size of bins as the bin type with 10 as the bin size and select okay to create the age groups separated by decade then drag and drop this new bin to add it on the x-axis and use the x button on the previously used age column to remove it from the chart to investigate further with the analysis feature let’s select the first bin with decreasing values right click on it select analyze and then explain the decrease just as with the analysis in the first visualization this action causes a number of visuals to appear these help us to identify all relevant aspects that might have contributed to the decrease in the age group above 40 years now let’s explore another useful aspect of the analyze feature in the bar chart which shows product category and status you may notice that road bikes have an unusually high number of canceled orders to investigate what might have caused this right click on the blue cancelled bar for road bikes and select analyze if you select find where this distribution is different a variety of visualizations are generated these illustrate the factors that played a significant role in the large number of cancellations of orders for road bikes this feature can highlight contributo factors such as country and location product cluster and more every visualization generated by the analyze features includes a thumbs up and a thumbs down option on the upper right corner this allows you to provide feedback to PowerBI regarding the usefulness of its analysis for your report when you are using the explain the increase or explain the decrease features you have the flexibility to select different visualizations to display the results that best suit your analysis requirements finally if the analysis feature provides an insightful visual that you’d like to include in your report you can quickly add it to the report by selecting the plus sign button in the top right of the visualization in this video you explored how to generate valuable insights from your data using the analyze feature in Microsoft PowerBI in this demonstration you learned how to work with diverse visualizations and interpret the results effectively the analyze feature provides you with advanced analytics automatically generating visualizations from your data sets aiding you to automatically detect patterns trends and anomalies in your data time series analysis involves analyzing a series of data in chronological order to identify meaningful information and reveal trends in this video you will explore how to create an insightful report analyzing adventure work sales data over a period of 3 years time series analysis involves analyzing a series of data in chronological order to identify meaningful information and reveal trends in this video you will create an insightful report analyzing Adventure Works sales data over a period of 3 years in your PowerBI report three Adventure Works data sets have already been imported these are sales product and date you will now add four visualizations as the basis for the time series analysis first add a simple card visualization with sales amount as its field second add a horizontal clustered bar chart with product in its y-axis and sales amount in its x-axis using the filter pane add a top 10 analysis on the visualization by sales amount so the highest selling products are highlighted line charts and scatter plots are the two most common visualizations used in the time series analysis with the first two basic visualizations already created let’s add these two types of graphs to the report add a line chart and include the date field from the date table in the x-axis this should not include the date hierarchy use sales amount from the sales table in the y-axis add a fourth visualization which is a scatter plot use the sum of total product cost from the sales table in the x-axis add the sales amount from the sales table to the yaxis include the category field from the product table in the legend section and the sum of sales amount from the sales table in the size section resize and move all the visuals so that they are better placed on the page now that the visualizations are created let’s explore how time series analysis can give you different perspectives on these visuals before you can create a time series analysis you must first import a custom animation visual from Microsoft AppSource microsoft AppSource is an online store offering custom visualizations that are built by industry-leading software providers to access the Microsoft App Store first select the ellipses in the visualizations pane and then select the get more visuals option this will take you directly to the PowerBI custom visuals in Microsoft app source search for the term play access to find the certified play access dynamic slicer visualization when you have located it choose add you should now have the play access button imported into the visualizations pane now let’s explore how to use the play access button as a dynamic filter in the report the play access button automatically filters all the other visuals using the chronological order of the date field that is added to it first select the new playaxis visualization in the visualization pane add month from the date table as a field this will ensure that the play access visualization will filter the report in a monthby-month sequence in the format your visual section there are three different formatting options that you may use specifically for the playaxis visual first there is animation settings it is possible to set the animation to auto start or to run on a loop for a specified time frame the second option is the time which you can use to modify the rate of filter transition here you will set it at 750 milliseconds which is a smooth transition speed the next format option relates to the color of the visual and specifically the color of each action of the play access button in this area you can specify colors for play pause stop previous and next actions the last format option is enable captions if you set this feature to on the button shows the value of the field that you have inserted and how it changes during the animation press play on the play access button to watch the sales data change month by month the play access button makes the report interactive by updating all the visuals simultaneously this provides a dynamic picture of the data outcomes over time and provides a more detailed analysis of the trends in adventure works sales you now know how to do a time series analysis and implement the playaxis visualization you can also use the play axis to conduct time series analysis decision makers in all areas of businesses require answers to very similar questions typical questions asked of the data analyst might be can we compare daily sales against the sales average is there a way to uncover trends in order quantity within our visualizations can we manually add a sales target threshold into our visualizations the senior management at Adventure Works consult with their data analyst Lucas they would like to see key information such as trends or averages to be clearly visible on certain visualizations lucas identifies reference lines as the key Microsoft PowerBI feature which will fulfill this requirement a reference line is an additional element that can be added to a visualization to draw attention to a key insight or piece of information powerbi offers a variety of reference lines that can be added to a visualization to include an additional measure for comparison with the data points the implementation of the line is based on integral calculations in the line type you’ve selected or on settings which you can customize let’s explore the different types of reference lines an average line represents the average value of a data series it is useful for identifying how individual data points relate to the overall average a median line shows the median value of a data series it is particularly helpful when dealing with skewed data distributions a percentile line identifies a specific percentile value such as the upper percentile within a data set helping you understand data distribution an x-axis or yaxis constant line is a straight line that represents a constant value on a visualization it is used to indicate a fixed threshold target or benchmark value for comparison a trend line reference line helps to identify trends or patterns in data different types of trend lines can be added to capture relationships in data it’s important to note that each visual within PowerBI supports its own set of reference lines this means that not every reference line type might be available for every type of visual powerbi intelligently offers reference lines that are contextually relevant to the type of data and visualization you’re working with for instance certain reference line types like trend line and average line are more applicable to line charts or scatter plots where data trends are easier to discern other reference lines like min line and max line are often used in bar charts to quickly visualize data ranges in some visualizations such as maps reference lines are disabled due to their limited interpretability within the visual context in the next few minutes you will be able to follow a practical demonstration on how to implement reference lines in PowerBI reporting this PowerBI report has two data sets already imported customer and sales you will create three graphs and add reference lines to them as another layer of visual information first create an area chart add age bins as the x-axis value and sum of order quantity as the yaxis value and resize it on the screen next add a line chart use order date as the x-axis value without using the date hierarchy and order total as the y-axis value resize this visual finally add a horizontal bar chart include location as the yaxis value and order total as the xaxis value and resize it to fill the screen now let’s add reference lines once you have selected the area chart a magnifying glass icon appears in the visualizations pane selecting this opens the analytics pane this pane lists the types of reference lines that can be added to the visualization add a trend line by selecting the on button a reference line appears which depicts the trend of order quantity over age groups it shows that older people order significantly less than younger people you can use the options below the trend line to adjust the line color transparency and style so that it stands out more for the next example select the line chart you will now add an average line which will help identify the days where the order total amount was above or below the average of each day in the analytics pane select average line and add line when the average line appears the choices underneath can be used to format it or to add a data label lastly in the bar chart it is important to easily identify the locations which are over a minimum target threshold select the bar chart in the analytics pane select constant line and add line add 3000 as the constant line value format the line if required it is now obvious that three locations Chicago Shanghai and Buenosaurus are below target thresholds of order total when choosing visualizations keep in mind that they do not all support reference lines for example if you change the bar graph to a map you can see that the line disappears in the analytics pane the message analytics features aren’t available for this visual appears you’ve now explored how adding reference lines to visualizations can highlight trends and data sets and simplify comparative analysis between data points adding reference lines to your report extends the capabilities of visual customization and allows you to meet the diverse demands of different business scenarios planning for the future is crucial for all businesses one business may need to plan for seasonal fluctuations in orders or revenue another may need to plan for growth and/or expansion what is critical in either situation is that key decision makers have reliable data and information and that they also have a realistic picture of future outcomes data analysts use forecasting to examine previous trends and patterns in business to predict whether they will continue and how they can affect future outcomes microsoft PowerBI contains a forecasting tool which can assist in this process renee at Adventure Works is currently formulating a 2-year development plan for the department she manages she has already been impressed by the reports that she has seen in PowerBI she approaches Lucas the data analyst to see if there are any visualizations available that could apply predictive models and forecast results lucas informs her that one of the core charts in nearly every report is already equipped with forecasting capabilities she’s excited to find out more the forecasting tool in PowerBI is directly built into line charts and it allows analysts and business users to predict future trends and values based on historical data they can make informed decisions and plan more effectively users can tailor their predictions to align with specific business needs and data patterns with forecasting options let’s look at three important concepts confidence interval in forecasting is the range of values within which the actual feature outcomes are likely to fall with a certain level of confidence it quantifies the uncertainty associated with a forecast for example a 95% confidence interval indicates that there’s a 95% likelihood that the actual future values will fall within the forecasted range this helps decision makers understand the potential variability in the predictive values seasonality refers to recurring patterns or cycles that appear at regular intervals in time series data patterns could be daily weekly monthly or yearly they often result from external factors like holidays or seasons or economic cycles recognizing and accounting for seasonality allows forecasting models to capture the expected fluctuations in data that repeat over time lastly ignore the last is a feature that allows users to selectively exclude the most recent data points from the historical data set when generating forecasts in PowerBI anomalies or abrupt changes in the data may occur in the latest periods which might distort the forecasted results by ignoring the last few data points users can focus the forecasting model on the more stable and representative patterns in the earlier data now let’s step through a practical example of including forecasting results in a line chart forecasting in PowerBI starts with a line chart adventure Works sales and date data sets have been imported into a new report in the visualizations pane select a line chart to add it to the canvas add date from the date table to the x-axis do not add the date hierarchy add sales amount from the sales table to the y-axis this basic configuration is all you need to apply forecasting to access the forecasting capabilities select the line chart then select the magnifying glass to open the analytics pane of the visuals select forecast in the list and turn it on a predictive section has already been added in the line chart select the arrow on the left to open the forecast settings options is the first and most important section here you can define the rules for how the forecasting line will be drawn units is set to points points refers to the date unit currently used in the visualization in forecast length you can specify a number of these date units and this will determine the length of the forecasting line in this case to forecast a whole year of values select 365 points to forecast a whole year period for confidence level select 90% confidence interval and select apply the forecast line also contains options to customize the line select the forecast line select a blue color so that it is similar to the actual line with the style option you can choose a dashed dotted or solid forecast line adjusting the transparency setting changes the visibility of the forecasted plot the confidence band choices allow you to customize the style of the upper and lower bounds changing it from fill to line the none choice will display no confidence bounds at all the forecasting feature in Microsoft PowerBI can create predictions of future trends from historical data adding these to your reports can provide you with valuable insights you are now familiar with using forecasting in a line chart and with concepts such as confidence intervals seasonality and ignore the last you’ve learned how to capture recurring patterns and how to allow for uncertainty these skills will allow you to design reports containing accurate forecasting the accurate anticipation of future outcomes will drive informed decisionmaking understanding the forces driving sales trends is a continuous concern for businesses advanced analytics tools are an accessible avenue to understanding these forces this is precisely the avenue your team proposes to navigate within Adventure Works sales data set with the robust capabilities of Microsoft PowerBI’s key influencers visuals you aim to identify all primary factors contributing to the rise and fall of sales figures in this video you’ll discover the power of the key influencer visualization an advanced analytics visualization in PowerBI you’ll learn how to include it in a PowerBI report and use it properly to obtain valuable information the key influencer visualization is one of the main advanced analytics visualizations in PowerBI it uses advanced algorithms to uncover relationships buried within data shedding light on the influential factors behind specific outcomes whether you want to understand the triggers behind a surge in sales or the reasons for a sudden decline the key influencers visual offers a concise snapshot of what truly matters now let’s explore the capabilities of the key influencers visual let’s start with an empty report with imported adventure work sales data select the key influencer icon on the visualization pane to add it to the canvas your aim is to apply AI insights to analyze the factors behind increases and decreases in the sales amount to do this drag and drop this sales amount field from the sales table in the analyze field the key influencer visual is now declaring that there are no fields in explain by requesting any number of relevant fields to the sales amount to initiate the analysis an AI analysis on all those factors will take place locating which of them are the main contributors behind sales amount surges and decreases to ensure the visualization provides insightful results you can add various relevant fields to the analysis for example let’s add the country region field from the customer table and the color and subcategory fields from the product table notice that as you add fields the visualization is already running a background analysis on the correspondence between the sales amount with all fields added in the explain by section let’s observe the results the top influencers affecting the sales amount are displayed on the visuals left side you can view the analysis results in detail by selecting any of them let’s select the red color influencer to delve deeper into the analysis when you select an influencer bar chart with a color field an analysis of sales amount compared to the average of sales per color displays you can observe the influence the red and silver products have on the sales total at a glance in contrast with the multi and white colors that barely made any sales to analyze the factors behind low sales amounts select the what influences sales amount box to change it to decrease apart from highlighting the key influencers affecting the sales these advanced visuals also group these influencers showcasing segments of influencers that played a significant role in sales increases or decreases select the top segments option in the upper border of the visual and in the field when is sales amount more likely to be choose high to identify the segments that perform well in sales now select the largest circle to view the results red road bikes have the biggest impact on sales with mountain bikes in the second position in this video you’ve explored the key influencers visualization an advanced analytics feature in PowerBI in just a few minutes with the support of AI algorithms powering the key influencer visual you extracted insights from your data set shedding light on the driving factors behind sales trends whether positive or negative you can also incorporate advanced analytics into your reporting process elevating the quality and depth of your analytical insights the marketing team at Adventure Works was fascinated by the impact the previous advanced visualization key influencers had on their data set they are now eager to explore what other advanced visualizations can accomplish your manager Addio wants to introduce decomposition trees another specialized analytics tool in Microsoft PowerBI if you’re wondering where and how to include the decomposition tree visual in a report this video is for you in the next few minutes you’ll be introduced to the decomposition tree and how to use this visual to navigate through data hierarchy levels which refer to the arrangement of data points in a structured format where elements are organized into levels or tiers based on their relationships you’ll also learn how to activate its AI potential letting the visual guide you through the critical factors behind outcomes but first what are decomposition trees the decomposition tree visual in PowerBI lets you visualize data across multiple dimensions it automatically aggregates data and enables drilling down into your dimensions in any order it is the optimal solution when analyzing the hierarchical structure of data being an AI visual it can also leverage the hierarchical graphical representation of the visualization to automatically explore dimensions based on certain criteria here is an example of how the decomposition tree breaks down Adventure Works sum of sales amount into hierarchical groups referred to as branches to analyze the distribution of the amount in its subcategories the user can navigate through the branches manually by selecting any data point or enable the AI capabilities of the visual to automatically navigate through the branch based on the most influential components to start our journey with decomposition trees let’s launch a new report using the Adventure Works sales date and product data set locate and select the decomposition tree visual in the visualizations pane to add it to the report readjust the visual so it fits the whole screen add the sales amount into the analyze field before looking into its AI powered capabilities let’s explore the basic functions of decomposition trees decomposition trees excel at analyzing data structured in a hierarchical fashion so let’s find a structure built like this in the data set navigate to the data view of the report and to the product table you can see that each model belongs to multiple supercategories which have the following sequence product model subcategory and category let’s add this hierarchy to the decomposition tree to utilize its basic features add all four components of the hierarchical structure into the explain by field in any order a plus sign appears just right of the sales amount bar navigate through the hierarchy components in the order they are being used in the data set to get a complete breakdown of the sales amount between products in the data set although you can use the plus sign in any order you want utilizing the hierarchy sequence will give the best decomposition possible hit X anytime to remove a column from the decomposition tree and use the lock button to prevent a user from removing it now that you have a basic understanding of the decomposition tree let’s look at its AI capability to explore this potential let’s remove the model and product fields and add two other dimension fields to the chart color from the product table and year from the date table start at the first level of decomposition the category and select the plus sign you can now see that besides the columns added on the explain by field there are two more options high value and low value with a light bulb on their left side by selecting either one of them the decomposition tree will automatically choose the main driving factor between all fields added in the explain by section and highlight it for you to look at its capability select the high value of accessories to identify that the helmet subcategory was the driving factor of the accessory sales while in the clothing category the main reason behind the accumulation of the high amount was the superb clothing sales of 2019 on the other hand by removing the generated column and selecting a low value in the bikes category you can identify that blue colored bikes were the lowest performing attribute in bike sales with each lowest point being in 2020 in this video you learned about the capabilities of the decomposition tree an advanced visualization in PowerBI the decomposition tree is a unique tool for ad hoc exploration and root cause analysis of the factors behind any outcome in a data set combining both basic features with advanced AI capabilities it can convert information into valuable insights and contribute to business decision making by providing a deeper understanding of the underlying insights in a data set in the modern age of technology where information is all around us imagine you could uncover a map that reveals the hidden pathway that leads to success this is the exciting world of identifying patterns and trends in Microsoft PowerBI a journey that transforms raw data into secrets for success and numbers into opportunities this module gave you the experience of a modern-day explorer equipped not with a compass but with PowerBI’s analytical tools so let’s briefly recap some of the key concepts covered in the identifying patterns and trends module your foundation of identifying patterns and trends was laid through an introduction to analytics in PowerBI and its statistical summary capabilities you are equipped with the knowledge needed to incorporate a range of statistical functions into your reports supported by practical examples and a detailed cheat sheet of available statistical functions within DAX language you learned the importance of grouping similar data points into segments to highlight hidden patterns to empower you in this concept you explored PowerBI’s grouping bin and clustering techniques which helped match the precise needs of your analysis covering histograms top-end analysis and continuous and categorical axes you gained even more tools to include analytics in your data sets advancing and focusing on trend identification you engaged with the exceptional tools of the analytics pane including reference lines error bars and forecasting these tools significantly enhance chart information depth enabling not just data point comparison but also future trend prediction these tools have the capacity to explain data fluctuations providing a variety of insightful visuals that you can instantly add to your reports moreover you gained an initial glimpse into PowerBI’s ability to generate insightful visualizations via the analyze feature automatically these tools have the capacity to explain data fluctuations providing a variety of insightful visuals that you can instantly add to your reports lastly your introduction to AI visuals in PowerBI completed the picture you learned how to conduct root cause analysis within your reports using specialized visualizations like key influencers and decomposition trees these visuals are invaluable for uncovering key drivers behind data set fluctuations you also explored the Q&A visualization a powerful tool capable of transforming any business user into a data analyst formulating queries and crafting visualizations this natural language processor empowers you to translate language into graphs with remarkable efficiency ultimately your journey through the identifying patterns and trends in PowerBI has equipped you with a multi-dimensional toolkit from mastering statistical functions to unraveling hidden insights through segmentation and powerful analytics techniques you’ve become a data explorer skilled at revealing the story within the numbers with the ability to predict trends and harness AI powered visuals you are now better prepared to translate data into strategic decisions imagine yourself as an explorer in a maze of data surrounded by a vast and complex landscape of information somewhere deep within beyond the twists and turns lies pathways to hidden insights and unchartered opportunities awaiting discovery navigating through this data maze without proper guidance or tools could mean missing out on these hidden treasures entirely microsoft PowerBI serves as your modern-day explorer’s toolkit equipped with advanced mapping techniques helpful clues and expert data navigation it helps you cut through the noise interpret the data patterns and go directly to the heart of the insights buried within during this course you’ve transformed from a curious data wanderer into a skilled navigator prepared to guide businesses like Adventure Works toward newfound opportunities and business success using data analysis and visualization in this video you’ll consolidate critical lessons from your journey through this data analysis and visualization with PowerBI course you’ll have a refreshed understanding of creating visually engaging dashboards and reports you’ll also recall concepts related to making your PowerBI dashboards and reports more userfriendly accessible and inclusive sharing your dashboards and reports with users and optimizing reports using DAX language and using visualization and AI in PowerBI to perform data analysis and identify patterns and trends your journey began with a foundational understanding of PowerBI acting as your compass you delved into the details of PowerBI service PowerBI desktop and PowerBI mobile in this part of the course you are introduced to choosing between PowerBI Pro and PowerBI Premium the limitations and advantages of each and how these choices impact data storage sharing and collaboration capabilities you also became well-versed in the administrative interface getting to grips with workspace creation and data set management this was like understanding the maz’s structure and its very pathways setting the course for your data journey you learned how permissions and roles in PowerBI can influence the accessibility and security of your data much like how an explorer’s team is structured based on roles and expertise in navigation you gained insight into diverse visualization forms from simple bar charts to more complex waterfall and funnel charts your journey went beyond surface level exploration introducing you to the DAX language for calculated columns and measures to make your visuals more dynamic and informative you also explored advanced customization options such as using slicers for real-time data manipulation or conditional formatting to highlight key metrics these tools became guiding tools for precise data interpretation you also picked up the importance of visual hierarchy and storytelling along the way realizing that a well ststructured report can convey a narrative that empowers decision makers making your insights both accessible and inclusive became your next focus you learned how to make your PowerBI dashboards and reports accessible to users with disabilities this involved implementing high contrast color schemes adding alt text to visuals and ensuring tab navigation compatibility moreover you explored the built-in translation features of PowerBI ensuring minimal data language barriers these strategies ensure your data exploration is inclusive and reachable for all additionally you covered how to create mobile responsive reports understanding that accessibility also pertains to the variety of devices used to access data navigating through advanced functionalities was your next challenge here you deepened your knowledge of PowerBI’s more robust features such as using drill down and drill through functionalities to navigate between different layers of your data you also tackled data modeling understanding how to create relationships between various tables and sources your expedition delved deeper to uncover query parameters and their role in making your reports dynamic and interactive these tools enable you to interpret the data in the maze precisely without losing sight of the broader context you even ventured into APIs and custom connectors expanding the realms of data sources you can bring into PowerBI finally you were introduced to PowerBI’s AI capabilities like text analytics and the integration of machine learning models you explored time series analysis to forecast trends and discovered how to generate predictive models understand correlation and create data simulations your exploration continued to discover how to generate predictive models understanding correlation and create data simulations this makes it possible to predict and prepare for future trends much like an experienced explorer reading signs from the environment to prepare for what lies ahead you were guided through the process of automated machine learning in PowerBI making it possible to create predictive models without indepth programming knowledge like finding shortcuts and secret pathways within the maze as you conclude this course take a moment to reflect on your expedition you began as a budding explorer and now stand as the guide of others navigating through the intricate and sometimes bewildering maze of data analytics with confidence you’ve mastered the navigational tools and the instruments at your disposal with PowerBI and learn the art of reading and interpreting data in its deepest forms remember the world of data is vast and the technology that helps us navigate it is ever evolving you’ve acquired the skills strategies and insights to embark on countless more adventures but the maze remains boundless with every question you answer you’ll discover new ones that provoke your curiosity and challenge your understanding that’s the beauty and the challenge of data analytics embrace the ongoing quest for knowledge wisdom and growth with optimism in your heart and curiosity as your guide the best adventures still await congratulations on completing the data analysis and visualization with PowerBI course your dedication and hard work have paid off and you’ve gained knowledge skills and tools that will help set you on a path to excel in the world of data analysis you have successfully covered the following topics adding visualizations to reports and dashboards applying formatting choices to visuals adding useful navigation techniques to reports designing accessible reports and dashboards and using visualizations to perform data analysis you should now be well grounded in data analysis and visualization with Microsoft PowerBI you’ve learned how to use the power of data visualization and reporting in PowerBI to create compelling data stories and use formatting navigation and filtering to create interactive user-friendly and accessible reports that are engaging and informative from using visualizations and AI features to uncover data trends and patterns to sharing your insights effectively you are now better positioned to support businesses like Adventure Works in making datadriven decisions and driving business success but remember this is just one step on your data analysis journey by completing all the courses in this program you’ll receive the Microsoft PowerBI Analyst Professional Certificate from Corsera this program is an excellent opportunity to enhance your proficiency in data analysis in PowerBI and gain a qualification that opens doors to entry-level positions in the data analytics field this program will also help you prepare for exam PL300 Microsoft PowerBI data analyst by successfully completing the PL300 exam you’ll earn the Microsoft Certified PowerBI data analyst certification which will position you well to begin or advance your career in this role this globally recognized certification is industry endorsed evidence of your technical skills and knowledge the exam measures your ability to prepare data model data visualize and analyze data and deploy and maintain assets to complete the exam you should be familiar with Power Query and the process of writing expressions using data analysis expressions or DAX which you will learn about throughout the program to learn more about the PowerBI data analyst certification and exam visit the Microsoft Certifications page at http://www.learn.microsoft.com/certifications your journey through this course has not only provided you with essential skills in data analysis but also has laid the groundwork for your future endeavors your ability to recognize different visualizations apply formatting choices design accessible reports and dashboards and perform data analysis using PowerBI will undoubtedly set you apart in the world of data professionals but there’s still more to learn and room to grow so why not register for the next course in the program whether you’re a novice in the data analysis field or an experienced technical professional completing the entire program will showcase your knowledge of and proficiency in analyzing data with PowerBI your dedication to learning and growing in the world of data analysis is commendable and you should be proud of your progress and accomplishments your commitment will show prospective employers that you are capable motivated and driven and eager to learn it’s been a pleasure to be part of your educational journey wishing you all the best as you continue to explore the endless possibilities that data analysis with PowerBI has to offer congratulations once again and best of luck hello and welcome to the creative design in PowerBI course businesses and organizations obtain data from many sources these include government financial economic health and scientific data to name just a few as a data analyst it might be your job to extract insight from this large pool of data you could use Microsoft PowerBI to import this data and create data models but how will you then present the results of your work would you agree that a more creative presentation approach is required especially when dealing with large volumes of data you might aim for a more userfriendly presentation of the data so we’ve designed this course to give you the skills you need to visually share your data insights with your intended audience in this course you will learn how to creatively design dashboards reports and charts you’ll make visuals that the audience can quickly understand and you’ll know when and how to include specialist elements such as videos streaming data and QR codes as part of your business intelligence presentations you’ll be introduced to the theory and practice of visualization and design this includes the design principles of data display and visualization let’s now quickly summarize the course material to give you an overview of all you’ll study in this course you’ll begin by learning how to create a cohesive report design based on the characteristics of your target audience you will identify key information so that you can produce audience focused reports in week two you’ll learn how good design enhances the comprehension of data in your reports you’ll apply visual clarity use multi-dimensional visualizations insert map visualizations and implement custom visualization such as Python-based visualizations with these methods you can design powerful report pages that improve the enduser experience then it’s time to visit the concepts of dashboard design and storytelling you’ll compare the design of a dashboard with the design of a report and you’ll explore the principles of data storytelling advanced dashboard features such as embedding media and QR codes are part of your studies this week during the course you can watch pause rewind and re-watch the videos until you’re confident in your skills consolidate your knowledge by consulting the course readings and measure your understanding by completing knowledge checks and quizzes in addition the course discussion prompts allow you to share and chat with other learners by connecting with your classmate during discussions you can help grow your network of contacts your studies prepare you for a final project and a graded assessment that you’ll undertake in the last week of this course in the project you’ll get a pre-made Adventure Works data set and model in PowerBI your challenge is to use the data to prepare reports for the sales team and the executive board you’ll need to use data storytelling and cohesive design you’ll also be asked to use the data to highlight new business opportunities after this hands-on learning you will complete a final graded assessment be assured that everything you need to complete the assessment is included in the course and of course as part of your preparation for assessment you can always review the content of any lesson to revise the relevant videos readings exercises and quizzes businesses need data sourcing preparation and analysis presenting the insights gained is often the last part of this data processing it’s a key factor in ensuring that the benefits of the analysis are understood by all stakeholders is this course for you hopefully the outline of the course content and topics will help you decide you don’t need an IT related background to take this course it’s for anyone who likes using technology and has an interest in presenting the results of data analysis whatever your background to complete this course you need to have access to some resources you need a laptop or desktop computer with a recommended 4 GB of RAM an internet connection and a Windows operating system version 8.1 or later it should have a .NET Framework version 4.6.2 or later installed and a subscription to Microsoft Office 365 you will also need to install PowerBI Desktop available as a free download the courses in this program prepare you for a career in data analysis when you complete all the courses in the Microsoft PowerBI analyst professional certificate you’ll earn a Corsera certificate to share with your professional network taking this program not only helps you to become job ready but also prepares you for exam PL300 Microsoft PowerBI data analyst in the final course you’ll recap the key topics and concepts covered in each course along with a practice exam you’ll also get tips and tricks testing strategies useful resources and information on how to sign up for the exam finally you’ll test your knowledge in a mock exam mapped to the main topics in this program and the Microsoft Certified Exam PL300 ensuring you’re wellprepared for certification success earning a Microsoft certification is evidence of your real world skills and is globally recognized a Microsoft certification showcases your skills and demonstrates your commitment to keeping pace with rapidly changing technology it also positions you for increased skills efficiency and earning potential in your professional roles the topics covered in the practice exam include prepare data model data visualize and analyze data and deploy and maintain assets in summary this course introduces you to how a data analyst using Microsoft PowerBI applies data design techniques to create compelling stories through reports and dashboards i hope you are ready to start creating compelling and cohesive reports and dashboards using the best visual techniques to optimize audience focus i don’t have to tell you that a social media photograph gets way more likes and shares than a message that contains text only we choose to look at images first your brain processes visual data thousands of times faster than text that’s the main reason we prefer visual communications it’s also why right now all over the world people are using data visualization software to make sense of large complex data of course humans communicated visually long before we had technological power let’s check in on how we progressed from using just numbers for data presentations prepare to understand the real meaning behind the numbers as our understanding of the impact of visuals increased the approach to creating visualizations changed and in 1933 Harry Beck created the London Rail Underground Map inspired by electrical circuit diagrams it simplifies a complex layout by focusing not on rail line geography but on how a commuter uses the rail system it’s a visual style still used today to make data easier to understand visualizations that successfully connect with users have a lasting impact on how we communicate data let’s say you want to use data visualization to illustrate a much larger rail network it could be 10 times bigger or a thousand times bigger scale it to 100,000 times and you have an idea of the data volumes now available data visualization tools help us understand big data in the world around us just compare older 2D maps to how satellite mapping reveals a different vision we can zoom in for more detail to give a granular understanding of the area zoom further into a city’s layout and reveal data insights with visual markers while always being able to place our insight in the context of a global landscape businesses benefit from data visualization by understanding the impact of their decisions businesses can create better products and services that improve the lives of their customers but data visualization is not just for business it improves data accessibility for governments organizations and citizens for the first time we all have access to detailed and accurate data about the planet professor Hawkins from the University of Reading created global warming strikes like this a simple visual with no text no numbers but its message of the danger of global warming is clear despite technological advances the goal of data visualization remains the same to make data accessible and easier to understand imagine a world where large-scale decisions are better understood through visualizations of this data you can use data visualization tools to enhance your communication skills reveal insights on a global scale and help build a better world how do you choose an outfit from your wardrobe when choosing which clothes to mix and match it’s important to know what colors go well together after all you want to look your best the same goes for your reports and dashboards to look their best they need to have the best mix of colors and shades that’s why you are now being introduced to color theory in this video you’ll explore color theory its basic concepts and how it assists you in creating presentations and data graphics color theory is the collection of design rules and guidelines used to communicate with users through effective color schemes color theory involves the meaning and use of colors and how to pick the best colors in different situations to build harmonious and visually captivating color combinations as a data analyst understanding the principles of color theory is essential for creating visually captivating and effective designs colors can evoke emotions convey messages and enhance the impact of reports color theory is a practical guideline for the visual effects of color combinations it includes the color wheel color harmony color psychology and color symbolism it gives you a powerful toolkit to create visually pleasing and meaningful designs the color wheel represents the relationship between colors it consists of primary colors red blue yellow secondary colors which are mixes of primary colors such as orange green and purple and intermediate or tertiary colors which are mixes of primary and secondary colors the color wheel guides your choice of colors leading to color schemes that create harmonious compositions color harmony is another important concept color harmony refers to the arrangement of colors in a specific design that is visually pleasing to the viewer you create visual balance and enhance the overall impact of your design by choosing the correct color combination here are a few methods used to combine colors into a color scheme complimentary colors this system uses opposite hues on the color wheel analogous colors uses groups of colors that are next to each other on the color wheel triadic is a color concept that uses a three-pointed triangle selection of colors from the color wheel monochromatic color combinations use several variations of the same color the psychology of color is one of the most important aspects to consider during your design colors can evoke emotions and influence behavior for instance when designing marketing materials for Adventure Works outdoor adventure products incorporating vibrant and energetic colors like orange and yellow can evoke feelings of excitement and enthusiasm colors can often carry symbolic meanings and cultural associations different cultures may interpret colors differently so it’s important to consider cultural context when selecting colors for global designs for instance while red may symbolize luck in Eastern Asian cultures it can represent danger in some Western cultures by understanding color symbolism you can ensure that your designs effectively convey the intended message across different cultural backgrounds given the importance of color theory it’s crucial to consider accessibility when working with color in design as not all individuals perceive colors in the same way color blindness is a condition where individuals have difficulty distinguishing certain colors or perceiving color differences the most common type of color blindness is red green color blindness where individuals have trouble differentiating between shades of red and green to ensure that your designs are accessible to individuals with color blindness use color combinations that have sufficient contrast this means avoiding color combinations that may appear similar to individuals with color blindness it’s recommended to use high contrast color combinations such as black text on a white background to improve readability additionally providing alternative ways of conveying information beyond color is crucial for example if you’re using color to indicate different categories or data points consider also using patterns labels or symbols to supplement the color coding this ensures that individuals with color blindness can still understand and interpret the information accurately by considering color theory and accessibility together you can create designs that are not only visually appealing but also inclusive and accessible to a wider range of individuals mastering color theory is a vital skill for any artist designer or creative professional by understanding the principles of the color wheel color harmony color psychology and color symbolism you can create visually captivating designs that effectively communicate messages and evoke emotions in your audience as you embark on your colorful journey at Adventure Works let color theory be your guide in transforming ordinary designs into extraordinary visuals if I tell you that the temperature is very hot what color comes to mind most people answer in the range of orange to red color is a crucial design element for business intelligence dashboards and reports to make them visually intuitive and understood by all viewers by the end of this video you will understand how colors evoke psychological associations and convey symbolic meanings let’s explore the science of color in communicating datadriven stories in business communication colors serve as navigational tools directing users attention and facilitating efficient information access here are some roles colors can play in designing your reports and dashboards background is the color of your report or dashboard background or the background of an individual visual within the report use low saturation colors that is a color that is not too vivid rich or intense then the background will not distract users from the main story the dominant or primary color gives viewers the first impression of the color theme it’s typically used in a lot of elements to create contrast within your report an accent color is used for focal points of your report capturing users immediate attention examples include call toaction buttons alerts and warning messages semantic colors are colors that have an
actual meaning and they aid a seamless comprehension for example commonly employed colors for alerts are red for bad orange represents average and green signifies good semantic colors are usually used for conditional formatting on texts and charts once you choose colors for your reports you can create a color palette powerbi can upload a color palette as a JSON file to design a custom theme for your reports and visualizations by using a JSON file you can create a report theme file that standardizes your charts and reports making it easy for your organization’s reports to be consistent use these colors to amplify insights for example identify certain values or groups within your data that are good or bad use contrasting colors to differentiate between different values use shades of the same color to demonstrate the strength or weakness or various grades for instance using shades of the same color in a geographical visual to represent the ascending or descending values of sales use a dull color for something less important and a bright color for crucial information at Adventure Works you must create a report showing a table of sales data with profit margins the profit margins will be emphasized using effective color combinations while considering accessibility requirements let’s explore color selection in data visualization launch Microsoft PowerBI desktop and open the project salesbyear.pbix pbix navigate to the report view of PowerBI desktop to the report containing a table with sales and profit margin values and a column chart emphasizing the profit margin to remove the sum of prefix from the column titles go to the visualization pane and in the columns list doubleclick on the column name and delete the sum of text this can be done for all columns that need to be renamed to change the theme of the visualization navigate to the view tab of PowerBI and select the accessible city park theme from the theme drop-down list this will change the entire color combination for the current report the theme contains colors that satisfy accessibility requirements to ensure accessibility for the broadest range of consumers you can increase the font size and change the font color throughout the report to maximize visibility and contrast for instance increase the font size of the table values to 18 point select the table and navigate to format visual visual expand the value section and change the font size to 18 expand the column header section and change the font size to 18 then to accommodate the new size of the table move and resize the two visuals the next task is to highlight the most valuable information in the table the profit is the most important information for the executives you can use color psychology to emphasize this section of the visual select the table visual and go to the visualization pane in the columns list select the drop- down arrow beside the profit margin column and move the cursor to conditional formatting in the drop- down list this opens a submen of the drop-own list font color is what is needed from this list this opens the font color format dialogue box for the profit margin column values select rules from the format style drop-own menu and select values only from the apply to section profit margin is selected under the what field should we base this on section leave this column selected next to define the rules for the first rule the process is to select the greater than or equal to symbol and enter zero for the value then select number from the drop-own list just after the and part of the rule select less than and write max in values and select number from the drop-own list finally in the then part of the rule select the green color from the theme color selection section to set up the second rule continue to select a new rule with the plus icon to add a new rule to the list in the first control select greater than or equal to from the drop- down list and remove zero it will automatically select min and then select number from the drop-own list after the and of this rule select less than write zero in the values and select number from the drop- down list finally select a red color from the theme colors select okay the conditional formatting will change the color of the text to red if the profit margin is in the negative range this is a format that the company executives expect it allows them to quickly assess this part of the report to colorize the column chart representing the profit margin select the chart and in the visualization pane navigate to the format visual tab from the expand the column section where you can assign individual colors to each column select a red color for financial year 2022 and keep the green for 2020 and 2021 finally change the text size of the column chart to 12 point this means in format visual changing the font size for x-axis values y-axis values title and data labels that example transformed a report lacking clear visuals and without Adventure Works branding into an attention-grabbing report by the intelligent use of colors as a report designer understanding the key role of color is crucial to creating visually compelling and impactful work you get a report or a page of information on your screen how do you decide if the content is important enough for you to read many designers include headlines subheadings and other design devices such as callouts elements like these highlight key parts of the information allowing you to decide faster if the content is relevant to you you’ll use similar tactics in your Microsoft PowerBI report and dashboard designs over the next few minutes you will be introduced to the concepts of positioning and scaling by strategically placing and sizing visual elements such as charts tables and text you guide the viewer’s attention and indicate the level of importance of the information let’s say you are asked to create a complex report for Adventure Works to present the company’s annual revenue growth by region to achieve effective positioning and scale you place a bar chart in the middle of the report clearly displaying revenue figures for each region to provide additional context you position a map visualization alongside the bar chart showing the geographic distribution of revenue growth by placing the two different visual elements together you can enable viewers to make connections between regions and their respective revenue performance for the most effective delivery you must plan your report think about the positioning of different portions of data use scaling techniques and create a good user experience in your report positioning is the strategic placement of visual elements within a report to guide the viewer’s attention and convey key information it’s essential to consider the flow of information and the logical sequence in which the audience will consume it the placement of data and insights can significantly impact how they are perceived for example when presenting sales figures for Adventure Work’s latest product line you would position the most important metrics such as revenue and units sold at the top of the report this ensures that viewers immediately grasp the success of the product line before diving into further details additionally you must pay attention to the logical flow of information you arrange sections of the report in a way that follows a natural progression enabling viewers to easily navigate through the data supporting details such as product specifications or regional sales performance are strategically positioned below the main metrics providing contextual information to support the overall narrative now let’s explore scaling scaling refers to the relative size and proportions of visual elements within a report it is important to recognize that finding the right scale is crucial for ensuring readability and visual clarity heading and titles are carefully sized to be larger and bolder drawing the viewers’s attention to important sections for instance when showcasing the company’s quarterly sales performance you can use a larger font size for the title to make it stand out and capture the viewer’s interest in contrast data labels and annotations are scaled down to avoid overwhelming the viewer with unnecessary information additionally the scale of charts and graphs should be carefully considered to represent the data accurately access labels tick marks and legends should be appropriately sized and positioned for easy interpretation by maintaining consistency in the scale of measurement across multiple charts and graphs in your reports you enable the viewers to make meaningful comparisons and draw insights effectively overall the positioning and scale of information in report design should aim to create a visually pleasing and intuitive experience for your audience by effectively organizing and presenting data you can enhance understanding facilitate analysis and effectively convey your message for report design mastering the art of positioning and scale is vital by considering the logical flow emphasizing key information and balancing scale you create visually compelling and informative reports that captivate viewers as a data analyst adopting these principles can elevate your report designs and effectively communicate insights to your audience adventure Works has a salesperson performance Microsoft PowerBI report with total sales and quantity sold however the visuals are randomly positioned and the information is overwhelming the task is to redesign the report to better present the data let’s explore how this is done the report contains a clustered column chart showing total sales by year and salesperson a clustered chart showing quantity by salesperson a card showing the top three salespersons and the company logo the first issue with the current report is the density of information presented in a single visual for example the column chart of total sales by year and salesperson is busy with too much information and the second is that all the visuals are randomly located on the report canvas to begin the redesign in the view tab from the theme drop-down options activate the accessibility city park theme themes are standardized color schemes that can be applied to your entire report to maintain consistency throughout your report the accessibility support in this theme includes a color palette that provides contrast between content background and adjacent colors so the text and graphics are legible to ensure accessibility for the broadest range of consumers increase the font size and change the font color throughout the report to maximize visibility and contrast to make the text color of the axis titles and labels consistent throughout the report customize the theme to do that navigate to the view tab and in the themes dropdown select customize current theme the customize theme dialogue appears select advanced from the middle pane and select a black color for the second level elements select apply then select the total sales by year and salesperson column chart in visualization build visual scroll down and remove the salesperson field from the legend section the legend is busy with too much information in a small area the primary objective of the chart is to show the total sales per salesperson by removing the salesperson field and creating a slicer we can present the same information with better clutter-free visuals resize the column chart and drag it to the left of the canvas then navigate to visualization format visual visual to expand the Xaxis and scroll down to change the title toggle to the off position move the second chart out of the way for now for the first chart going to visualization format visual visual expand the column section and select FX to open the conditional formatting dialogue box in the dialogue box select the total sales from the drop-own of what field should we base this on section then select the black color in the lowest values check add a middle color and select a green color for the midval section select the darker green color for the highest value section then select okay to finish setting up conditional formatting the conditional formatting converts the columns to the shades of green and black color that you specified with the shade based on the column value it also adds a color legend to the column chart the legend is an unnecessary element in the chart that can be deleted to make the design cleaner to remove it go to visualization format visual visual legend and turn the toggle to the off position finally change the text size of the chart X and Yaxis and data labels to 12 points as the original visual was created to represent the salesperson’s performance add a salesperson slicer to the report to do this from the data pane bring the salesperson field from the salesperson’s table to the report canvas and select the slicer option from the visualization pane selecting the slicer go to visualization format visual visual slicer settings options there from the style drop-own list of options select the drop-own choice resize the slicer and drag it to the top right position of the report canvas next select the sum of quantity by salespersons column chart and replace the salesperson field from the x-axis with the year field from the order date column of the sales table the reason for this change is that we have a salesperson slicer and we can create a consistency between it and this chart by having year on the xaxis then the salesperson slicer will interactively present the sales generated by each salesperson in each year from visualization format visual general expand title and rename it as quantities sold rename the y-axis label as quantity sold then remove the x-axis title apply conditional formatting to the column colors remove the color legend and change the text size the column chart is resized to the same size as the previous one and dragged to position it parallel with the previous visual next resize and drag the top three salesperson’s card below the slicer and adjust the position and size accordingly for better visibility and accessibility change the text size and color of the salesperson’s name on the card go to format visual visual expand the card section change the title font size to 18 and color to black finally drag the Adventure Works logo to the top left of the canvas and add a report title of salesperson’s performance the report now has a structured layout with a logical flow of all the information originally presented this report demonstrates that proper positioning and information density adjustments improve comprehension and engagement placing visual elements optimizing scale and ensuring clarity of labels allows organizations to effectively communicate insights and make datadriven decisions in the realm of report design the organization and presentation of information plays a crucial role in capturing the attention of viewers in this video you will explore the concept of cohesive pages and the importance of striking the right balance between chaos and cohesion in report design drawing inspiration from Adventure Works you will delve into how thoughtful design choices contribute to cohesive pages that effectively convey information and captivate audiences before going into the dynamics of chaotic versus cohesive pages let’s recap the significance of cohesion in report design in previous videos you learned how elements such as color positioning and visual hierarchy contribute to cohesive designs by utilizing consistent color palettes strategic positioning of elements and clear visual hierarchy designers can create reports that are visually appealing easy to navigate and convey a unified message consider that your company Adventure Works needs to showcase its product lines performance across different regions in a report to create a cohesive page you need to employ a clean and structured layout you have to utilize consistent color schemes such as using brand colors to highlight important information and differentiate regions graphs and charts are thoughtfully positioned aligned and scaled to facilitate easy interpretation in this scenario a chaotic page would feature disorganized graphs overlapping text and a mix of unrelated colors leading to confusion and a lack of clarity chaotic pages suffer from a lack of structure coherence and intentionality they are characterized by cluttered layouts conflicting color schemes and elements positioned inconsistently chaos not only hampers visual appeal but also creates confusion and hinders effective communication of information in an Adventure Works report a chaotic page may include confusing graphs overlapping text and inconsistent use of color making it challenging for viewers to understand the intended message when working for Adventure Works you recognize the significance of cohesive pages and strive to create designs that engage and inform viewers effectively by adopting cohesive design principles you ensure that your reports are visually appealing organized and easy to navigate for example when presenting quarterly sales performance you carefully arrange key metrics in a logical flow utilizing a consistent color palette that aligns with their brand identity this approach creates a cohesive page that guides viewers through the information in a structured and comprehensible manner adventure Works demonstrates how thoughtful design choices contribute to cohesive pages you ensure that fonts colors and other visual elements align with the brand identity creating a consistent and recognizable aesthetic throughout your reports by utilizing whites space effectively you allow elements to breathe and improve readability clear headings and subheadings along with intuitive navigation elements further enhance the overall cohesion and user experience by incorporating these steps into your report design process you can improve cohesiveness and create visually appealing reports that effectively communicate information cohesiveness is not just about aesthetics but also about facilitating understanding and engagement for the intended audience creating a clear visual hierarchy is essential for guiding viewers through the report and highlighting key information use font size color and formatting to differentiate between headings subheadings and body text ensure that the most important elements stand out and draw the viewers’s attention adopting a consistent color scheme throughout the report enhances cohesiveness and strengthens brand identity choose a color palette that aligns with the company’s branding guidelines and use it consistently across charts graphs text boxes and other visual elements this consistency helps to establish visual harmony and reinforces the overall design aesthetic pay attention to the positioning of elements within the report ensure that related information is grouped together logically and presented in a sequential manner use alignment and spacing techniques to create a sense of order and structure avoid cluttering the page with unnecessary elements and maintain sufficient white space to enhance readability and visual appeal utilize grids and guides as design aids to achieve precise alignment and spacing grids help maintain consistency and alignment across different sections of the report while guides assist in positioning elements accurately these tools provide a framework for maintaining cohesiveness and ensuring that elements are visually aligned consistency and typography is crucial for creating a cohesive look and feel choose fonts that are legible and align with the overall design style use a limited number of font styles and sizes to maintain consistency throughout the report consider the readability of the chosen fonts and ensure that they are suitable for the target audience regularly review and refine your report design to identify areas for improvement seek feedback from colleagues or stakeholders to gain fresh perspectives analyze the report’s effectiveness in communicating the intended message and make necessary adjustments to enhance cohesiveness continuous improvement is key to achieving optimal results in the dynamic world of report design finding the balance between chaos and cohesion is essential for creating engaging and impactful pages by recapping the importance of cohesion exploring chaotic examples and showcasing the best practices you have gained insights into how color positioning and other design elements contribute to the creation of cohesive pages as you embark on your own report design journey remember the value of cohesive pages thoughtful design choices including consistent color schemes strategic positioning and attention to visual hierarchy can elevate your reports and captivate your audience by creating designs that balance order and clarity you will effectively communicate your message empower viewers with valuable insights and leave a lasting impact let’s take a poorly designed sales performance report and redesign it into a cohesive report the report view of PowerBI desktop displays a sales performance report called adventurework sales.pbix the report is poorly designed with randomly placed visuals and lacks coherence the redesign will change colors reposition and scale visuals and format text the report contains two line charts one funnel chart and two card visuals a logo and a report title the first step is to change the theme from the theme drop-down activate the accessible city park theme to ensure accessibility and impose a consistent style the theme contains colors that satisfy accessibility requirements customize the theme to enhance the label and access colors to ensure accessibility for the broadest range of consumers increase the font size and change the font color throughout the report to maximize visibility and contrast now drag the company logo to the top left of the report canvas also drag the title box to align with the logo let’s change the color of the title to black and make the text bold to align with the color palette of the theme select the sum of total sales card visual and rename the title as revenue to match the intent of the data in visualization format visual general effects change the background to theme color 2 both cards will have the same background color differentiating them from the report background and letting the viewer know that they both hold related data and contain the most valuable information in visualization format visual visual callout value change the font size to 32 and change the color to white to indicate the importance of this item for category label change the color to white and font size to 18 for better visibility against the new background then repeat these steps for some of quantity card visual and rename that visual as units sold now reposition both card visuals to the top right of the canvas and make sure these are of the same size because they are of equal importance you can rescale the card by selecting and dragging any side of the visual next select the sum of total sales by month line chart and rename it to a more appropriate title of revenue by month remove the x-axis title by turning the title toggle to the off position navigate to visualization format visual visual expand x-axis and scroll down to turn the title toggle to the off position x-axis represents monthly sales with the month name the month title on the axis does not add any relevant information rename the y-axis to total sales USD to clarify the sales details and currency now to add grid lines to the line chart in visualization format visual visual grid lines select dashed as style and black as the color next select the sum of total sales by month and country line chart and change its title to revenue by country remove its xaxis title as done in the previous chart and rename the y-axis as total sales USD next to format the legend navigate to visualization format visual visual and scroll down to the legend section in the legend section turn the title toggle button to the off position change the text size to 12 points and select the top right position from the position drop-down list of options the legend title is redundant because the country names provide sufficient information add grid lines to match the other visuals ensuring items such as title legends axis values and font size are formatted consistently for all the visuals helps report cohesion select the funnel chart and rename the title to revenue by category in visualization format visual visual conversion rate labels toggle to off as this is not relevant to the sales go into visualization format visual visual and expand the color section and select FX to open the conditional formatting dialogue in the dialogue select the total sales from the drop-down of what field should we base this on section then select a blue color called theme color five for the lowest values check add a middle color and select mid green theme color one for the midvalue section select the dark blue color theme color two for the highest value section select okay to apply conditional formatting converts the bars to shades of blue in descending order of sales amount dark blue represents the highest sales values next change the text size of the funnel chart to 14 points for better accessibility and visibility likewise change the font size of the axis titles and labels of both line charts to 12 points finally rescale and reposition the visuals making sure the distance between the visuals is equal to maintain design integrity adjust the position by dragging and rescale by selecting and dragging any side of the visual it’s good practice to review your work and possibly invite comments from colleagues a quick review right now suggests some slight improvements for instance to finish increase the size of the titles on each chart to 18 points that’s a demonstration of how to create cohesion in a report by applying and customizing an accessible theme ensuring consistent formatting for all visuals and scaling and positioning visuals in a logical hierarchical way to deliver a coherent data story imagine you’re planning a musical performance but you are playing for two different audiences one a group of classical music enthusiasts and the other a crowd of young energetic music lovers satisfying both audiences is a challenge it’s like the challenge you have when presenting data understanding your target audience is crucial and catering to their unique needs is the key to success it’s impossible to please everyone but the data must be readily understood by the majority with essential insights highlighted for your specific audience a key visualization success factor is understanding the audience you must tailor presentations to the specific needs and preferences of the target audience that is the specific group of people that your content is intended to reach it is the group of individuals most likely to be interested in or benefit from your data identifying and understanding the target audience is essential for communication and allows tailored strategies that can connect with this specific group’s preferences needs and characteristics every audience has unique characteristics including their level of technical expertise roles and responsibilities demographic information and other specific needs in this video you will explore the importance of knowing the audience and how the characteristics of your target audience influence the creation of your data presentation because of their characteristics you may be able to identify an audience’s needs an executive board needs highle summaries and key performance indicators while a marketing team wants detailed customer insights and marketing analytics when considering the target audience for a report or presentation assess some factors this will help identify the audience’s characteristics and needs enabling you to tailor your design to meet their specific requirements here are some key factors to consider identify the different roles or job functions of the potential users for example are they executives analysts marketers or sales representatives each row may have distinct data requirements and preferences determine the audience’s level of expertise and familiarity with the subject matter or the software being used are they beginners intermediate users or advanced professionals this helps you gauge the complexity of the information and the level of detail needed understand the goals and objectives of the audience what specific information or insights are they seeking for example executives may be interested in highle performance summaries while analysts may require more detailed data for in-depth analysis determine the specific information needs of the audience what kind of data or metrics are most relevant to their decision-making process for instance marketing teams may focus on customer demographics and campaign performance in contrast finance teams may require financial metrics and profitability analysis consider the preferred communication style of the audience some individuals prefer visual representations and charts while others prefer textual reports or interactive dashboards adapting your content to their preferred format enhances engagement and understanding assess cultural and demographic factors influencing the audience’s preferences and understanding this includes language preferences cultural nuances and accessibility considerations recognize the time constraints of the audience are they busy executives who require concise and summarized information or do they have more time for in-depth exploration tailoring the level of detail and presentation format can ensure that the information is effectively conveyed within the available time frame by considering these factors you can gain valuable insights into the target audience and align your report or software design to meet their specific needs once the target audience is identified the next step is to use data visualization techniques to address audience requirements it’s important to find the right balance between providing the required data and ensuring that it is understood by most of the audience when creating for diverse audiences it is crucial to simplify complex concepts and avoid jargon or technical terms that may be unfamiliar to non-technical stakeholders adventure works for instance may use clear and concise language to explain intricate manufacturing processes or market trends which your internal team would be familiar with however if presenting to external partners or users from outside the company they may be unfamiliar with manufacturing processes and therefore the technical terms should be avoided it’s important to identify and highlight the most relevant insights for the target audience for instance when presenting to the executive board the focus may be on financial performance market share and strategic initiatives on the other hand when presenting to the marketing team you can focus on customer behavior campaign effectiveness and market segmentation by tailoring the content to the specific interests of each audience data presentations become more engaging and actionable incorporating examples and scenarios that your audience is familiar with can help them connect with the data when presenting to the executive board a case study on the success of a recent product launch or a comparison of sales performance across different geographic regions can provide valuable insights similarly presenting market research findings or customer feedback to the marketing team can help them fine-tune their strategies and campaigns knowing the audience is vital in creating impactful data presentations by understanding the target audience’s needs preferences and roles within the organization data analysts can tailor their presentations to ensure maximum impact and understanding focusing on simplifying complex concepts highlighting relevant insights and using real world examples specific to the audience can significantly enhance the effectiveness of data presentations balloons are great fun at every party they brighten the room and raise the celebration mood but the same balloons that you used at a retirement function you don’t expect them to work as well at a kid’s birthday party for that party you’ll have balloons in different shapes and colors it’s the same situation when it comes to presenting data designing with the end user in mind is the key to success in data visualization the age range of the target audience is a vital consideration age related design considers the unique needs preferences and capabilities of different age groups in this video you’ll explore the significance of age related design in Microsoft PowerBI and discover specific considerations when designing visualizations for younger children aged 5 to 12 teenagers adults aged 18 to 64 and older adults aged 65 and above before exploring age related design considerations let’s briefly revisit the fundamentals of color theory color plays a crucial role in data visualization evoking emotions conveying meaning and aiding comprehension when designing for different age groups it’s important to select colors that are visually appealing to the group easily distinguishable and aligned with the intended message now let’s examine age related design in detail designing for younger children requires a simplified and engaging approach use vibrant and engaging colors younger children are attracted to bright and bold colors a visually stimulating color palette can capture their attention and enhance their engagement use simple and intuitive icons complex visual elements can overwhelm young children choose simple and recognizable icons that are easy to interpret interactive features such as buttons or dragable elements make the experience more interactive and enjoyable for young users incorporate playful illustrations and characters for example adventure works could use animated bicycle characters or friendly animal mascots in their visualizations to make the content more relatable tell a story through the data to capture the imagination of younger children adventure works could create a virtual journey such as showcasing different bicycle models in color and visually appealing environments for adults use a clean and professional design choose a visual style that meets the target audience’s expectations avoid excessive use of playful elements or overly casual designs ensure the visual elements have sufficient contrast and use clear readable typography for easy comprehension use text that is clear legible and easily readable choose appropriate font sizes typography and contrast to enhance readability adults appreciate a clear and intuitive user interface use logical navigation structures like menus and breadcrumbs to help users quickly navigate the content streamline the user interface and minimize complex interactions consider the audience’s needs for efficient data analysis and decision-m design dashboards and reports that provide relevant information quickly and concisely incorporate advanced visualizations appropriately consider using advanced charts graphs and interactive elements to provide deeper insights and facilitate data exploration allow users to personalize their dashboards or reports according to their preferences and priorities providing customization options can enhance user engagement and satisfaction designing for older adults requires additional focus on clarity legibility and ease of use use large and well spaced elements aging eyes may need help with small text or densely packed visuals enlarge fonts and provide ample spacing between elements to enhance readability and prevent visual clutter designing for different age groups requires consideration of their unique characteristics and needs by incorporating age related design principles you can create Microsoft PowerBI visualizations that cater to the specific requirements of groups like younger children and older adults from vibrant colors and interactive elements for children to clear typography and simplified interactions for older adults every design decision should prioritize the target audience’s ease of understanding and engagement age related design is one important aspect of creating inclusive and compelling visualizations continually exploring and understanding the needs of diverse user groups will help you focus the features of PowerBI to deliver impactful and accessible data visualizations for all imagine you’re preparing a delicious meal carefully selecting the finest ingredients your focus is on the flavors that will make the meal great in a similar way when presenting data focusing on the key details is crucial much like those food ingredients your audience craves the most relevant and impactful insights prioritizing key information ensures your message fulfills and satisfies the audience understanding the needs and preferences of your audience allows you to focus on the most relevant data points highlight outliers and provide the right level of detail for effective communication in this video you will explore the importance of prioritizing key information in Microsoft PowerBI and how it can enhance data insights for your audience before exploring the details of prioritizing it is vital to know your audience and their specific needs for instance presenting to the executive board requires a highle overview with emphasis on the big picture and key insights while presenting to a sales team may require more detailed information about performance evaluation consider a report for the executive board with an overview of quarterly sales and an emphasis on product categories the data also indicates that the executives need to focus on France and the United Kingdom for their marketing efforts by understanding your audience you can tailor the presentation to their specific needs ensuring that the key information is appropriately highlighted it allows you to customize the content format and level of detail in your presentation by adapting the presentation to the preferences knowledge level and goals of the sales team you increase the chances of delivering a compelling message that meets their needs when presenting data it is essential to capture the attention of your audience quickly by focusing on headlines or the most important findings and trends you can convey the main message effectively in the case of Adventure Works annual sales report key headlines may include overall revenue growth top selling product categories and regions with significant sales increases by highlighting these headlines you provide a clear and concise overview that immediately grabs the audience’s attention in any data set there are often outliers or data points that deviate significantly from the norm these outliers can provide valuable insights or indicate areas that require attention by highlighting them visually such as using color or annotations you draw the audience’s focus to these critical data points for example adventure works may have a particular product that experienced a sudden spike in sales or a region that underperformed compared to others by highlighting these outliers you prompt further exploration and discussion ensuring that the audience does not overlook essential information while headlines and key findings are crucial it is also essential to provide access to detailed information for a closer inspection when appropriate different audience members may have different levels of expertise or specific questions that require a deeper dive into the data in tailoring presentations the availability of detailed information for closer inspection should be carefully considered aligning with the needs and preferences of the specific audience for instance in an annual sales report from Adventure Works presenting to the executive board may emphasize highle trends revenue figures and strategic directions while a presentation to the sales team might delve into granular details like regional performance customer segments and sales targets adapting the level of detail ensures that each audience receives the information that aligns with their decision-making requirements optimizing the impact of the presentation microsoft PowerBI allows for interactive exploration where users can drill down into specific data points or filter the information based on their interests by providing this level of detail you enable further analysis and empower your audience to extract insights relevant to their specific needs the definition of significant information can vary across different audiences what may be crucial for one group may not be as relevant to another therefore it is crucial to adapt your presentation to align with the preferences of your audience for example the executive board may prioritize overall revenue and market share while the sales team may be more interested in product specific details or customer segmentation by understanding these preferences you can ensure that the key information presented is meaningful and resonates with your audience prioritizing key information in Microsoft PowerBI is a critical skill for effective data visualization and communication you can enhance data insights by understanding your audience focusing on headlines highlighting outliers providing access to detailed information and adapting to audience preferences the key to successfully prioritizing information is understanding your audience and tailoring your presentation to meet their specific needs picture a vault where your most valuable possessions are stored now imagine that this vault doesn’t have a strong lock leaving your treasures vulnerable to theft just as you’d prioritize security for your valuables safeguarding data is paramount in our digital age data the lifeblood of modern organizations is subject to a range of threats cyber attacks breaches and unauthorized access ensuring the security of this digital gold mine isn’t just a choice it’s a necessity let’s explore the world of data security where the keys to protection lie in understanding the risks implementing robust measures and fostering a culture of vigilance in the world of data visualization ensuring the security of data is of utmost importance from protecting sensitive information to maintaining data integrity incorporating robust security measures is crucial in this video you will explore the significance of security in data visualization and discuss key considerations for safeguarding data throughout the visualization process adventure Works a fictional multinational bicycle manufacturer is used as an example to illustrate the concept of data security in practice data visualization often involves working with sensitive information such as customer data financial records or proprietary business insights ensuring the security of this data is essential to maintain trust comply with regulations and protect against unauthorized access or data breaches let’s examine the key aspects of security and data visualization controlling access to data is vital to ensure that only authorized individuals can view or interact with specific data sets by implementing role-based access control data can be restricted or served in a controlled manner to the individuals who need to access it this helps protect sensitive information and reduces the risk of unauthorized data exposure additionally access logs and audit trails can be implemented to track and monitor data access providing accountability and visibility into data usage in Adventure Works you implement role-based access control to ensure that sensitive data is accessible only to authorized individuals in data visualization processes for instance the finance team has access to financial data while the marketing team can view customer demographics for targeted campaigns this granular access control prevents unauthorized individuals from accessing data beyond their scope safeguarding sensitive information anonymizing data is an effective technique for protecting privacy and confidentiality by removing personally identifiable information or replacing it with pseudonyms the data can be used for analysis and visualization while preserving privacy anonymization techniques such as generalization suppression or noise addition ensure that individuals cannot be identified from the data generalization involves simplifying or aggregating data to a higher level of abstraction often to protect privacy or reduce complexity suppression is the deliberate removal of certain data elements to prevent identifying individuals or sensitive information noise edition introduces controlled random variation into the data to make it more challenging to deduce specific details about individuals or confidential data these techniques are commonly used in data anonymization and privacy preservation to strike a balance between sharing useful information and safeguarding sensitive details ensuring data remains useful while reducing the risk of privacy breaches organizations should follow best practices and guidelines for data anonymization considering factors such as the nature of the data regulatory requirements and the intended use of the visualizations in Adventure Works you conduct market research and collect customer feedback to protect customer privacy you employ data anonymization techniques when visualizing the data personal information such as names addresses and contact details are replaced with pseudonyms or aggregated to preserve anonymity this allows Adventure Works to analyze and prevent valuable insights without compromising the privacy of customers maintaining data integrity is crucial to ensure the accuracy and reliability of the visualized information data integrity aspects include data validation error detection and consistency checks data validation involves verifying the accuracy and integrity of input data to ensure it meets predefined criteria error detection focuses on identifying mistakes or anomalies in data helping prevent erroneous information from causing problems consistency checks ensure that data conforms to established standards or matches other related data maintaining a reliable and cohesive data set these practices collectively help maintain data quality minimize errors and ensure that information is reliable and useful for decision-making and analysis implementing data validation rules and performing regular audits help identify and rectify any anomalies or inconsistencies in the data ensuring the visualizations reflect accurate and reliable insights furthermore employing data encryption techniques can prevent unauthorized modifications and tampering of the data maintaining its integrity throughout the visualization process in Adventure Works you prepare quarterly reports on sales performance which are shared with the executive board to ensure data integrity you implement data validation checks to detect any anomalies or errors in the sales data by cross- referencing the data with your customer relationship management system or CRM and performing consistency checks Adventure Works ensures the accuracy and reliability of the visualized sales information this data integrity provides the board with confidence in making informed decisions based on reliable insights when transferring data between different systems or sharing visualizations with stakeholders it is essential to prioritize secure data transmission using encrypted connections such as HTTPS or SSLTS ensures that data is encrypted during transit making it difficult for unauthorized individuals to intercept or manipulate the data https hypertext transfer protocol secure is a protocol that provides secure communication for website connections allowing user data to be transmitted in an encrypted manner this encryption relies on security protocols such as secure sockets layer SSL or transport layer security TLS secure sockets layer transport layer security SSLTS is used to ensure privacy and integrity during data transmission over the internet protecting user data from malicious attacks and ensuring its security these protocols enhance users online experience by providing a more secure environment when conducting online transactions and sharing sensitive information additionally organizations should consider secure file sharing methods such as using virtual private networks or VPNs for the connections using two-factor authentication or 2FA for authenticating users using Microsoft one drive for business Google workspace or Dropbox business for enterprise level cloud storage solutions and using secure protocols like secure file transfer protocol or SFTP and also utilize secure cloud-based platforms for distributing visualizations s ensuring data remains protected throughout its journey adventure Works collaborates with external partners and distributors sharing visualizations and sales data for joint business planning to ensure secure data transmission you utilize encrypted connections such as SSL TLS when sharing sensitive information over the internet this encryption protects the data from unauthorized access during transit maintaining the confidentiality and integrity of the shared visualizations and data data visualization often involves working with data that is subject to legal and regulatory requirements such as general data protection regulation or GDPR compliance with these regulations is crucial to protect individuals rights and maintain legal obligations data visualization practices should adhere to the relevant regulations including obtaining appropriate consent anonymizing data when necessary and implementing necessary safeguards organizations should stay informed about evolving data protection regulations and ensure their data visualization processes align with the correct legal frameworks adventure Works operates in various regions with different data protection regulations when visualizing data they ensure compliance with relevant regulations such as GDPR they obtain appropriate consent from customers anonymize data where necessary and implement necessary security measures to protect personal information this ensures that Adventure Works aders to the legal requirements and maintains the privacy rights of individuals security is a fundamental aspect of data visualization ensuring the confidentiality integrity and availability of data by implementing robust security measures such as access control data anonymization maintaining data integrity secure data transmission and compliance with data regulations organizations can build trust protect sensitive information and deliver reliable insights to their stakeholders as the importance of data continues to grow prioritizing security in data visualization is essential for maintaining the confidentiality and integrity of information in today’s datadriven world kim grew up in a small town in rural America the town had seen better days the region’s economy was in decline there were few career prospects for a young woman kim had to stay in her hometown and take whatever jobs she could find luckily she was an avid social media fan with a recent smartphone the phone allowed her to connect online even though the town’s wired internet connections were slow and often failed completely she vented her career and life frustrations on social media and very soon she got many suggestions for alternative careers and educational paths kim explored the opportunities available to her taking advantage of the low barrier of entry offered by the internet she used her phone and computer to take online courses and to research business ideas she had an eye for fashion and makeup an affinity for emerging styles and an ambition to succeed that combination led her to establish a business venture offering a few products online luckily for Kim the launch of her online business coincided with the upgrade of the town’s broadband to fiber connectivity yes you can work from anywhere with an internet connection but if you’re at all competitive it’s nice to be somewhere that has fast internet speeds the world is now a global village the internet is at the heart of this transformation and is an integral part of our everyday lives that’s why the need for better speeds and greater coverage has been felt around the world in the USA average connection speeds increased from 25 megabytes per second in the past to over 100 megabytes per second in recent times this is largely due to the widespread adoption of fiber optic technology which gives us faster speeds and improved coverage kim started slowly but her business grew as more and more people in her small town began to connect to and use the internet more because of its better speed her business expanded as the world grew more connected through fast internet connections kim started to use data from her customers to visualize and identify preferences and grow her business further despite the lack of local resources Kim was able to run a global business from her small town people both in rural and urban areas can access the internet easily with predictable costs and 247 access thanks to new technologies such as mobile broadband connections on 4G and 5G when traveling Kim can run her business using her smartphone connected to a cellular network or using one of the many Wi-Fi hotspots supplied by cities across the world the rise of global internet connectivity allowed Kim to access a wide array of resources with fast access to a global network she was able to stay upto-date with the latest trends in international business she made connections with professionals in other countries and was soon collaborating on new business deals and markets she couldn’t have considered before what was once an impossibility is now a reality for Kim she continues to explore global internet connectivity and use customer data analysis to expand her international business and explore new opportunities welcome to this high-level recap of the lessons covered this week this summary will help you revise the concepts of visualization and design during the course various adventure work scenarios were used as real life simulations of a multinational bicycle retailer operating in multiple countries these scenarios are designed to facilitate understanding and provide relatability and will be mentioned again in this recap as you review color theory positioning scale and density of information chaotic versus cohesive pages knowing the audience age related design prioritizing key information and security in data color theory is a crucial guideline for mixing colors and understanding the visual impact of specific color combinations it includes concepts like the color wheel color harmony color psychology and color symbolism by grasping these principles you gain a powerful toolkit for crafting visually appealing and meaningful designs the color wheel illustrates the relationships between colors including primary secondary and tertiary colors enabling you to navigate various color schemes for harmonious compositions color harmony focuses on arranging colors pleasingly in a design achieved through complimentary analogous triad or monochromatic combinations enhancing balance and impact color psychology explores how colors evoke emotions and influence behavior helping you use colors strategically for specific messages for example using yellow and orange can often evoke vibrant and energetic emotions symbolic meanings and cultural associations of colors are also essential ensuring effective communication across diverse cultural backgrounds mastering color theory empowers designers to create captivating designs effectively convey messages and evoke desired emotions making color theory a guiding force in transforming ordinary designs into extraordinary reports and dashboards color is a fundamental component in report design and data visualization impacting the quality and effectiveness of reports color influences emotions perceptions and the overall visual impact of your data visualization each color holds unique psychological associations and symbolic meanings generating diverse emotional responses for example warm colors like red and orange convey energy passion excitement and attention or warning while cool colors like blue and green evoke calmness serenity and harmony by skillfully selecting and combining colors designers can effectively convey the intended emotional message in report design while also considering cultural interpretations for global designs positioning in report design involves strategically placing visual elements to guide the viewer’s attention and convey essential information adventure Works recognizes the importance of this ensuring key data points like revenue and units sold are prominently placed at the top of a report the logical flow of information is also considered with supporting details arranged beneath the main metrics creating a natural narrative for easy navigation scaling information in the report and dashboard design is also crucial for clarity visual hierarchy and emphasis proper scaling optimizes space ensures responsiveness and reduces cognitive load chart selection plays a pivotal role in optimizing scale of information for example bar charts are used for presenting nominal and original scales while line charts work with interval and ratio scales once an appropriate chart is selected all associated elements can be scaled proportionately according to the degree of emphasis overall mastering the art of positioning and scale enhances report designs creating engaging informative reports that effectively communicate insights to the audience positioning in design involves arranging visual elements to guide attention and convey messages effectively adventure Works understands this importance ensuring key data is presented clearly and avoiding overcrowding techniques like grouping related info consistent spacing and visual hierarchy are employed to enhance information density while white space prevents clutter allowing viewers to focus their attention aligning elements guides the narrative and helps the flow of information proper positioning and information density are crucial in data visualization for comprehension and engagement enabling organizations to communicate insights efficiently cohesive page design is crucial contrasting with chaotic layouts that lack structure and coherence cohesive designs engage viewers utilize clear visual hierarchies and maintain a consistent color scheme aligned with the brand identity thoughtful positioning effective use of whites space and strategic typography contribute to organized visually appealing reports the incorporation of grids guides and regular reviews will refine the design ensuring a cohesive presentation of information by mastering these principles you create compelling reports that communicate effectively and leave a lasting impact on your audience the crucial first step in creating a successful report or presentation is identifying the target audience’s unique characteristics such as their roles expertise goals information needs and preferred communication style adventure Works for instance uses clear language and visualization elements to explain complex concepts while highlighting relevant insights for different groups such as the executive board or marketing team where possible incorporate real world examples and scenarios to help the audience connect with the data this targeted approach ensures data presentations effectively convey meaningful insights and contribute to the business success of Adventure Works to optimize data visualization designing with the end user in mind is crucial and age related design is a significant aspect to consider designing for all age groups requires understanding their unique needs by following age related design principles Microsoft PowerBI users can create visually appealing and engaging visualizations that cater to the specific requirements of different age groups the goal is to prioritize ease of understanding and engagement for the target audience prioritizing key information is a crucial aspect of data presentation by understanding your audience you can tailor your presentation to meet their specific needs ensuring that the most relevant data points are appropriately highlighted when presenting data capturing attention quickly is essential identifying outliers and important data points is another critical strategy providing access to detailed information for closer inspection is essential for those in your audience who need to drill down to reveal more data that’s part of adapting to your audience’s preferences prioritizing key information in Microsoft PowerBI is a critical skill that enhances data visualization and communication by considering your audience focusing on headlines highlighting outliers providing detailed access and accommodating audience preferences you can drive more meaningful decision-making based on data insights during your data visualization work security has a vital importance when dealing with sensitive information this includes data such as customer data financial records or proprietary business insights ensuring proper data security is crucial for maintaining trust complying with regulations and preventing unauthorized access or breaches by implementing robust security measures such as access control data anonymization maintaining data integrity secure data transmission and compliance with data regulations organizations build trust protect sensitive information and deliver reliable insights to their stakeholders access control involves controlling who can access specific data sets reducing the risk of unauthorized exposure you can implement role-based access control granting access only to authorized individuals and ensuring that sensitive data is protected data anonymization preserves privacy by removing identifiable information allowing analysis and visualization without compromising personal details maintaining data integrity is crucial to ensure the accuracy and reliability of the visualized information data integrity aspects include data validation error detection and consistency checks compliance with data regulations such as general data protection regulation or GDPR is essential and you can obtain consent from customers anonymize data as needed and implement security measures to comply with relevant regulations during this week you explored color theory positioning scale of information and information density chaotic versus cohesive pages knowing the audience age related design prioritizing key information and security and data by applying these techniques you will have more control over data visualization and design in Microsoft PowerBI the difference between insight and noise is clarity is the message of your report clear to the viewer or is the insight hidden by the noise in your presentation crafting compelling visualization in PowerBI is a necessity in this video you will learn to transform raw data into captivating stories where charts and graphs are not just shapes they bring essential clarity to your story data visualization helps convey complex information in a way that is easy to grasp and interpret microsoft PowerBI offers a wide range of visualization options from simple bar charts to intricate custom visuals allowing you to tailor your presentations to your audience and data however the true impact of data lies not just in its presentation but also in the clarity and visual appeal of the visualization when considering the importance of clarity charts data and visuals are all crucial components clear and visually appealing charts make it easier for stakeholders to understand complex data the right chart type can simplify complex information making it accessible to broader audiences data is only valuable when it communicates an insight and supports a decision visual impact ensures that your data presentation is engaging and persuasive cluttered visuals can lead to misinterpretation and therefore erroneous conclusions visual clarity in your reports reduces the risk of drawing incorrect insights let’s explore some best practices to create visual clarity and impact selecting an appropriate visual to present the data is critical for ensuring clarity and visualization it helps to display data accurately for instance a pie chart can be used to present a data set showing parts of a whole this might be a breakdown of total sales by each product category but what if you have 20 product categories pie charts will get cluttered and difficult to read if the data set is too complex break it down into smaller more digestible parts you can create summarization and aggregation measures within your data model you can employ drill down functionality of PowerBI to present details about your data although you can use colors to highlight key data points overuse of colors can lead to confusion you need to include clear and concise data labels for data points in your chart type avoid overcrowding the chart axis as this creates clutter in your chart and the overall report becomes unreadable you need to maintain a formatting consistency across all charts of your report pages you can use and customize report themes to ensure a cohesive look the data quality also contributes to the visual clarity of the report visualizations are only as good as the data quality they represent you need to make sure the data is clean accurate and formatted when choosing a chart for your report consider key elements such as the data type the message the context and the audience understand the nature of your data is it numerical categorical or geographical this helps you decide the appropriate chart type determine the data story you want to convey in your report are you showing comparison trends distribution or proportions this influences the chart selection evaluate how your visualization will be used dashboards presentations and interactive reports require distinct types of charts and visuals consider your audience’s familiarity with data visualizations select a chart type that connects with their experience although PowerBI provides tools and the flexibility to create stunning visuals it’s up to you as a data analyst and report designer to use them to eliminate clutter and impart visual appeal by prioritizing clarity selecting an appropriate chart and following best practices you can transform your data into captivating and meaningful stories that deliver insights in the dynamic world of data visualization creating visually appealing and compelling reports is essential for effective communication and decision-making however as you design these reports you must not forget about accessibility in the context of data reporting and visualization accessibility refers to the design and implementation of reports that can be easily used and understood by all individuals including those with disabilities this involves creating reports in a way that accommodates various needs such as providing alt text for visuals ensuring sufficient color contrast enabling keyboard navigation and providing compatibility with screen readers ensuring that your reports are inclusive and accessible to all users regardless of their abilities is a crucial aspect of responsible and user centric report creation because of its global operations Adventure Works executive management want to design its reports and dashboard to be used by a broader audience therefore as a data analyst your task is to consider the accessibility features of PowerBI before you plan and execute data analysis and design reports and dashboards now let’s explore a project file in PowerBI to learn how to create reports that are userfriendly and accessible to all audiences the project file contains three data tables sales products and region the first task is to create a line chart by dragging the total sales month and country fields from these tables into the respective wells of the line chart visual next create a donut chart representing the total sales by product category select the total sales and category fields to add to the chart for users with visual impairments these visuals may not be accessible add alt text to make your reports inclusive select the line chart and access visualizations format visual then general and scroll down to the alt text box enter the following descriptive text for the line chart monthly regional revenue analysis for adventure works this description acts as a text alternative that screen readers can access this lets users understand the content even if they cannot see it your users can also expand a specific visual from the report or dashboard select the line chart then select the focus mode icon on the top right corner of the visual the chart fills the entire screen select back to report to exit focus mode you can also view the data in a tabular format that is more screen reader friendly from the visual context menu select show as a table from the drop-own list this displays the line chart with a data table visual and report page titles are important accessibility features that serve as reference points let’s add some access visualizations select general then select the chart title provide a descriptive title of the chart like month sales by country next you need to name your report pages select the page number and rename the page to better represent the data both the X and Yaxis titles should also be readable and provide sufficient information in the line chart a color on its own might not be sufficient to convey information use markers to help distinguish the different data sets used in the visual select the line chart and turn the markers toggle to the on position select a different shape marker for each country you can configure the marker shape size and color for each line powerbi’s tab order feature provides a way to arrange all visual elements logically to accommodate keyboard users this ensures a natural order of visuals that keyboard shortcuts can access navigate to the view tab of PowerBI desktop and access the selection pane from the show panes group this opens a selection pane with two tabs layer order and tab order in the tab order tab you can rearrange the order of visuals in your report you must ensure screen readers effectively interpret and convey visuals and text this way you can ensure that the report is properly interpreted and conveyed to users with screen readers finally choose an appropriate accessibility theme and the high contrast windows option from the view tab to help ensure report accessibility this generates contrasting text and background colors to help make the content readable for users with visual impairments or color blindness if you use a high contrast mode in Windows PowerBI desktop automatically detects which high contrast theme is being used in Windows and applies those settings to your reports lastly test your reports with diverse users including those with disabilities to gather feedback and identify accessibility issues real world feedback helps you improve report design there are accessibility features available in PowerBI to help you successfully create a report design that can be accessed by a wide range of consumers integrating PowerBI accessible features into your workflow is not a limiting factor in designing compelling reports and dashboards it is the correct way to generate reports usable by a broader audience including those with disabilities you created a canvas of charts and graphs in Microsoft PowerBI to visualize your data but as you review your report it seems incomplete it’s as if one piece of the puzzle is missing that critical piece is the assessment of its clarity and impact a report is not just a collection of individual charts its clarity and its impact come from combining these visual elements into a compelling narrative this video will explore strategies and best practices to ensure your PowerBI reports are not just a canvas of information but are visually compelling engaging and impactful guidelines for creating an impactful report include deciding on the report objective establishing a visual hierarchy using branding and themes carefully composing the report employing storytelling techniques and optimizing the report performance for the best user experience what do you intend to communicate in your report and what is your target audience having a clear understanding of these aspects guides your design decisions the use of visual cues such as size color and visual placement builds the visual hierarchy to emphasize key insights or data points and assist navigation use branding and themes to help create a professional report design brand guidelines enforce a consistent style that adds credibility to your reports when composing your report consider layout and composition factors such as whites space alignment and screen real estate optimization whitespace means ensuring proper spacing between report elements like headings visuals and brand elements alignment is about aligning report elements to create a structured layout and a sense of order that emphasizes the data story screen real estate refers to the available space on the report canvas of PowerBI finding the right balance between presenting enough data to get your message across while avoiding overwhelming your audience is crucial when dealing with a lot of data points think about incorporating interactive elements like tool tips slicers and drill through such features keep the main visual clear but allow users to expand specific data points telling a story with your data significantly enhances the engagement and impact of your PowerBI report sequence items on the report canvas to make a natural storytelling flow for example a clear introduction key insights supporting details and finally a conclusion slow loading or unresponsiveness leads to a poor user experience that can diminish the impact of a report optimize report performance by eliminating unnecessary data minimizing complex DAX logic and aggregating data choosing an appropriate chart type based on the data type is critical in designing a clear and impactful report we will now explore use cases strengths and limitations of some commonly used chart types bar charts can compare discrete categories or values displaying rankings and trends over time easy to interpret useful to display data with few categories can come in the form of a bar chart where the bars display horizontally and in a vertical orientation when it displays as a column chart not suitable for continuous data and can become cluttered with too many categories display trends and patterns over time with a line chart to identify changes in data over a continuous scale excellent for visualizing time series data and to display multiple series for comparison less effective for comparing individual data points and not suitable for categorical data pie and donut charts display the composition of a whole showing parts of a percentage and they emphasize relative proportions easy to understand and they work well with a small number of categories not suitable for use beyond eight categories scatter plots are great for visualizing the relationship between two numerical values identifying outliers and spotting correlations it reveals patterns clusters and trends and is effective in displaying highdensity multi-dimensional data the visual may be overwhelming with too many categories a gauge chart displays a single value in relation to a predefined target such as key performance indicators or KPI provides a visual representation of performance against a goal not suitable for displaying multiple data points tree map is ideal for visualizing hierarchical data structures showing the proportions of categories within a whole visualizing hierarchical relationships by effective use of space and color coding may not be suitable for non- hierarchical data and it gets complex when there are deep hierarchies a strategic approach to report design in Microsoft PowerBI can create a clutter-free and engaging data story by having a clear objective maintaining a visual hierarchy implementing consistency and adhering to best practices in all design choices such as chart selection you can create a report that makes the best impression on the audience data is not just numbers it is a compass that guides you through the maze of business performance highlighting exactly where you underperform and where opportunities await a key performance indicator chart is one way to transform numbers into insights stories and to uncover hidden messages from raw data often used for sales marketing and customer service KPIs act as performance benchmarks measuring progress and identifying trends a KPI visual typically displays a single metric and its performance against a target or baseline this makes it easier for viewers to quickly judge performance and identify problems microsoft PowerBI has a built-in KPI visual but gauge charts and bullet charts can also be used to present KPI values kpi measures a value and shows trends and status the value is the main measure that you want to evaluate for instance current sales the element you want to compare the value with is the target for example the sales target the trend is how the value performs over time for example are the sale values going upward or downward the KPI visual can be adjusted from a desktop design to a version that works well on mobile devices to optimize a KPI chart for mobile devices keep the charts layout uncluttered use appropriate font sizes and contrasting colors focus on presenting the essential data points and avoid excessive decorative elements adventure Works wants insight into sales figures and an assessment of sales targets let’s design a sales performance KPI visual in PowerBI desktop and optimize it for mobile devices first launch PowerBI desktop and open the adventure work sales report to create a KPI chart to track sales performance against the target drag the total sales and target fields from the sales table to the report canvas powerbi automatically generates a column chart from these values you don’t need this chart so select KPI visual from the visualization pane to convert it to a KPI this action results in an empty chart with no data hover the cursor on the information icon the icon indicates that both values and trend axes are needed for this chart the three elements of the KPI chart are in the build visual tab of the visualization pane these elements are value target and trend to compare the sales values with the target add the total sales measure to the value section of the visual for the trend axis add months to view monthly sales trends remove the target values and drag the month field from the order date hierarchy to the trend axis this action generates a KPI visual that charts sales values by month it’s like creating an area chart with month as an axis and sales as values the main value indicated in the visual is sales but is this total sales or a filtered value the value represented at the center of the KPI visual is the last data point shown in the trend axis this means that if the trend is a month then this is the last month sales only in this report it’s the sales for December 2018 if the data set contains sales for multiple years then the value indicates the sales for December of all years if the data set contains the values for the full year then it’s for December but what if you only have sales for certain months access the visualizations tab then format visual visual and date turn on the date toggle to display the values date you’ve presented the sales data but must compare the value to the target drag the target measure from the sales table to the target section of the KPI visual adding the target generates color coding in the visual by turning the value and the area chart red an exclamation mark appears beside the value indicating that the sales values are behind the target the target is represented as the goal by default the percentage difference between the sales and the target is displayed in parenthesis which is minus 6.59% in the current report if the sales values meet or exceed the target then the color of the value and area chart turn green with a check mark next you must format the chart using font style and size changing color or adding background color for instance you can choose the sentiment color red as bad or red as good based on the nature of the value lastly optimize the KPI visual for mobile devices navigate to the view tab and select mobile layout drag the KPI visual from the page visuals pane to the mobile layout page positioning and rescaling the visual to adjust it the visual is now optimized for mobile devices a KPI chart represents the sales trend against the target value with the help of KPI visuals Adventure Works can identify which product region or sales representative is underperforming and as a result devise strategic decisions for performance improvement the key to revealing insights from raw data is using the appropriate visualization techniques have emerged using specific data types and analytical methods to produce tailored visualizations dotplot is one such visualization that is popular when presenting categorical data in relation to a numerical value to display the relationship between two numeric variables you can create a scatter plot that defines the correlation between variables a variation of a scatter plot is a bubble chart that can display the relationship between three variables the third variable represented in the size of a bubble a bubble chart is like a dot plot but instead of numeric data you use categorical information on the x-axis dotplot charts are a simple yet effective data visualization technique used to display the distribution of data points along a single axis in a dot plot chart each data point is represented by a dot and dots are stacked vertically above the corresponding data values on the axis this makes dot plots especially useful for visualizing the distribution and frequency of categorical data powerbi does not have any visual named dotplot or dot chart but you can create a dot plot by converting a scatter chart to a dot plot however there are certain custom visuals available in the PowerBI marketplace that are used to directly create dot plots in PowerBI let’s quickly check on a few reasons dot plots make such a useful chart type a dotplot chart is easy to use it is easy to interpret for non-technical users it’s particularly useful when visualizing categorical data giving a clear comparison between categories it displays the distribution and patterns in the data it can visualize a large amount of multi-dimensional data and it’s a compact chart that’s cell phone friendly adventure Works needs insights into regional product category sales performance they need to know the quantity sold for each category and the revenue per country the challenge is the number of variables to be presented in a single visual as a PowerBI analyst you can deploy a dot plot to present categorical information such as category or country on the x-axis sales on the y-axis and quantity as the size of the dot let’s jump into PowerBI and use a dot plot to analyze and visualize the Adventure Works information open the Adventure Works sales project the PowerBI core visualization pane has a no dot plot or dot chart visual so you need to begin with the scatter chart and convert it into a dot plot adventure works must present sales quantities country and category data drag the sales and total quantity sold measures from the key measures table to the report canvas powerbi autogenerates a column chart select the scatter chart from the visualization pane to convert the column chart to a scatter chart powerbi autofills the x-axis section with sales and the y-axis field with total quantities sold this is your scatter chart the sales data is numeric but you need to bring categorical data to the x-axis drag the country column from the region table to the x-axis field of the visual and move the sales data to the y-axis next drag the category column from the product table to the visuals legend section when I hover the cursor on a single dot in the chart a tool tip appears displaying the country category and sales amount for the category in that country to add more data drag the quantity sold measure from the key measures table to the visual size section the dot size changes in proportion to the quantity sold the tool tip now displays quantity information in addition to the previous data the chart still resembles a bubble chart to change it navigate to the format visual tab and expand markers in the shape drop-own list select the square dot you could also select distinct shapes for each category the dot size can also be adjusted here next format the aesthetics first add a chart title description then adjust the legend position legend title and font size format the axes to display clear labels and titles add and format the grid lines then add background color to improve the report’s accessibility select different shapes for each category finally you must add analytics lines select analytics in the visualization pane represented by a magnifying glass icon to display a range of different analytical lines expand the average line drop-down and select add line to add an average line to the chart format the line color and toggle the data label button to the on position to add average sales value data other analytical lines can be added to the chart as required adventure Works analytical needs were fulfilled by presenting categorical data in a single visual the dotplot chart allows you to visualize multi-dimensional data with more than two variables and categorical information instead of numerical values on the x-axis of the chart interactive visualizations breathe life into data revealing hidden patterns and relationships between variables powerbi’s core visualization pane offers a visual where numbers are transformed into dynamic bubbles bubble charts can depict multi-dimensional data in a single view making intelligent use of space in addition to the X and Y axes a third dimension of data is represented through the size of each bubble this approach enables you to highlight complex relationships between variables and identify patterns that might not be immediately evident in traditional two-dimensional scatter plots the bubble charts ability to convey multiple data dimensions simultaneously gives analysts and decision makers deeper insights into their data these insights can lead to more informed choices and strategies across a range of applications such as market analysis financial planning sales performance evaluation and resource allocation one example of applying a bubble chart effectively is in market analysis suppose you are analyzing the performance of various products within different markets the X and Y axis can represent market share and revenue while the bubble size corresponds to the total number of units sold by examining this data in a bubble chart you can discern valuable insights such as which products are dominant in specific markets based on market share and revenue and how sales volume relates to these factors highdensity data refers to data sets containing a substantial number of data points which can lead to visual clutter and hinder effective data interpretation with bubble charts you visualize data point density and use sampling techniques to manage data representation on the chart by adjusting the size of the bubbles or employing dynamic filtering options you can focus on specific areas of interest and maintain a clear and coherent chart despite the data’s complexity adventure Works wants to get insight into their data about the performance of different product colors the correlation between total revenue and profit margin the management wants to know the number of units sold of each product color sales profit margin product color and quantity together make the analysis and visualization challenging you can utilize a bubble chart in Microsoft PowerBI desktop to give all the required information in a single visual let’s transform those raw numbers into dancing bubbles of information and help Adventure Works make datadriven decisions about product colors the data model displays information on total sales and profit margin measures the product table has product color information to begin visualizing profit margin and sales select scatter chart from the visualization pane to add a placeholder visual to the canvas drag the sales and profit margin measures from the key measures table on the data pane to the x and y axis this generates a scatter chart with a single data point to make the chart more interesting bring a third data dimension to the chart fields this converts the scatter chart to a bubble chart then drag the color column from the product table to the legend field of the visual the tool tip now displays information about the total sales amount of a specific color product and the profit margin associated with that product color adventure Works needs to know the unit sold so bring the quantity sold measure from the key measures table to the size section of the visual another important feature of bubble charts is the play axis which you can use to animate your visuals drag the year field from the order date hierarchy from the sales table to the play axis now you can also analyze the data by year select play on the left side of the axis powerbi animates the bubbles to represent the variations in sales quantities and profit margins over the years next navigate to the analytics tab represented by a magnifying glass in the visualizations pane add a medium line based on sales and another for profit margin these chart lines provide analytics on the median sales and profit values the analytics pane provides interesting insights about the data now you need to format the chart first change the bubble shape and size to convey additional information and insights select visualization format visual visual and then markers in the shape dropdown change the shape of an entire series or individual categories in the size section adjust the size you can apply further formatting by changing the font style size and color adding background color and so on adventure Works can now visualize dense and multi-dimensional data in a compelling visualization to draw meaningful insights for future strategic plans in this video you discovered how a bubble chart delivered an engaging visualization to Adventure Works about the correlation between profit margin and sales based on the product color units sold and year you also explored the analytical capabilities of the bubble chart by adding the median and average lines to the chart to convey additional insights about the data you are working with a large data set when you discover that no one is interested in the data that’s a big surprise to you then you realize that it’s the insights people want presented not the data when dealing with data sets containing an abundance of data points presenting the information without overwhelming the viewer is vital in this video you will explore advanced display techniques in Microsoft PowerBI techniques such as presenting highdensity data using maps drills and 3D visualizations in PowerBI highdensity data is where you have a large amount of data points or values within a small area on a visual it often leads to visual clutter and makes it challenging to accurately interpret the visual some techniques to handle highdensity data include use aggregations and summarization drill through and drill down color coding such as heat maps and geographical maps and using 3D and custom visualizations let’s check some PowerBI visualizations that use these techniques and evaluate their potential for use in reports the first one to explore is heat maps heat maps are a powerful tool for visualizing the density and distribution of data across geographical regions or grids using color gradients to represent values heat maps allow viewers to quickly identify patterns trends and hotspots within large data sets for example imagine you are analyzing sales performance across various regions for Adventure Works a heat map could represent the sales figures using a color spectrum highlighting regions with the highest sales in vibrant hues while cooler shades indicate lower sales the heat map visualization is not available in the PowerBI core visualization pane you can import a heat map from PowerBI marketplace you can also use a Python-based heat map visualization in PowerBI you will learn about that option later in the course another visual to consider for highdensity data is called tree maps tree maps are ideal for displaying hierarchical data and comparing the proportions of data points across different levels in a tree map each rectangle represents a category and its size correlates with the proportionate value it represents this technique allows viewers to analyze the overall composition and the data point breakdown in a single visual for instance you can use a tree map to display the distribution of sales by product categories and subcategories within Adventure Works now let’s explore the functionality of drill through and drill down where analysts and viewers can dig deeper into the data a drill down in PowerBI allows users to move from a higher level of detail to a more granular level while a drill up does the reverse for example Adventure Work sales data is plotted on a time scale the viewers can use drill down to look at the sales data on a data hierarchy that goes from a year to each quarter to month and all the way down to a daily level there are two drill through situations to explain chart drill through lets users explore additional detail within a visual by clicking on specific data points for example in a bar chart representing sales figures for various products at a summary level selecting a specific bar say product 3 can trigger a drill through action revealing a detailed report highlighting sales trends in various regions product details and customer information related to that specific product page drill through allows users to navigate to a different page with associated information this advanced technique is especially valuable for creating summary pages with high-level insights while two-dimensional visualizations are more popular 3D visualizations can offer a new dimension of insights for instance a 3D scatter plot can showcase the distribution of products with a three-dimensional space revealing potential correlations and patterns such as a presentation of a product’s performance based on three parameters: price sales volume and customer satisfaction a 3D map can present data points in an interactive three-dimensional map space 3d mapping adds a sense of depth and realism to geographical data making it easier for users to identify spatial trends and analyze data use Microsoft PowerBI’s advanced display techniques to extract insight from large complex data sets while considering enduser requirements master highdensity data display drill through capabilities and the world of 3D visualization to improve your PowerBI reports and deliver impactful insights do you only access your social media accounts from a desktop computer no like most of us you probably spend most of your internet time on a mobile device accessing data on the go has become the norm decision makers expect to be able to access critical information anytime anywhere as a report creator you must be able to optimize report layouts for mobile devices that way you ensure your insights appear on smaller screens without losing clarity and usability creating a mobile friendly report layout involves careful consideration of visual placement font sizes and content organization to do that use the tools and settings in the mobile layout canvas of Microsoft PowerBI when optimizing a report for mobile one of the key considerations is responsive design a responsive layout automatically adjusts to fit different screen sizes and orientations ensuring that the report looks and functions optimally on various mobile devices such as tablets and smartphones the adaptability is crucial as mobile devices come in various screen sizes it ensures report access without the user needing to zoom or scroll horizontally another critical aspect of mobile optimization is the selection of visuals and data presentation not all visuals are suitable for mobile viewing due to their complexity or size you must choose visuals that convey essential insights while maintaining readability on smaller screens simplified visuals such as line charts bar charts and KPI cards are often preferred for mobile layouts as they can present data clearly font sizes play a crucial role in mobile optimization text that appears legible on a desktop monitor might become challenging to read on a smaller mobile screen use appropriate font sizes that ensure readability without straining the user’s eyes headers and labels should be clear and concise while data points should have sufficient spacing to avoid clutter in addition to visual elements interactivity is another aspect to consider when optimizing mobile devices you must choose visuals that convey essential insights while maintaining readability on smaller screens some interactions such as tool tips and drill through actions may work fine on desktops but might not translate well to touch-based mobile devices test and adjust interactions to ensure a smooth and intuitive mobile user experience as a best practice testing your mobile optimized report on various devices is crucial to identify potential issues and ensure consistency across different platforms emulating different mobile devices or using responsive design testing tools can help verify the reports performance and appearance on various devices adventure Works executive management wants to visualize its product sales summary it must be a mobile friendly sales summary dashboard so that it can be accessed anytime anywhere let’s use PowerBI desktop to optimize the Adventure Works sales summary report for mobile viewing before optimizing a report for mobile it is essential to review its current layout and design you need to identify elements that may not translate well to smaller screens and those that require adjustments to maintain readability and user friendliness let’s optimize the adventure work sales summary report for mobile devices the report contains one column chart representing the yearly sales amount a donut chart displaying sales by country or region and two card visuals showing sales and profit to begin navigate to the view tab and select mobile layout the mobile layout page has three panes: visualizations page visuals and mobile layout the page visuals canvas displays all the visual elements of the original report the mobile canvas has a precise grid layout for rescaling and repositioning the visuals on the screen with snap to grid functionality additionally you can select the checkbox lock objects from the view ribbons page options this action locks the visual elements in place to avoid any accidental movement use this once you are satisfied with the position and scale of your visual next drag all visual elements from the page visual pane and drop them to the mobile canvas one at a time first move two card visuals to the mobile canvas align both cards to the top side by side of the mobile screen now the main values on the card visuals are no longer visible so navigate to visualizations then visual expand the call out and in the value section change the font size to 18 in the label section change the font size to 12 in the spacing section change the vertical spacing to five pixels you can adjust font size independently for mobile and desktop versions of reports repeat this formatting for the second visual make some fine adjustments in positioning and scaling of the cards to optimize the readability and design next drag and drop the column chart to the mobile canvas enlarge the chart to fill the screen size and align it below the two card visuals finally move the donut chart to the mobile canvas enlarge it to fill the screen below the column chart in the mobile layout the donut chart legend values are not completely visible a small arrow is visible on the right end of the legend this suggests navigating for more information navigate to visualizations visual and expand legend in the position drop-own menu select center left you can also adjust the font size if necessary this changes the position of the legend from the top to the left all values are now visible without further navigation you can perform more adjustments for scaling the visuals and aligning them in the mobile layout screen the Adventure Works sales summary report is ready for anytime anywhere access on mobile devices optimizing report layouts in Microsoft PowerBI for mobile devices is an essential step in meeting the needs of today’s onthe-go business environment the world of data visualization continues to evolve and Microsoft PowerBI is at the forefront of introducing innovative ways to present and interpret data one of the latest additions to PowerBI’s visualizations is the shape map a feature that allows users to create geographic visualizations to uncover insights from geographical data in this video you will delve into the concept of shape map visuals their purpose and cover a step-by-step guide on how to add and configure them in your PowerBI reports adventure Works have recently expanded into territories across the globe as an analyst you realize the traditional table and chart visuals might not effectively communicate the geographical aspects of analysis you can use shape map visuals in PowerBI to better represent geographical and sales data to better showcase data topics such as population density competitor location and market demand across different regions a shape map visualization empowers users to tell stories using geographical data unlike traditional map visuals that plot data on a geographical map shape maps go a step further by enabling users to work with custom regions or shapes such as countries states or provinces sharing your report with a PowerBI colleague requires that you both have individual PowerBI paid licenses or that the report is saved in premium capacity powerbi Premium provides extra features like the ability to store more data cloud features and improved performance for PowerBI workspaces you can also use it to deploy reports and data sets and share content with users reliant on free licenses let’s help Adventure Works to craft a shape map visual to better present their performance across various geographical territories the shape map visual is only available in PowerBI desktop and in preview mode since it is in preview it must be enabled before you can use it to enable the shape map you need to select file options and settings options global preview features then select the shape map visual checkbox followed by okay you will then need to restart PowerBI desktop after making this selection now you need PowerBI to display the Adventure Works shape map visual the data set contains two fields sales and states these fields contain state names and corresponding sales amounts in PowerBI desktop after the shape map visual is enabled you select the shape map icon from the visualizations pane to add a shape map placeholder to the report canvas after adding the shape map to your report canvas you should add data to the data fields drag the state field to the location well and drag the sales field to the color saturation well of the map visual you can select the view tab to change the color scheme to a more accessible one such as accessible city park if you have an additional data set like product category or product color you can move them into the legend well to create a divergent color in this case as there is no category available in the data set you can apply gradient colors to the map go to format visual visual fill colors and turn the gradient toggle to the on position then add light blue for the minimum purple for center and black for the maximum you can also change the border color to black and three width now you need to display the map keys select the map settings dropdown then view map type key this action opens a dialogue that lists the map keys these keys are for US states you can change the map type to view keys for other countries if required the next option in this menu is projection you can use this option to present a 3D object on a 2D map powerbi selects Alber’s USA map style by default but three other options are available one option is equi rectangular this is a cylindrical projection that converts the globe into a grid each cell in the grid has the same size shape and area merc is another option this is a cylindrical projection with the equator depicted as the line of tangency polar areas are more distorted than equictangular projections and finally there’s orthographic this is a projection from an infinite point as if from deep space it gives the illusion of a three-dimensional globe next you’ll access the zoom dropdown and toggle on the zoom on selection and manual zoom options these options allow you to zoom in on states when selected finally to format the chart title access the general tab then expand the title drop-down and use the design effect options to change the title’s properties as required in this video you learned about shape map visuals discovered their purpose and explored a step-by-step guide on how to add and configure them in your PowerBI reports you specifically learned how to create a shape map visual with color coding to represent the sales amount for Adventure Works cororoplathth maps also known as filled maps stand out as a powerful tool for representing and analyzing spatial patterns by color coding geographical regions based on data values Cororopath maps offer a compelling way to visualize variations in data across different locations in this video you will explore the fundamental aspects of Cororoplathth maps their use cases and examples of the type of data best suited for this visual format adventure Works executive management realizes that simply looking at raw data in a tabular or columner format is not sufficient to comprehend the regional distribution of scales they need a visual that instantly communicates the variations in sales across various geographic regions as an analyst you can resolve this issue by employing the Cororapath map visual in PowerBI which allows you to present sales data on a geographical map with color-coded regions to indicate sales performance across various territories a cororoplath map is a geographic representation in which areas such as countries states or regions are shaded or patterned to illustrate quantitative data values each region on the map is assigned a color or pattern that corresponds to a specific data value allowing viewers to identify patterns and trends instantly the intensity of the color or pattern represents the magnitude of the data value enabling easy comparisons and highlighting regional disparities corroplath maps are most effective when the data being visualized has clear geographic boundaries when designing a cororopath map it is crucial to carefully select colors or patterns that are easy to interpret and distinguish using a color scale that smoothly transitions between values can enhance readability it is also essential to provide a clear legend or data scale to help users understand the relationship between colors or patterns and the corresponding data values now let’s consider some detailed use cases for cororoplath maps cororoplathth maps are ideal for visualizing population distribution across different regions by shading regions based on population density or total population you can quickly identify densely populated areas and areas with sparse populations corroplath maps are widely used to showcase various economic indicators such as GDP per capita unemployment rates or poverty levels across different geographic regions this helps policymakers and economists in understanding the economic disparities and making informed decisions corropath maps are valuable in displaying health and education related metrics such as disease prevalence vaccination rates literacy rates and school enrollment levels they provide insights into regional health and education challenges and aid and resource allocation cororoplath maps can effectively display environmental data such as air quality temperature variations or levels of pollution these maps help environmentalists and policy makers in assessing environmental conditions and devising appropriate conservation strategies but how can a cororoplath map best help adventure works in their business activities one example is to break down sales performance data per country as well as per state within those countries in this example of the United States states with higher sales are represented by darker shades while lighter shades indicate lower sales corropath maps offer a captivating way to explore and comprehend data patterns through geographic visualization their ability to showcase variations in data across different regions makes them a popular choice for a wide range of use cases from health economic indicators environmental data and population distribution with Cororoplath maps data analysts researchers and policy makers can gain valuable insights and make datadriven decisions with geographical context as an essential tool in the data visualization toolkit cororoplath maps assist in deeper understanding of the world around us cororopath maps have become an essential tool in data visualization for representing and analyzing data in a spatial context cororoplath maps also known as field maps are particularly effective in displaying quantitative data across geographical regions in this video you will explore the steps to create and utilize field maps in PowerBI focusing on a scenario involving the Adventure Works company by the end of this video you will have the skills to configure and display data on a cororoplath map allowing you to transform complex data sets into insightful visualizations before diving into creating a cororopath map it’s crucial to know how to select the appropriate data for analysis in the context of adventure works let’s consider a scenario where the company wants to understand the sales performance across different regions in a specific country the data should include at least two columns one representing the geographical regions and the other containing the relevant quantitative data such as total sales revenue or profit corresponding to each region in PowerBI creating an effective data model is the foundation of any compelling visualization the data should be structured in a way that PowerBI can understand the relationship between the geographical regions and the quantitative data you must ensure that the columns representing regions are in text format and contain matching names or codes for the regions present in the map data visualization similarly the quantitative data should be in numerical format for accurate analysis with the data model ready it’s time to create a corropath map visual in PowerBI to achieve this you can navigate to the visualizations pane and select the filled map option and PowerBI will automatically detect the columns representing the geographical regions and the quantitative data and position them on the respective fields to enhance the visualization and make it more meaningful you can customize the coroplath map further powerbi offers several customization options to help you fine-tune the visual representation for example you can adjust the color scale to highlight different intensity levels of the data making it easier to interpret variations additionally you can format the map’s title legend and other visual elements to suit your report’s aesthetics and readability let’s apply the steps mentioned above to a specific scenario involving Adventure Works a multinational bicycle manufacturer the company wants to analyze its sales performance across various states in the United States and identify regions with the highest and lowest sales for the very first step map and cororopath map visuals are disabled you must enable them by accessing file options and settings options global then security then check use map and filled map visuals the Adventure Works data set contains two relevant columns state for the geographical regions and sales for the quantitative data representing sales revenue in each state you must ensure that the state column is formatted as text and each state name matches the corresponding states in the map data visualization similarly the sales column should be in numerical format in this instance you will format it as currency you can select the visualizations pane and click on the filled map icon drag the state field to the location well and sales to the tool tip well of the visual to apply the color coding to the map visual go to visualizations format visual and then visual select fill colors and then select the FX icon to apply conditional formatting in the conditional formatting dialogue box add three rules for the color coding of the map based on sales values based on the data the maximum sales value is $400,000 and the minimum value is $81,000 so you can define the following rules rule one all sales values between $80,000 and $149,000 must be colorcoded yellow rule two all sales values between $150,000 and $249,000 must be red rule three all sales values between $250,000 and the maximum value must be purple you then expand the map settings in the style drop-own list you will select a map style powerbi has five styles: Aerial dark light grayscale and road you will select the aerial map style expand the controls option and turn auto zoom to the off position turn the zoom buttons and lasso tool to the on position this gives you control over zooming into a specific area of the map to make the corroplath map more informative you can customize the color scale to represent varying sales levels across states regions with higher sales revenue can be displayed in darker shades while regions with lower sales values can be represented in lighter colors formatting the map title and adding a meaningful legend will help convey the information more effectively lastly you can access the general tabs title dropdown to format the title of the visual and apply other effects as required cororopath maps are powerful tools that empower businesses to visualize and understand data across geographical regions with their ability to display data variations using color intensity these maps provide valuable insights into spatial patterns and trends by following the steps outlined in this video and applying them to a scenario involving adventure works you can master the art of configuring and displaying data on a corupath map in PowerBI in the ever evolving landscape of data visualization map visuals have emerged as powerful tools for presenting geographical data in an engaging and informative manner powerbi Microsoft’s robust business intelligence platform offers a range of features to create compelling map visualizations that can reveal insightful patterns and trends in this video you will explore essential tips and tricks to optimize your map visualizations in PowerBI ensuring that you leverage the full potential of your geographical data map visualizations hold the potential to unlock a wealth of insights from your data especially when dealing with geographical information however it’s essential to optimize these visuals to effectively communicate your insights to your audience adventure Works operates in multiple stores across different cities and states the North American sales manager asks you to present a report of sales for various states and cities as a PowerBI analyst your task is to create a comprehensive analysis of sales across various regions using map visuals a single layer of analysis in map visual might only provide a summary level of information about sales to dig deeper into states and cities you need to create geo hierarchy and map visual of PowerBI let’s go through adventurework sales data and create a geo hierarchy using filled map visuals in PowerBI launch PowerBI and open the project adventurework sales.pbix report the report contains two data tables a fact internet sales table and a geography table in map visualizations defining a precise location is especially important this is because some designations are ambiguous due to the presence of one location name in multiple regions for example there is a Southampton in England Pennsylvania and New York adding longitude and latitude coordinates solves this issue but if the data set does not have this information you will need to make sure to format the geographical columns as the appropriate data category select the country column from the geography table and navigate to column tools then properties in the data category dropdown select country format the data category for a state province name and city columns as state or province and city respectively a global icon appears before the field name this tells PowerBI that this is a geographical data type you will collapse the geography table and expand the fact internet sales table you then select the sales amount column from the fact internet sales table and format the data type as currency within two decimal places select the field map icon from the visualization pane to place a map placeholder in the report canvas you can then enlarge the placeholder to create the geo hierarchy drag the country state province name and city columns from the geography table to the location field of the map visual make sure the order of the fields is country then state province name and finally city next drag the sales amount field from the sales table to the tool tip field of the map visual to differentiate the states based on the sales you should color code the map open the conditional formatting dialogue box by selecting the FX icon from the fill colors in the conditional formatting dialogue box select yellow for minimum red for center and purple for maximum the data set contains sales data of various countries but you only want to present sales data for the United States expand the filter pane and under the country option select United States adding depth to map visualizations leverages geo hierarchies you can drill down from country to state state to city and so on at the top right corner of the map visual in the report canvas are arrow icons these arrows represent the drill down functions used to access the hierarchy of the data first select the downward arrow to turn on the drill down function when the drill down mode is on the arrow is highlighted with a black background now select the downwards double parallel arrow to go to the next level of the hierarchy in the current example selecting the double arrows takes us to the US country level alternatively you can also select the country on the map to go to the next level of the hierarchy you can then hover the cursor over California the tool tip displays the sales value for the entire state in the tool tip is a drill up and a drill down text with icons you can select these icons to either go one step up or one step down in the hierarchy select drill down to access the city level it is important to note that the color of the drill down will be the same color as the higher level view so it may need to be modified for accessibility purposes at the city level the tool tip displays all data from country to city with relevant sales amounts there’s no drill down option because city is the last level of the hierarchy in this report however you can create a more granular hierarchy by adding postal code and stores to the location save the project to your local computer making sure to apply all changes before exiting PowerBI you should now understand how to use data to create geo hierarchies powerbi map visualizations are a powerful and dynamic tool for data analysts seeking to explore understand and communicate geographic data in this video you’ll learn to explore the map visuals interface and display and configure a map adventure Works has created a filled map visual with geo hierarchy let’s help the company format this map by exploring the control options PowerBI offers you launch PowerBI and open the file adventurework sales.pbix go to visualizations and select format visual then visual then expand the map settings dropdown in the style dropdown you can select from the five map styles supported by PowerBI road style is selected by default let’s select aerial from the drop- down list expand the control section to reveal the three zoom options auto zoom zoom buttons and the lasso button auto zoom is automatically turned on you must also turn the zoom and lasso buttons to the on position this provides more control over the map to highlight a specific region the last option in map settings is geocoding culture by default PowerBI sets it to auto leave it as it is to further format the colors of the map visual open the conditional formatting dialogue box where you can modify the colors as needed with the current selection these colors represent the sales data across various states and cities yellow represents the states with the lowest sales values purple represents the states with the highest sales values next you can rename the labels and titles to make the visual clutter-free and help users identify specific places on the map double click on the state province name field in the location well of the map visual and rename it as state in the tool tip field rename sum of sales amount to sales go to visualizations format visual and then general change the title of the map visual to a more descriptive title like sales distribution by location you can configure and format the information that appears when you hover over a specific region on the map expand the tool tips option scroll down to the background and change the color to light green you can use the other options to further format the style and size of the data displayed on the tool tip you have now created a filled map with geo hierarchy and explored the various control and formatting options in PowerBI remember presenting information alone is not sufficient you must also use formatting and design to create engaging dashboards and reports in PowerBI in this video you learned how to explore the PowerBI map interface and display and configure a map powerbi offers various visualization options to display geographical data effectively two popular choices for mapping data are shape maps and filled maps known as corroplets both of these visualizations enable users to present geographic data in a visually engaging and informative manner in this video you will delve into the key differences between these two map types exploring their unique features use cases and the data they utilize as a business analyst working at Adventure Works you need to present regional sales data across different countries in PowerBI you have two options to choose from: filled maps or shape maps a filled map allows you to display color-coded regions based on a metric like sales for various geographical areas while shape maps provide more flexibility for customization the final selection should be based on the visualization requirements shape maps provide a platform for users to create their own custom visualizations by importing geographic data in the form of vector files the vector files used in shape maps are typically in the top too JSON format which is a file format used for storing geographic data topojson files allow for compact and efficient data representation as it reduces the data size and loading times in web applications and visualizations with shape maps users can visualize regions countries states or even custom territories by utilizing their own data sets there are three key features of shape maps to consider: customization precision and data complexity through customization users have the flexibility to use their data and design custom regions based on unique geographical boundaries or territories with precision shape maps can accurately represent non-standard geographic regions that are not predefined in standard geographical data sets by handling data complexity since users provide their geographic data shape maps are ideal for visualizing intricate boundaries and smaller regions filled maps or corropathlets are a type of map visualization that leverages predefined geographical boundaries provided by PowerBI’s
built-in mapping capabilities users can assign data values to regions represented by the map’s predefined shapes filled maps use color shading to represent data values allowing users to visualize data distribution across various regions the key features of Cororoplath maps are simplicity filled maps offer a straightforward approach to map visualization as they utilize predefined shapes without requiring additional custom data sets quick insights with field maps users can quickly gain insights into data distribution and patterns across various regions bing maps integration filled maps benefit from Bing Maps extensive geographic database providing accurate and up-to-date boundary information there are four main differences between shape and filled maps let’s consider these differences and how this would impact on your decisions when working with geographical data the primary distinction between shape maps and filled maps lies in their data sources and customization options while shape maps allow users to import their custom geographic data filled maps utilize predefined geographical boundaries from Bing maps this difference impacts the level of customization and the ability to visualize specific non-standard regions imagine Adventure Works wants to visualize its complex sales territories each with unique boundaries defined by the company’s specific business needs in this scenario shape maps will be a better choice with Shape Maps Adventure Works can import its custom geographic data creating precise and granular visualizations that accurately represent their sales territories the ability to use custom-defined administrative boundaries ensures that Adventure Works can tailor the map to its unique requirements making shape maps the perfect choice for this task shape maps represent data by associating values with custom regions created by users offering precise and granular visualizations filled maps use color gradients to represent data values within predefined regions providing a more generalized view of data distribution across larger geographic areas adventure Works wants to show its sales densities across different regions they want to get a quick high-level overview of how sales are distributed with field maps Adventure Works can quickly assess sales densities by country or region using color gradients providing insights without the need for customdefined boundaries shape maps are best suited for scenarios that require complex geographic representation such as visualizing sales territories customer distribution or customdefined administrative boundaries filled maps with their simplicity and quick insights are ideal for showcasing highle data patterns such as population densities sales performance by country or regional sales growth field maps benefit from Bing Map’s geographical database which ensures accurate and up-to-date boundary information this integration simplifies the process of creating visualizations especially for users who do not have access to specialized geographic data sets adventure Works faces a challenge they want to showcase sales performance by country highlighting regional sales growth but they also want to maintain a level of precision here’s where the choice between shape maps and filled maps becomes crucial shape maps with their custom regions could offer the precision needed to visualize specific sales trends however if a more generalized view is acceptable filled maps can quickly provide insights across larger geographic areas striking a balance between detail and simplicity in conclusion shape maps and field maps are two valuable map visualization options in PowerBI each catering to different use cases and data requirements in the realm of data visualization geospatial information can be a gamecher the ability to visualize data on maps not only adds context but also unlocks new layers of insights powerbi offers a range of map visualizations and one standout feature is its integration with Azure maps azure maps are part of the broader Azure location-based services family also called Azure LBS they provide a comprehensive platform for building geospatial solutions including mapping searching routting and traffic services azure maps visual provides a rich set of data visualizations for spatial data on top of a map it connects to a cloud service hosted in Azure to retrieve location data such as map images and coordinates that are used to create the map visualization it has several advantages compared to other map visualizations including seamless integration with Azure services advanced geospatial features scalability performance enterprisegrade security and developer friendliness details about the area are sent to Azure to retrieve images needed to render the map canvas also known as map tiles data in the location latitude and longitude buckets may be sent to Azure to retrieve map coordinates a process called geocoding in this video you will delve into what Azure maps are how to add them in PowerBI and provide a step-by-step guide to set up and configure an Azure map for Adventure Works competitor analysis by state now you will learn Azure maps and its usage in PowerBI reports you are working as a data analyst in Adventure Works company and you have public sales report data from a competitor you will configure an Azure map for Adventure Works competitor analysis by state you can enable the Azure Map PowerBI visual by selecting the Azure maps icon from the visualizations pane a disclaimer text appears on the screen regarding Azure Maps use of data access model view to view the data model tables the data model contains three data tables a reseller sales fact table a geography table and a reseller dimension table all these tables are related by one to many relationships you return to report view drag the country field from the geography table to the location well of the Azure map visual then drag the reseller measure from the reseller dimension table to the size well of Azure map visual the bubble size proportionally represents the number of resellers in each region to further analyze the reseller for each product line of Adventure Works drag the product line field from the reseller dimension table to the legend well of the visual this adds color coding to the bubble and displays the number of resellers for each product line in each country you can create a geo hierarchy by bringing other fields from the geography table to analyze the granular data further however in this video let’s just focus on the country level next let’s explore some formatting and control settings go to visualizations format visual visual and then map setting you can select the style of the map from the style dropdown select road from the available options in the bubble layer section you can configure the size shape and color of the bubbles the bubbles minimum size is very small so let’s change the size to 15 pixels in the size option of the bubble layer change the color of each bubble slice based on the product line you will also add category labels to the map for accessibility let’s increase the font size to 12 and reduce transparency to 25% lastly you can format the Azure Map title color text style and so on by following the steps outlined in this lesson you can seamlessly add configure and utilize Azure Maps to perform advanced analysis as you continue to explore the possibilities of Azure Maps and PowerBI you’ll be empowered to create compelling visual narratives that go beyond numbers helping you make informed decisions driven by location intelligence cycling is a peaceful and calming leisure activity that anyone can enjoy many people use their bicycles to get outdoors and enjoy the countryside or to go on camping trips with friends but in the business of bicycle manufacturing it’s a constant battle to grow sales and find new markets one way Adventure Works seeks new opportunities is by using data analysis it recently conducted some competitor analysis and that data tells an interesting story its main competitor is performing really well in specific European regions that’s an intriguing insight but the big question is what is the reason for that success what is it about the market that makes it different from elsewhere and is it something that Adventure Works can learn from does it have a product to satisfy the demand in this region the Adventure Works team does some more research to figure out what their competitor is doing right they check on sales volumes the products that do well and the areas of Europe that are supplied by competitors an analysis of competitor marketing tactics reveals that they’re selling to a specific young female demographic in particular regions they’re using a lot of focused social media marketing to get their message to the target audiences the findings point to the frustrations that young female cyclists have with their choice of bike types for city and suburban commuting to bring more depth to the data insights Adventure Works decides to analyze city demographic data where its competitors are most successful focusing efforts on these areas leads to the discovery that there are market demographics that are a perfect match for some Adventure Works products so what can Adventure Works do to compete in the identified regions and markets to find out more the team dive further into the demographic and marketing data the data analysis team then uses the data discoveries to create geographical visualizations the visualizations identify patterns and trends that can lead them toward the development of a new marketing strategy finally it’s time to present the new market plan to the company’s management team examining the new report of the targeted regions it compares the data to its own target audience for bike ranges adventure Works uses the collected data to design their own strategy to target a similar demographic the marketing staff brainstorm ideas for social media adverts influencers and other marketing tactics in areas that the target audience is spending most of their time jaime the CEO believes it has the potential to be very successful and is confident that this plan will help compete with her rivals in these regions data analysis is a powerful tool to help discover new business markets creative use of chart visuals and map visualization can help identify new opportunities and grow business through sales data analysis and competitor data analysis Adventure Works identified a market that they had not yet entered but competitors were already performing well in by the visual analysis of data it found market segments that matched its product line this was valuable insight and led it to new customers and new regions that have a high potential for continued growth powerbi offers several core visuals readily available on the visualization pane but what if the type of visualization you require doesn’t exist in PowerBI you can create it with custom visualizations in this video you’ll explore what custom visualizations are why they matter and how to create them adventure Works needs a visualization to explore its sales data however none of the existing visualizations in PowerBI are appropriate so Adventure Works needs a custom one find out more about custom visualizations then help Adventure Works build its own so what are custom visualizations custom visualizations are userdefined visual elements that extend the capabilities of PowerBI beyond the built-in visual options they enable you to create unique tailormade visuals that cater to specific business and visualization requirements enhancing data’s clarity and impact but why do visualizations matter because of their ability to help address unique needs every organization has its unique analytical requirements with custom visualizations you can create visuals that directly resonate with your organization’s specialized needs custom visuals also offer insights that standard visuals might not be able to convey as effectively this can help you uncover the trends and patterns hidden within your data for example through its custom sales data visuals Adventure Works might discover that it sells more bicycle repair equipment in the winter months custom visualizations can be installed in PowerBI from different sources you can import custom visuals created by developers from the PowerBI marketplace certified PowerBI visuals are available in AppSource microsoft or its partners develop these visuals which can be downloaded from PowerBI desktop you can create custom visualization in PowerBI using Python or R programming languages these visualizations are imported from a file on your local computer you can also develop PowerBI visuals to meet your analytical or aesthetic needs if developing in R or Python then it’s recommended that you use an integrated development environment or IDE such as Visual Studio Code also known as VS Code python is a powerful open-source programming language often used for data analytics it’s very versatile and offers a rich ecosystem it’s beginnerfriendly and backed by community support making it a great language for data professionals it also offers pre-written code bundles or libraries for creating visualizations like Seabor and Mattplot lib using R or Python to develop your own PowerBI visuals or to customize existing ones is an optional expertise you may wish to pursue it if you have a coding background a familiarity with Python or want to extend your skill set into this area before creating a visualization you need to load some data for it luckily Python has built-in data set examples that can be imported and can be used to create new data sets for this demonstration Python has already been installed in PowerBI and the relevant libraries and data sets have been imported so the first step I need to take in PowerBI desktop is to enable Python scripting i navigate to file and select options and settings then select options this opens options where I can select Python scripting always ensure PowerBI has detected the Python installation path under detected Python home directories if you need to you can copy and paste the path from your Python installation i select okay now I am ready to use Python and PowerBI python and PowerBI is used in two ways the first purpose is to import data the second is to create custom visualizations let’s explore the first method and import some data python libraries contain sample data sets that you can import to PowerBI i navigate to the get data dropdown and select more this opens the get data dialogue in the search bar I write Python the Python script appears on the right side of the window i select Python script and then select connect a Python script dialogue box appears on screen from here you can write a Python script to import sample data from Python libraries for instance I can write a Python script to import your data set into PowerBI desktop the code creates a data frame by importing the pandas package of Python with the required columns and associated values once I execute the code PowerBI opens the navigator window with a data set named sample data set the data set appears under the data pane on the right side of the PowerBI interface when I select load to load the data set it can now be used to create visualizations in PowerBI powerbi offers a wide range of core visualizations custom visualizations provide several unique advantages that contribute to more effective data communication improved insights and tailored solutions python with its rich set of libraries and ability to handle data manipulation visualization and machine learning tasks make it an essential tool for data professionals as a data analyst it’s important to be able to extract the insights you need from your data and engagingly present them integrating Python with PowerBI allows you to explore your data more deeply to reveal further insights and present the data through sophisticated visualizations in this video you’ll learn how to add a Python-based visualization to PowerBI Desktop adventure Works is analyzing its data sets and realizes that the core PowerBI visuals don’t provide a comprehensive view of its data you can help the company generate a more sophisticated analysis by leveraging a Python-based visualization in PowerBI let’s learn more about adding a Python-based visualization then help Adventure Works python is a powerful scripting language that relies on libraries these libraries like mattplot lib and seabor can be integrated with powerbi to create dynamic and sophisticated custom visualizations although python provides useful features and libraries it still has a few limitations and it’s important to be aware of these limitations before designing visuals python’s data set size is limited to 150,000 rows and has an input limit of 250 megabytes all data fields from different tables must have defined relationships between them or you’ll encounter an error python visuals refresh after each update filter or highlight external Python scripts might raise security concerns using R or Python to develop your own PowerBI visuals or to customize existing ones is an optional expertise you may wish to pursue it if you have a coding background a familiarity with Python or want to extend your skill set into this area to get you more familiar with custom visualizations let’s demonstrate a Python custom visualization in PowerBI desktop for this demonstration Python has already been installed in PowerBI and the relevant libraries and data sets have been imported so the first step is to create a visualization using the imported sample data set i navigate to visualization pane and select the Python visual icon this opens a dialogue called enable script visuals select enable a placeholder for a Python visual image appears in the report canvas and a Python script editor appears at the bottom of the report page a Python script can only use fields added to the value section by creating a data frame you can add or remove fields while you work on your Python script powerbi desktop automatically detects field changes as I select or remove fields from the value section supporting code in the Python script editor is automatically generated or removed i drag all the fields from the sample data set table to the value section of Python visual based on the selection the Python script editor generates the code the editor creates a data set called dataf frame with the fields I added to the value section duplicate rows are removed from the data and the fields are grouped the first visual will be a scatter plot graph that generates insights between the age and weight fields of the sample data set in the Python script editor I write the code to draw a scatter plot graph that measures age on the x-axis and weight on the y-axis i execute the code to import the mattplot lib Python library which creates the plot finally I select run from the top right corner of the Python script editor title bar to generate the Python visual on the report canvas next to generate another Python visual using Adventure Works data I open the Adventure Works Sales PowerBI project the data model contains four related data tables: sales products salesperson region i make sure the data tables relate to each other using appropriate relationships without these relationships you cannot use the fields from the different tables to create Python visuals the visual required for Adventure Works is a bar chart of total sales by each country to create this visual drag the total sales field from the sales table and the country field from the region table to the value section of the Python visual the editor creates a data set called dataf frame with the fields I added to the values section duplicate rows are removed from the data and the fields are grouped to create a column chart I write the Python script under the paste or type your script code here then I run the script the script draws a plot with total sales on the y-axis and country on the x-axis the script imports the metplot lib visualization library which generates the bar chart you can customize the visuals for color size data values and other attributes by modifying the Python code or importing other libraries that’s an example of creating Python-based visuals in PowerBI both by importing and with Adventure Work sales data set integrating Python with PowerBI helps to move a sophisticated data analysis to a compelling presentation however even though Python-based visualizations expand the capabilities of PowerBI they also have some limitations to consider such as Python’s limited data set size and they do require specialist expertise to implement in PowerBI welcome to this highle recap of the concepts and techniques covered this week this summary will help you revise the lessons on the design of powerful report pages during the course simulations of adventure work scenarios were used in videos and exercises these scenarios are designed to facilitate understanding and provide relatability the items we will review are clarity and visual impact accessibility considerations for Microsoft PowerBI creating and formatting KPI and dotplot charts how to visualize highdensity multi-dimensional data map visuals such as corroporath and shape maps and custom visualizations including adding a Python-based visualization in the first lesson on visual clarity in reports you learned to transform raw data into a story using charts and graphs that expressed the essential narrative of your data charts data and visuals are all crucial components of the clarity and visual appeal of data visualization selecting the correct chart type simplifies complex information making it easier for stakeholders to understand your presentation design with your audience in mind consider how familiar they are with data visualizations and then select visuals and chart types that are appropriate for their background and experience you must use your design ability to create visual impact and clarity one technique to use to do this is to eliminate clutter when building reports and visualizations don’t neglect accessibility produce reports that can be easily used and understood by all individuals including those with disabilities production should include alt text for visuals sufficient color contrast keyboard navigation and compatibility with screen readers the key areas of impactful report creation include deciding on the report objective establishing a visual hierarchy using branding and themes carefully composing the report employing storytelling techniques and optimizing the report performance for the best user experience when deciding on an appropriate chart type consider recommended use cases for the chart its strengths and its limitations by having a clear objective maintaining a visual hierarchy implementing consistency and adhering to best practices in all design choices such as chart selection you can create a report that makes the best impression on the audience kpi charts are often used to illustrate performance benchmarks measure progress and identify trends you can use the Microsoft PowerBI built-in KPI visual or use gauge charts and bullet charts to present KPI values dotplot charts are used to visualize the distribution and frequency of categorical data by displaying data points along a single axis for instance you can use a dot plot to represent category information on the x-axis sales on the y-axis and sales quantity as the size of the dot bubble charts depict multi-dimensional data in a single view for instance to analyze the performance of various products in different markets the X and Y axis represent market share and revenue while the size of the bubble is related to the total number of units sold with bubble charts you visualize data point density and use sampling techniques to manage data representation on the chart when creating reports PowerBI has many built-in capabilities that support ease of use and help your productivity they include app navigation ribbon navigation and navigation and key panes such as the visualization pane and the selection pane as a designer should you have any other disabling factors you have accessibility options that allow you to operate and design in Microsoft PowerBI you explored advanced display techniques in Microsoft PowerBI such as techniques to present highdensity data and the use of maps drills and 3D visualizations for instance you could use a heat map to illustrate sales figures using a color spectrum a tree map to display hierarchical data and compare data point proportions for sales data plotted on a time scale users can use drill down to look at the sales data on a data hierarchy that goes from a year to each quarter to month and all the way down to a daily level powerbi gives you the ability to use chart drill through and page drill through is a technique for creating summary pages with highle insights 3d visualization such as 3D mapping adds a sense of depth and realism to data making it easier to identify trends and analyze data as a report creator you must optimize report layouts for mobile devices to ensure reports display properly on mobile screens one of the key techniques to optimize a report for mobile devices is the use of responsive design powerbi’s shape map visualization reveals insights from geographical data cororoplathth maps visualize variations in data across different locations by color-coding geographical regions based on data values a popular use case for cororopath maps is to display environmental data such as air quality temperature variations or pollution levels for any PowerBI map visual it is vital to properly prepare the data this includes cleaning formatting handling missing values and optimizing for performance one key feature of PowerBI map visualizations is its integration with Azure maps azure maps are part of the broader Azure location-based services family also called Azure LBS custom visualizations are userdefined visual elements that can create unique tailormade visuals for specific visualization requirements custom visuals created by developers can be imported from the PowerBI marketplace certified PowerBI visuals are available in app source and they can be downloaded from PowerBI desktop you can also create custom visualization in PowerBI using Python or R programming languages to help you design powerful report pages you explored various features this week such as clarity and visual impact for charts and reports accessibility considerations for Microsoft PowerBI creating and formatting KPI and dotplot charts how to visualize highdensity multi-dimensional data map visuals such as cororroplath and shape maps and custom visualizations including adding a python-based visualization by applying these techniques you will be better able to create powerful report pages in Microsoft PowerBI data is a treasure and with Microsoft PowerBI analytical powers you can explore it in a variety of ways but what do you need to explore this treasure a treasure map to see the big picture or a magnifying glass to analyze the details that’s the difference between a dashboard and a report your dashboard will provide a high-level analysis of the data that has been analyzed in one centralized place dashboards are a simplified overview of the big picture designed to highlight key metrics for quick monitoring and decision-making reports are comprehensive and analytical designed to dive deep into data while in your report you are able to analyze the finer details of this data add filters slicers and drill through functions in this video you will learn more about the key differences between PowerBI dashboards and reports discovering their use cases along the way jamie the Adventure Works CEO needs to visualize an overview of the company’s performance including sales marketing customers and so on the sales and marketing directors need to explore more granular data to identify trends outliers and anomalies within the data as a principal PowerBI analyst you need to decide on a dashboard design that will work perfectly to present to the CEO with summary level visualizations but for each of the directors you need to create detailed reports about sales and marketing now let’s delve into the primary differences between dashboards and reports both PowerBI dashboards and reports serve distinct purposes and have unique design considerations before exploring design approaches let’s try to understand the fundamental differences between dashboards and reports let’s start by listing some key characteristics of PowerBI dashboards powerbi dashboards are concise summarized displays on underlying reports in PowerBI they typically contain a single canvas or page offering a high-level view of metrics and key performance indicators also called KPIs dashboards are designed for quick decision-making and monitoring they can also include visuals tiles and widgets from different reports when it comes to creating and designing a dashboard in Microsoft PowerBI you can only do it in Microsoft PowerBI service the Microsoft PowerBI service sometimes referred to as PowerBI online is the software as a service part of PowerBI you generate a dashboard and PowerBI service using visual elements and tiles as well as pin an entire page of a report to your dashboard first you have simplicity and focus dashboards are concise and focus on key metrics they avoid clutter and unnecessary visual elements and prioritize the most critical information for quick decision-making next you have visual hierarchy visuals need to be arranged in a logical sequence the use of size color and placement emphasizes the significance of information that is presented lastly there is mobile responsiveness you must ensure your dashboard is responsive and visually appealing on a variety of devices such as tablets and mobile phones it is important to use responsive design principles to adapt to all screen sizes now let’s turn our attention to PowerBI reports powerbi reports are detailed and structured documents often consisting of multiple pages or tabs they are also designed for in-depth analysis and exploration of data containing tables matrixes and visuals that provide detailed insights powerbi reports support filtering drill through and slicers for interactive exploration to maximize report impact for all types of viewers you must consider three major areas of design layout and structure interactivity and storytelling let’s start with layout and structure you need to use a clear and logical structure to guide report users through the data utilize page numbers titles sections and headers to improve report navigation next you have interactivity in the report design you must consider adding slicers filters and drill down and drill through functionality to access granular data finally storytelling reports are designed to tell a datadriven story you need to use text boxes annotations and narratives to explain valuable insights arrange visual elements in a logical sequence to guide users about the introduction main body and the conclusion of the story before exploring an example of using dashboards and reports let’s touch on charts in PowerBI and how they interact with dashboards and reports appropriate chart selection to match the type of data being presented is essential to designing both reports and dashboards in PowerBI chart selection is critical in data visualization as it directly impacts the effectiveness of data communication the choice of chart will determine how your audience understands and interprets data because a dashboard is based on your underlying reports it is essential to make the correct chart selections for the data in your reports for your task for Adventure Works you need to create multiple dashboards for the CEO as well as the sales and marketing directors let’s start with the CEO Jamie with a tailored dashboard with data presented to meet their specific needs with this dashboard you should focus on designing a dashboard emphasizing highlevel insights key performance indicators and strategic information in a visually appealing layout based on this typical dashboard layout often includes these six categories first is an executive summary this section may include KPIs in the form of card visuals such as revenue profit margin year-over-year growth and market share next up is sales performance this may include charts showing revenue expenses profit trends and time comparisons the third category is market overview which represents market share trends and competitive analysis the fourth category customer metrics can include customer retention and acquisition rate charts the fifth category is operational performance in this category production output customer satisfaction and departmental performance visuals can be included finally you have strategic initiatives completion status for key initiatives in the form of progress bars and charts illustrating project timelines and milestones can be presented in this section for the sales director you need to design reports with drill down and drill through modes for detailed and granular data analysis for the drill down and drill through modes to work you can break down the report into individual pages these pages are sales performance overview geographical analysis product analysis salesperson’s performance and timebased analysis each of these pages needs to be designed with appropriate structure and chart selection based on data you want to present lastly let’s consider what is required for the marketing director’s report the marketing director will need to see data related to Adventure Works marketing channels how campaigns are performing and a categorization of customers for the marketing director the report content should contain an overview marketing channel analysis campaign performance customer segmentation and recommendation and insights this will provide the marketing director with a good starting point to begin assessing their department and that concludes our summary of dashboard versus report design in Microsoft PowerBI designing a dashboard and designing a report are distinct processes with unique objectives reports offer in-depth analysis and exploration of granular data while dashboards provide high-level overview for quick decision-making and monitoring of key metrics consider a PowerBI dashboard that feels like it was designed just for you precisely delivering the insights you need to drive your decisions this dashboard is designed to optimize your experience the end user making your work easier creating user centric dashboards in PowerBI is not about displaying a collection of charts and graphs it is about solving specific problems for your users with important data indicators prioritized high on the page trends and performance comparisons further down the page and general information towards the bottom in this video you will learn about getting a better understanding of your audience creating user centric dashboards as well as exploring some examples of these dashboards so how can you better understand your audience when designing your PowerBI dashboards you will likely have a baseline of knowledge depending on the products or services your company offers but what else can be done to help understand your target audience let’s look at four methods you can use they are identifying the end users defining user needs establishing users data literacy and finally identifying the preferred devices of users let’s begin by identifying the end users end users are the individuals or groups who will be interacting with and generating insights from your dashboards identifying your audience helps tailor the dashboard to their specific needs and preferences next you must define user needs each user group may have distinct data requirements and objectives you need to work closely with each user group to determine the specific data they work with and how you can visualize them you can do this by identifying key metrics relevant to their roles allowing you to select what is presented on their dashboard having established the end users and their needs you must now consider their level of data literacy are they data savvy or do they need a simplified data interface for example a sales team will need the most accessible data they are used to working with as opposed to a finance team that may be used to more complex data sets and charts lastly you must consider the device preferences of your audience consider the devices they are using most frequently are they accessing dashboards on laptops tablets or mobile devices this will help you make selections optimized for device specific dashboards let’s consider an example where this is put into practice the Adventure Works sales director received a sales performance dashboard that she did not like as it was difficult to comprehend the visuals on the dashboard realizing she is unable to use the current dashboard to assist in decision-making she passed the dashboard and underlying reports to you to make necessary improvements when you open the dashboard you look to identify the issues the dashboard might look impressive at first glance but there are many problems remember a dashboard should be understandable and actionable but currently this dashboard is neither there are data shortcomings as well as design shortcomings in this dashboard the data shortcomings include the area chart displaying sales by category is not appropriate here the donut chart shows sales by country without any legend the tree map used to display sales by product subcategory is too busy with too many colors the top five products by sales column chart is not relevant to the sales dashboard with regards to the design there are a similar number of issues the salesbyear column chart has a negative value but is the same color as the positive numbers key metrics of the dashboard such as revenue units sold and profit are not presented appropriately overall there is no color and style uniformity in the entire dashboard based on a brief analysis of the dashboard it can be easily concluded that the dashboard is neither understandable nor actionable your task is to redesign the dashboard focusing on key metrics including the relevant information for salespeople and visually appealing colors and charts let’s redesign this dashboard by following these steps select visuals that effectively convey your intended message when you design user specific dashboards you might want to import custom visuals in PowerBI to meet the specific needs of your audience next place the most critical information at the top of the dashboard based on the requirement gathered use key performance indicator tiles to highlight key metrics maintain consistency in your design including the color schemes fonts and layouts if you choose a color to convey positive figures ensure it is consistent with all graphs and charts ensure you employ responsive design techniques when designing your dashboard many end users access dashboards from their mobile devices therefore you need to make sure the dashboard is visually appealing and functional on smaller screens create a narrative flow within your dashboard text boxes card visuals and annotations can guide users through the data visualization if you implement these best practices to redesign the dashboard you will create a dashboard which is understandable and actionable this dashboard is concise relevant to the sales manager and maintains consistency in terms of theme and color palette all the charts are appropriate for the data type presented let’s finish this example by outlining some user specific dashboards you would design for other departments in Adventure Works for the marketing team your dashboard would monitor marketing campaign effectiveness visualize social media engagement provide demographic and geographic insights about the target audience and display competitor analysis on various product lines if you were tasked to develop a customer support team specific dashboard you would track customer support ticket data display customer satisfaction scores provide a real-time view of open tickets and escalations as well as highlight frequently reported problems these are just guidelines in real life situations you need to tailor your dashboard according to your user requirements once you have crafted and designed a user specific dashboard it is essential to conduct testing and receive user feedback to ensure that the dashboard meets their needs and expectations user feedback can especially add value to improved iterations of your dashboard creating user centric dashboards is about two things is it understandable and is it actionable to do this you need to identify your target audience understand their needs their data literacy and the devices they use to engage with dashboards you should now understand the effective use of visuals how to remain consistent in your color selection and selecting the most appropriate data for your audience imagine you are working for Adventure Works when you receive a request from your manager Addio Quinn who is traveling abroad for a business meeting they need an up-to-date overview of the company’s sales performance in a dashboard format adio may not be able to access the dashboard on a large device such as a computer or laptop while traveling therefore your primary goal is to create and optimize the dashboard so Adio can access the required information on the go using their mobile device in this video you will learn about how you can optimize dashboards for mobile phones and Microsoft PowerBI mobile optimization of PowerBI reports and dashboards is not just a trend it is a necessity in modern business intelligence applications there are three reasons in particular why mobile optimization is so important they are accessibility real-time decision-making and enhanced user experience mobile optimized dashboards ensure that actionable insights are accessible to users who rely on smartphones as their primary device the second reason is real-time decision-making executives directors and managers need up-to-date information at their fingertips to make strategic decisions on the go lastly you have enhanced user experience a welloptimized dashboard improves the user experience making it easier for users to interact with and understand data let’s explore how you can optimize the Adventure Work sales dashboard for cellular devices a dashboard is a single canvas of data visualization displaying the current state of the business based on underlying reports in PowerBI service you want to optimize a sales summary dashboard for mobile devices log to your PowerBI service all reports data sets and dashboards are listed in my workspace select my workspace from the left navigation pane of the PowerBI canvas and select the sales summary dashboard to open it this is an existing dashboard created from a report published from PowerBI desktop in my workspace dashboards are distinguished by clock icons once the dashboard is open select the arrow beside edit from the top menu and then select mobile layout from the drop- down options this opens the phone dashboard edit view the phone layout screen has two panes edit mobile layout and unpinned tiles the unpinned tiles pane contains all tiles that are unpinned from the dashboard you can resize and rearrange any tiles to fit the phone view the desktop version of the dashboard will not change you can also unpin any tile from the phone view if it does not fit or is not needed in the edit mobile layout screen the tiles of the sales summary dashboard are not in the correct order you can resize reposition and rearrange the tiles in the mobile layout once you drag and resize a tile other tiles in the dashboard adjust their position automatically instead select unpin all tiles from the top menu bar this will unpin all tiles and move them to the unpinned tiles pane this will allow you to start the design from scratch you can now pin individual tiles and resize them in a sequence to the mobile layout pane the three card visuals contain a snapshot of information about sales and profit you can then pin these three card visuals to the top of the mobile layout screen select the pin icon on the top right corner of the tile to pin the visual on the mobile screen next pin the yearly profit tile to the mobile screen below the card tile you can pin the sales by year and sales by category tiles side by side below the yearly profit tile on the mobile screen next pin the sales by country tile and sales by salespersons below the existing tiles you can enlarge the sales by salesperson tile to display the entire data set the top five products tile is not related to the sales summary dashboard and is not needed in mobile screen so you can leave that tile on the unpinned tiles pane you can resize and rearrange the tiles according to your analytical and audience requirements if you are still unhappy after you have completed these changes you can either reset tiles or unpin all tiles reset tiles returns the dashboard to its original state while unpin all tiles moves all tiles from phone screen to unpinned tiles pane when you’re satisfied with the phone dashboard layout you can switch to web view by selecting web layout from the top menu bar powerbi automatically saves the mobile layout once a dashboard has been completed you can view it on your cell phone you will need to download and install the PowerBI mobile app and log into your account all dashboards are listed in my workspace the ability to access and act on data insights while on the move is an essential element of today’s fast-paced business landscape by ensuring your mobile dashboards are accessible enable real-time decision-making and enhance the user experience you will set yourself up for success optimizing PowerBI dashboards for mobile devices ensures that the decision makers have access to the data they need when they need it leading to better and instant decisions given the amount of data sources available a single dashboard can never display all of the available data as a data analyst you must manage multiple dashboards and reports in Microsoft PowerBI let’s say you need to design multiple but similar dashboards for example you might need these dashboards for managers in different countries designing each dashboard from the beginning each time is not good practice in this video we will explore features in the Microsoft PowerBI service that can accelerate your workflow when creating and managing multiple dashboards there are two different workflow approaches you can use in PowerBI service making a copy of a dashboard and pinning elements from one dashboard to another there are many occasions when a copy of a dashboard helps your workflow these include using a dashboard as a template testing dashboard versions making regional versions of a dashboard and working databases that have the same data structures and types you can use an existing dashboard as a kind of template to create a new dashboard use this technique when you work on scenarios that closely resemble each other in terms of structure and flow of information the procedure is to build the first dashboard copy it rename it and then edit this copy modifying it to reflect the second data scenario to test dashboard performance create a duplicate of a dashboard modify it and test its performance against the original version for global operations you may need to create slightly different versions of a dashboard to match the culture language or norms of various countries or regions when you get a new database that has the same data structure and types as the existing data set you can duplicate the original dashboard and use it as a template for the new data set the second technique to handle multiple dashboards in PowerBI service is copying a visual element between the dashboards for example imagine you have a custom visual tile in a dashboard that you want to include in another dashboard in your workspace you can simply pin the tile from one dashboard to another without navigating back to the original report the source of the tile does not change meaning that the pinned tile links back to the original source report where it was created if the original content changes all dashboards pinned to it will also be updated to create and copy dashboards you must use the Microsoft PowerBI service you can view dashboards in Microsoft PowerBI service and in Microsoft PowerBI mobile dashboards are not available in PowerBI desktop therefore you need to publish all your reports to PowerBI service before creating and managing dashboards to create a copy of a dashboard you must be the creator of the dashboard if someone in your team shared a dashboard with you you cannot duplicate it you cannot pin tiles from dashboards shared with you only from dashboards created by you let’s open PowerBI service and explore some techniques to manage multiple dashboards to duplicate a dashboard log into your PowerBI service and open the workspace that contains your dashboard select the dashboard to duplicate from my workspace navigate to file and select save a copy from the drop-down a duplicate dashboard dialogue opens here you need to give an appropriate name for the duplicated dashboard select duplicate a duplicated dashboard is saved in the same workspace as the original one now the dashboard can be opened and modified to satisfy the analytical requirements some of the tasks you can perform include move resize and delete tiles add or pin new tiles share your dashboard with colleagues and team members the next task is to pin a tile from one dashboard to another open the product sales dashboard from my workspace and hover the cursor on the tile to pin then select more options and select pin tile from the dropdown in the pin to dashboard dialogue from the drop-down select either an existing dashboard to pin to or create a new dashboard and pin the tile to that when you select pin a success message appears at the top right corner indicating the visualization has been pinned to the selected dashboard open the dashboard to check the pinned visual further operations can now be performed on the pinned visualization like resizing renaming and moving you can duplicate a dashboard and pin a tile from one dashboard to another in Microsoft PowerBI service in real world data analysis working on many dashboards and reports is a frequent practice being able to quickly replicate a dashboard and copy visual elements between dashboards is a valuable addition to your skill set content with a visual always attracts more viewers than non-visual content visually rich media such as photos images videos and animations significantly contribute to the impact of content eye-catching visuals help to onboard and engage viewers informative visuals enable them to focus on and understand your message in this video you’ll discover media elements you can integrate into your dashboard and explore the benefits they bring to your workflow microsoft PowerBI service supports many media types in a dashboard including text boxes images videos web content and live streaming or real-time data there are many benefits to using media elements such as their ability to enhance data context create engagement reinforce branding provide instructions and present a summary visual content such as images and videos provide a context to data for example you can use images to display product photos company logos location maps and use video footage for a manufacturing or promotional video clip to help users understand the data being presented still images and motion graphics make dashboards more engaging and assist effective storytelling videos or animations for instance can be included to narrate the story behind the data making it more relatable and impactful reinforce an organization’s branding by including company logos and product images in your dashboard animations and video clips about a company’s corporate culture manufacturing process or marketing campaigns are some examples that can be included the use of short video clips containing instructions on how to navigate dashboards and interact with data effectively is another helpful application of media in dashboards images and icons can be used to present a visual summary of data making it easier to quickly grasp key insights you can include live streaming as a media element in a dashboard powerbi’s real-time streaming updates your dashboard data automatically and constantly any PowerBI visual or dashboard can be used to display and update real-time data and visuals the streaming data that feeds your updates can come from social media sensors such as a point of sale terminal or sensors detecting changes in light heat or motion service usage such as metering the consumption of power or other utilities or any time-sensitive data there are three types of data sets designed to display on real-time dashboards and tiles push data set streaming data sets and pubnob streaming data sets a push data set is where the data is pushed to PowerBI service from any live streaming data set such as SQL server when the data set is created the PowerBI service automatically creates a new database in the service to store the data with a push data set you can create visuals reports and dashboards as with any other report visual because the data is stored in PowerBI service you can pin any visual to the dashboard from your report and on the dashboard visuals are updated in real time whenever the data is updated powerbi only stores data from a streaming data set in temporary caches which expire quickly with a streaming data set the data is also pushed to PowerBI service from any data set that is constantly updating like SQL server or Amazon web services Oracle and so on a streaming data set is not stored in PowerBI memory as a result it has no underlying data set physically saved in PowerBI that means you cannot use regular report functionality in PowerBI like using filters and slicers in your report for drill down functions and to create interactivity the only way to use a streaming data set is to add a tile to your dashboard and use the streaming data set as a data source called custom streaming data in PowerBI service the tile is then optimized to quickly display real-time data you can choose any visual you want on the tile and the benefit of a streaming data set is that the visual always displays live data we can also use something called the PubNub streaming data set pubnub is a platform for building realtime applications it works with the minimum of delay which is called low latency this is because no data is pushed to PowerBI all realtime data is live streamed from PubNub it is a solution that has high reliability and is scalable meaning that its reliability and performance are retained as your audience grows this is a vital feature since your audience will expect the real-time changes to be instant regardless of how many viewers are online pubnub manages this by being scalable over globally distributed data centers pubnub is compatible with platforms across web mobile and internet of things powerbi is one of these platforms that can read an existing PubNub data stream the PowerBI web client uses the PubNob software developer toolkit or SDK to read an existing PubNub data stream the PowerBI service stores no data because the web client makes this call directly you must list any approved traffic from your network to PubNub as allowed like a streaming data set PowerBI does not store data so you cannot use any report building functionality you can visualize a PubNob streaming data set by adding a tile to your dashboard and configuring a PubNub data stream as the data source tiles based on a PubNub data source are optimized to quickly display real-time data pubnub is a streaming service that means it is a platform that helps build and operate real-time interactivity for mobile web and internet of things it is useful for real-time use cases that require security scalability and reliability the three types of data sets you can use to display real-time data are push data set streaming data sets and pub streaming data sets in PowerBI with the push data set you can create reports visuals like you usually do with an imported data set and then pin the visual to the dashboard streaming data sets and PubNob streaming data sets are not stored in PowerBI memory and therefore do not allow you to create any report visuals to use those you create a dashboard tile and connect a live streaming data set directly to the visual on the tile choosing a streaming method depends on factors such as where the data set is hosted what the analytical requirements are and what infrastructure your organization has available live streaming brings many benefits including live streaming updates enable users to access current data in real time this is especially valuable for monitoring rapidly changing metrics or critical data points dashboards with live updates can include alert mechanisms that trigger notifications when specific conditions are met live data streaming allows organizations to respond quickly to market changes operational disruptions or emerging trends team communication is improved through real-time collaboration and live data updates enable organizations to adjust forecasts and strategies based on the most recent data incorporating media elements like still images motion graphics and live streaming updates helps to transform your PowerBI dashboard using dynamic engaging and real-time visuals these visuals not only enhance the user experience but also empower users to respond quickly and make decisions about changing business conditions a sales summary dashboard that you created has all the required sales data but it fails to engage the audience the addition of media elements can help in this video you’ll learn how to add and format dashboard media elements to help enhance user experience powerbi service allows you to incorporate media elements such as still images and motion graphics into your dashboard log into a PowerBI service account open the sales summary dashboard from my workspace we’ll add three media elements to the dashboard a text box a still image and a video clip you need to add a tile to your dashboard to place an image text box or video select add a tile from the edit drop-down the add a tile dialogue appears where you can select the media type to add a dashboard heading select the text box and select next the add a text box tile window appears on the right side of the screen where the title and description can be added add text to the content section such as this dashboard displays the most up-to-date sales information of Adventure Works next format the text to increase the size color and indentation change the font size to 16 bold the color to black and center it tick the check box to display the title and subtitle of the tile you can also set a custom link and add either an external link or a link to another PowerBI dashboard or report from my workspace hyperlinks can also be added to the content section of the text box next let’s add the Adventure Works logo to the dashboard if you want to place your company logo or any other image to your dashboard you need to publish the image online and create a URL link with http colon or https colon you must also make sure that security credentials are not enabled to access the image you cannot add SVG file types to a PowerBI dashboard from the add tile window select image and then next in the detail section to display the title above the image tick the display the title and subtitle checkbox when placing something like the Adventure Works logo you don’t need to enable the title and subtitle now to enter the image URL the Adventure Works logo is already published to Google Drive and the URL was generated without any security credentials which is added here to the URL section to hyperlink the tile select set custom link and then select external link you need to enter the URL of the external source to make the tile a hyperlink select apply and a logo image is added to the dashboard and you can rescale and reposition the tile within the dashboard the last media element to add is a video only YouTube and Vimeo links are supported from the add tile window select video a video information window appears where you need to add information about the video to display the title and subtitle of the video tick the check box display the title and subtitle we will leave the title and subtitle off for this demonstration add a video URL to a clip hosted on YouTube or Vimeo to add the hyperlinks tick the check box set custom link under functionality select external link and add the video URL you can add the video link to open in a new browser tab or add a link to an entire playlist viewers can watch the video on the dashboard tile and also select a hyperlink to navigate to the entire playlist to watch further videos in the same tab select the no option from the open custom link to open the custom video link in a new tab select apply a video tile is added to the dashboard and you can resize and reposition the tile as needed once you add a media tile to your dashboard you can go back and make any changes to the text box change the video URL and so on to make changes select the title and hover the cursor on more options indicated by three dots on the top right corner of the tile and select edit details then the edit tile window opens where you can make and apply changes to the media tile you should now be familiar with adding media elements to the dashboard and formatting them to help create an engaging and captivating user experience with the help of images and videos you can transform your dashboard into an immersive and informative tool you don’t ever want your end users to have to type in a URL they may not type it at all because it’s too much effort or worse still they may type it incorrectly fail to reach your site and give up a QR code is a better solution that avoids the end user having to type in anything it’s short for quick response code a QR code is a two-dimensional barcode that contains information in a machine readable format qr codes consist of black squares arranged on a white square grid typically in a square shape qr codes can store different types of data including text URLs contact information phone numbers and more qr codes are a valuable addition to PowerBI dashboards and reports they enhance user interactivity and data accessibility qr codes are useful in PowerBI dashboards because codes can be generated for specific reports and dashboard tiles in Microsoft PowerBI service users can scan the QR code using their mobile devices to instantly access the associated content without any manual navigation this feature is especially useful for onthe-go access to critical information external web sources or documents can be linked to QR codes providing users with additional context or supporting information related to dashboard data qr codes can be used to gather user feedback or conduct surveys directly from the dashboard since QR codes are mobile friendly they align with the growing trend of mobile business intelligence users can scan codes using their smartphones making data consumption more convenient and accessible the marketing department can use QR codes for instance linking to promotional materials or campaigns related to the data presented on the dashboard you can create a QR code for a dashboard tile and PowerBI service or for a PowerBI report to better understand the use of QR codes consider this scenario to help manage sales reporting and streamline order placement Reneie the Adventure Works marketing manager wants to have quick and easy access to key sales metrics she also wants to share the measures with the sales team to track the sales progress using PowerBI service you can fulfill her analytical needs by adding the power of a QR code reini can share the QR codes among her team members and any stakeholders to give them quick access to relevant data let’s explore PowerBI service and discover how to generate a QR code for a report or dashboard tile in PowerBI service you can generate QR codes for either the entire report that you published from PowerBI desktop or for an individual tile of a dashboard you can create a QR code in the PowerBI service for tiles in any dashboard even in dashboards that you cannot edit let’s check both processes log into PowerBI service and open the sales summary dashboard in the dashboard there is a tile representing sales by salesperson you can generate a QR code for this visual element of the dashboard select the more options from the upper right corner of the tile represented by three dots and select open and focus mode from the drop-down powerbi opens the visual in a full screen in focus mode select more options from the upper right corner of the menu bar and choose generate QR code from the dropdown a dialogue with the QR code appears from here you can scan the QR code or download it as an image which can be shared by email or print to display it in an office or a public place where colleagues can access the information if you want to print the QR code make sure to print it at 100% or actual size if the data in the tile is updated the sales manager can monitor the sales performance you can select exit focus mode to go back to the dashboard next to generate a QR code for the entire PowerBI report open the Adventure Works PowerBI report from my workspace select file and choose generate QR code from the drop-down a dialogue with the QR code appears and you can use the QR code as mentioned previously you can scan the QR code from the PowerBI app on a phone to directly access the visualization qr codes can be generated using the built-in capabilities of Microsoft PowerBI both for a dashboard tile and an entire PowerBI report strategic integration of QR codes and PowerBI can streamline the workflow leverage the power of mobile technologies and enhance the user experience whether it is for efficient data access or engaging user interaction QR codes are a valuable addition to your PowerBI dashboards and reports have you ever accidentally started watching a film halfway through remember how confused you felt and how many questions you had to ask the other viewers before you finally understood the character and the plot if a Microsoft PowerBI report or dashboard does not tell a cohesive story then the employees and stakeholders who view them can feel a similar confusion transforming raw data into a meaningful narrative is a vital skill for the data analyst effective data storytelling serves as a bridge between the analysis of the data and communication of the results it combines the art of storytelling with the science of analytics to convey insights and findings in a compelling way with a multinational organization like Adventure Works where employees and stakeholders are spread across different regions effective data storytelling is particularly important in this video you will explore the main components of data storytelling and discover the benefits of a good data story data storytelling is the art of using data and visuals to build compelling narratives which helps to convey a message highlight trends and engage a wide audience at its core it involves presenting data in a way that captures attention facilitates understanding and informs decision-making you can achieve effective storytelling by combining three distinct components in a well scripted way which can lead the report users to the insights produced by your analysis let’s explore those components at the core of data storytelling is the data itself this includes raw information facts and statistics that you have collected when the data has been processed and analyzed you can then identify the primary message you want to convey the use of a business analytic tool such as PowerBI can help to provide the context throughout your data story in addition the data provides the context that the audience needs to interpret the analysis presented to them next you design the journey the audience will take towards your primary message identifying the start and end points and any key data points along the way a narrative provides structure context and meaning to your data a well-crafted narrative explains the significance of data outlines the key findings and guides the audience through the story’s progression it might include explanations interpretations and implications based on data insights data visualization is the representation of data using charts graphs maps and other visual elements by choosing appropriate and effective data visualizations you allow viewers to quickly grasp information viewers can identify the trends patterns and insights that might be challenging to discern from raw data alone in the context of data storytelling visual elements educate your audience on your proposed theory by creating a connection between the visual elements and your narrative you can engage the audience and present both detailed and summarized data points these three components work together to create a datadriven story that communicates information and insights effectively and can even create an emotional response the data provides evidence substance and context visualizations aid in comprehension and the narrative ties everything together into a cohesive and compelling data story effective data storytelling can have a positive impact on the stakeholders directly involved and your organization as a whole some benefits of successful data storytelling include engagement engaging stories capture and hold the audience’s attention this engagement is vital for conveying critical messages next is enhanced understanding good data storytelling simplifies complex information and highlights key points making it accessible to a broader audience the visualizations and narratives help them to understand datadriven insights without requiring them to have advanced technical knowledge to capitalize on this you need strong communication data storytelling ensures that analysis is not limited to data analysts or data scientists it facilitates communication between different departments and disciplines within an organization fostering collaboration at the heart of datadriven stories is the purpose of solving problems datadriven stories help identify problems and opportunities by revealing patterns and trends it also encourages proactive problem solving through business analytic tools lastly there is effective reporting whether you are working in research business or academia data storytelling enhances the effectiveness of reports and presentations it transforms dry data into engaging narratives that captivate audience attention and involvement data storytelling is a transformative approach to data analysis and communication you can leverage the power of narrative data and visualization to convey insights effectively by mastering data storytelling you can add value to your data and insights and offer value to your audience and industry when you think about data and the story it can tell you need to think of it as a traditional story that you’ve read in books or watched in movies it contains the same elements of traditional stories like a setting characters a situation of conflict overcoming this conflict and a resolution to the story as an analyst you need to build your data story around these traditional storytelling methods by the end of this video you will have explored how elements of traditional storytelling can be translated to your data story in Microsoft PowerBI data contextualization establishes the environment and background against which the datadriven story unfolds your setting includes the details about the data sources the time frame and the broader context in which the analysis takes place for instance if you are analyzing sales data for a specific year in Adventure Works the setting would include details about the industry the market conditions and the company’s current financial status next up are the characters of your data story these are the individuals involved in the analytical process this includes data analysts data scientists and other stakeholders such as business leaders collaborators and external partners in a data story each character plays a unique role data analysts are the main characters who explore and interpret the data the main audience of your analysis such as CEOs or directors are supporting characters to the data story stakeholders are impacted by the insights driven from the data like many great stories conflict is central to your data story in this context the conflict is the business problem or data challenge it is the central issue that the data analyst aims to resolve for example your problem could be a decline in sales a drop in customer satisfaction or any other business issue determined through data analysis the conflict sets the stage for your analysis and drives the story towards the resolution finally there’s the resolution to the data story the resolution in the data story is the result of the analysis where insights are presented and actionable recommendations are made the resolution should provide a clear path of action based on datadriven insights and findings for example if the conflict is declining sales the resolution might involve strategies to boost sales like targeting specific customer segments launching a season specific marketing campaign and so on let’s explore how as a Microsoft PowerBI data analyst you would implement story elements to address a real world challenge at Adventure Works the story unfolds at Adventure Works headquarters where the company’s CEO Jaime is meeting with leadership to discuss the declining sales of Adventure Works products threatening the company’s future as a PowerBI data analyst and report designer you are the main character of this data story you are determined to uncover insights and anomalies from the data that will lead the company out of its sales slump a secondary character is the Adventure Works CEO Jaime jaime is considered a visionary CEO known for her adventurous spirit and belief in the company’s potential she is eager to make strategic decisions based on your analysis to move the company towards new heights the challenge facing Adventure Works is a steady decline in sales over the past two years the decline is causing concern among various stakeholders of the company including Jaime the executive leadership recognizes the company needs a datadriven solution to identify the reason for the decline and devise strategies to reverse the trend as the principal analyst you explore the company’s sales data from this 2-year period you investigate customer demographics seasonal trends and product performance through effective data visualization you uncover three significant insights first the sales of mountain bikes have outperformed other products in the same subcategory during the spring and summer months secondly by delving into customer feedback you discovered a compelling pattern of customers consistently praising the durability and quality of Adventure Works mountain bikes lastly you revealed a correlation between decreased marketing efforts and the months of declining sales based on your results it became clear that the company’s reputation for producing rugged and durable products is a hidden gem that can be capitalized on and that a consistent and effective marketing campaign is the missing piece of the puzzle to increase sales now you reached the resolution of this data story after working on data visualization and exploration you presented your report to the executive meeting and the CEO the committee decides to immediately address the identified issues based on your findings the marketing team drafts a roadmap to focus their efforts on promoting the durability and quality of their mountain bikes based on these findings the CEO Jamie provides a directive to the marketing director to increase the campaigns by targeting the competitive advantage Adventure Works has over their competition reliability with a datadriven strategy in place Adventure Works can now embark on a new journey as the company emphasizes the durability of its bikes and expands into new markets Adventure Works reignites their essence of exploration and sales begin to rise once more you have crafted a datadriven story of transformation for Adventure Works through data analysis and storytelling the company identifies outliers correlations and patterns to their problem this insight helps the company to rediscover its core strength and plan its future efforts accordingly a collection of numbers and charts on a report canvas in Microsoft PowerBI does not always tell a captivating story however with the science and art of data storytelling you can turn data context into your story setting turn stakeholders into characters and frame a business problem into a conflict and resolution the data storytelling process is an integral part of presenting data analysis it involves transforming datadriven insights into a narrative that is engaging and informative and leads to action and resolving the conflict in this video you will delve into the full process of data storytelling and how you can relate it to the data analysis process let’s start by outlining the eight steps you will cover they are goal data collection and preparation data analysis and exploration data visualization audience consideration communication feedback and iterations and actions and decision-making the data analysis process typically begins with defining a clear goal and a hypothesis of what you expect to uncover in your analysis analysts theorize about the relationship between the variables in the analysis and what they expect to discover from the data connecting this to data storytelling it is crucial to understand what message or insight you want to convey through the data this end goal guides the entire storytelling process data is collected from a source cleaned transformed and prepared for analysis as you learned in previous lessons this process might include merging data sets removing errors and duplicates handling missing values and so on in data storytelling your work begins with prepared data therefore it is essential to have a well ststructured data set that aligns with the goal of your story this ensures that the story is based on accurate and relevant information the data analysis and exploration stage involves statistical analysis hypothesis testing and data exploration techniques to uncover patterns trends and relationships in the data these findings are the heart of data storytelling you need to select the most critical insights that align with your goal such as key trends correlations anomalies or any other significant findings visualization is the key component of data analysis allowing you to explore and communicate data patterns effectively it plays a significant role in determining how receptive your audience is to receiving complex information to create effective visuals to support the goal of your story you need to choose the appropriate chart type relevant to your data effective visualization can help to reveal patterns trends and findings from your data provide context interpret results and articulate insights streamline data so your audience can process information and improve audience engagement you need to create a dashboard using data visualization tools in PowerBI to present these findings a data dashboard is used to manage information and for business intelligence a dashboard provides a single canvas to organize and present valuable information in a logical sequence the dashboard is the single location where the audience can understand the connections between the data story and the hypothesis you made initially data storytelling places a strong emphasis on the audience you need to tailor your story to your audience’s background their knowledge of the topic and business requirements the narrative is designed to resonate with the audience data storytelling involves dynamic and engaging communication this includes presentations interactive reports and dashboards you need to collect feedback from team members and other stakeholders which helps you refine your narrative visuals and overall storytelling approach to better meet your audience’s needs data storytelling is not just about providing information it aims to inspire actions having established your goal at the start of the storytelling process it should link back to the actions and decisions the compelling visuals and narrative aim to motivate stakeholders to make informed decisions backed up by accurate data and insights presented data storytelling is changing the way we consume information storytelling with data imparts a human dimension to often complex and cryptic data sets filled with numbers and statistics crafting a narrative plays a role in this process but the ability to comprehend and convey information is crucial for constructing a compelling narrative and leading to effective decisions congratulations on completing dashboard design and storytelling in Microsoft PowerBI you learned about using design principles to improve the visual impact of a dashboard and tailoring the design to the users interacting with the dashboard you also explored data storytelling and how it is a compelling way of transforming raw data into a data narrative that informs engages and inspires action let’s recap what you learned and the key takeaways from each topic you began by learning about improving dashboard and report design in Microsoft PowerBI dashboards are created in PowerBI service and are based on underlying reports dashboards are typically a single canvas of information presenting the current state of the business reports are designed from a variety of data sources in PowerBI desktop and typically contain multiple pages reports support the use of slicers and filters to enhance interactivity for users having established your knowledge of dashboards and reports you then learned about how to identify and focus on the end users in an adventure works scenario reports generated with data from various sources may contain information about the company’s inventory or sales the growth of the company in different regions about salesperson performance or best performing product categories the purpose of your analysis is a dashboard that contains only the relevant information needed by your target audience for example if you want to design a dashboard for the finance department you first need to identify the relevant data from the available data set you must visualize and present the information necessary for the finance team with all irrelevant data emitted when creating a user centric dashboard your ability to prioritize and visualize relevant data is a major step in engaging your audience you then learned about optimizing dashboards for mobile phones in the lesson you learned how to optimize dashboards for cellular devices how to allow for accessibility considerations and how to create dashboards for real-time decision-making and an enhanced user experience keep in mind though you need to be the owner of the dashboard to make any changes having completed the lesson on improving dashboard design you then learned about other dashboard elements you learned about working with multiple dashboards specifically how to duplicate a dashboard duplicating dashboards is especially important when you need to test the performance of a new dashboard with slight variations or to distribute a slightly different dashboard for other departments or regions another tool that you learned about is pinning a specific tile from one dashboard to another you can pin the tile from one dashboard to another without navigating back to the original report the source of the tile does not change meaning that the pinned tile links back to the original source report where it was created you then learned about incorporating media elements such as images videos and animations and text boxes to your visualization you learned about types of media which can positively impact the dashboard and its engagement with the audience you learned in this lesson how to add and edit various media files to the dashboard from PowerBI service you also learned what factors you must consider ensuring they work correctly for example an image file can only be displayed when it is published online with a URL without security credentials lastly in this lesson you gained hands-on experience in creating QR codes for various dashboard tiles and entire reports in PowerBI service a QR code is a feature that enables you and business users to access the most critical information on the go this can also be used to collect feedback conduct surveys and add external web links to your dashboard the last lesson in this module covered the principles of data storytelling data visualization and narrative are the three fundamental components of data storytelling effective data storytelling can have a positive impact on the overall analytical process benefits of data storytelling include engagement enhanced understanding communication problem solving and effective reporting next you went through an example of data storytelling for adventure works you learned about the principles of setting a stage identifying the conflict assigning the roles to various characters of the story and conflict resolution throughout the storytelling process then you learned about the storytelling process via eight steps they are goal data collection and preparation data analysis and exploration data visualization audience consideration communication feedback and iterations and actions and decision-m in the context of data analysis these steps cover the entire process from data collection and cleaning to databacked decision-m in real world scenarios you will come across examples of poor storytelling which need to be improved before they are presented to your audience choosing the wrong chart type designing a random dashboard canvas and inconsistent use of colors are all common mistakes you need to avoid while crafting a dashboard for your data story you should now have a better understanding of how to optimize your dashboard visuals and how to incorporate data storytelling best practices to create effective dashboards and reports the skills you’ve learned over these weeks will enable you to create data stories that capture user attention enable them to recognize the goals of your data analysis and generate effective solutions for your business congratulations on completing this course on creative design in Microsoft PowerBI microsoft PowerBI is not just an analytical tool it provides opportunities to implement creativity into your reports and designs to better engage dashboard and report users let’s recap what you have learned over the last few weeks reflecting on the key takeaways you started your learning journey by exploring color theory and the key role of color in building reports color theory is the collection of designs rules and guidelines used to communicate with users through color schemes you applied color theory and the role of color principles to improve a report for Adventure Works following on from this you explored appropriate positioning and scale of information while designing your PowerBI reports strategic placement of visual elements such as charts and graphs in a logical sequence within reports increases their user impact in addition consistent scaling within various chart types in accordance with the data type and structure also ensures the effectiveness of design next you learned how to avoid chaos in your PowerBI reports maintaining cohesion and consistency to your report building you also implemented the principles of chaos and cohesion practically to generate a cohesive design in PowerBI throughout this course you learned that the key to successful visualization is knowing your audience you must tailor your PowerBI presentations to meet the needs and preferences of your audience you must tailor your PowerBI presentations to meet the needs and preferences of those interacting with and using them during this lesson you learned how several factors such as job role user objectives information needs and cultural considerations influence your audience’s requirements you then switched to another crucial factor that plays a pivotal role in report design and that is age differences in your audiences colors are significant when designing PowerBI visualizations for various age groups appropriate formatting of a report that reflects the analytical message concisely while maintaining the design principles is key in report design and finally an important aspect of working with data is data security you learned about keeping data secure through data anonymization and how it can be achieved now let’s turn our attention to visual clarity in reports visual clarity at both chart level and report level affects the impact of your reports in this lesson you explored how to choose the correct chart type for the type of data you are visualizing you learned the data type the message and the audience all play a role when selecting a chart type branding visual hierarchy and the business objective are some of the factors that impact your visual clarity at report level next you covered both theoretical and practical aspects of accessible report design in Microsoft PowerBI many built-in tools can be employed to consider people with visual impairments while retaining an engaging and compelling report design following this you gained a thorough understanding of important chart types in PowerBI you gained hands-on experience in designing a key performance indicators or KPI chart a dotplot chart and a bubble chart a KPI chart is significant as you can visualize the current values against a predefined target value with trend axis in place a scatter plot chart along with its variations dot plots and bubble charts are of special significance because of their ability to display multi-dimensional and highdensity data in a single visual with these charts you can visualize categorical information on the charts x-axis having delved into the topic of charts you also explored advanced tools within PowerBI desktop to display complex data structure like tree maps heat maps and drill through and drill down functionalities of PowerBI to conclude this section on visual clarity in reports you learned how to optimize your PowerBI reports for mobile devices joining the wave of dynamic mobile business intelligence geographical data is the part of every business that requires special visual needs powerbi has various map visuals to visualize the location-based information you explored various map visuals through examples and with a hands-on experience shape maps and corropath maps also called filled maps are the two most common map visuals azure maps is a new map visual within PowerBI that offers more control and formatting options through map layers to accomplish the growing need to combine visualizations with complex data structures sometimes PowerBI core visuals are unable to fulfill your analytical requirements this is where you can leverage custom visualizations the PowerBI app source provides a range of custom visuals that are developed by partners and tested by Microsoft for quality and accuracy you learned how to download install and format a custom visual in your core PowerBI visualization pane you have gained a thorough understanding from installing Python to using it for your custom visualization python along with its rich and versatile visualization libraries such as mattplot lib and seabour provides an entire new avenue of dynamic and interactive visualization within powerbi having learned about designing powerful report pages you turned your attention towards dashboard design and storytelling the dashboard is a distinct component of the Microsoft PowerBI ecosystem you began by exploring the differences between a PowerBI dashboard and report as both offer several benefits and serve distinct purposes a PowerBI dashboard represents a snapshot of information displaying the current state of business and is a single canvas of visualization with key insights and KPIs a report is designed for granular data analysis that might consist of multiple pages with drill through and drill down functionalities you learned how to publish your report to PowerBI service create a dashboard and optimize your dashboard for mobile phones remember you can only create and optimize dashboards in PowerBI service the reports you generated using data from various sources might contain information about inventory sales regions growth of the company salesperson performance and best and worst performing product categories the product of your analysis is a dashboard that must contain only the relevant information needed by your target audience in the real world you need to work on multiple reports and dashboards simultaneously in this context you explored ways to streamline your workflow by duplicating a dashboard and pinning a visual element from one dashboard to another media elements are an integral component of a dashboard in the digital era adding images text boxes and videos to your dashboard can have a significant impact on audience engagement you gained practical experience in integrating media elements such as images and videos to your dashboard the fast-paced business landscape requires continual access to up-to-date data powerbi’s live streaming capabilities allow you to integrate real-time data to your dashboard for faster and on-time decision-making you learned that there are three types of live streaming data sets that PowerBI service supports push data set streaming data set and pub streaming data set only push data set is physically stored in PowerBI memory allowing you to build reports on top of the data set effective data storytelling serves as a bridge between the analysis of the data and communication of the results it combines the art of storytelling with the science of analytics to convey insights and findings in a compelling way you gained a thorough understanding of the components of data storytelling the narrative the data used and visualization and how these elements weave a data story next you learned the elements and the process of data storytelling with Adventure Works scenario with the eight-step process you crafted an engaging data story for Adventure Works the eight steps of data storytelling are goal data collection and preparation data analysis and exploration data visualization audience consideration communication feedback and iterations and actions and decision-making lastly you learned that effective data storytelling can have a positive impact on the overall analytical process benefits of data storytelling include engagement enhanced understanding communication problem solving and effective reporting as you have now finished your recap of this course you should take a moment to reflect on your learnings before embarking on the final project assessment and course quiz be sure to recap your learnings additional resources and previous quizzes and best of luck as you complete your journey congratulations on completing the creative design in PowerBI course your hard work and dedication have paid off you’ve made significant progress on your data analysis learning journey and you should now have a thorough understanding of the theory and practice of visualization and design including the design principles of data display and visualization this course provided you with a strong creative design foundation in Microsoft PowerBI this should allow you to modify your report designs to build cohesive reports and to produce audience focused reports aimed at target audiences you learned that to enhance the comprehension of data and improve the enduser experience you can apply visual clarity use multi-dimensional visualizations insert map visualizations and implement a custom visualization exploring the concepts of dashboard design and storytelling you compared the design of a dashboard with the design of a report examined the common steps involved with data storytelling and discovered advanced dashboard features such as embedding media and QR codes your PowerBI knowledge of visualization and design will help you to create better reports and dashboards well done for completing another step in your data analysis education by passing all the courses in the program you’ll earn a Microsoft PowerBI analyst professional certificate from Corsera this program is a great way to expand your understanding of data analysis and gain a qualification that will allow you to apply for entry-level jobs in the field and will help you prepare for the PL300 exam by passing the exam you’ll become a Microsoft certified PowerBI data analyst it will also help you to start or expand a career in this role this globally recognized certification is industry endorsed evidence of your technical skills and knowledge the exam measures your ability to prepare data for analysis model data visualize and analyze data and deploy and maintain assets to complete the exam you should be familiar with Power Query and the process of writing expressions using data analysis expressions or DAX you can visit the Microsoft certifications page at http://www.learn.microsoft.com/certifications to learn more about the certification and exam this course has enhanced your knowledge and skills in the fundamentals of creative designing in Microsoft PowerBI but what comes next there’s more to learn so it’s a good idea to register for the next course whether you’re just starting out as a novice or you’re a technical professional completing this program demonstrates your knowledge of data modeling in PowerBI you’ve done a great job so far and you should be proud of your progress the experience you’ve gained will showcase your willingness to learn your motivation and your capability to potential employers it’s been a pleasure to embark on this journey of discovery with you wishing you all the best as you continue to pursue your studies and develop your career working with PowerBI involves working with many different assets like reports and dashboards managing all of these can be a difficult challenge so we’ve designed this course to equip you with the skills you need to deploy and maintain PowerBI assets during this course you’ll explore the role of PowerBI in business deploying assets in a PowerBI workspace and the role that security and monitoring play in safeguarding reports and dashboards in PowerBI let’s take a few minutes to preview what you’ll learn you’ll begin with an introduction to the role of PowerBI in business with a focus on data flow data flow in business refers to the movement of information within an organization this movement or flow occurs in the following stages collection processing analysis and decision making once gathered the data is cleaned or standardized it’s then transformed data analysts use the refined data to generate insights the data is analyzed using PowerBI service this software offers many advantages for analysts it’s accessible scalable and offers collaboration tools and data backup and recovery features the data analyst is the central figure in this process they possess important skills and expertise in extracting valuable insights from data an important skill that all data analysts must possess is understanding structured query language or SQL data analysts use SQL to interact with the SQL databases that store the data analysts can connect to a SQL database using import or direct query modes import mode loads data directly into PowerBI direct query mode connects PowerBI directly to the source database an analysis is presented in the form of a report a report can be static or dynamic a dynamic report explores multiple areas of interest its results are presented in the form of visuals these reports also facilitate using whatif parameters that permit interactive adjustments to modify visualizations and generate insights into potential scenarios next you’ll explore how to deploy assets in a workspace a workspace is a specialized area in PowerBI that holds important assets there are two types of workspaces in PowerBI the first is a personal workspace which you can use to store your content the second is a shared workspace where a team can collaborate on reports and dashboards workspace roles determine how individuals can interact with workspaces workspace roles include viewer contributor member and admin you can manage these roles using PowerBI’s manage access feature next you’ll learn how to monitor workspaces by monitoring a workspace you can measure its impact and make changes to increase its usefulness you’ll also explore the topic of data sets and gateways in PowerBI a data set must contain the latest available information you can use a scheduled or incremental refresh to ensure accurate data and you can promote and certify data sets to inform your team where to access the most current and reliable data you’ll also explore establishing a secure reliable connection between your on- premises data and PowerBI service using data gateways there are three types of gateways in PowerBI the on premises data gateway the on- premises data gateway personal mode and the Azure virtual network or V-Net data gateway which type of gateway you choose depends on the setup of your organization and its specific data management and security requirements you’ll also learn how PowerBI deployment pipelines move content through the following life cycle stages development testing and staging or production another useful feature for maintaining your workspace is the lineage view this view shows the data journey from source to destination with all the connections in between impact analysis shows how changes to your data can impact or affect different assets in your workspace next you’ll explore the role that security and monitoring play in safeguarding reports and dashboards in PowerBI you’ll first explore how to share information safely and identify sensitive data sensitive data is essential information that if leaked could damage the company’s reputation finances or privacy you can safeguard data using PowerBI’s authentication tools you can also use sharing links to control who you share information with and use sharing permissions to determine what they can do with the data sensitivity labels are also another useful method of safeguarding data access to data sets is governed by data permissions these ensure that only authorized individuals can access data you can configure permissions in PowerBI to safeguard your data you’ll also review rowle security for safeguarding data rowle security or RLS controls which individuals can view data based on predefined roles and rules there are two types of role security static RLS restricts users to specific data dynamic RLS uses data analysis expressions or DAX to adjust realtime data access based on user roles finally you’ll explore subscriptions and alerts in PowerBI you can subscribe to reports and dashboards a PowerBI subscription is an automated delivery system that provides daily data snapshots as emails or notifications you can use the subscriptions pane in PowerBI to manage your subscriptions as well as subscriptions PowerBI also offers data alerts these automatic customizable notifications inform users when specific conditions or thresholds have been met or exceeded you’ll also complete exercises in which you’ll put your new skills into practice by helping adventure works with PowerBI knowledge checks which will test your understanding of these topics and additional resources in which you’ll consult Microsoft learn articles to help you explore these topics in more detail in the final week of this course you’ll undertake a project and graded assessment in the project you’ll prepare configure design and develop a data model for a fictitious online company called Tailwind Traders finally you’ll have a chance to recap what you’ve learned and focus on areas you can improve upon throughout the course you’ll engage with videos designed to help you build a solid understanding of data modeling in PowerBI watch pause rewind and rewatch the videos until you are confident in your skills then consolidate your knowledge by consulting the course readings and measure your understanding of key topics by completing the different knowledge checks and quizzes this will set you on your way toward a career in data analytics and form part of your preparation to take the PL300 Microsoft PowerBI data analyst exam by the end of the course you’ll be equipped with the necessary skills to work effectively with data models in PowerBI good luck as you start this exciting learning journey data is integral to business success but how that data arrives at the business is also important in this video you’ll learn about the flow of data in business and how it can be managed to help generate insights lucas is helping Adventure Works to develop its latest business plan this requires collecting all available data about the business to ensure that Adventure Works plan is as informed as possible this involves exploring what kind of data adventure works can analyze how it makes its way to the business and the techniques the company can use to prepare it for analysis but first let’s begin with the question what is data flow data flow in business refers to the movement of information within an organization this movement occurs in stages the first stage is collection where data is gathered from various sources such as Excel spreadsheets and SQL databases the second stage is processing where data is cleansed and transformed to prepare it for meaningful analysis during the next stage analysis advanced analytics and algorithms are applied to the processed data to uncover trends patterns and insights that inform business strategies the last stage is decision-making during this stage informed decisions are made based on the analyzed data guiding actions and adjustments within the business to optimize processes and achieve objectives and there are processes within business that govern aspects of data like how it is acquired stored manipulated and shared to support business operations and objectives let’s begin with the first stage data collection at Adventure Works data is collected from a variety of valuable sources firstly the Adventure Works e-commerce platform acts as a primary source capturing customer transactions web store browsing behavior and purchase history this platform integrates seamlessly with the customer relationship management or CRM system which compiles customer insights and interactions the point of sales systems in Adventure Works physical stores provide realtime data on instore purchases and customer foot traffic the company collaborates with suppliers who share inventory and sales data ensuring a streamlined supply chain social media platforms serve as another essential source offering insights into customer sentiment engagement and trends once the data is collected it then needs to be processed this vast amount of data is managed through SQL databases that securely store these records in tables you’ll learn more about SQL later in this course for now you just need to know that the SQL database is the center of Adventure Works data operations it links all aspects of the business and it provides an overview of business operations and customer interactions this empowers Adventure Works to make informed decisions for continued success with such a vast amount of information flowing through the system ensuring the accuracy and reliability of the data is paramount the two main steps in this stage of the process are data cleansing and transformation let’s explore these steps more closely data cleansing is the process of examining correcting and standardizing incoming data this removes inconsistencies from the data ensuring that it’s reliable and accurate for instance Adventure Works can standardize customer addresses at the data source by ensuring all addresses are collected and stored in the same format using consistent data types this provides a consistent foundation for shipping and billing this process not only refineses the quality of the data but also establishes a solid foundation for subsequent analysis once cleansed the data then flows through pipelines where transformation steps come into play the process of data transformation involves working with aggregations applying calculations and enhancing data for example Adventure Works can aggregate sales figures from different locations for an overview of regional performances these pipelines act as a bridge for the data to undergo a series of carefully designed transformations before it’s ready for analysis and reporting this stage of the process ensures that the insights derived from Adventure Works data are precise and actionable this helps to drive informed decisions for the company’s continued success after cleansing and transformation the refined data is now ready for analysis the results of this analysis form the foundation for insightful reporting for example Adventure Works can generate sales insights from its regional sales data these insights then form the basis of a report that offers a clear business snapshot now that Lucas has generated the required insights he passes the report on to management once Adventure Works management obtain a copy of this report they can use its insights to make decisions about the business the report indicates low sales of its new mountain bike model based on this insight Adventure Works might try a new marketing campaign for this model to help improve its sales beyond Adventure Works various industries harness data in unique ways to drive their operations for example the public transportation sector uses data from its routes travel times and ticket sales to optimize schedules allocate resources efficiently and enhance the overall commuting experience for passengers other sectors that make use of data include food companies those dealing with perishable goods are impacted by weather and temperature so they must collect and analyze meteorological data cold storage facilities rely on real-time temperature monitoring to preserve the quality and safety of products and they might also increase production in anticipation of a heat wave these examples illustrate how different sectors leverage data to make informed decisions this enhances their efficiency and competitiveness in the market you should now be familiar with the flow of data within a business and how this data is used to generate insights and make decisions an effective data flow is essential for generating insights for informed decision-making in today’s datadriven world the ongoing management of data is crucial for businesses to make informed decisions enhance efficiency and gain a competitive edge in this video you’ll learn how a company like Adventure Works can leverage its data assets using PowerBI service to become a datadriven enterprise and the importance of the continued maintenance of these assets adventure Works has set a goal of becoming a datadriven enterprise by the end of the year to achieve this goal the company must make the most of its data assets so its data analysts have configured custom reports and dashboards in PowerBI to monitor inventory levels track customer preferences analyze market trends and assess product performance let’s explore how the company can leverage and manage these assets to drive strategic decision-making in a datadriven enterprise like Adventure Works data isn’t just information it guides strategic choices resource allocation and maps the pathway for future growth during this transition to a datadriven mindset PowerBI service is used to deploy and maintain data assets as you’ve previously learned PowerBI service is a cloud-based platform used for data analysis it’s a centralized hub where teams can collectively work on reports and dashboards ensuring that everyone has access to the most up-to-date information this ensures that insights remain current and relevant and it empowers Adventure Works to make informed decisions swiftly and accurately unlike its desktop counterpart the service offers the following advantages it’s accessible for remote teams offering flexibility and collaboration across geographic distances adventure Works can use the service to scale up or down to accommodate changing business needs teams can also easily add or reduce resources without extensive hardware and infrastructure investments powerbi service also offers real-time collaboration features for documents and projects improving productivity and teamwork and it provides data backup and recovery reducing the risk of data loss due to hardware failures or other unforeseen events now that you’re more familiar with its advantages let’s explore how the Adventure Works data analysis team makes use of PowerBI service as you discovered earlier Adventure Works can deploy PowerBI service assets like reports and dashboards to monitor inventory levels track customer preferences analyze market trends and assess product performance all in real time let’s find out more about the insights PowerBI service can generate in these areas powerbi service can help to monitor inventory data data analysts can track inventory turnover rate order fulfillment accuracy shipping and delivery times and return rates adventure Works can track existing and emerging customer preferences this information can be used to differentiate its product offerings and stay ahead of competitors adventure Works can also use data to analyze market trends the company can identify opportunities for new product development or enhancements to existing products ensuring Adventure Works remains relevant and it can study trends in pricing to adjust costs to stay competitive and maximize profits other areas of the business that Adventure Works can monitor include product performance powerbi service can deliver information on the performance of individual product lines this information can include the best and lowest selling products and data from online product engagement and product recommendation effectiveness can guide decisions for the purchasing and marketing teams this ensures Adventure Works maintains a competitive advantage in a dynamic market it’s not just retailers like Adventure Works who use PowerBI service in today’s datadriven landscape businesses and organizations across various industries rely on the continuous maintenance of data assets to help guide decision-making for instance in the health care sector accurate and up-to-date patient records are critical for providing quality care a hospital’s ability to access a patients medical history in real time can be a matter of life and death in the finance industry investment firms require accurate data on stock prices and market trends to make timely investment decisions and as the Adventure Works examples demonstrated understanding customer behavior and preferences is vital for online retailers to tailor their offerings and marketing strategies effectively as these examples show data assets help to inform every sector of enterprise you should now be familiar with how a company like Adventure Works can leverage its data assets using PowerBI service to become a datadriven enterprise and the importance of the continued maintenance of these assets whether it’s optimizing supply chains fine-tuning logistics or tailoring marketing strategies the need for continuously maintained data assets is universal deploying and maintaining assets is not just an advantage but a prerequisite for success in today’s business world data analysis is essential and data analysts are central players in this data analysis process extracting invaluable insights from raw information in this video you’ll explore the pivotal role of a data analyst and the profound impact they have on organizational success adventure Works relies heavily on data analysts to help make sense of its data and generate insights to drive business success and there are certain skills and traits a company like Adventure Works looks for in its analysts let’s find out more about the skill sets Adventure Works values and the contribution that its analysts make to the company a data analyst is expected to possess specialized skills in statistics math and programming they use advanced tools to analyze big data and find hidden trends and anomalies that others might miss a data analyst creates reports and visualizations that combines complex information into simplified insights these reports and summaries help decision makers to navigate the business landscape they spot opportunities for improvement automation and cost reduction helping to make processes more efficient and boost the organization’s competitiveness data analysts enforce data protection rules they detect and fix weaknesses safeguarding organizations from harmful breaches and data leaks now that you’re familiar with the skills a data analyst must possess let’s examine some examples of where a data analyst can offer invaluable insights and solutions a data analyst at Adventure Works can employ advanced analytics to segment customers based on behavior demographics and preferences for instance a data analyst might identify a segment of Adventure Works customers who prefer outdoor gear by tailoring marketing messages and promotions to this group the company can increase sales for outdoor related products this enables targeted marketing for higher sales conversion and enhanced customer loyalty data analysts can also use past sales data trends and seasonality to forecast product demand and optimize stock accordingly a data analyst may discover that certain products have a seasonal demand spike by adjusting inventory levels and promotions accordingly Adventure Works can prevent overstocking and reduce carrying costs this leads to higher profitability because Adventure Works can avoid the risk of excess stock data analysts can also generate insights into sales by studying the purchasing patterns of customers to discover which products sell together most effectively through market basket analysis a data analyst might find that customers who purchase hiking boots often also buy outdoor gear adventure Works can use this insight to create bundled promotions that encourage customers to purchase these items together these insights help Adventure Works to meet the needs of its customers and increase its sales in an online industry stopping fraud is vital data analysts use realtime checks to spot suspicious transactions keeping Adventure Works safe financially and protecting its reputation a data analyst may set up alerts for transactions that deviate significantly from a customer’s typical behavior for instance if a customer suddenly makes a high-V value purchase after a history of smaller transactions it could trigger a fraud alert you should now be familiar with the pivotal role of a data analyst and the profound impact they have on organizational success data analysts are essential for helping businesses drive insights and progress as the examples you’ve just explored demonstrate data analysts help to make informed decisions improve operations drive innovation and reduce risks sql or structured query language is a powerful language with many advantages for data analysts working with large enterprise databases in this video you’ll learn about the importance of SQL how it helps with data storage and queries and how it integrates with Microsoft PowerBI adventure Works has just hired some new traininee data analysts it needs these analysts to generate insights from its SQL databases but several of them are unfamiliar with this tool let’s explore the answers to some of their questions about SQL to discover how it helps enterprises like Adventure Works the first question these new trainees have is what’s a SQL database at its core a SQL database is a system for organizing and storing data in a structured format when we refer to a structured format we mean that data is structured or organized so it can be located quickly when required for analysis a SQL database excels in handling structured data its framework is built of tables rows and columns this means that all data is stored in specific categories and analysts can find the data they need with minimal effort for example Adventure Works needs to retrieve bicycle data for a report it can create a SQL query that accesses the product category column in the products table where a list of all bicycle types in stock can be found as this example shows a strong business case can be made for SQL databases through their structured and reliable framework however another advantage of SQL databases is that they facilitate complex queries for quickly extracting specific subsets of data this is important for generating reports and insights data sets are also constantly expanding which requires scalability and a larger data set requires more complex methods of data retrieval you can retrieve data from large databases using techniques like partitioning and indexing finally SQL databases can be accessed by multiple users or applications at the same time an entire team of Adventure Works data analysts can access the SQL database simultaneously without causing a conflict or slowdown this is an important advantage for a business as we’ve discovered the main advantage of a SQL database is its storage capabilities the next question that the new data analysts have is how does this storage work sql databases store data using a method called normalization you might be familiar with this method from previous courses normalization divides data into multiple related tables each with a specific purpose it’s like tidying a room by putting similar things in separate boxes as you discovered earlier SQL databases also use indexing indexing is the technique of assigning a unique number to each row in a table this acts like a table of contents in a book making it easier to locate information as a data analyst it’s also important for you to understand that the real power of SQL isn’t just its storage capabilities the ultimate benefit of a SQL database is its ability to return information through SQL queries sql queries are statements written in SQL they instruct the database to perform a specific operation like returning all records in a table or just a specific subset so you must study the syntax and structure of SQL statements carefully to extract the necessary insights as efficiently as possible for example Adventure Works data analysis team has created a SQL query that returns all bike data from the products table however they can also create a more complex SQL query that returns data only on bikes that cost $1,000 or more the new data analysts are now more familiar with the basics of SQL so their final question is how does a SQL database relate to PowerBI just like PowerBI SQL databases are used by businesses of every size to manage and organize data by integrating SQL databases with PowerBI data analysts can use these tools to create compelling visualizations and reports that turn raw data into actionable insights having explored the basics of SQL alongside Adventure Works new data analysts you should now be familiar with the importance of SQL how it helps with data storage and queries and how it integrates with PowerBI sql is an essential tool for data analysts to help generate the insights businesses need develop a good understanding of SQL and you’ll be an asset to any enterprise powerbi is a powerful tool for extracting
data and it can also be integrated with a SQL or structured query language database to generate even greater insights into your data in this video you’ll explore the structure of a SQL database the steps to connect it to PowerBI and some examples of connection modes adventure Works has recently migrated its data sets to a SQL database the company has tasked Lucas with connecting this database to PowerBI so that it can begin to analyze its data let’s explore the basics of integrating PowerBI and SQL databases then follow along with Lucas as he establishes the connection to begin here’s a quick overview of a SQL Server a SQL Server is a relational database management system or RDBMS developed by Microsoft it provides a secure and scalable platform for storing managing and retrieving data sql servers organize data into structures called databases where they’re stored in tables with rows and columns this makes it easy to retrieve and work with specific data sets users can interact with SQL databases by creating SQL queries that send instructions to the database so your next question might be how do I connect to a SQL database establishing a connection between PowerBI and a SQL database requires three pieces of information the name of the server the database name and your credentials here’s how these pieces of information work together to provide access the server name identifies the location of the database server the gateway to your data the database name is the database within the server you intend to access and the credentials are typically the username and password that grant access permission to the server these details provide a secure and efficient foundation for linking your analytical tools there are two primary modes available for connecting your data in PowerBI import mode and direct query in import mode data is loaded directly into PowerBI for fast and responsive visualizations however the data is static so it might need to be refreshed to reflect realtime updates on the other hand direct query mode connects PowerBI directly to the source database this enables real-time analysis but potentially leads to slower performance due to continuous queries to the database which one you choose depends on your business needs when making your decision balance factors like data size update frequency and performance requirements to communicate with this infrastructure you need to construct queries written with SQL for example Lucas can use a basic select SQL query to retrieve sales data from the database the select command initiates the retrieval of data from the database in other words you’re instructing the database to select specific data in this query the asterisk signifies that we want to retrieve all columns from the specified table the from clause specifies the table from which we want to retrieve the data or the source of the information we’re interested in in this instance we need the rows and columns from the Adventure Works sales table finally the wear clause adds a condition that filters the resulting table rows based on specified criteria in this query product category road bikes indicates that we’re interested in records in the product category column that match the road bikes value now that you’re up to speed with the basics let’s work with Lucas to establish a connection between PowerBI and the Adventure Works SQL database select get data from the home ribbon tab to import data from any PowerBI source a pop-up window with all available data source connectors appears type SQL in the search bar to locate the SQL Server database connector identify the required connector and select connect this opens the SQL Server database window where you must input the database details the SQL server is the server’s IP address containing the database or its identifying name in this instance the Adventure Works server name is FG7N373 and the database name is MSDB next ensure that import is selected as the data connectivity mode to load the table in the PowerBI file memory these settings should suffice for your connection to all database tables the next step is to create a SQL query to retrieve the required data set expand the advanced options then input a SQL select query to retrieve all road bike data from the product category column in the Adventure Works sales table finally press okay next you must provide credentials to connect to the required database and extract the sales data select the database tab and input your database credentials make sure the correct database level is selected then select connect to establish a connection between PowerBI and the database table a warning appears stating that an encrypted connection to the database is missing we can ignore the warning for this example scenario and select okay however it’s good practice to use an encrypted connection in a realworld PowerBI environment a preview of the data set appears on screen you can select transform data to interact with the data set in power query editor or select load to connect to it directly in this instance we’ll select load to connect to it directly once the required rows are loaded navigate to data view if your loaded data is present as a table then this confirms that the connection has been established successfully you’ve now explored the structure of a SQL database the steps to connect it to PowerBI and some examples of connection modes by integrating PowerBI and SQL you can greatly enhance the power of your data analysis powerbi generates static reports that offer a snapshot of data at a fixed point in time however it can also generate dynamic reports which adapt and respond to your business needs in this video you’ll explore the basics of dynamic reports an overview of PowerBI parameters and how to generate dynamic reports using parameters over at Adventure Works Lucas is preparing sales reports however instead of generating a new static report for each aspect of the business he wants to create one report that can serve several different purposes dynamic reports are the perfect solution up to this point you should have experience working with static reports these offer fixed snapshots of data like total sales revenue over January however dynamic reports can be adapted and transformed based on user specifications dynamic reports can be modified using parameters to change how they display information as the data analyst you can decide which parameters inform the report this means that its content is always aligned with your business needs you can also adapt your parameters for different scenarios or you can switch between data sources in real time with this alignment an organization gains more value from one single report this saves time optimizes resources and leads to more efficient and effective reporting practices as you’ve just learned dynamic reports are created using parameters in the context of PowerBI parameters are dynamic variables that influence the data displayed in the report parameters are like dials and switches on a control panel if you update your parameters your report updates accordingly there are many different examples of parameters including numerical values text inputs and boolean or true false settings parameters also accept default values or free form text there are many options for customizing your parameters for example Lucas is developing a sales report that must analyze monthly sales data in North America he can set up a parameter to analyze sales on a continual month-by-month basis or input a custom date range he can also set parameters to filter data by region so that the report focuses only on North America or he could set up a custom region name to focus on a specific area of interest like monthly sales data for states on the West Coast powerbi parameters are the cornerstone of dynamic reporting empowering users like Lucas to customize their data views let’s explore a few more examples of how parameters can be used with dynamic reports you can use parameters to explore high levels of data granularity with dynamic data selection and filtering for example as you’ve just discovered Lucas can analyze specific areas of interest in his data using custom ranges this helps to deliver greater insights for adventure works parameters also enable dynamic data source connections with parameters you can switch between data sources like databases files or application programming interfaces also known as APIs this is great for dealing with evolving data environments or multiple data repositories parameters can be used to analyze existing business situations or create new what-if scenarios for example Lucas can create financial forecasts by inputting growth rates expense projections and revenue assumptions as his parameters this generates a range of potential revenue outcomes for Adventure Works leveraging PowerBI parameters through scenarios helps Adventure Works to explore multiple outcomes helping to create datadriven business decisions you should now be familiar with the basics of dynamic reports PowerBI parameters and how to generate dynamic reports using parameters by using dynamic reports you can align your data more closely with the needs of your business and gain maximum value from one single report dynamic reports are an interactive userfriendly way of viewing and analyzing data and offer much more powerful insights than traditional static reports in this video you’ll learn how to create a dynamic report using a SQL database and PowerBI parameters lucas must generate a dynamic report for Adventure Works that analyzes the company’s sales data across multiple regions the report must extract data from a sales table in a SQL database it then needs to use parameters to alter the displayed region according to user selections the first step is to create a connection select get data from the home ribbon tab select SQL Server from the list of options the SQL Server database dialogue box appears on screen input the server name in the server field and the database name in the database field ensure that the import mode option is checked for data connectivity mode import mode should be selected by default next you need to retrieve and load the data for your report expand advanced options input a SQL select query that retrieves all table columns from the Adventure Works sales table containing data or values for sales in Asia select okay to execute the query input your database username and password credentials to access the SQL server select connect then okay on the encryption warning finally select load to load the database table into your report the table shows data from sales in Asia as specified in the where clause of the SQL select query the next step is to format the table and visualize the data the table’s default name is query one rename the table to sales now you need to visualize the sales as a table graph select the table visualization then expand the columns of the sales table select the product category product region and order total columns finally you need to increase the size of the text to make it more visible navigate to the format pane of the visualization increase the table’s values to 15 point font size increase the column headers to 16 point font size resize the table to fit the values and center it on screen next you need to create parameters to make the connection dynamic navigate to the transform tab on the home ribbon to access power query editor once in power query you can view the data set table you’ve connected to you can now create a new parameter to access the dialogue box for creating new parameters access the home tab select manage parameters then new parameter these actions open the manage parameters window you can configure your parameter as follows name it region parameter select text as the data type ignore suggested values as it’s not required for this project finally add Asia with single quotes as the current value select okay to create the parameter now you need to assess your parameter by adjusting your SQL query right click on your sales query in the query editor then select advanced editor your code appears on screen in the advanced editor dialogue box replace Asia in your code with the amperand symbol and region parameter check the bottom left hand corner of the dialogue box to ensure no syntax errors have been detected then select done you need to grant permission for this query to run select edit permission and then run select close and apply to return to report view now you need to test that the report is dynamic select transform data from the home ribbon and select edit parameters change Asia to Europe select okay then select apply changes to refresh the data set select run to enact your changes the data set modifies itself to display sales in Europe adventure Works now has a dynamic report that it can use to explore its sales data across multiple regions and you should now be familiar with the process steps for creating a dynamic report using a SQL database and PowerBI parameters a dynamic report typically offers insight into one area of interest at a time however with a multialue dynamic report you can explore several areas of interest at once in this video you’ll learn how to create a multialue dynamic report in PowerBI adventure Works needs to transform its current dynamic sales report into a multialue dynamic report that offers insight into its sales data across multiple regions simultaneously let’s create this report for the company using PowerBI the first step is to create a spreadsheet containing the required values to be passed to the SQL query it must use single quotes for text values however to include a single quote at the beginning of your text in Excel you need to use double quotes this indicates to Excel that you’re typing a single quoted text access the transform data option to open Power Query Editor select and import the product region selection Excel spreadsheet check the box for sheet one and select okay to add it to the editor once the sheet is loaded in the editor rename column one to region selection now you need to create a function to match the database table rows with the user selection in the spreadsheet select the sales query from the queries menu right click on the query and select create function from the list of options in the create function window type the following function name get sales data from regions select okay power query creates a folder that contains all parts of the function the next step is to invoke your custom function this ensures that the database table records match the spreadsheet column values in other words you import only the relevant data select the other queries folder and select sheet one then access the add column ribbon tab and select invoke custom function this action opens the invoke custom function window name the new column invoked function data select the get sales data from regions function query and select region selection as your region parameter then select okay your data set shows a new invoked function data column containing the required sales regions you can use the double arrow button on the top of the new invoke function data column to expand the data avoid using the original column name as a prefix this would make the column names too long it should only be used if combining multiple columns of the same name in the same function might cause confusion select okay to load the data this loads the database table columns and rows with a product region that matches the spreadsheet selections double click on sheet one in the queries pane and rename it to sales function select close and apply to return to the report view access the visualization pane and select the table icon select the following columns from the data pane product category product region and order total as you select these columns the table visualization is populated with the data from each one next select the format painter on the visualization pane increase the font size of the table’s values to 15 point and the column headers to 16 point for greater visibility then resize the table return to the spreadsheet and change Asia to Europe then save the document return to PowerBI and select the refresh option from the home tab the new multialue region selection from the spreadsheet is shown in the database table results your multivalue dynamic report is now ready to present to Adventure Works this report lets the company select and analyze sales from multiple regions for greater insight you should now be familiar with the process of creating a multialue dynamic report in PowerBI dynamic reports show information on your current data but with whatif parameters you can dynamically alter reports to observe hypothetical outcomes or scenarios in this video you’ll explore the concepts of whatif parameters and scenario-based analysis and you’ll review the process steps for applying these concepts to your reports adventure Works has raised its monthly order amount target lucas its data analyst must determine the target to meet next month’s sales goals lucas can use whatif parameters to forecast scenarios and identify the required sales target before we explore how Lucas can carry out this task let’s review the basics of whatif parameters a whatif parameter is a customdeefined variable that can make interactive adjustments within a PowerBI report you can adjust your parameters to change your visualizations and generate insights into future scenarios the main purpose of whatif parameters is to enable dynamic scenario analysis this means users can explore various hypothetical scenarios without the need for complex calculations or creating multiple versions of the same report instead a single report can be transformed into a versatile tool capable of adapting to various business contexts for example Adventure Works can use whatif parameters to create sales forecasts the company’s data analysts can tweak variables like sales growth rates seasonality factors or marketing budgets they can then instantly observe how these adjustments affect projected revenue sales and revenue targets this level of interaction empowers users to make informed decisions based on realtime insights while what if parameters offer tremendous flexibility it’s important to recognize when and where they can be most effective they’re most effective in scenarios with many variables that can significantly impact outcomes and where it’s important to be able to quickly assess these outcomes what if parameters can be applied across a range of industries organizations and use cases for financial analysts they facilitate stress testing of financial models and evaluation of risk scenarios marketing professionals can use them to optimize advertising budgets and forecast campaign outcomes supply chain managers can simulate various demand scenarios to fine-tune inventory levels once you have the available data the possibilities of whatif parameters are near endless now that you’re more familiar with whatif parameters let’s help Lucas perform a scenario-based analysis for Adventure Works lucas must create a whatif parameter to forecast the sales required in February to reach the new monthly target of 70,000 using the data from the sales report to help him first navigate to the modeling tab select new parameter and numeric range from the drop-own menu the parameters dialogue box appears on screen input the details as follows name the new parameter forecasted increase assign it a decimal data type input one as the minimum amount and two as the maximum then input 0.1 as the increment this creates 10 steps between one and two and set the default to one finally check add slicer to this page and select create a slicer is added to the page expand its settings on the visualization tab select vertical list as the style and turn on single select so a value is always selected resize the visual to fit the left side of the report navigate to the data pane and expand on the forecasted increase table to identify what has been created by the whatif parameter first there is the column that’s currently being used in the slicer which contains a list of numbers based on the parameter settings this was created by the generate series function secondly a measure contains the option selected in the slicer captured by the selected value measure you also need a third measure to handle the desired calculation to create it select new measure from the ribbon and name it forecast amount add the sum of order total column multiplied by the forecasted increase value measure now you need to add this measure to the analysis navigate to the column chart and access the build visual settings add the measure to the yaxis of the visualization since the parameter is set to one the forecasted results of the calculation is the exact same number as the current total you can cycle through the options to view more scenarios the whatif parameter dynamically modifies the visualization one forecast shows that a 1.6 increase in the total amount is enough to reach the monthly target you should now understand the concepts of whatif parameters and scenario-based analysis and the process steps for applying these concepts to your reports what if parameters in PowerBI offer a transformative approach to data analysis by providing the ability to dynamically adjust variables and instantly visualize the impact they empower users to make more informed decisions data scientists and data analysts and big tech companies already use SQL and other languages for advanced data analysis this gives leadership valuable insights into overall productivity and what the weak spots may be leading to evidence-based strategic decisions they can create comprehensive customer profiles to better understand their customers needs leading to targeted marketing initiatives businesses can look at supply chain analytics to figure out where production delays or bottlenecks happen but what impact can data science have on a larger global scale some cities are already using data analytics to inform decisions about urban planning to lead to a better quality of life for their inhabitants ultimately working toward being recognized as a smart city singapore Oslo New York and Paris the list goes on imagine a city planned entirely based on data analysis a city that takes the innovations all those cities already use and incorporates them into one place what would that look like welcome to Data Topia during its inception urban planners and data scientists work together to develop an exact ratio of residents to schools to shops to restaurants to healthcare facilities to green spaces and so on ensuring that all these amenities are accessible to all residents all the time there are no traffic jams in Datatopia real time data analytics and predictive models provide timely and actionable insights to traffic management centers using cameras sensors and GPS data from vehicles this is used to adjust traffic lights dynamically and reduces congestion by improving the efficiency of intersections digital signs display realtime traffic information to drivers suggesting alternate routes when congestion is about to occur real time analytics automatically detect traffic incidents and alert authorities leading to quick response times to minimize disruptions and improve safety data topians don’t have to worry about overflowing waste bins all bins have been fitted with sensors that detect when they are nearing full capacity triggering timely waste collection and preventing overflows landfill usage and recycling rates are carefully monitored using realtime analytics this data is used to inform sustainability initiatives water use cleanliness of public spaces and energy use is also monitored in Datatopia street lights dim when roads are empty to reduce energy consumption green energy systems power the city and smart grids optimize power distribution predictive analytics have shown that 38% of Dattopians will be over 65 in the next 10 years health care measures such as hospital capacity and resource allocation are carefully managed to accommodate the aging population data analytics identifies trends and patterns within the population to target preventive interventions and improve overall health outcomes this includes identifying at risk populations and tailoring interventions to specific groups education is very important in Datatopia educators can analyze attendance records coursework completion rates and other data to identify at risk students early in the academic year early warning systems can trigger interventions to prevent dropouts and improve student success analytics are also used to recognize high achievers who may benefit from advanced coursework statistical algorithms are used to predict student outcomes this drives decisions in allocation of university course offerings in the city data science is used in resilience planning in Datatopia predictive analytics ensure that the city has resilience strategies in place to cope with various challenges such as cyber threats economic downturns or natural disasters this data is used to improve emergency response times and the deployment of emergency services during a crisis datatopia seamlessly integrates information and technology to create a healthy and sustainable urban ecosystem we may not quite live like the people of the imagined data topia just yet whether it seems like a dream or a nightmare to you it’s clear that with the ever evolving landscape of the practical application of data analytics we may be getting one step closer every day congratulations on reaching the end of these lessons on PowerBI in enterprise during these lessons you explore data’s role in large enterprises let’s take a few minutes to recap what you learned in these lessons you first learned how data flows through an enterprise you discovered that data flow refers to data movement within an enterprise this movement occurs in the following stages: collection processing analysis and decision-m in a large enterprise data flows in from a variety of sources its flow is governed by processes influencing how it is acquired stored manipulated and shared once gathered the data must be cleansed and transformed to prepare it for analysis data cleansing is the act of standardizing data so that it is reliable and accurate data transformation is the act of transforming data as it flows through pipelines once cleansed and transformed the refined data is ready to inform strategic decisions as its insights are revealed through PowerBI reports organizations use these reports insights to become datadriven enterprises data isn’t just information it guides strategic choices and helps to map a pathway to growth powerbi service is used by many businesses to generate datadriven insights this is because of the advantages that it offers it’s accessible for remote teams it scales to meet data growth it offers real time collaboration and data backup and recovery and it’s you who helps organizations to take advantage of these benefits the data analyst is the figure that plays a central role in extracting valuable insights from this data a data analyst brings several important skills to an enterprise they provide analytical expertise they create reports and visualizations with data that drive decision-making they generate insights that identify room for innovation and they help to identify and mitigate risks next you learn about SQL and its role in enterprise sql or structured query language is used by data analysts to interact with SQL databases data is stored in a SQL database that stores data in a structured format this means data is organized so that it can be located quickly when required sql databases also store information using normalization and indexing to make it easier to locate data sql databases offer many advantages for enterprises they’re great for storing data they facilitate complex queries they can scale to meet the demands of a growing business and they can be accessed by multiple users at the same time sql databases return information through SQL queries data analysts must be familiar with SQL syntax to create queries that extract the required data to connect to a SQL database you must identify the location of the server and the database on the server that you need you then need to provide credentials to gain access you can connect your data using import mode or direct query mode import mode loads data directly into PowerBI direct query mode connects PowerBI directly to the source database you can communicate with this infrastructure using SQL queries for example Adventure Works can use SQL select queries to extract information on bicycles sql databases and PowerBI servers also facilitate the use of dynamic reports dynamic reports can alter between views based on user selection you can also create multialue dynamic reports that simultaneously explore several areas of interest within your data sets both can be modified using parameters to change how they display information this provides more value than standard reports as a data analyst you can decide which parameters inform the report once they align with your business needs you must connect PowerBI and a SQL server to create a dynamic report you then need to create a SQL query to retrieve and load the data from the SQL database once loaded you need to visualize the data typically in graph format finally you must configure parameters to analyze the data multi-dynamic reports are more difficult to create this is because they require the use of custom functions to be invoked in a data set powerbi reports also make use of a whatif parameter a whatif parameter is a custom-defined variable that can be used to make interactive adjustments within a PowerBI report you can adjust your parameters variables to change your visualizations and generate insights into future scenarios they’re most effective in scenarios with many variables that can significantly impact outcomes that must be assessed quickly throughout these lessons you also completed several knowledge checks that tested your understanding of the concepts and processes you explored you also encountered additional resources which presented you with links to further reading materials that you can use to enhance your understanding of the role of PowerBI in enterprise you’ve now reached the end of this summary it’s time to move on to the module quiz where you can test your knowledge of these topics this is followed by the discussion prompt where you can discuss what you’ve learned with your peers you’ll then be invited to explore additional resources to help you develop a deeper understanding of the topics in this lesson best of luck working with PowerBI service requires managing many different reports dashboards and data sets keeping track of these can be a demanding task fortunately you can use the workspace feature to manage your data assets in this video you’ll explore PowerBI service workspaces their advantages the types of workspaces available and best practices to follow when using them lucas has been tasked with managing several different reports and dashboards for Adventure Works he can use PowerBI service workspaces to keep all these data assets in one place using personal and shared environments let’s explore how workspaces can help Lucas manage Adventure Works assets powerbi service workspaces act like specialized rooms in a house each workspace hosts distinct data sets reports and dashboards this is great for data analysts because it helps with organized and efficient data management several features of workspaces make them useful for data analysts these include organization access control collaboration and streamlined updates let’s explore these features beginning with organization workspaces offer data analysts great organizational potential each workspace is a unique container for related reports dashboards and data sets this helps keep your data tidy and easy to locate workspaces also provide access control safeguard your data from unauthorized users with your workspac’s access control features depending on the workspace you can determine who can see or edit the content for example Lucas can configure his workspace so that only other members of the data analysis team can view it this is especially useful when working on confidential data or collaborating with specific teams workspaces also enable collaboration between teams shared workspaces are like conference rooms they’re spaces where Lucas and the data analysis team can discuss and refine data insights it’s not just about storing reports but building them together workspaces help keep content updated with workspaces you can streamline updates to your projects updating or modifying data is much easier with everything in its right place whether pulling in new data or revising visualizations having a structured workspace ensures consistency and clarity now that you know more about workspaces and their advantages let’s explore the different types available there are two main types of workspaces these are personal and shared workspaces both serve a different purpose let’s review their differences to find out more a personal workspace is like a private room in your house it’s your space where you can arrange things to your liking and work on projects privately here you’re in total control outsiders don’t have a key ensuring your work remains confidential and undisturbed shared workspaces let team members collaborate they can bring together their individual data insights and blend them into a collective narrative it’s a space designed for collaboration allowing multiple users to add edit and refine reports and dashboards simultaneously how you manage and utilize your workspace is crucial for effective data analysis adopting certain best practices can significantly enhance your efficiency and output one important best practice involves regular cleanup periodically review and remove outdated reports or data sets from your workspace this proactive approach ensures optimal performance and prevents potential confusion from irrelevant information you must also establish clear naming conventions for your data assets consistency is key when naming your reports dashboards and data sets this practice aids easy retrieval and benefits all users especially in shared workspaces you must also frequently review your access controls assign access levels based on roles and responsibilities to maintain data security and prevent unintended modifications for example over at Adventure Works Lucas must continually monitor who can access his team’s shared workspace to ensure only data analysts can view its assets in the digital realm safeguarding your work is paramount ensure that you back up your work regularly regular backups protect against unexpected data losses ensuring continuity in your projects on a large team like Lucas’ frequent backups are vital it only takes one mistake from one team member to lose important data and finally you should also encourage open discussion and collaboration with your team members you can do this by fostering a culture of continuous feedback you can refine data visualizations optimize reports and foster a more collaborative environment by actively seeking and implementing suggestions adhering to these best practices ensures efficient data management and creates a conducive environment for team collaboration you should now be familiar with PowerBI service workspaces their advantages the types of workspaces available and best practices to follow when using them as you’ve discovered through Lucas and his team workspaces can greatly benefit your data analysis projects as a PowerBI data analyst you’ll frequently collaborate with others in shared workspaces so it’s important that you understand how to create and manage these workspaces in PowerBI service in this video you’ll explore the process steps for creating a workspace and learn how to keep its content updated over at Adventure Works Lucas needs to create a collaborative workspace for his data analytics team a PowerBI service shared workspace is the perfect solution let’s help Lucas create and manage this workspace log into PowerBI service navigate to the lefthand sidebar to access the platform’s tools select workspaces to display the available workspaces for now Lucas only has access to my workspace his personal space select my workspace to access the space and reveal its contents the workspace contains reports dashboards and data sets however other team members need to collaborate on these assets to create a shared workspace for the team navigate to workspaces and select new workspace the create a workspace dialogue box appears on screen in this dialogue box you can input a workspace name assign a domain for your workspace and upload an image you can also use advanced settings to assign members for now let’s just input adventure work sales as the workspace name then select apply now that we’ve created the workspace we must upload some content select upload then select a PowerBI report the report and its data set and dashboard are uploaded to the workspace and ready to share however if any changes are made to the report in PowerBI desktop it will need to be uploaded again to the shared workspace to ensure these changes are reflected for all other users to demonstrate this let’s open the report in PowerBI desktop and make a quick change in the report select the order total by product color visualization select the ellipsus symbol then select sort access and modify the order by sort ascending all values on the x-axis are now sorted by ascending order total save the report and return to PowerBI service open the report again in the workspace screen this version does not reflect the change we made in PowerBI desktop so we’ll have to upload it again return to the workspace screen and select upload select browse and locate the updated report a warning appears stating that a data set with the same name already exists select replace and upload the new version of the report once the new version of the report is uploaded you can open the report and view your changes the updated chart is now visible in the report indicating a successful upload you should now be familiar with the process steps for creating a workspace and keeping its content updated by knowing how to build and manage shared workspaces in PowerBI service you can work effectively with your teams to generate insights and help drive business success running a shared workspace involves managing a lot of different people everyone must be assigned the correct roles and permissions to ensure the team works together effectively in this video you’ll explore workspace roles and the different types available and learn how to configure them lucas has created a new shared workspace for his Adventure Works colleagues to collaborate on the company’s latest reports he now needs to identify who requires access to the workspace and assign the correct roles to everyone let’s work with Lucas to assign roles to the team just as you wouldn’t let everyone in a company have the keys to every room roles determine who can do what in digital workspaces these roles ensure that each person has only the access required to do their part of the job nobody is granted unnecessary permissions that could lead to accidental disruptions or security risks in PowerBI service workspace roles are the backbone of efficient and secure collaboration workspace roles include viewer contributor member and admin let’s explore these roles in more detail beginning with viewer viewers are the audience they can look but can’t touch in other words they can view content without modifying or managing anything lucas can assign this role to managers stakeholders or anyone else who needs to be in the loop without directly impacting the workspace next is contributors contributors are there to add and modify content but they can’t adjust access permissions or delete items lucas should assign this role to those focused on adding content they can contribute to selecting content but don’t need to make bigger workspace adjustments workspaces also host members members can contribute to the content by adding and editing assets they can also add other members or collaborators with lower permissions however they cannot delete the workspace or manage user roles lucas can assign this role to regular team members who need to work on data or perform analysis and might also need to add others to the project and finally there’s admins admins oversee the workspace they have full control from adding editing and deleting content to managing user access and even deleting the workspace lucas can assign the role of admin to himself or another individual tasked with overseeing the entire project or workspace the chosen admin can keep the project running smoothly while ensuring everyone else performs their roles as required now that you’re more familiar with workspace roles let’s help Lucas to manage the roles in his shared PowerBI workspace lucas has uploaded the project’s report data set and dashboard in the adventure work sales workspace however roles must be assigned before the team can collaborate on this workspace first select manage access from the workspace environment all team members with access to the workspace are listed here for now it’s only Lucas who has access to add a new team member to the workspace and assign a role select add people or groups a brief information box appears stating that viewers cannot edit content in the workspace to add a team member search for their name or email in the search box for the first example let’s add Adio our fellow data analyst assign Adio the contributor role so he can collaborate on the content and press add adio is now added to the workspace next let’s add Renee the marketing manager as a viewer this role lets her access the workspace to view insights without making any changes lastly the IT department must be assigned the role of admin this role grants full permissions from content management to user access control locate the admin account in the search box select the admin role and add it to the workspace all roles have now been assigned select the back arrow to view the roles that everyone has been assigned select the down arrow on their permission to modify a role and alter it to another role for example Renee needs to be able to add users from her team to the workspace reassign her role to member to grant her these permissions having helped Lucas and his team organize their workspace you should now be familiar with workspace roles and the different types available and how they’re configured always configure workspace roles correctly to ensure your project runs smoothly and set your team up for success workspaces are useful for storing and collaborating on content but it’s important to keep this content organized and easily accessible workspace apps are a great way of organizing your content efficiently to be located quickly and easily in this video you’ll explore the basics of workspace apps their advantages and learn how to create one in Adventure Works each department accesses its reports and dashboards through PowerBI however navigating this content on PowerBI is complex and timeconuming as a solution Adventure Works wants to create departmentspecific apps so that each department can access its reports and dashboards quickly and efficiently let’s find out more about apps in PowerBI service and how adventure works can incorporate them an app in PowerBI is a collection of important assets like dashboards reports and data sets packaged together for ease of access these assets can be bundled together under a workspace they can then be published to the PowerBI service this enables a streamlined sharing and distribution mechanism for PowerBI content there are a few reasons why businesses like Adventure Works prefer to use apps to access content on PowerBI service one reason is ease of access with apps users don’t have to search through numerous reports and data sets everything they need is in one package this makes it quick and easy to locate content apps also facilitate version control when an app is updated users automatically see the latest version this ensures that everyone is on the same page apps also help with security apps maintain the same level of data security as individual reports access can be restricted to authorized users only and data can be secured at row level so users can only view what you want them to view these security measures are great for protecting your data finally apps can also be customized apps can be tailored for specific departments or roles within an organization for example Adventure Works can customize the app to show marketing data for the marketing department sales data for the sales department or financial data for the accounting department this makes Workspace apps incredibly flexible tools for data distribution now that you’re more familiar with PowerBI apps let’s explore the process for creating an app in PowerBI service adventure Works has created a workspace called Adventure Works Sales this workspace holds all content related to the company’s sales like reports and dashboards to create an app for this workspace select the create app option this opens the build your app window the window contains three tabs setup content and audience in the setup tab you must input key information about your app this includes the name description logo and color scheme you can also add contact information for publishers or other important individuals name the app Adventure Work Sales and add sales app as the description once you’ve input the required information select add content to move to the next tab in this tab select the add content option to add reports to the app adventure Works requires the orders report and product sales report select and add the reports once added the reports appear in the left sidebar you can preview the reports or adjust their order select the symbol to the left of the orders report and drag it to the bottom so it appears last in the app you can also select the down arrow on the right of add content to add separate sections to your apps let’s link to the Adventure Works site select add new section to add a new section the new section appears in the list rename it Adventure Works internal site press the down arrow again select add link name the link Adventure Works website and add the link in the opening field box select content area then in the section field box select Adventure Works website select add to add the link to the app then select next add audience to move to the next section the audience tab you can use the audience tab to manage access to your application anyone who can access the workspace can access the app by default you can add more users or groups from the search box or you can share your app with the entire organization for now let’s restrict access to workspace users select publish app to complete the process it might take a few minutes for the app to publish once it’s ready select go to app to view it the app is ready to use with the Adventure Works website as its landing page you can use the sidebar on the left to navigate its contents you should now be familiar with Workspace apps their advantages and how to create them in PowerBI service as you continue to work with PowerBI service use Workspace apps as useful tools to organize your content for quick access and more efficient projects workspaces are a useful tool for developers but how do you determine how widely used or effective your reports are with PowerBI workspace metrics features you can monitor the usage and effectiveness of your workspace content in this video you’ll learn about the importance of monitoring workspace and report usage utilizing the current report metrics and the new preview feature and you’ll explore how usage metrics enhance report and workspace efficiency lucas is responsible for monitoring the performance of his team’s PowerBI workspace and its content a strong understanding and efficient deployment of usage metrics will help Lucas monitor the effectiveness of his workspace and reports let’s explore these topics in more depth and find out how they can help Lucas monitoring workspace usage in PowerBI involves tracking how reports and dashboards are accessed used and shared within a workspace it provides a window into the effectiveness and reach of the deployed data solutions the insights gathered from this data enable data analysts to make informed decisions on optimizations security and resource allocation it’s important to understand how your content is used to measure its impact and effectively guide your efforts usage metrics act as feedback showing how reports and dashboards are accessed within the organization for example you might discover that your team references several reports daily or a certain dashboard isn’t receiving the number of views it should you can use these datadriven insights to improve the performance of these assets monitoring report performance ensures relevance efficiency and responsiveness aligning your work with organizational needs and user preferences monitoring is mainly performed using the PowerBI services usage metrics reports or monitoring reports you can enable these reports for every workspace giving insights into how frequently users access them the initial usage report in PowerBI primarily focuses on individual report metrics providing details such as the number of views shares and user interactions on a per report basis for example Adventure Works evaluates the performance of its global marketing reports by tracking views and user interactions the company also measures how the report has been shared to gauge engagement across its worldwide workforce the usage metrics report is instrumental in understanding the performance and user engagement of your workspace reports powerbi service offers its users the option to switch to a preview version of the new workspace metrics feature this new feature expands monitoring from individual reports to the entire workspace providing additional insights into report performance some of these insights include aggregated metrics which encompass all KPIs analyzed in the old usage reports and add report performance information this feature compiles all of Adventure Work’s previously analyzed KPIs and integrates report performance data to provide a comprehensive set of metrics other insights include the typical opening time of the report with daily and weekly breakdowns lucas uses this data to track the average report loading times to help ensure a smooth user experience and this feature also provides information on all workspace reports instead of a specific one lucas uses this data to understand how his reports are performing so he can improve their content you can also access a detailed FAQ article containing all relevant capabilities and a description of this rich new feature to run and access the usage metrics data you’ll require the following prerequisites you need a PowerBI Pro or premium per user PPU license to run and access the usage metrics data however the usage metrics feature captures usage information from all users regardless of the license they’re assigned to access usage metrics for a report you must have edit access to the report and finally your PowerBI admin must enable usage metrics for content creators your PowerBI admin may have also enabled collecting per user data in usage metrics ensure these prerequisites are established before running or accessing the usage metrics data in this video you’ve learned about the importance of monitoring workspace and report usage utilizing the current report metrics and the new preview feature and you explored how usage metrics enhance report and workspace efficiency monitoring workspace usage with PowerBI’s workspace metrics preview feature improves our understanding of data usage across the organization aligning with informed decision- making and resource efficiency as a data analyst your role includes tracking how users engage with your data with the workspace usage report you can review insights into workspace activity and user engagement you can then use these insights to optimize your data and reports in this video you’ll learn how to enable the workspace usage report feature in PowerBI generate and navigate a usage metrics report for a specific workspace report and interpret key metrics to gauge user engagement and report interaction lucas has uploaded a product sales report to his workspace he needs to check that his data analytics team has reviewed this report lucas can use the usage metric and workspace usage reports to monitor the team’s engagement with his product sales report let’s help Lucas achieve his goal by guiding him through this process the usage metrics report in PowerBI is important for understanding how individuals interact with reports and dashboards it is an insightful report that can be launched and viewed on any workspace report the new workspace usage report feature enhances this by providing even more detailed insights it allows a closer look at how workspaces are used not just individual reports thanks to these reports users can now view an enhanced overview of basic report metrics the report usage tab lets users better understand each report’s performance with more detailed usage metrics that provide data on topics like views and users the report performance tab provides a breakdown of a report’s effectiveness with detailed insights into specific report interactions and their impact users can also use the report list tab to explore how all the reports in the workspace are performing making it easy to compare their performance and success and the FAQ tab provides easily accessible answers and guidance adventure Works can use the new workspace usage report feature to align resources and strategies with actual user interaction and needs enhancing their performance and user experience now that you’re more familiar with usage reports and the new workspace usage report feature let’s create one for Lucas from the PowerBI home screen navigate to workspaces and select the adventure work sales workspace here you can view the content uploaded to this workspace to enable the usage metrics report on the product sales report hover over the report item and select the ellipsus symbol to access the reports options locate and select the view usage metrics report option to launch the monitoring report if this is your first time accessing the usage report PowerBI will need a few moments to create it in the usage metrics report you can find information on report views and unique views by day total report views and a list of all users who access the report there are also slicers available for your data that can filter the usage report based on distribution method this feature highlights users that the report was shared to or workspace users who access the report you can also slice based on the platform the users use to access the report either from a browser or mobile lastly you can even filter by viewing the usage of separate report pages to enable the new monitoring feature toggle the new usage report to on this transforms the usage report to the new workspace usage report this new feature contains four separate pages with monitoring tools on the first page report usage you can identify metrics like the old report with updated visualizations and separate graphs instead of slicers for example you can see that 100% of report access has been conducted through PowerBI.com instead of mobile also selecting pages on the bottom right visualization shows that the order report page takes up 57% of the views on the second page report performance you can see the loading time of the report based on date user country of browsing and the internet browser used this is a significant page when troubleshooting long loading times on reports on the third page report list the new usage report feature allows users to monitor the usage of every workspace report from this single view you can see the familiar tools from the old usage monitoring report now enabled through all workspace reports the fourth and last page FAQ contains a detailed guide on all metrics and terminology used in this new monitoring feature it explains the usage of every tool in detail all this information can easily be exported to Excel and analyzed making monitoring and reporting on the workspace usage easier than ever in this video you’ve learned how to enable the workspace usage report feature in PowerBI generate and navigate a usage metrics report for a specific report within a workspace and interpret key metrics to gauge user engagement and report interaction with these reports you can optimize your workspace and its reports so that they meet the needs of your team by now you’re familiar with generating insights into data insights are generated from data sets and these data sets in turn rely on timely accurate data flow from different sources over the next few minutes you’ll learn about the basics of data sets in PowerBI service explore the relationship of data sets to data flows and reports and compare scheduled and incremental refreshes in data sets adventure Works data sets are dynamic they’re continually updating as they receive new data from different sources the company must ensure that its reports capture this latest data so they’ve tasked Lucas with integrating its data sets and data flows let’s take a closer look at how data flows into data sets a data set in PowerBI is a collection of data you import or connect to this data can come from a single source or multiple sources once captured it forms the basis for your reports and dashboards every data set’s unique structure and metadata influences the analysis you can perform let’s break down this relationship further as the previous example shows data sets act as a bridge between data flows and reports in PowerBI data flows collect and transform data from various sources like SQL databases and Excel files these data sources are then loaded into data sets these data sets a collection of processed data feed into the reports this enables analysts to derive insights effortlessly the symbiotic relationship ensures a streamlined data flow from extraction to visualization let’s look at an example of how the Adventure Works sales department can use data flows to consolidate and prepare data for analysis an adventure works data flow may collect sales data from different regions using a complex network of data sources it then cleans this data by removing duplicates and transforming the remaining data into a unified format once this process is complete the cleansed and transformed data is loaded into a data set data analysts can use this data set to create a report to analyze sales trends compare regional performance and identify growth opportunities it’s important to remember that all data sets must be frequently refreshed to include updated data this is to ensure that your insights are as current as possible you can manually refresh your data set any time but with PowerBI you can also plan a refresh to occur automatically there are two main ways to automatically refresh your data in PowerBI service a scheduled refresh and an incremental refresh both refresh mechanisms are vital for maintaining the accuracy and relevance of data in the PowerBI service let’s take a closer look at these methods a scheduled refresh is a set routine where the entire data set is refreshed at specific intervals for example Lucas has scheduled a daily refresh for 2 a.m each morning in the Adventure Work sales workspace to ensure data remains current however be careful when using scheduled refresh it could be resource inensive for large data sets an alternative more resource efficient method is to use incremental refresh unlike a scheduled refresh an incremental refresh only updates the parts of the data set that have changed as you saw in the previous example Lucas sets a scheduled refresh at 2 a.m daily for the primary sales data set to capture the previous day’s data however he can also set an incremental refresh every hour for the continuously updated online sales data set this incremental refresh captures new sales data without reprocessing the entire data set this way Lucas efficiently keeps data sets current ensuring reliable analysis and reporting at Adventure Works both refresh methods help Lucas keep his reports timely and actionable you should now be familiar with the basics of data sets their relationship with data flows and reports and understand the difference between a scheduled and incremental refresh data sets are central to PowerBI and they’re a valuable part of your analytical toolkit leverage data sets effectively for greater insights and informed decision-making powerbi is a fantastic service for data analysis however to get the most out of it you must ensure it has a secure and stable connection to your data with PowerBI gateways you can create a strong safeguarded bridge between PowerBI services and your on premises data over the next few minutes you’ll discover how to connect data with PowerBI gateways explore the different types and uses of gateways and learn how to set up and manage gateways adventure Works stores large amounts of data on premises lucas and his data analytics team must connect to this data securely and reliably using PowerBI the team can leverage PowerBI gateways to establish a secure and reliable connection between on premises data and PowerBI service so why is PowerBI gateways a solution for Adventure Works powerbi gateways establish a secure and reliable connection or bridge between your on- premises data and the PowerBI service on Microsoft’s cloud this connection allows PowerBI service to access and retrieve data from on premises data sources this enables organizations to keep their data secure while benefiting from the PowerBI services cloud-based analytics and sharing capabilities powerbi gateways interact with on premises data in two ways the first is a data refresh gateways facilitate the scheduled refresh of data sets pulling the latest data from the source to PowerBI for example Lucas can use the gateway to schedule a daily refresh of adventure works on premises sales data this ensures that the sales team has the latest figures ready for analysis in PowerBI every morning the second type of interaction is query execution gateways help execute queries against the data source to retrieve updated data lucas opens the latest iteration of Adventure Works sales data report and executes a query to identify yesterday’s total sales the gateway helps Lucas to execute the query against the sales report there are three main types of gateways in PowerBI each suited to different scenarios the on- premises data gateway the on- premises data gateway personal mode and the Azure virtual network or V-Net data gateway which type of gateway you choose depends on the setup of your organization and its specific data management and security requirements let’s find out more about each type beginning with the on- premises data gateway the on premises data gateway suits multiple users sharing and refreshing data across many Microsoft services including PowerBI it’s very versatile which makes it useful for diverse organizational setups the gateway supports all types of connections from PowerBI like import data scheduled refresh direct query and live connection quick access to and support for these connections is important in real time data interaction for example each Adventure Works department requires access to different data sets stored on premises these data sets can be managed centrally with an on premises data gateway this setup lets multiple users refresh and access the data they need across different Microsoft services next let’s review the on premises data gateway personal mode the personal mode is tailored for single user scenarios it supports connections to local data sources such as SQL Server and Excel which is useful for individual users or analysts it’s also designed to be easy to set up and once setup is complete the gateway requires no additional configurations for data sources this offers a much less complex solution for business analysts who want to publish and refresh PowerBI reports with minimal hassle however this gateway supports only one type of connection import data or scheduled refresh and it’s designed only for PowerBI so it doesn’t support other applications lucas can use the personal mode of the on- premises data gateway to manage data sets he doesn’t want to share with the rest of the team with this straightforward setup he can refresh the data without going through the central gateway and finally there’s the Azure virtual or V-Net data gateway the Azure virtual network or V-Net data gateway best suits complex organizational setups by offering enhanced security and data management features within a virtual network it helps cut the costs or overheads of installing updating and monitoring on premises data gateways by virtually bridging PowerBI to supported Azure data sources this gateway securely communicates with the data source executes queries and transmits results to the PowerBI service as Adventure Works grows it requires better security and data management a V-Net is a great solution it enables secure data transfer and the ability to manage the data environment it provides a secure pathway for data that adheres to the company’s organizational security policies and it keeps data refreshed and readily available for analysis in PowerBI you should now understand how to connect data with PowerBI gateways the different types and uses of gateways and how to set up and manage gateways with a strong understanding of gateways you can establish an efficient and secure connection between your on premises data and PowerBI impactful insights depend on access to the latest data an analysis based on outdated data isn’t of much use to anyone configuring a regular PowerBI data refresh ensures your reports and dashboards are consistently synced with the latest data by the end of this video you’ll understand the importance of configuring a data set refresh and know how to configure a scheduled ondemand and incremental refresh adventure Works needs daily updates on its marketing campaigns and sales so Lucas must ensure that the reports and dashboards his team relies on for analysis contain the latest available data let’s help Lucas configure a data set refresh so his team is working with up-to-date information first access the adventure works sales workspace the workspace contains a new report on marketing campaigns access the report settings to plan a scheduled refresh select schedule refresh from the settings to navigate to the data set refresh settings the last refresh failed because the credentials weren’t entered when the data set was uploaded to the cloud navigate to the data source credentials category and select edit credentials this report is connected to the Adventure Works SQL database so input your Adventure Works SQL database username and password then select sign in next navigate further down the menu and expand the refresh settings toggle the setting on to activate the scheduled refresh check that the refresh is configured daily between 6:00 a.m and 100 p.m coordinated universal time or UTC the scheduled refresh is now ready navigate back to the workspace once the credentials are set you can manually refresh the data set whenever needed to demonstrate let’s refresh the orders report hover over the report and select the circular arrow this is the refresh icon selecting this icon performs an ondemand manual refresh of the data set next let’s configure an incremental refresh on the sales transaction report navigate to Power Query Editor on the PowerBI desktop to issue an incremental refresh you must now create two parameters one that determines when the refresh begins and another that states when it should end select manage parameters then new parameter in the manage parameters dialogue box name the first parameter range start assign it a date time parameter type and provide January 1st 2000 as the current value right click the parameter and select duplicate to create a copy this copy is now your second parameter rename it range end next select the sales table and identify the order date column select the columns down arrow access date time filters then custom filter in this window keep the rows where order date is after or equal to select parameter and input range start for the and option select before parameter and range end on the second row your configuration is now ready select okay then close and apply to return to PowerBI desktop right click on the sales table and select incremental refresh toggle the incremental refresh on configure the settings to archive data older than two years and incrementally refresh data from the last seven days each data set refresh will now remove transactions that occurred over two years ago and they’ll refresh only transactions that occurred in the last seven days note that as the info box states the report must be uploaded to the PowerBI service for the refresh policies to occur apply your changes and save your report lucas and his team are now working with the latest data and you should now understand the importance of configuring a data set refresh and how to configure a scheduled on demand and incremental refresh great work analyzing data involves working with many different data sets so it’s important to distinguish reliable data sets from unreliable or misleading ones to ensure your insights are accurate with PowerBI you can endorse promote and certify reliable data sets to clarify which ones you and your team should work from in this video you’ll understand the importance of data set endorsement differentiate between promoting and certifying data sets and learn how to promote a data set in the PowerBI workspace over at Adventure Works the sales workspace is cluttered with many data sets it’s difficult for Lucas and his team to determine which ones to work with lucas decides to identify and endorse reliable data sets to help his team maintain data integrity in their workspace let’s discover more about endorsing data sets then use our new knowledge to help Lucas and his team endorsing data sets involves identifying and marking reliable data sources in your workspace to ensure your team works with quality content you can endorse data sets in PowerBI from the endorsement and discovery menu data set endorsement in PowerBI comprises two levels promoting and certifying promoting a data set indicates that you trust its content and view it as ready for organizational use when you promote a data set a promoted icon appears next to it in the workspace when a data set is flagged as trusted it becomes easily discoverable and the team knows it’s reliable you can also certify a data set this is a higher level of endorsement it symbolizes that the data set meets the company’s stringent quality and compliance standards however content certification is a big responsibility only authorized users can certify content so this option is typically only available to workspace owners over at Adventure Works Lucas is the workspace owner that means he is the only team member who can certify data sets next let’s review the process for endorsing content in PowerBI by helping Lucas promote reliable data sets access the Adventure Works sales workspace to view all available data sets select filter then data set the team has been using the marketing campaigns report a lot recently it’s filled with high quality data that has delivered many great insights lucas has decided it can be endorsed as trustworthy content to begin the endorsement process hover over the data set to reveal the ellipsus symbol select the ellipsus then settings in settings locate and expand the endorsement and discovery section check the promoted option then check make discoverable so other users can identify the endorsed data set select apply to finish configuring the settings select adventure work sales from the navigation pane to return to the workspace navigate to the right of the workspace the marketing campaigns report data set is now marked as promoted the promoted flag draws the attention of the workspace users to the report and lets them know it’s suitable for analysis great work you’ve helped Lucas identify and endorse a reliable report that his team can use for analysis and you should now understand the importance of data set endorsement be able to differentiate between promoting and certifying data sets and know how to promote a data set by endorsing data sets you ensure your team works with and draws insights from reliable and consistent data anna oversees quality at the Spiro Car Company today she has a big meeting with senior leadership spiro has been manufacturing electric vehicles for the last 8 years and business is booming or at least it was lately there have been concerns about manufacturing time and quality business has slowed sales have dropped and morale is low and Anna unsurprisingly is worried luckily one thing Anna never worries about are statistics they never lie each machine in the assembly line reports statistics to a central database in the manufacturing facility unfortunately dumping data on her manager’s desks won’t solve the problem this time she has heard her colleagues discuss using PowerBI for analyzing data but Anna prefers the old ways and stores everything locally on a central database but what if she could somehow convert her data stack into a coherent interactive visual if so she would be one step closer to figuring out where quality is slipping and more importantly providing the leadership team with the answers they need she meets with Dennis and outlines her predicament he explains the on premises gateway to her this gateway will bridge the gap between Anna’s on premises data and PowerBI and best of all the data transfer is completely secure this means that she can access all the features of PowerBI using the data stored locally on her laptop a great solution after a quick guide through the basics from Dennis and a chat with it about requirements Anna is ready first she installs the gateway on the database server and signs in with her work account to register the gateway anna can now connect all the data she stores locally to reports and dashboards in PowerBI she can even configure a refresh schedule or perform an ondemand refresh she starts running reports building rich data visualizations and identifying interesting business insights she discovers that the main issue in the Spiro manufacturing supply chain process is a delay in delivering the car’s high-capacity battery packs the supplier also fails to deliver enough batteries which leads to further delays the quality slips as the assembly team tries to make up for these delays anna can’t believe how straightforward it was to convert her on premises data using the gateway and the best thing about it she doesn’t have to say goodbye to her older methods of storing her data locally anna arrives at the leadership meeting with an interactive dashboard to outline her findings and a plan to resolve the issue senior leadership decide to use Anna’s data analysis to develop a remediation strategy spiro switches to a more reliable supplier for their battery packs and they put better measures in place to review quality analytics so they can act before another issue occurs thanks to Anna Spiro’s business once again is booming when deploying content in PowerBI it’s important to ensure the data is safe and that the change is handled efficiently that’s why analysts make use of structured deployment over the next few minutes we’ll explore PowerBI’s deployment pipelines for streamlined project management in this video you’ll learn about PowerBI’s deployment pipelines recognize the importance of separate environments and explore how to enhance data security through structured development over at Adventure Works Lucas has been tasked with using PowerBI service to improve the company’s development process he must ensure that the data of all new content deployed to the workspaces remains accurate and secure during the report development stages let’s help Lucas achieve this deployment pipelines in PowerBI help content move smoothly through development testing and production stages this allows for controlled testing and validation of content before it reaches end users let’s explore these three stages of deployment in more detail first we’ll examine the development environment here developers can add new content without changing current reports this is the first step in the deployment process this is where developers can create and modify PowerBI reports any errors or issues at this stage have no impact on the existing production data for example Lucas improved a sales report by adding a new visual in the development stage ensuring it matched branding guidelines next let’s explore the test environment this is where a small group of testers review and test new reports for issues before they’re used in production providing feedback and checking for bugs and data problems here reports are validated for accuracy performance and any potential bugs before moving to the production environment for example Lucas can move his new visual from development to the testing phase this will allow for the testing team to check the accuracy and performance of the new visual lastly we’ll investigate the production environment once new reports and features are tested they’re ready to be used by the end users in the production environment this is the last step in the process for example once Lucas’ new visual has been validated through testing it is moved to the production environment once in the production environment users and stakeholders will be able to use the new feature however not all three development environments must be included in a deployment pipeline for example the testing phase could be excluded if it’s not considered necessary there are several benefits of a structured development life cycle by having distinct environments you can ensure that unvetted changes do not corrupt the production data a structured life cycle allows for comprehensive testing ensuring that the data remains accurate and reliable and deployment pipelines provide a streamlined process for managing changes enabling better control over the development process let’s find out how a structured development process helped Adventure Works in a realworld example lucas improved a sales report by adding a new visual in the development stage ensuring it matched branding guidelines after moving it to the test environment and thorough validation the report went to production this example showcases how PowerBI’s deployment pipelines ensure a smooth and accurate transition of content benefiting data accuracy and decision-making at Adventure Works using PowerBI’s deployment pipelines for a structured development process ensures safe data handling in this video you’ve learned about PowerBI’s deployment pipelines the importance of separate environments and enhanced data security through structured development with PowerBI’s deployment pipelines you can effectively manage changes with separate environments allowing for accurate and secure sales data while reducing risks and improving control and efficiency it’s important to catch potential errors in your pipelines to ensure your data is accurate for end users with PowerBI deployment pipelines you can catch these errors and ensure a smooth transition from development to production in this video you’ll learn how to access and configure a PowerBI service deployment pipeline how to allocate existing workspaces to their respective environments and how to oversee and monitor deployment history and settings a minor error in PowerBI report development could mislead end users lucas needs to use deployment pipelines to ensure changes are tested to enhance reliability and efficiency let’s guide Lucas through this process access the deployment pipeline icon on the left navigation pane on the PowerBI service homepage on smaller screens you might need to select the more ellipsus button in the navigation pane to locate and select the deployment pipelines an introductory screen with the pipeline capabilities appears select create a pipeline to begin streamlining the data processes the create a deployment pipeline window appears on the screen enter sales pipeline as the pipeline name and sales reports deployment pipeline as the description then select next three default environments appear on the screen you can add more environments by selecting the add button and naming them you can also remove environments by selecting the bin icon for this example let’s keep only the development and production environments of PowerBI we’re now on the deployment pipeline page note that the workspaces assigned to the environments must be created beforehand in this case the main workspace we’ve been using has been renamed to Adventure Works Sales Development highlight it in the development environment and select assign workspace next select the newly created Adventure Works Sales Workspace in the production environment and assign it after assigning both a warning pop-up appears indicating differences in content between the two environments select deploy in the test environment to confirm that the changes made by users in development have been approved they can now be deployed in the production environment where end users have access select deploy to begin the process a green tick appears at the end indicating that the two environments are now synced and no new changes are to be deployed for now several important features of the pipelines appear in the top ribbon you can adjust the pipeline settings from the ribbon manage access to the environment and view the deployment history the history contains necessary information such as the deployment user the number of items deployed and the final process status lucas has improved Adventure Works sales reports you can do the same by setting up a deployment pipeline to ensure smooth transitions from development to production minimizing errors and enhancing data integrity in this video you learned how to access and configure a PowerBI service deployment pipeline allocate existing workspaces to their respective environments and oversee and monitor deployment history and settings maintaining a workspace often requires updating its components however an update to one component could affect multiple others with lineage view and impact analysis you can understand how your components are related and how changes impact the workspace in this video you’ll learn about the core concepts of data lineage and impact analysis the functionality and benefits of the lineage view and you’ll also explore the impact analysis feature and its role in data management over at Adventure Works Lucas needs to update the SQL server his workspace depends on however several other workspaces also depend on this same server lucas must determine what components rely on this server and how they’ll be impacted by the changes he makes to it you can help Lucas by working with him to incorporate lineage view and impact analysis into his workflow let’s begin by understanding what these terms mean lineage view simplifies data tracking by showing its journey from source to destination it visually connects data elements by revealing the relationships between data sets data flows reports and dashboards these data elements are presented using a parent child relationship the parent child relationship shows how data elements are connected in a sequence parents are the starting points and children follow as subsequent steps in the data journey this helps to provide a clear picture of the connections between the data in your workspace lucas can use lineage view to manage his workspace by identifying and updating outdated data sets this ensures that his team works from the most recent and accurate reports another valuable tool in PowerBI is impact analysis impact analysis complements lineage view it helps you to understand how changes in your workspace affect different components it provides an overview of how data is used this feature helps you to make informed decisions when modifying data your data sets are intertwined with your reports workspaces and dashboards a change to one asset can affect multiple others once you understand how changes impact your workspace you can inform the rest of the team and ensure everyone can use the updated data effectively now that you’re more familiar with lineage view and impact analysis let’s explore how Lucas can incorporate them into his workflow when you log into a workspace you are presented with the default list view this view displays workspace items such as reports and dashboards to switch to the lineage view select the lineage view icon this view is only available to the admin contributor and member roles in lineage view you can explore the relationships between all your workspaces content for example in the adventure work sales workspace a SQL server database serves as the data source for both data sets in the workspace reports have also been created for both data sets additionally both reports have visualizations pinned to a single dashboard the sales dashboard selecting any component brings up a window with its details on the right hand side of the screen select the SQL server as this is the component to be modified selecting this component brings up information such as the server and database name the privacy and authentication methods and the status of the gateway which indicates that the connection is currently active select the X icon to close the window data sets also display their last refresh date and time you can refresh a data set on demand by selecting the refresh button this is the basic lineage flow in a workspace workspaces with larger data pools are more complex various reports could stem from a single data set this generates numerous end dashboards the show lineage button on every component is helpful in these situations you can select the arrow to highlight the entire lineage flow the most important feature of the lineage view is impact analysis select the screen icon on any lineage component to open the impact analysis window in this instance select the Adventure Works SQL Server data source the impact analysis window displays all components a SQL Server data source change affects the affected components are referred to as child items the asset you modify is the parent item in this instance modifying the Adventure Works server the parent item would impact six child items spread across three different workspaces you can also view the list of child items by type or workspace by selecting the buttons on the right before you modify the server you need to notify all team members impacted by your actions you can use the notify contacts feature to message all affected individuals you can also add a note to describe the impact in this video you learned about the core concepts of data lineage and impact analysis the functionality and benefits of the lineage view and the impact analysis feature and its role in data management lineage view and impact analysis in PowerBI boost data management you can easily track data history keep data updated and understand changes and effects these features make decision making smarter and data management smoother you interact with many different assets in your workspace and it’s important that they can be accessed quickly however some assets like reports can take longer to load the more you use them luckily PowerBI offers a caching feature you can use to optimize your workspac’s performance in this video you’ll learn about the fundamentals of query caching in PowerBI how caching interacts with import mode and the application of caching adventure Works data analysis team has been using the marketing campaign report heavily as a result of all these changes the report takes longer to load each time it’s accessed the team needs to make use of caching to improve the report’s performance let’s find out how caching is the process of temporarily storing query results this enhances performance by minimizing the time and resources required to fetch data accessed regularly for example the analytics team queries the marketing campaign report hundreds of times daily each query involves retrieving and processing significant data from the database this can strain the system and slow down the reporting process caching helps by saving frequently requested data like the marketing campaign report so it doesn’t need to be fetched from the database every time this speeds up the analytics process and reduces strain on the system there are many benefits to query caching first it offers faster performance with caching you can return reports and queries faster especially for frequently used static data sets it also preserves bookmarks and filters so that they don’t need to be reapplied or reset each time a query is run caching also offers personalized data access each user receives their own cached query results for a personalized experience query caching also follows all security rules which means that caching maintains data security without compromising compliance and lastly caching reduces the computing load on your workspace saving resources however query caching has certain limitations it is exclusive to import mode and not applicable for direct query and live connection modes not all users have access to query caching it is only available with a PowerBI premium or embedded subscription there are also other potential limitations clearing the cache when switching from on to off can cause a brief delay for ondemand queries and finally during data set refreshes the query cache updates and may impact performance with high query volumes now that you’re more familiar with query caching let’s help the Adventure Works data analytics team make use of this feature to improve their reports performance first open the Adventure Works sales data set where the report is located this report is used often which affects its loading speed so it’s a good candidate for query caching to use query caching hover over the marketing campaigns report data set select the ellipsus symbol and choose settings from the options in the settings menu navigate to and expand the query caching options query caching is turned off by default to enable query caching select on and then select apply this caches all bookmarks and filters on the initial report page the report will now open faster if you try to disable query caching a pop-up appears this pop-up warns that turning off query caching will result in saved queries being deleted the next time someone opens the report they may experience a slight delay during their first use this applies to both options with query caching disabled in this video you’ve learned about the fundamentals of query caching in PowerBI how caching interacts with import mode and the application of caching using query caching in PowerBI improves report speed and resource efficiency streamlining your data analytics journey it’s a smart way to optimize performance maintaining uninterrupted service connectivity in PowerBI is important for timely and accurate data analysis by understanding the most common connectivity challenges and how to troubleshoot them you can perform analysis without issue in this video you’ll learn about the most common connectivity issues in PowerBI how to rectify refresh failures caused by credential modifications and the process of configuring notification settings for multiple users over at Adventure Works Lucas has been alerted to a supply chain optimization project report that failed to update because of a credential change to troubleshoot this issue he must fix the schedule he also needs to add another team member to the notifications in case the updates fail again when he’s unavailable let’s help Lucas fix the report and ensure that Adio is notified the next time there’s a problem but before we do let’s learn more about troubleshooting service connectivity issues powerbi service connection problems can lead to data set refresh failures with various causes to fix this a clear troubleshooting plan is needed this involves checking the gateway configurations resolving data refresh issues and ensuring data source settings are correct by following this process users can improve service connectivity leading to smoother data analysis in PowerBI it’s also important to correctly set up notification settings to alert the right people about refresh failures this ensures quick action can be taken to resolve any issues let’s start by exploring some of the most common connectivity issues as you’ve just learned most connectivity issues in PowerBI fall under the umbrella of three main categories the first of these we’ll explore is gateway configuration the first step is to check the gateway connectivity status by verifying that a gateway connection is active and running on your data sources the next step is to ensure you’ve selected the correct gateway choosing the correct gateway facilitates a reliable connection to your data sources this ensures that your reports and dashboards have the most accurate and up-to-date information and you must also check that you’re using the latest gateway version an updated gateway ensures a solid connection between PowerBI and your data sources another category is data refresh issues this can include issues like unsupported data sources that do not support refresh operations understanding the nuances of these data sources and rectifying such issues is essential for ensuring that your reports reflect the most current data it’s also important to perform a scheduled refresh check testing the accurate configuration of the scheduled refresh is vital in preventing data latency a well-configured scheduled refresh guarantees that your data is updated regularly and that the insights derived from your reports are based on the latest available data finally there are also data source settings an example of this is data source misconfigurations addressing any misconfigurations in your data source settings promptly ensures uninterrupted data retrieval a malfunctioning data source may prevent the connection with PowerBI blocking the refresh processes and there’s also credential verification verifying the credentials for your data sources helps prevent unauthorized access and resolve connectivity issues ensuring the credentials are accurate and upto-date is fundamental for maintaining a secure and reliable connection to your data sources let’s discover how these issues can be solved by taking a few moments to help Lucas troubleshoot his PowerBI connection navigate to the supply chain optimization project workspace to address the data set that failed to refresh a red exclamation mark next to the refreshed column indicates that the refresh has failed to complete select the warning icon to view details of the error in the report settings menu immediately when opening the settings Lucas identified that the last scheduled refresh failed this resulted in the refresh being disabled by PowerBI so the error resulted from this failed refresh let’s troubleshoot this error scroll down and check the gateway and cloud connection options verify that the personal gateway is running on the database and does not pose an issue with the connection between the data source and PowerBI the next set of options data source credentials states that the data source failed due to incorrect credentials this is the cause of the connection issue select edit credentials to fix this and enter the new login credentials leave the rest of the settings as they are and select sign in the connection has now been reactivated scroll down to the refresh settings expand the options and select on to enable a daily refresh in the next section check the these contacts box to add AIO to the contacts list adio will now be notified if a refresh failure occurs again in the future in this video you learned about the most common connectivity issues in PowerBI how to rectify refresh failures caused by credential modifications and the process of configuring notification settings for multiple users by rectifying credential errors reconfiguring scheduled refreshes and ensuring the right individuals are notified about refresh failures you’ll ensure the accuracy and timeliness of your data congratulations on reaching the end of these lessons in deploying assets during these lessons you explored creating monitoring connecting to and maintaining workspaces and data sets in PowerBI let’s take a few minutes to recap what you’ve learned so far you began the first lesson by exploring the concept of a workspace you learned that a workspace is a specialized area in PowerBI that holds important assets like data sets reports and dashboards its advantages are that it helps to organize assets for easy management provides security through access control as only permitted users can access workspaces a workspace also enables collaboration teams can use them to build reports and workspaces let analysts update or modify data quickly there are two types of workspaces in PowerBI the first is a personal workspace which you can use to store your own personal content the second is a shared workspace where a team can collaborate on reports and dashboards always follow best practices in your workspace like performing regular cleanups establishing clear naming conventions safeguarding your data regularly backing up your work and seeking feedback from your team on improvements that could be made to the workspace the process of creating a workspace is very straightforward a workspace can be created by selecting the new workspace option from the workspaces tab in PowerBI when creating a new workspace you must consider workspace roles workspace roles determine who can perform each task workspace roles include the following viewers can view content but can’t modify it contributors can add and modify content members can alter content and add new members and admins have full control over the workspace assets and its members you can manage these roles using PowerBI’s manage access feature during this lesson you also created a shared workspace for Adventure Works where Lucas’ team could collaborate on reports in the next lesson you learned how to monitor workspaces this involves tracking how reports and dashboards are accessed used and shared within a workspace by monitoring a workspace you can measure its impact and make changes to increase its usefulness monitoring is performed through usage metrics and monitoring reports these reports provide details like how a report was used or an overview of a report’s performance you can create a usage metrics report in a workspace from a reports options list there are also slicers for your data that can filter report data powerbi automatically creates a usage metric report data set when you create a usage metric report the credentials for accessing this report must be carefully managed so that it can be refreshed and accessed as required in the third lesson you explored the topic of data sets and gateways in PowerBI a data set is a collection of data you import or connect to it can come from one or multiple sources the captured data forms the basis of your reports the captured data must be the latest available information this ensures that your reports are accurate you can use a data refresh to ensure accurate data a scheduled refresh is a routine that refreshes an entire data set at specified intervals you can configure a refresh by selecting the scheduled refresh feature from your reports options ensure you enter the correct details and credentials so PowerBI can access the report an incremental refresh updates only the parts of the data set that have changed this is a more resource efficient alternative you can configure an incremental refresh from Power Query Editor this involves creating two parameters determining when the refresh begins and when it ends promoting and certifying data sets lets you inform your team where to access the most current and reliable data promoting a data set indicates you trust its content and it’s ready for use certifying a data set states that it meets the company’s highest standards you can promote and certify data sets from PowerBI’s endorsement and discovery menu you also explored establishing a secure reliable connection between your on premises data and PowerBI service using data gateways these gateways enable you to perform a data refresh or query execution securely there are three types of gateways in PowerBI the on premises data gateway the on- premises data gateway personal mode and the Azure virtual network or V-Net data gateway which gateway you choose depends on your organization’s setup and its data management and security requirements you also practiced your new skills with an exercise in which you configured a data set for Adventure Works you also worked through a knowledge check which tested your knowledge of these topics and an additional resources item in which you explored Microsoft learn articles on data sets and gateways in the fourth and final lesson you learned how to maintain workspaces and data sets you began the lesson with an overview of development life cycles powerbi contains deployment pipelines that help move content through the following life cycle stages development in which new content is added testing in which content is reviewed for issues before it’s used in production and production when reports and features are deployed to end users the benefits of a structured development life cycle include data safety data integrity and efficiency and control you can access the deployment pipeline in PowerBI from the navigation pane this feature can create customize and manage pipelines or environments another useful feature for maintaining your workspace is the lineage view this simplifies data tracking by showing the data journey from source to destination with all the connections in between impact analysis helps you understand how changes to your data can impact or affect different assets in your workspace you can alternate between these views in PowerBI you’ve now reached the end of this summary it’s time to move on to the module quiz where you’ll test your knowledge of the topics you’ve covered best of luck data analysts often find themselves working with sensitive data as such they often need to think about the responsibility of handling such information safely in this video you’ll learn how to identify sensitive data and review measures that can be taken to protect data at Adventure Works a data breach could lead to legal trouble loss of trust and a competitive disadvantage safeguarding sensitive data is important for protecting its reputation and success data analysts must handle sensitive data with care so how do we tell the difference between regular data and sensitive data sensitive data contains important information about a business or its stakeholders that if mishandled could cause harm or misuse here’s a simple rule if it’s information that could damage the company’s reputation finances or stakeholder privacy it’s sensitive data for example general sales figures for a particular region might be considered regular data but a detailed list that breaks down customer details financial records employee information or even proprietary business knowledge is sensitive data any information that offers intimate knowledge that isn’t meant for circulation can be classified as sensitive the consequences of mishandling sensitive data can have multiple serious consequences both at business and employee level for example an email containing sensitive product designs for Adventure Works next big launch is inadvertently sent to an external vendor a mishap could give competitors an advantage or lead to legal problems if designs were patented also think about the impact of an employese’s personal data leak this could breach privacy laws resulting in fines and harm trust between employees and management one mistake can bring financial losses legal troubles and brand damage as you navigate the world of data it’s important to be equipped with a security toolkit let’s explore the various measures that can be implemented to ensure data remains in safe hands before a user can access a report they need to prove that they are who they say they are adventure Works operates globally so everyone accessing the PowerBI platform must be verified an authentication system requires users to input a unique identifier that ensures only authorized personnel can access data once a user is authenticated the system determines what data they are permitted to access this protects Adventure Works from internal leaks and unauthorized external breaches in PowerBI you can define roles for users as each role has specific permissions tied to it since employees within Adventure Works have varied job functions PowerBI allows roles to be customized ensuring data is distributed on a need to know basis for instance a product management analyst role might be permitted to see inventory levels reports while the human resources analyst can access employee reports regularly reviewing and updating these roles is essential to ensure they align with organizational needs and changes another measure used to protect sensitive data is rowle security rowle security or RLS is like a detailed filter where users can view only the data rows they are supposed to based on their role or identity for example a regional manager for North America at Adventure Works might only need to view sales data for North America and not Europe rls ensures specific rows of data in PowerBI are shown only to authorized users safeguarding regional strategies and preventing potential conflicts of interest another measure used to safeguard data is encryption adventure Works intellectual properties such as proprietary bicycle designs and vendor contracts are invaluable the company can use encryption to ensure that only authorized individuals can read this data as data moves between systems or across the internet it is susceptible to interception encrypting this data ensures that even if someone gains unauthorized access they can’t decipher the information this helps protect business interests as a global company Adventure Works data is often accessed from around the world encrypting data while it’s being transmitted ensures it can’t be accessed and misused finally there’s also data masking data masking allows you to work with obscured versions of sensitive data enabling you to verify transactions without risking financial security it strikes a balance between transparency and security for Adventure Works sometimes you might need to work with data without knowing the exact details in these instances you’ll need to use the technique of data masking for instance you might need to verify the last four digits of a customer’s credit card without seeing the whole number data is powerful but carries great responsibility in PowerBI every data point represents Adventure Work’s commitment to its global community you should now know how to describe sensitive data and understand the measures that can be taken to protect data protecting data preserves trust in the company’s vision your choices today shape tomorrow’s outcomes as a data analyst you’ll often need to send very large files to other people fortunately you can use PowerBI’s link sharing feature to grant access to reports without transferring large files or losing their interactivity in this video you’ll explore sharing a URL in PowerBI service different types of links and how to generate a URL or link to share a report at Adventure Works data analysts are constantly building useful and dynamic reports powerbi’s link sharing feature allows them to quickly distribute these reports to multiple teams with a simple link let’s find out more about how this works in PowerBI when you share a link you’re essentially giving someone a URL to access your report or dashboard directly in a web browser a link is fast efficient and doesn’t require downloading large files however it does pose security risks which means that access must be carefully managed powerbi offers different sharing options for links let’s explore some of these the first category is people in your organization for example you’ve built a report on Adventure Works yearly sales trends and want to share it with the whole sales team when you select people in your organization anyone with an Adventure Works email can open the report using the link this means only those within the organization can view those insights the next category is people with existing access you’ve shared a report with the product management team perhaps containing confidential info about a new touring bike prototype when you use the people with existing access option only those you’ve already permitted can view the report others at Adventure Works won’t be able to view it even if they find the link the final category is specific people in certain situations a specific person may need access to a report tailored to their project by using the specific people option you can ensure that only the individuals you explicitly mention can view the report other individuals can’t access it unless you permit them however configuring who can access the link is just as important as configuring what the individual can do with the data provided by the link configuring data protection is vital failure to do so could result in unauthorized access to sensitive customer and employee data leading to legal issues privacy breaches and a tarnished reputation sharing permissions is a vital tool for protecting data permissions safeguard your data by determining who can access it in large companies like Adventure Works these protections are crucial let’s explore two common sharing permissions in PowerBI re-share and build permissions data and insights must move between departments in big companies like Adventure Works re-share permissions let people share with others which can be great for sharing important information quickly but it can also cause problems each time it’s shared again the original context can get lost leading to misunderstandings or the wrong people accessing the data build permissions lets others use the data you’ve shared recipients with build permissions can merge data as needed for richer analyses but they can’t change the core data however using this power wisely is essential to avoid cluttered less useful reports now let’s demonstrate an example of how you can generate a link to share using PowerBI first start by navigating to PowerBI service on the left sidebar select workspaces and select the specific workspace where your desired report is located browse through the list of reports and select the title of the report you wish to share this opens the report and provides a live interactive view of its contents it’s always good practice to review the report before sharing to ensure it’s the correct one towards the top left corner of the screen locate and select the share icon which resembles an arrow the share button provides different mechanisms for report distribution in the window that opens just above the email address field select the people in your organization with the link can view and share option choose the people in your organization permission level from the available options ensure you uncheck the option allow recipients to share your report by toggling this option off you ensure that the content is only viewed by its intended audience once you have selected the desired permission level select the apply button near the bottom of the send link window is the copy link button depicted by a paperclip icon when you opt to share via a link PowerBI generates a unique URL that directs users to your report by copying this link you’re grabbing the address of the live version of your report once copied you can paste and share this link just like any other web link when a user clicks on it provided they have the required permissions they’ll be directed to the report on PowerBI service where they can interact with it live remember always to consider the sensitivity of the data when selecting an option next let’s configure build permissions for the reports data set access your data set from the workspace hover over the record select the ellipses or three dots to the right of the data set’s name and select manage permissions in the manage permissions pane select add user and then input the names or email addresses of the users or groups you want to grant build permissions to in the permissions dropdown select allow recipients to build content with the data associated with this data set this allows users to create new reports or visuals based on this data set coupling it with reshare ensures they can distribute their creations to others to restrict re-sharing simply uncheck the reshare option after configuring the permissions as desired select the grant access button having explored sharing via links you should now be familiar with sharing a URL in PowerBI service the different link types and generating a URL or link to share a report links and their related permissions are instrumental for sharing your reports safely in the business world data is power but it must be handled responsibly data analysts often work with sensitive client and employee data which must be safeguarded carefully fortunately they can use PowerBI’s data sensitivity labels to protect this information in this video you’ll learn how to identify data sensitivity labels and how to work with data sensitivity labels at Adventure Works customer and employee information needs to remain confidential lucas has just completed a new sales report this data is confidential so it’s important that he correctly labels the report as so let’s learn more about data sensitive labels and how Lucas can use them to categorize data powerbi’s data sensitivity labels allow you to categorize data and safeguard the company’s reputation and trust they act like digital tags showing the level of confidentiality data requires they guide users on how to handle data responsibly these labels are part of a security system across Microsoft’s products when you apply them in PowerBI you set the data sensitivity level properly using these labels ensures data protection especially when sharing or exporting there are six different categorizations of data sensitivity labels used in PowerBI personal public and general and there’s also confidential highly confidential and restricted let’s learn more about these labels by exploring how Adventure Works makes use of them in PowerBI from the left sidebar of PowerBI select Workspaces then select the workspace that contains the report or dashboard you wish to configure in this instance you need to configure Lucas’ sales report inside the workspace choose the sales report with the report open select the title at the top of the screen in the drop-own menu access the sensitivity label dropdown if you haven’t applied a label before you might find that the label reads none or no label in a faded gray color signaling its dormant state select the sensitivity label drop-down to show the range of available options select confidential for the current report let’s take a moment to review these labels the personal sensitivity label denotes data linked to specific individuals but not intended for the wider organization for example a junior data analyst might share information with a senior data analyst this information is valuable but doesn’t need to go to the entire company adventure Works often creates content for a wide audience including customers stakeholders and the public this content is labeled as public for example a brochure showcasing Adventure Works new bike range for an exhibition is intended for wide distribution without any restrictions the general sensitivity label is for information meant for the broader internal audience without specific sensitivities like Adventure Works monthly newsletters which cover company events and other general news this information is for all employees not external stakeholders and the general label keeps it freely accessible within the company the confidential label deals with sensitive information across departments this label is for data that needs careful handling it’s for valuable data that’s not intended for everyone like PowerBI reports shared between data analysts the highly confidential label safeguards Adventure Works critical innovations it’s for essential sensitive data like research into new products or markets this label ensures limited access protecting valuable information for project insiders at the highest level of data sensitivity is the restricted label for adventure works it means maximum secrecy and caution it’s for data that requires extensive protection like top executives discussing mergers acquisitions or critical contracts the restricted label keeps this monumental data as secret accessible only on a need to know basis now that you know the different labels let’s label the sales report select confidential for the current report the selected label appears near the report’s name at the top of the screen this signifies that you’ve successfully labeled your report in this video you learned how to identify sensitivity labels and how to work with sensitivity labels not all data is the same certain data must be treated more carefully than others use tools like data sensitivity labels to protect the integrity and confidentiality of your data many people think sensitive data leaks only happen because of a targeted attack from cyber criminals but sometimes unintentional internal leaks can be just as damaging meet Daniel daniel has been part of the Adventure Works team for the last 3 years as an IT specialist daniel’s life is busy and with his first kid on the way increasingly expensive while he’s happy at Adventure Works he sometimes wonders if he could earn more working elsewhere one day Daniel answers an IT help desk call from Maya on the payroll team daniel has never met Maya but he’s happy to help when she reports a problem opening Microsoft Excel attachments after a few minutes of troubleshooting Daniel has no success daniel asks Mia to send him an example of one of the attachments so he can check if it works from his side maya is anxious to get the issue resolved and without thinking she sends him the top email from her inbox which happens to be from HR when Daniel opens the attachment he discovers that it’s a complete list of salaries for all Adventure Works employees he’s a bit surprised to see this but he closes it down and helps Mia to adjust some of her trust center settings she verifies that this resolved the issue and they end their call daniel continues his work but before he logs off for the day curiosity gets the better of him he knows he shouldn’t but he reopens the attachment he received earlier from Maya he accesses the tab labeled IT department he sees his name and salary no surprises there he spots some names from the management team and he’s shocked by what some of them earn maybe he should consider management then he notices some other names these are names of colleagues on the same team as him friends he can’t resist looking at their salaries some are on a pretty similar pay scale to him but other team members earn significantly more per month he’s got no idea why this might be but he’s not happy he closes the spreadsheet logs off and heads home later that night Daniel can’t stop thinking about the salaries he saw it seems so unfair that people doing the same work as him earn more and some just joined Adventure Works in the past year daniel has been there over three years however the spreadsheets information is limited and doesn’t tell the full story the people on the list with higher salaries hold advanced qualifications that justify their higher pay and Daniel is in line for a promotion and a sizable salary increase next month in recognition of his hard work he has a bad night’s sleep and is not in a good mood when he arrives at the office the next day while he’s grabbing a muchneeded cup of coffee he bumps into Katie he confides in her about the salary information he saw the day before katie is annoyed too later that day she tells Caleb who then tells Sam and so it continues word is spreading and employee engagement has taken a hit daniel and Sam decide they’ve had enough of feeling undervalued and they accept slightly better paid positions with another company katie Caleb and the others have stayed where they are but they are not feeling very motivated with reduced headcount and disengaged staff the rest of the company has noticed that the quality of service from the IT help desk is slipping such a simple mistake could have been avoided if HR had used sensitivity labels with encryption settings on their sensitive files even if Mia had still inadvertently shared the Excel file with Daniel he would have been denied access to the file due to insufficient permissions life at Adventure Works would have carried on normally and Daniel would have received his muchdeserved promotion data helps businesses generate insights make decisions and succeed however not everyone in the business needs access to all its data sensitive data must be safeguarded with data permissions in this video you’ll learn about the risks of sensitive data and how to evaluate and safeguard these risks adventure Works relies heavily on data from sales reports to make decisions around its product lines however some of the Adventure Works sales reports also contain sensitive information on profit margins this information should be visible to senior leadership only let’s look at how PowerBI data set permissions can be used to restrict data access to only those who need it to perform their roles first let’s define what we mean by PowerBI data set permissions at the core of every datadriven organization lies its data sets data set permissions are the gatekeepers to these data sets as they’re like a series of digital locks and keys they’re permissions that ensure that the right individuals have the necessary keys to access specific data they strike a balance between accessibility and security all employees of Adventure Works have their own designated roles data permissions act as boundaries ensuring that everyone has access only to the data they need for their role the available permission types are read build reshare write and owner the first permission type we’ll explore is the read permission the read permission in PowerBI grants users the ability to view and understand data sets without altering the original content for example the marketing team at Adventure Works may need to look at the product sales report to analyze the effectiveness of marketing campaigns and promotions but they don’t need to alter this report in this case the read permission is sufficient it permits access while minimizing the risk of unintentional data modifications preserving data
integrity next we’ll explore the build permission the build permission enables users to construct visuals PowerBI reports and dashboards based on the available data without modifying the source data itself at Adventure Works the finance team responsible for creating and maintaining the sales data sets often find that sales representatives and product managers who have legitimate reasons to access the data are unintentionally changing key financial figures while exploring the reports this not only leads to incorrect financial analysis but also disrupts the financial team’s workflow by utilizing the PowerBI build permission the sales and product team can format the data for analysis without the risk of inadvertently altering it sharing information is central to collaborative environments like Adventure Works the reshare permission enables users to distribute specific data sets or reports to other users or teams permitted to access this information before a product launch at Adventure Works the finance team can use the re-share permission to share a tailored readonly data set with the marketing team this means the marketing team can optimize their advertising campaigns based on realtime sales data while the finance team is able to safeguard the integrity of their financial reports now we’ll examine the right permission the right permission in PowerBI allows users to alter data users with this permission have the authority to make modifications to the actual data sets adventure Works product development and marketing teams need access to the company’s sales and customer data granting the right permission allows the teams to not only view the data but also make specific updates and additions to the data set for example they can record customer feedback update product specifications and add marketing campaign results this permission when used cautiously ensures that Adventure Works data remains current and relevant however it comes with the caveat that any modification should be made with caution to prevent misinformation finally we’ll explore the owner permission much like the CEO overseeing every aspect of Adventure Works having an owner of the business data ensures centralized data governance the owner permission grants comprehensive control over data sets encompassing the capabilities of all other permissions owners can modify share build and even restrict access to data owners ensure that the correct data is available to the correct people safeguarding sensitive information while also fostering a culture of openness where needed with overarching control they are the custodians of data’s trajectory ensuring it aligns with the broader vision of the organization in this video you’ve learned about the risks of sensitive data and how to evaluate these risks and safeguard data these permissions promote data governance and integrity by ensuring that users only access the data relevant to their roles leading to more accurate analyses and informed decision-making as a data analyst you must ensure that your data sets are accessed only by relevant individuals and at the required permission levels so it’s important that you can configure data set permissions effectively in this video you’ll learn how to add and manage permissions for a data set in PowerBI adventure Works must share its sales report with the wider data analytics team however some team members must be assigned different data set permissions than others let’s help Adventure Works assign permissions as required upon successful login navigate to the icons on the lefth hand navigation pane select the workspaces icon select the Adventure Works workspace the Workspaces pane is where all your current and future workspaces reside browse through the data sets to find the Adventure Works product sales data set remember each data set can represent different departments or analytical perspectives once selected a new view appears on screen this screen provides useful details about the data set such as the current storage location the last date refreshed as well as existing reports and dashboards that currently use the data set find and select the file drop-down in the top left corner when this option is selected additional options appear such as download this file and manage permissions from the drop-down select manage permissions this option lets you oversee who can view or edit this data set a link section appears on screen these are sharable URLs that have been generated for this data set they act as direct gateways for users to access the data set without navigating the entire PowerBI interface each link outlines its creator who has access and the type of permissions assigned it allows you to maintain a clear shared data record ensuring that old links can be retired or renewed as needed next to the links tab select direct access the direct access tab enables you to grant direct access to a specific individual or group within Adventure Works here you will find the names of people and groups with access their email addresses and the type of permissions assigned select the add user button to add a new user you can input email addresses or names and PowerBI will suggest matches from your organization in this case you need to provide ADIO another data analyst access to the report once you’ve selected Adio you must assign in permission levels check the box that corresponds to the desired permission level for now you just need Adio to be able to read the data set assign read permissions you can add a personalized message explaining the reason for granting this access once you have selected grant access an email notification is sent to the user a new record appears in people and groups with access indicating that the user has been successfully granted access next you must remove access for the employee Kai as he’s no longer part of the project to remove access for a user or a group first locate their name in the people in groups with access section each name is followed by details such as the permission level and the date the access was granted next to each name is an ellipsus or three vertical dots which reveal additional options when selected within this menu locate the remove access button a confirmation pop-up appears select remove access it’s crucial always to be sure when revoking access to a data set as it can result in delays in accessing critical reports and dashboards upon removal the user’s name disappears from the people and groups with access list this immediate feedback confirms that the revocation action was successful finally you need to grant right access to Lucas identify his name in the list and select the ellipsus to bring up the menu select add right to assign right permission it’s important only to assign right access to people with the necessary understanding and responsibility you should now understand the process of granting and removing access to specified users with PowerBI these permissions help keep data in check and accurate by letting users access only the data they need for their roles improving analysis and decision-making data analysts often share sensitive data with people outside of the organization this means the correct permissions must be assigned when sharing links to this information to keep it secure in this video you’ll discover how to maintain data security and integrity when sharing information outside of your organization adventure Works needs you to share a PowerBI sales report with a new partner to prepare you for this task let’s explore the importance of maintaining the security and integrity of the data when sending it to outside stakeholders when sharing PowerBI reports externally it’s essential to protect sensitive data and respect privacy boundaries to prevent potential harm to the company and its stakeholders this involves carefully controlling what information is shared and maintaining strict security measures you can control this information using techniques like user licensing sharing permissions and rowle security or RLS there’s also data masking and anonymization report embedding and external sharing settings let’s explore these techniques in more detail when sharing PowerBI reports with external partners or vendors it’s important to ensure they have the right PowerBI Pro licenses for smooth access an Adventure Works admin can assign and oversee these licenses through the Microsoft 365 admin center requiring ongoing monitoring to maintain compliance and prevent violations next is the use of rowle security or RLS using rowle security is crucial especially when sharing sales data with external vendors adventure Works can ensure vendors see only relevant table data this technique keeps other sensitive information in the same table safe and inaccessible we’ll explore this more in a later lesson next let’s examine data masking and anonymization to protect sensitive data Adventure Works uses data masking and anonymization techniques this involves replacing real data with fake or pseudonmous data in Power Query allowing external partners to analyze trends without accessing Adventure Works sensitive information another technique is report embedding when Adventure Works shares PowerBI reports externally they choose secure embedding methods like publish to web or embed code they use these options carefully considering the data sensitivity before deciding which one to use this is important to keeping data confidential and limiting report access to the right people these embedding methods allow you to add reports to external platforms while keeping control over who can see and access the data next is external sharing settings to enable external sharing Adventure Works adjusts their PowerBI service settings controlled by the PowerBI admin these adjustments include various configurations to maintain the company’s security standards such as authorizing users or groups for external sharing and setting content restrictions they can also control the links expiration time and mandate authentication for external users to access shared content lastly let’s examine the use of sharing links adventure Works boosts report security by creating safe links with clear permissions making them a safer sharing choice these links can have expiration dates and be limited to specific users reducing the chance of unauthorized access you can use these features to share a sales report with the new partner so that it can only view required data in this video you discovered how to maintain data security and integrity when sharing information outside your organization as you explore and share data always be sure that you retain its integrity and confidentiality data analysts are often required to share sensitive data with multiple teams and departments this can pose a problem if the wrong individual accesses specific data fortunately you can use rowle security or RLS to ensure that your data remains accessible and protected in this video you’ll learn about the importance of maintaining data integrity how to evaluate and safeguard these risks and how RLS regulates data access adventure Works needs your help to manage data access for its global team of employees and customers effectively you can use role level security in PowerBI to tailor data access by region and role ensuring data integrity and confidentiality companywide let’s explore the basics of rowle security and how you can use it to help adventure works we’ll begin with an explanation of what we mean by rowle security rowle security or RLS ensures that only authorized individuals can access the right data this helps to preserve the security and integrity of your overall data sets in other words rowle security controls who sees what data based on predefined roles and rules it’s especially important when many different actors are interacting with the same data essentially it ensures that each person can view only the data they need and sensitive information is safeguarded let’s explore some of the advantages of implementing rowle security rowlevel security gives you precise control over who views what this helps prevent accidental data leaks by safeguarding sensitive data from unauthorized users as an organization expands its data scales and increases in its complexity rls makes it easier to handle these more complex data access needs you can use RLS to establish new rules for accessing data without starting from scratch compliance and auditing play a vital role in any organization rls helps companies comply with data privacy regulations it simplifies auditing by keeping track of who can access what for companies like Adventure Works data breaches pose a significant threat rls reduces the risk of data breaches with RLS even if someone unauthorized gets into a PowerBI report they can’t see data they aren’t assigned to this adds a layer of security against data breaches while there are many benefits to rowle security there are also several potential issues you could encounter if it’s not managed correctly using security layers especially dynamic RLS can slow down data retrieval because it filters data in real time monitor performance especially with big data sets to keep things running smoothly rowle security often requires maintenance that’s why regular checks and updates as roles and access needs change are important periodically review the RLS settings to make sure they still work well for your organization to ensure that the correct access is given to the correct individual when you set up RLS test it thoroughly to ensure the rules work and give the right access regular testing helps prevent data leaks and keeps everything working as expected next let’s explore the different kinds of rowle security static and dynamic static rowle security in PowerBI creates predefined rules to control data access based on user roles it restricts users to specific data ensuring that they only see information relevant to their roles for example a new hireer on your team has been tasked with analyzing sales of mountain bikes in North America this means they should not have access to sales data for other products or regions with static rowle security you can establish clear rules that ensure they can only access data related to sales of mountain bike products in North America dynamic rowle security in PowerBI adjusts real time data access based on user roles this permits users to view only the data that’s relevant to them at any given moment dynamic rowle security uses DAX or data analysis expressions formulas and user roles in PowerBI to filter data based on specific conditions these conditions could include user attributes or affiliations stored in a database for example your new hire has successfully analyzed sales of mountain bikes in North America so they’ve been tasked with analyzing sales of mountain bikes in other regions this means that PowerBI can now grant them access to data for other regions with dynamic row security the system can adjust its access so the new hire can view sales data for specific regions as required in this video you’ve learned about the importance of maintaining data integrity how to evaluate and safeguard these risks and how it regulates data access you should now be familiar with the basics of rowle security and how it ensures that data remains accessible and protected by using rowle security you can ensure that each entity gets the correct data in the right situation as a data analyst it’s important to control access to your data so that others can only view information relevant to their roles a useful method of safeguarding data is configuring security at the table row level in this video you’ll learn how to configure static rowle security on a data set in PowerBI your team member Addio Quinn needs access to the latest sales reports to analyze sales data from North America let’s configure static rowle security so Adio can only view the data required to complete his task to begin select the modeling tab then choose the manage roles option in the manage RO section you need to create a new role with the relevant permissions for audio select the create button to add a new role right click on the new role and choose rename rename the role as marketing North America to maintain a structured and organized role management next select the table you want to filter in this case it’s the sales table then right click on the table name and select add filter to specify which data rows this role can view choose the region field from the drop-own list and add it to the table filter DAX expression area the table filter DAX expression is where you define the limits for each RO’s data view it’s crucial to be precise about the data accessible to users in this role select the region field and input a relevant DAX expression stating that the region’s value should equal North America this DAX expression ensures that AIO can only view North American data to verify if the expression works as intended select the check mark icon in the top right corner of the manage roles window after creating your DAX expression select save to confirm your changes and establish clear visibility boundaries now you need to ensure that everything works correctly select view as and test the configuration choose the marketing North America role and select okay to view the data from a user’s perspective and verify its accuracy once you’ve completed your check select stop viewing to exit the view as ROS feature be sure to save your settings after saving your RO definition go to the home tab and select publish in the publish to PowerBI dialogue box choose Adventure Works the current PowerBI workspace you’re working in click the select button powerbi publishes the report to your chosen destination the time required for this process may vary based on the report size and your internet connection a new dialogue box confirms your report’s successful publication access the Adventure Works workspace and locate the newly published report and data set identify the data set with the same name as your report it’s now available in the PowerBI service and can be adjusted for user access select the ellipses next to your data set name to open a list of options choose security from the list to display the role level security settings from here you can assign user roles in the role level security settings locate the role you created in PowerBI desktop marketing North America then access the members area and enter Adio’s email address this action assigns Adio to the role of member and grants him access to North American marketing data next select add then select save to enforce the role assignments locking in the user access levels if Adio attempts to access data outside of North America he will see blank visuals as he only has access to marketing data related to the North American region you should now be familiar with the process steps for configuring static row security on a data set in PowerBI as a data analyst it’s your job to keep data safe and accurate so make sure that you always configure static role level security as required during a project the roles and needs of your users may often change which requires constant updating of data access permissions that’s a lot of work if you’re using static rowle security however with dynamic rowle security you can adjust data access automatically as roles change in this video you’ll learn how to configure dynamic rowle security or RLS on a data set in Microsoft PowerBI and how to assign validate and publish a report secured with dynamic RLS access PowerBI and open the Adventure Works product sales report locate and select the modeling tab in the ribbon area at the top of the screen on the modeling tab locate the security group in this group select the manage roles choice a dedicated manage roles window opens this is the area where you can define and manage roles create a new role using the manage roles dialogue box name the new role dynamic sales access now you need to apply filters select the role you just created then locate and select the table you wish to apply a filter to in this case it is the sales table next right click on the table name and select add filter select the email field from the drop- down list to add it to the table filter DAX expression area this area establishes visibility boundaries for each role determining what data each user can view you must now formulate a DAX expression that equates data from the table’s email column to the user principal name function the user principal name function fetches the user’s email address it then filters data dynamically by limiting the user to rows or data that match their email address for instance Lucas who works in sales and marketing can only access data relevant to his marketing campaigns this ensures he can’t access confidential data from other business areas to verify the syntax of your DAX expression select the check mark icon on the top right side of the manage rolls window if the expression is correct select save in the bottom right to confirm the change to the role once the role has been created and configured it must be tested to ensure it works as required select the view as choice on the modeling tab this opens a view as roles dialogue box then select the other user choice and enter Lucas’s email address then select okay you can now view the data as if you were Lucas if you are content with the validation exit the view as ROS mode by locating and selecting stop viewing at the top of the window save your changes to ensure your created role is not lost this ensures that all your configurations are stored securely after saving the role definition select the home tab and select publish in the publish to PowerBI dialogue box choose your current workspace and then the select button depending on the size of the report and your internet connection the publication process could take a few moments a new dialogue box confirms that your report has been published successfully next locate the newly published report and data set the data set can now be configured for user access select the ellipses security choice next to the data set name select security from the list this displays the rowle security settings of the report the role you created in PowerBI desktop is displayed in the left pane once the role is selected on the left email addresses can be added in the members pane on the right type in Lucas’s email to assign him to that role and give him access to specific data areas next select add and save to enforce the role assignments locking in the user access levels you can repeat this process for other users as required adventure works can now distribute the report with the knowledge that its data is safeguarded and you should now understand how to configure dynamic rowle security and assign validate and publish an RLS configured report searching for daily reports in PowerBI can be a time-consuming task wouldn’t it be great if they arrived automatically in your inbox at a set time each day thankfully you can configure this setup with report and dashboard subscriptions over the next few minutes you’ll learn how to set up subscriptions to your reports and dashboards and review the advantages of this setup every morning Lucas reviews his PowerBI workspace for new reports and dashboards this is a time-consuming process by configuring subscriptions he could have these assets delivered directly to his email subscribing to reports and dashboards in PowerBI offers a wide array of advantages let’s take a closer look at those benefits a PowerBI subscription is an automated delivery system that sends daily scheduled snapshots of your reports and dashboards as an email or as a notification this turns a tedious manual process into a seamless and automatic one one of the main benefits of subscribing to reports and dashboards is quick access to data once there’s a new update you and all other subscribers receive an instant update or alert this ensures that decision makers always operate with the most current data with a subscription Lucas can ensure that his sales and marketing insights are always drawn from the most recent reports and dashboards subscriptions also boost efficiency and productivity manually pulling up the same report day after day is a tedious task but you can automate this process with subscriptions your teams can prioritize more important tasks and dedicate more resources to analysis and insight instead of wasting time fetching reports with a subscription to the weekly sales dashboard Lucas could receive the latest sales and marketing data every Monday at 6:00 a.m sharp receiving regular reports fosters a sense of routine and consistency in data consumption with set delivery intervals users can create structured time slots dedicated to datadriven assessments a shared understanding is key to effective collaboration when multiple team members or teams subscribe to the same reports it establishes a uniformity in the information they base their decisions on everyone is working from the same version of each report now that you’re more familiar with the benefits and uses of subscriptions in PowerBI let’s configure a subscription for Lucas so he has quick access to the most up-to-date data all your reports dashboards and data sets are listed in your workspace select the report you’re interested in to open it once the report loads navigate to the top toolbar select the ellipses next to the edit button to open more options in a drop-own menu from these options select subscribe to report the subscriptions pane appears on screen you can use this pane to configure your subscription as follows first give your subscription a memorable name especially if you plan to set up multiple subscriptions decide how often you want to receive this report for example should it be daily weekly or even monthly depending on your chosen frequency set the specific time you’d like the report sent if you want other colleagues to receive this subscription add their email addresses here remember you also need access to the report to view it you can also add a custom message in the email received when the report is sent once you’ve set up your subscription select save and close or save to activate it you’ll then receive confirmation that the subscription is now active depending on your settings you’ll begin receiving the report via email based on your selected frequency select an existing subscription to view its details you can modify pause or cancel your subscription from this menu lucas now has daily automated access to sales and marketing reports and dashboards this gives him more time to analyze data and generate insights and you should now know how to set up subscriptions to your reports and dashboards and the advantages of this setup with PowerBI subscriptions you’ll work more efficiently consistently and faster this leaves you more time and opportunities to generate insights to help your organization achieve its goals much of your daily work as a data analyst involves analyzing data to generate insights but what if PowerBI could generate and deliver these insights to you with PowerBI data alerts you can receive automated insights that save time and effort in this video you’ll explore the benefits of data alerts and learn how to set up an alert in PowerBI at Adventure Works Lucas monitors and analyzes data for events like a spike in sales or a slowdown in production or shipping times however manually uncovering these insights takes time it would be much more efficient to configure data alerts that flag these events automatically let’s find out more about data alerts and how Lucas can use them for more efficient monitoring data alerts are essentially automatic notifications set up within PowerBI they inform users when specific conditions or thresholds in a dashboard are met or exceeded and these alerts can be customized to cater to a range of business needs there are many different benefits to data alerts a major benefit is real time decision-making data alerts notify data analysts immediately when specific metrics reach a predefined threshold this instantaneous awareness means decisions can be made quickly organizations can adapt to real-time changes in the business environment at Adventure Works Lucas can use data alerts to monitor sales spikes in Europe for marketing campaigns this realtime insight allows the European sales team to adjust strategies for maximum impact quickly data alerts also help with efficiency and timesaving manually analyzing data takes time by configuring data alerts that monitor important conditions data analysts can direct their attention elsewhere confident they’ll be notified if something requires their attention for example Lucas previously spent hours checking website traffic following the launch of new marketing campaigns now thanks to data alerts he’s instantly informed of significant traffic changes which frees his time for other tasks instead of discovering issues after they’ve occurred and seeking solutions data alerts can notify stakeholders of potential problems before they escalate for instance an alert can be triggered if a manufacturing process at Adventure Works starts to slow the company can intervene immediately before the slowdown impacts the wider production line this proactive approach can mitigate risks and prevent minor issues from becoming major problems data alerts also ensure that all relevant parties are notified about important datadriven insights for example if Adventure Works launches a new marketing campaign in Germany data alerts can notify the marketing and IT teams of surging website traffic this synchronization ensures greater collaboration the marketing team can assess the campaign success while the IT team can scale server resources and finally data alerts are highly customizable this lets different teams or individuals set alerts based on what’s most important to their role or department a sales manager might set alerts related to sales metrics while a supply chain manager might focus on inventory levels this personalized approach ensures that each stakeholder receives the most relevant data instead of unnecessary information now that you’re more familiar with data alerts let’s help Lucas set up alerts in PowerBI in your workspace is a list of reports dashboards and data sets select the report you’re interested in to open it once the report loads navigate to the KPI visual you wish to create an alert for it’s important to note that PowerBI differentiates between reports and dashboards dashboards are a collection of tiles each representing a specific visual or information alerts can be set on tiles pinned from report visuals or PowerBI Q&A and only on gauges KPIs and cards hover over the visual to pin it from your report to a dashboard then select the pin icon this action opens the pin to dashboard menu you can select the dashboard to which you want to pin the visualization and even change its theme a confirmation message appears once you’ve pinned the visualization select the messages go to dashboard option to view your pinned visualization move your cursor over the tile of interest an ellipsus appears at the top right corner select it to reveal a drop-own menu with additional options for that tile select manage alerts from the drop-own menu this opens the core settings for alerts related to this tile on the alerts menu select add alert rule you can now define a new condition for alerts a clear descriptive name for an alert like drop in shipping time provides a clear context next choose a condition parameter like above or below and set a numeric value this value becomes your trigger point for instance if shipping times drop below a set number it’ll trigger the alert you can decide the alerts notification frequency depending on how critical the data is if it’s a vital metric like manufacturing uptime you might instead set up every hour alerts for less urgent data every 24 hours might suffice once you’ve configured the alert to your satisfaction select save this activates your alert it’s good practice to review your alerts regularly to access your active alerts just select manage alerts again you can view and manage your existing alerts from the manage alerts menu frequently reviewing your alerts ensures that they’re still relevant to your organization’s goals outdated alerts might cause unnecessary distractions or lead you to miss out on critical insights you should now understand the benefits of PowerBI data alerts and be familiar with the setup process data alerts are a great tool for delivering automated actionable insights that save you time increase your productivity and help you and your organization succeed emily is the CEO IT specialist designer head of HR delivery driver and chief coffee maker at Ecocraft Furniture you name it Emily does it along with a small but close-knit team of other crafts people Ecocraft specializes in producing highquality sustainable furniture founded just two years ago the company is already exporting its products to various countries across North America and Europe the raw materials for Ecocraft’s furniture such as sustainably sourced wood and eco-friendly paints are imported from different countries this means transactions often take place in multiple currencies this has been one of the biggest challenges for Emily and Ecocraft fluctuations on the currency markets can significantly impact production costs and profit margins the company needs a system to issue alerts when rates are favorable for making large purchases or setting prices for overseas markets this would help Emily and Ecocraft manage budgeting and financial forecasting powerbi is the perfect solution for Emily she can use it to track important business metrics sales supply chain status and currency exchange rates emily decides to set up alerts on PowerBI for currency exchange rate changes this will give her the information she needs to make sound financial decisions the first step is to collect data emily enlists the help of her tech-savvy friend Alex who helps her create a robust data pipeline together they source real time and historical exchange rate data for the currencies of the countries from which they import raw materials they also collect data on their purchase orders and expenses related to each supplier next they create a dashboard to monitor various key performance indicators the dashboard will also identify patterns and potential risks associated with currency fluctuations the exchange rate data and other vital metrics like sales and supply chain status are displayed in real time emily configures PowerBI to send custom alerts whenever currencies pair like when the US Canadian dollar or the US dollar to euro cross thresholds that impact the company’s financials she sets these alert levels based on historical data and current business needs for instance if the exchange rate for the euro increases by more than 5% in a week Emily will receive an alert armed with these alerts Emily is better prepared to mitigate currency risk when an alert triggers she can immediately assess the potential impact on her production costs and take necessary actions this could include renegotiating contracts with suppliers and hedging currency exposure or seeking alternative suppliers from more stable regions shortly after setting up the PowerBI dashboard an alert indicates that the US dollar to euro exchange rate has dropped to a favorable level based on this information the team orders raw materials from the European suppliers saving thousands of dollars as Emily continues to use PowerBI and respond to alerts she gains deeper insights into her business she can analyze which suppliers are more cost effective based on currency trends and adjust her sourcing strategy accordingly these datadriven insights help the company to make more informed decisions save money improve the overall efficiency of its supply chain and ultimately increase profitability over time the currency alerts become integral to Emily’s business this provides the stability she needs to pursue her mission of creating beautiful eco-friendly furniture for years to come the company plans to extend the PowerBI platform’s capabilities to other business areas solidifying data as a core component of its growth strategy emily’s journey with PowerBI is a testament to the power of datadriven decision-making congratulations on reaching the end of these lessons on security and monitoring in PowerBI during these lessons you explored the role that security and monitoring play in safeguarding reports and dashboards in PowerBI let’s take a few minutes to recap what you learned in these lessons you first explored how to share information safely and identify sensitive data sensitive data is essential information that if leaked could damage the company’s reputation finances or privacy if the information is employee related the leak could damage an organization’s and its workforce’s relationship fortunately you can safeguard data in PowerBI using the following methods authentication and authorization systems ensure that those accessing the data are who they say they are assigning clear roles and permissions ensures that individuals can only access certain data rowle security or RLS filters data so that individuals can only access relevant elements of data sets data encryption prevents data from being intercepted during transmission data masking lets you work with obscured versions of data so that you can only view the information required to complete your task you also learn that sensitive information can be shared using links these links offer sharing options so you can control who views the data these options include people in your organization who need the data people with existing access to the data or specific people that you include directly and you can decide what recipients can do with the data using the following sharing permissions they can reshare the data with others or make use of the data to perform analysis another method of safeguarding data is the use of sensitive labels these labels let you categorize data making it clear who can access it these categories include personal which denotes data linked to specific individuals public which is data for a wider audience and general meaning information meant for a wider internal audience there’s also categories that govern more sensitive data the confidential label means the information is sensitive and requires careful handling highly confidential relates to sensitive data on critical business innovations and the restricted label is used for data that must be treated with maximum secrecy and caution you then demonstrated your understanding of sharing information in PowerBI by applying sensitive labels to an Adventure Works data set in the next lesson you explored the topic of organizations and permissions you discovered that access to data sets is governed by data permissions these ensure that only authorized individuals can access data powerbi offers the following permission types the owner permission grants a user complete control of a data set the read permission permits users to view but not alter data the reshare permission permits users to reshare data the build permission lets users utilize the data for analysis and the write permission enables users to alter data you then learned how to configure these permissions in PowerBI using the manage permissions option this option lets you create and manage URLs for data access that can be shared with your team you also learned that data can be shared outside of an organization however it’s important to consider which safeguards are most appropriate to ensure the data remains confidential you completed this lesson with a knowledge check in which you tested your understanding of data permissions and you reviewed additional resources to help you learn more about PowerBI and data permissions in the third lesson you reviewed rowle security for safeguarding data rowlevel security or RLS controls which individuals can view data based on predefined roles and rules some of the benefits of RLS include granular control over data the ability to scale as your data grows assistance with compliance and auditing and a reduced risk of data breaches however RLS also gives rise to several potential issues it can impact performance by slowing down data retrieval it requires regular maintenance and it must be tested frequently there are two types of role security the first is static static RLS restricts users to specific data so they can only view information relevant to their roles the other type is dynamic RLS dynamic RLS uses data analysis expressions or DAX to adjust real-time data access based on user roles you completed this lesson by undertaking a knowledge check focused on rowle security and you reviewed some additional resources on this lesson’s main topics in the fourth and final lesson you explored the topic of subscriptions and alerts in PowerBI you can subscribe to reports and dashboards a PowerBI subscription is an automated delivery system that provides daily data snapshots as emails or notifications the advantages of subscriptions include timely access to information a boost in productivity because more tasks are now automated consistency in data consumption and enhanced collaboration teams can now work from the same data sets you can configure subscriptions using the subscriptions pane in PowerBI with this feature you can name your subscription decide how often you receive it and even include other colleagues you can also modify pause or cancel your subscription as you need as well as subscriptions PowerBI also offers data alerts these automatic customizable notifications inform users when specific conditions or thresholds have been met or exceeded some of the benefits of data alerts include realtime decision-making efficiency through automation proactive problem solving enhanced collaboration and customization and personalization you can configure data alerts in PowerBI the manage alerts feature lets you set conditions and thresholds that determine when you receive alerts finally you demonstrated your understanding of these topics by undertaking an exercise in which you configured a data alert for Adventure Works you’ve now reached the end of this summary it’s time to move on to the discussion prompt where you can discuss what you’ve learned with your peers you’ll then be invited to explore additional resources to help you develop a deeper understanding of the topics in this lesson congratulations on everything you’ve achieved so far you’ve now reached the capstone project during this course you explored the role of PowerBI in business deploying assets in a PowerBI workspace and the role that security and monitoring play in safeguarding reports and dashboards in PowerBI let’s take a few minutes to recap what you’ve learned so far you began with an introduction to the role of PowerBI in business with a focus on data flow data flow in business refers to the movement of information within an organization this movement or flow occurs in the following stages: collection processing analysis and decision making once gathered the data is cleaned or standardized it’s then transformed data analysts use the refined data to generate insights the data is analyzed using PowerBI service this software offers many advantages for analysts it’s accessible scalable and offers collaboration tools and data backup and recovery features the data analyst is the central figure in this process they possess important skills and expertise in extracting valuable insights from data an important skill that all data analysts must possess is understanding structured query language or SQL data analysts use SQL to interact with the SQL databases that store the data analysts can connect to a SQL database using import or direct query modes import mode loads data directly into PowerBI direct query mode connects PowerBI directly to the source database an analysis is presented in the form of a report a report can be static or dynamic a dynamic report explores multiple areas of interest its results are presented in the form of visuals these reports also facilitate using whatif parameters that permit interactive adjustments to modify visualizations and generate insights into potential scenarios next you explored how to deploy assets in a workspace a workspace is a specialized area in PowerBI that holds important assets there are two types of workspaces in PowerBI the first is a personal workspace which you can use to store your content the second is a shared workspace where a team can collaborate on reports and dashboards workspace roles determine how individuals can interact with workspaces workspace roles include viewer contributor member and admin you can manage these roles using PowerBI’s manage access feature in the next lesson you learned how to monitor workspaces by monitoring a workspace you can measure its impact and make changes to increase its usefulness you also explored the topic of data sets and gateways in PowerBI a data set must contain the latest available information you can use a scheduled or incremental refresh to ensure accurate data and you can promote and certify data sets to inform your team where to access the most current and reliable data you also explored establishing a secure reliable connection between your on- premises data and PowerBI service using data gateways there are three types of gateways in PowerBI the on- premises data gateway the on- premises data gateway personal mode and the Azure virtual network or V-Net data gateway which type of gateway you choose depends on the setup of your organization and its specific data management and security requirements you also learned how PowerBI deployment pipelines move content through the following life cycle stages: development testing and staging or production another useful feature for maintaining your workspace is the lineage view this view shows the data journey from source to destination with all the connections in between impact analysis shows how changes to your data can impact or affect different assets in your workspace next you explored the role that security and monitoring play in safeguarding reports and dashboards in PowerBI you first explored how to share information safely and identify sensitive data sensitive data is essential information that if leaked could damage the company’s reputation finances or privacy you can safeguard data using PowerBI’s authentication tools you can also use sharing links to control who you share information with and use sharing permissions to determine what they can do with the data sensitivity labels are also another useful method of safeguarding data access to data sets is governed by data permissions these ensure that only authorized individuals can access data you can configure permissions in PowerBI to safeguard your data you also reviewed rowle security for safeguarding data rowle security or RLS controls which individuals can view data based on predefined roles and rules there are two types of rowle security static RLS restricts users to specific data dynamic RLS uses data analysis expressions or DAX to adjust real-time data access based on user roles finally you explored subscriptions and alerts in PowerBI you can subscribe to reports and dashboards a PowerBI subscription is an automated delivery system that provides daily data snapshots as emails or notifications you can use the subscriptions pane in PowerBI to manage your subscriptions as well as subscriptions PowerBI also offers data alerts these automatic customizable notifications inform users when specific conditions or thresholds have been met or exceeded during these lessons you also completed exercises in which you put your new skills into practice by helping adventure works with PowerBI knowledge checks which tested your understanding of these topics and additional resources in which you consulted Microsoft Learn articles to help you explore these topics in more detail you’ve now reached the end of this recap it’s time to move on to the capstone project which will test your understanding of these concepts through a series of exercises best of luck you’ve reached the next stage of the capstone project you’ve worked hard to get to this stage and made good progress let’s recap what you’ve achieved so far in the previous set of scenarios that you’ve just completed you prepared sales data configured data sources and designed and developed a data model you’ll begin this next stage of the capstone by configuring aggregations for Tailwind traders these aggregations will help the company generate insights into its financial performance as part of this scenario you’ll calculate sales and profits data and record the performance of visuals using the performance analyzer these aggregations will help generate insights informing the company’s strategic decisions for the upcoming business year by completing this exercise you’ll demonstrate your ability to create timebased summaries determine median sales volumes and utilize the performance analyzer tool next you’ll transform the insights you generated from configuring aggregations into a sales report tailwind Traders needs a report that helps to inform sales decisions the company needs your help to generate such a report using its sales data to generate this report you’ll complete the following tasks create charts and cards to visualize your data and add a slicer to your report aside from the sales report Tailwind Traders also requires a report that displays key insights into its profits creating this report will be your next task you’ll generate this report by creating charts and cards to visualize the data creating a KPI and adding a slicer through this and the previous scenario you’ll demonstrate your ability to create different kinds of charts to display sales data and display important sales metrics using cards and KPIs in the next capstone scenario you’ll help Tailwind Traders create an executive dashboard tailwind Traders will use the dashboard to generate insights into its global performance the dashboard must focus on sales and profits and be accessible on mobile devices you’ll create this dashboard by pinning sales and profits card visualizations and KPIs to the dashboard and configuring mobile view for the cards KPI visuals and core visualizations by completing this scenario you’ll show that you can create an executive dashboard in PowerBI display sales summaries highlight profit metrics use card visualizations for quick insights and configure a dashboard that’s mobile friendly in the final scenario you’ll need to help Tailwind traders to generate quick and actionable insights into its data you can carry out this task using PowerBI subscriptions and alerts features you’ll complete this task by creating daily alerts for key metrics and creating subscriptions for the sales and profits overview tabs by successfully helping Tailwind traders to generate quick and actionable insights you’ll prove that you can configure subscriptions and set up proactive alerts if you encounter any difficulty with these scenarios remember that you can refer to previous learning materials like videos and readings for guidance you’ve already completed similar tasks in the other exercise items in this course so you’re more than capable of working through these scenarios best of luck congratulations on completing the Capstone project it’s been a lot of work but you finally reached the end your completed Capstone PowerBI environment should contain sales and profits reports visualizations of the key metrics in your reports pinned to an executive dashboard and you should also have configured alerts and subscriptions let’s take a few moments to recap the exercises you’ve completed by reviewing examples of what the completed dashboard should look like don’t worry if these examples don’t quite match your dashboard you can review these best practice examples in more detail when you access the exemplars in the first exercise you configured aggregations using DAX you created measures to calculate the following: yearly profit margin quarterly profit and median sales you then assessed the performance of these reports in the second exercise you created a sales report you then visualized the data in this report using charts you created a bar chart for loyalty points by country a column chart for quantity sold by product a pie chart for median sales distribution by country and a line chart for median sales over time you also created cards for stock quantity purchased and median sales in the third exercise you created a profit report you then visualized the data in this report using charts you created a bar chart for net revenue by product a donut chart for yearly profit margin by country and an area chart for yearly profit margin over time you then created cards for year-to-ate profit and net revenue USD you then set up a KPI for gross revenue USD and added a slider for your profit report finally you saved and published the report once your profits and sales reports were completed your next task was to create an executive dashboard to create this data you created a dashboard called Tailwind Traders Executive Dashboard you then pinned the following sets of visualizations to the dashboard sales overview core visualizations sales overview card visualizations profit overview core visualizations and profit overview card and KPI visualizations once you finished pinning your visualizations you configured the mobile view for the cards KPI visuals and core visualizations in the final exercise your main task was configuring the dashboards alerts and subscriptions you first created a daily alert for gross revenue USD that informs Tailwind Traders when gross revenue drops below $400 US next you created and activated a weekly subscription for the sales overview tab ensuring it could be viewed and shared in PowerBI you then created and activated a weekly subscription for the profit overview tab ensuring it could be viewed and shared in PowerBI you’re now ready to begin working through the exemplers where you can compare your PowerBI environment against the best practice examples in more detail congratulations you’ve reached the end of this capstone project course you’ve worked hard to get here and developed many new skills you made great progress on your PowerBI journey this course and all you have achieved is a culmination of all the previous courses you’ve completed in this specialization having completed this course you now understand the basics of PowerBI’s relationship with business you’re familiar with the process steps for creating monitoring and maintaining workspaces you can connect data sets and gateways you can securely share information with your team and the wider organization and you can manage subscriptions and alerts in your workspaces with this course you were able to reinforce and demonstrate the learning and practical development skill set you have gained throughout this program this was achieved through hands-on guided practice configuring a PowerBI workspace for Tailwind Traders the graded assessment further tested your knowledge of PowerBI after completing the final project it’s a great time to pause and reflect on your journey you can reflect on the completed course from several vantage points you could consider the links between this course and the previous ones you’ve completed or you could reflect on the process of completing the project for example what were the hardest parts of the project what was the easiest what experience did you gain from the project and would you benefit from revisiting previous courses whether you’re just starting as a technical professional a student or a business user this course end project proves your knowledge of the value and capabilities of database systems the project consolidates your abilities with a practical application of your skills but the project also has another important benefit it means you have a fully operational PowerBI workspace to reference within your portfolio this serves to demonstrate your skills to potential employers and not only does it show employers that you are self-driven and innovative but it also speaks volumes about you as an individual and your newly obtained knowledge you’ve completed all the courses in this specialization and earned your certificate in PowerBI the certificate can also be used as a progression to other role-based certificates you may go deep with advanced role-based certificates or take other fundamental courses depending on your goals certifications provide globally recognized and industry endorsed evidence of mastering technical skills you’ve done a great job and should be proud of your progress the experience you’ve gained shows potential employers that you are motivated capable and not afraid to learn new things thank you it’s been a pleasure to embark on this journey of discovery with you best of luck in the future welcome to the Microsoft PL300 exam preparation and practice course a significant milestone on your journey toward becoming a certified Microsoft PowerBI data analyst if you’re motivated to set yourself up for a career in the world of data analytics you’re on the right track your learning journey in data analytics with Microsoft PowerBI has culminated in this course carefully designed to equip you with the knowledge skills and competencies you need to excel in the Microsoft PL 300 exam as you delve into this course you’ll navigate key PowerBI features and concepts that are integral to the PL 300 exam these concepts encompass a broad spectrum including data preparation modeling visualization and asset deployment plus by the end of the course you won’t just be well prepared for the PL300 exam you’ll also be equipped with valuable insights into your future career prospects in data analytics with PowerBI your course journey begins with a comprehensive review of fundamental concepts associated with data preparation and loading in PowerBI you’ll cover a range of essential topics such as the journey from exam preparation to Microsoft certification mastering the art of acquiring data from diverse sources and data profiling and cleaning as well as the intricacies of data transformation and loading the next part of your course journey involves a detailed recap of core data modeling concepts in PowerBI representing another crucial step in your preparation for the PL300 exam this will entail a thorough recap of designing effective data models and the creation of model calculations using DAX or data analysis expressions additionally you’ll delve into implementing well ststructured data models and optimizing data performance for efficient and seamless analysis following your refresher in data modeling you’ll take a turn toward revisiting essential concepts linked to data visualization and analysis more essential components to your PL300 exam readiness this part of the course encompasses creating impactful reports and enhancing and elevating those reports to boost usability and storytelling plus you’ll also focus on developing your skills in recognizing patterns and trends within data which is invaluable in data analytics after covering these critical content areas you’ll shift your focus to the deployment and maintenance of assets within PowerBI here you’ll refresh your understanding of pivotal topics like establishing and managing workspaces and assets you’ll also work on your proficiency in the efficient handling of data sets a skill that’s fundamental to the work of a data analyst to complete this course successfully you’ll have the opportunity to apply the skills and knowledge you have gained to a practice exam specially designed to simulate the conditions of the PL300 exam this practical hands-on assessment will allow you to assess your readiness and identify areas that may require further attention or improvement furthermore you’ll receive additional study resources and materials to further enhance your preparation you’ll also have the opportunity to explore different roles and career prospects that will be accessible to you once you’ve successfully completed the exam and obtained your Microsoft certification in sum the objective of this course is to prepare you for the PL300 exam and support you in realizing the next steps towards a career as a PowerBI data analyst the course is structured to prepare you thoroughly for assessment and guide you in recapping and consolidating the concepts you’ve acquired throughout the program it aims to increase your confidence in your competence and ensure you are truly exam ready as with the other courses in this program the videos readings activities and quizzes will contribute to you consolidating your knowledge and serve as a way for you to measure your progress beyond preparing for the PL300 exam this course holds a much larger promise it’s about more than just gaining knowledge and skills in data analysis in PowerBI it’s about taking an important step in setting yourself up for a career in data analysis a field filled with opportunities and potential by completing all the courses in the program you’ll earn a Corsera certificate which you can use to proudly showcase your job readiness to your professional network furthermore the program with an emphasis on this exam preparation and practice course will prepare you for the Microsoft Exam PL300 which leads to a Microsoft PowerBI data analyst certification globally recognized evidence of your realworld skills so are you ready to achieve exam readiness and take a leap toward a career in data analytics with PowerBI congratulations on reaching the home stretch of this program and all the best as you embark on the exciting and promising learning journey that lies ahead this is the final course in the Microsoft PowerBI data analyst professional certificate which will guide you through taking the PL300 exam and earning the associated Microsoft certification by obtaining the Microsoft PL300 certification you can unlock various career opportunities enhance your knowledge and skills and cultivate a competitive edge in the job market exams are nothing new it’s likely that you’ve encountered similar challenges earlier in your career just like before it takes preparation to make the most of it and the more effective your preparation the more benefits you will reap from all your effort this video provides a quick overview of what you can expect from the PL300 exam the logistics around taking the exam and the steps you need to take to prepare for success you can take the PL300 exam online at your home or office through Pearson View online you can also take your exam with Pearson View at one of their worldwide test centers pearson View is a global leader in computer-based testing and assessment services their Onview platform employs several security measures to ensure a fair and secure testing experience you can schedule your exam for a specific date and time on the Pearson View website there are a few important things to do before the day of the exam these include a system check making sure your ID document meets the specified requirements and choosing the appropriate space to take the exam the PL300 exam is a proctored exam which means that you are monitored by a live proctor or exam supervisor through your webcam during the exam the proctor ensures that you follow the exam guidelines and don’t engage in any prohibited activities the proctor will also give you certain instructions during the check-in process on the day of your exam there are very strict rules about what items and actions are allowed while taking the exam which you’ll learn in greater depth later it’s critical to understand these policies because failing to adhere to them will result in the termination of the exam session let’s move on to the topics covered in the exam to succeed in the PL300 exam you should be proficient at using Power Query and writing expressions using DAX or data analysis expressions you should know how to assess data quality as well as understand data security including rowle security and data sensitivity the PL300 exam measures your ability to accomplish the following technical tasks: data preparation data modeling data analysis and visualization and asset deployment and maintenance there are certain percentages of exam questions relating to each of these categories knowing these percentages can help you focus your study schedule on the categories that carry the most weight and help you prepare in the most effective way you can look forward to exploring the specific ways in which the skills related to each of these categories might be assessed later you can also consult the detailed exam skills outlined provided by Microsoft effective exam preparation not only requires a lot of dedication but you also need to consider effective strategies for during the exam for instance you should consider the type of questions you might get and how to approach them some helpful strategies include reading every option before choosing a final answer and following a process of elimination when you are unsure you will learn more about these and other strategies later one of the best forms of preparation is to take a practice test before the exam this way you can monitor your progress and identify the areas that might require a little more attention later in this course you will take two mock exams each one will focus on the topics and key concepts covered in the previous courses and the skills measured in the PL300 exam this video gave you a bird’s eyee view of how the PL300 exam works what it tests and some core elements of an effective exam preparation strategy you’ve already put in a lot of hard work by engaging in course material exercises and assessments during this program you are in a good position for the final preparation before taking the exam the information and materials in this lesson will help you focus your preparation in this final stage toward earning the Microsoft PowerBI data analyst certification datadriven enterprises rely on data analysts to provide them with accurate and insightful analysis as you’ve learned finding the correct data sources is essential for data analysts to help businesses achieve their goals in this video you’ll recap the importance of identifying the right data sources and connecting to data sources with Microsoft PowerBI as you begin the data analysis process identifying what data is required and which sources can provide the data is the first step toward a successful analysis outcome for example when looking to increase sales your social media accounts and popular search engines become your key data sources to analyze marketing data similarly if you’re looking to improve customer satisfaction tracking the volume of support requests and resolution time from your customer support system is the key data source fortunately PowerBI comes with over 100 connectors to allow you to tap into the different data sources available to you these include spreadsheet sources such as Microsoft Excel user directory services such as Microsoft Active Directory SQL databases such as Microsoft Azure SQL databases and text files in various formats such as XML JSON and CSV plus Microsoft continues to add new connectors and update existing connectors each year now let’s explore how to connect to a data source in PowerBI in PowerBI desktop select get data followed by Excel workbook when the file browser opens navigate to the folder that your Excel file is in select the Excel file then open the navigator window will open displaying all the available sheets within the workbook select the check boxes beside the sheets that you want to import at the bottom of the navigator window are three buttons: load transform data and cancel selecting load will load the data directly without cleaning or transforming it for this example let’s select transform data to open the Power Query Editor and inspect the data powerbi will begin loading the data note that this may take a few minutes depending on your computer and the size of the worksheet once the data is loaded the Power Query editor will open power Query allows you to apply transformation operations to the data before loading it into PowerBI on the left side of the editor is the queries pane where each table is listed selecting a table will allow you to clean and transform its data each row of data in the table is listed in the main working view on the right side of the editor is the applied steps list this lists each of the transform operations being applied to the data and the order in which they are being applied note that if you need to change the source of the data query you can select the cog icon beside the source step this opens a window where you can change the file from which the data is loaded if you’re satisfied with the existing data source you can close the window by selecting okay in this example let’s use the data as is without cleaning and transforming it select the close and apply button in the top left corner of the editor to finish transforming the data and load it into PowerBI powerbi will begin loading the data with transformations applied to it again this may take a few minutes depending on your computer and the size of the worksheet once the data is loaded you can begin working with it to build reports and dashboards if you want to inspect the data after loading select the table icon on the left side of the interface to open the table view also known as the data view in this view you can inspect each table and each row of data working with data sources is an important aspect of the role of a data analyst this video revisited the importance of identifying the right data sources and how to connect to an Excel data source load its data using Power Query Editor and configure the data source settings by selecting the cog wheel next to the source step in the applied steps pane as you solve business challenges unlock new opportunities and optimize existing processes consider which data sources can provide the data you need to achieve your objectives powerbi with its more than 100 connectors makes it possible for you to harness these sources to their fullest potential with hundreds of connectors in Microsoft PowerBI it should be no surprise that a wide range of options are available when using these connectors previously when you used an Excel worksheet as the data source the data imported into PowerBI but for larger volumes of data importing may become a resource inensive operation this is where choosing a different storage mode like direct query comes in in this video you’ll revise the different storage modes available in PowerBI powerbi Desktop supports three different storage modes also known as connectivity modes or data set modes in PowerBI service import mode direct query mode and dual mode when you use import mode data is copied from the data source to PowerBI this allows quick access to the data locally however if the data source is updated after importing you must refresh the data source fortunately you can configure PowerBI to schedule refreshes at specific intervals such as daily or weekly when you use import mode consider how up-to-date the data must be for stakeholders to make datadriven decisions effectively another consideration when using import mode is the required storage space if you are working with an extensive data set storing all the data on your local device may not be possible in today’s datadriven world it is not uncommon to see data sets consuming several gigabytes of storage so what about data sources with significantly large volumes of data a scenario where import mode may be unsuitable by changing to direct query mode PowerBI will query the data source directly for data rather than importing it this means that when a report is displayed in PowerBI each visualization will send a query to the data source to request the required data to determine what connectivity mode is supported you can refer to Microsoft’s documentation for your chosen connector one disadvantage of using direct query is that it requires transferring query results from the data source every time a query is made depending on the volume of data this may take some time slowing down visualizations and reports to improve the user experience PowerBI also provides a dual mode this mode is a combination of the direct query and import modes depending on the query and data source PowerBI will store a local copy of query results and refresh the copy as needed this helps improve the responsiveness of visualizations and reports without importing all data into PowerBI as you build data models in PowerBI connecting to multiple data sources is common when your data model connects to multiple sources it is known as a composite model with composite models you can configure the storage mode for each table in the model for example let’s say you have two tables in your data model products and sales in a niche business the product data set might be a small Excel spreadsheet and the sales data a large data set stored in a SQL database in this scenario it would make sense to use import mode for the products table and direct query or dual mode for the sales table this would help ensure no slowdown in your reports and that the viewers have a good user experience but what about connecting to a data set on PowerBI service powerbi features a type of connector called live connection which allows you to use direct query with data sets published to PowerBI service powerbi service becomes an important data source for building reports and dashboards as an organization grows hosting data in PowerBI service allows the organization to have one source of truth to maintain consistency and accuracy in reporting the benefit of using live connection is that security rules can be applied to the data ensuring that company data remains protected from unauthorized viewers in this video you recaped import direct query and dual storage modes to help you choose between them choosing the right storage mode is important to ensuring a good user experience for different stakeholders if data retrieval is slow reports and dashboards will also be slow which may result in stakeholders not utilizing the insights unlocked by your data analysis as you proceed through the data analysis process carefully consider which storage modes are suitable for different data sources and how they should be configured query parameters are a useful feature in Microsoft PowerBI for simplifying a dynamic element of your data for example changing between a test data source and a production data source or filtering data from your data source in this video you’ll revise how to configure query parameters and the values that they use in the Power Query Editor there’s an Excel data source loaded containing stock orders for different business regions because the data set is quite large let’s use query parameters to filter the data needed to do this select the manage parameters button in the home tab of the ribbon menu this opens the manage parameters window to filter the data by country you need to add a country parameter in the manage parameters window select new in the name field enter country in the description field let’s add a note that this parameter filters the stock order data by country ensure that the required option is enabled so that report users must specify a value for this parameter for the type field let’s change the type to text as the country values are text values also since there’s a fixed list of countries in the data let’s change the suggested values to list of values in the list of values add the three countries present in the data the United States France and Germany for the default value select United States this will be the default value for users of this data set for the current value select United States then select okay this adds the parameter to the queries pane to ensure that the data source query utilizes the parameter select the stock orders query in the queries pane then select the filter button in the country column followed by text filters and equals which opens the filter rows windows in the filter rows window change the filter value button to parameter this will then change the equals filter to utilize the previously defined country parameter you can then select okay note how the data set is now filtered by the country parameter in the home tab of the ribbon menu select close and apply to load the data set to confirm that the parameter has been applied select the table view button also known as the data view in this view it is clear that the data set contains only stock orders for United States this matches the current value specified for the country parameter earlier to visualize how this parameter is used let’s create a simple report containing a card visualization navigate to the report view in the visualizations pane select the card visualization the visualization is then added to the report now select the visualization in the report in the data pane also known as the fields pane let’s select the unit price field this applies the unit price field to the visualization in the visualizations pane in the data field rightclick the sum of unit price and then select average the visualization now displays the average value of the unit price field in the data set to change the parameter you can select the drop-down of the transform data button in the home tab of the ribbon menu then select edit parameters in the edit parameters window let’s change the country parameter to France then select okay powerbi now displays a notification that there are pending query changes if you select apply changes the parameter change will be applied note that the average value in the visualization has changed this is because the data set has now been filtered for only stock orders in the France business region to confirm this let’s select the table view button in this view it is clear that the data set contains only stock orders for France in this video you recaped how to change the values in a parameter query parameters are a great way to filter your data queries dynamically as you begin building reports and working with more extensive and multiple data sets consider how you can use query parameters to reduce the scope of data being retrieved by PowerBI optimizing your reports and providing a better user experience as a business continues to grow so does the challenge of managing large volumes of data and ensuring that the data is wellformed and ready for analysis microsoft PowerBI’s data flows help to solve this issue by creating reusable data transformation logic in this video you’ll explore what a data flow is how it works and how to connect to one in PowerBI desktop maintaining a single source of truth is important in a datadriven enterprise it ensures consistent analytical conclusions are obtained from the underlying data one method of ensuring a single source of truth is by creating data flows in PowerBI service a data flow is a collection of tables that exist within PowerBI service you can add and edit tables in your data flow apply transformations and manage data refresh schedules directly from the workspace in which your data flow was created each table consists of columns and rows each cell in a table is known as an entity data flows promote the reusability of underlying data elements preventing the need to create separate connections with your cloud or on premises data sources if you want to work with large data volumes and perform the extract transform and load or ETL process at scale data flows with PowerBI premium scales more efficiently data flows act as data sources for your data sets in both PowerBI service and PowerBI desktop data flows can also act as data sources for other data flows however when using a data flow there are important considerations and limitations to keep in mind if a data flow links to another data flow the maximum number of linked data flows in the chain is 32 this is known as the maximum depth you need a PowerBI premium subscription in order to refresh more than 10 data flows across the workspace data flows are managed individually this means that there is limited visibility into dependencies between data flows in PowerBI data flows you can use parameters but you can’t edit them unless you edit the entire data flow when creating a data set in PowerBI desktop and then publishing it to the PowerBI service ensure the credentials used in PowerBI desktop for the data flows data source are the same credentials used when the data set is published to the service previously in this course you walked through how to create a data flow let’s take a moment to explore how to connect this data flow to PowerBI desktop launch PowerBI desktop and select more from the get data drop-down list of options in the get data dialogue box that appears select Power Platform from the left column and select data flows from the right column of the dialogue box then select next if you are connecting to the dataf flow for the first time a dialogue box opens where you need to sign into your PowerBI service account after you enter your login credentials select connect a navigator window appears displaying the workspace and the data flow you created previously expand the workspace and data flow to display the available tables the two tables that you imported during the creation of the data flow are available here select both tables fact internet sales and dim date followed by load the tables are loaded into the PowerBI model a process you may be familiar with you can establish relationships between the data tables and create reports and visualizations as you typically do with any data set once the data is updated in the source data set you need to go back to PowerBI service and refresh the data flow or configure the scheduled refresh of it you will revise scheduled refresh later data flows are a powerful feature that enable you to centralize your data as a single source of truth as an organization grows data flows help to encourage consistency and reuse of data leading to effective decision-making within the organization businesses operate with many data sources from SQL databases to Excel spreadsheets but with multiple data sources comes varying degrees of quality some sources may be perfect and ready for analysis but others require quality checks cleaning and transformation in this video you’ll revise the importance of inspecting data before loading it for analysis before loading a data source into PowerBI it is essential to evaluate whether the data source will provide the data that you require and if the format is compatible with PowerBI utilizing the wrong data for analysis can lead to incorrect conclusions being drawn or even worse wrong business decisions being made once you’re satisfied that the data is suitable the next step is to load it into PowerBI when you first load a data source PowerBI inspects the first 1,000 rows of data of each table to determine the data types of each column powerbi supports multiple data types such as numeric types date and time types text and true or false in most scenarios PowerBI will automatically determine the correct type however while this automatic feature is useful it is important to inspect the results of it in the data view also known as the table view of Power Query Editor incorrect data types can cause significant issues later when writing DAX queries building reports and analyzing the data if you need to change the data type use the Power Query editor to perform the transformation once the correct column types are established it is important to evaluate the statistical distribution of the columns in PowerBI this is done using three data profiling tools column quality column distribution and column profile let’s revisit each of these profiling tools starting with column quality column quality displays the percentage of data that is valid in error and empty in an ideal situation you want 100% of the data to be valid column distribution displays the distribution of the data within the column and the counts of distinct and unique values distinct values are all the different values in a column including duplicates and null values distinct tells you the total count of how many values are present on the other hand unique values do not include duplicates or nulls unique tells you how many of those values only appear once lastly column profile provides a more in-depth look into the statistics within the columns for the first 10,00 rows of data this column provides several different values including the count of rows which is important when verifying whether you imported your data successfully for example if your original database had 100 rows you could use this row count to verify that 100 rows were in fact imported correctly additionally this row count will show how many rows PowerBI has deemed as being outliers empty rows and strings and the min and max which will tell you the smallest and largest value in a column respectively this distinction is particularly important in the case of numeric data because it will immediately notify you if you have an anomaly in your data such as a maximum value that is beyond what your business identifies as a maximum now let’s recap how to access these profiling tools in the Power Query Editor a sales data set has just been loaded in the Power Query Editor the data set contains the transaction ID product ID quantity sales amount and other related data to inspect each column’s data type navigate to the transform tab in the ribbon menu to display the data type in the ribbon menu select the column and inspect its data type the data type is currently set to text for each column as the data in the first four columns are numeric update the first four columns to the whole number data type by selecting each column and changing the type in the ribbon menu note that when the data type is changed a new step is added to the applied steps list remember you can edit remove and reorder the steps in this list next let’s update the sales amount column to the decimal number data type and finally update the transaction date column to the date data type next you have to evaluate the column quality distribution and profile to do this navigate to the view tab in the ribbon menu enable the column quality column distribution and column profile options in the menu the view now updates with the corresponding statistics each column is 100% valid meaning there are no errors or empty values in the quantity column there are four distinct values and zero unique this means that among this data there are four values that occur in the quantity column but none of them are unique in the column statistic panel the count is 52 since there are 52 rows of data this is the correct number the minimum and maximum values for the quantity column are within the expected range for the business if there were any issues with this data further transformation would be required to clean the data you will learn more about transformation later in this course the data is ready for import navigate to the home tab in the ribbon menu and select close and apply profiling your data is important for ensuring accurate results later in the data analysis process without accurate data businesses can’t unlock the insights that they’re seeking remember accurate and consistent data is a requirement for a successful datadriven enterprise as you know by now datadriven organizations rely on data to make informed decisions and drive innovation however the effectiveness of such decisions is greatly dependent on the quality and consistency of the data poor quality data and inconsistencies can lead to expensive mistakes missed opportunities and damaged reputations in this video you’ll explore resolving inconsistencies and issues in your data let’s start by exploring the question what is data quality data quality refers to the accuracy completeness and reliability of the data as a future data analyst a key responsibility of your role is ensuring that data is of high quality before it is used stakeholders and decision makers rely on accurate data to assess performance and build strategies inaccurate or incomplete data can lead to inaccurate reports and misguided decisions such decisions could have significant effects on the business if the business is operating in a regulated industry such as pharmaceuticals the wrong decision could lead the business to fall out of compliance with regulation and be subject to fines or legal proceedings for example duplicate entries in your marketing data could lead management to overstock certain products increasing costs and negatively impacting the finances of the business the common types of inconsistencies and quality issues that can occur are duplicate rows empty or missing values and errors or invalid values fortunately PowerBI comes with tools to help analyze the quality of your data and resolve inconsistencies and errors previously you learned how to use data profiling tools to analyze a column’s quality distribution and profile which helps identify irregularities in your data you also learned how to ensure that the column has the correct data type now let’s revisit how to use the Power Query editor to resolve other data quality issues and inconsistencies here in Power Query is a data set that contains several data quality issues the first issue is that every row is duplicated to resolve this navigate to the home tab on the ribbon menu then select the remove rows button and select remove duplicates power Query has now removed the duplicates and added a step to the applied steps list for removing duplicates next there are some values in the transaction date column that are null the sales team has informed you that there was an error on their system and the date was the 1st of January 2023 to fix this select the replace values button under the home tab the replace values dialogue box appears here specify null as the value to find and 1st of January 2023 as the value to replace with select okay and the changes are applied again note that a new step is added to the applied steps list in the sales amount column one of the values is spelled as the words 500 instead of the number to fix this use the replace values dialogue again this time specifying the words 500 as the value defined and the number 500 as the value to replace select okay to apply the changes now that the quality issues are resolved return to the home tab in the ribbon menu and select close and apply to apply the changes maintaining data quality is a key aspect of being a data analyst by regularly evaluating and auditing your data you can help maintain the accuracy of your analysis and help organizations make effective decisions that will lead them to success data comes in different forms a telephone number is not the same as a block of text therefore ensuring these different forms are correctly represented and stored in table columns is important for accurate and consistent data collection and analysis in this video you’ll revise how to identify and transform column data types and how to create a new calculated column based on existing data in PowerBI a table consists of one or more columns of data as you add data to the table a new row is created in the table with a value in each column each column has a specified data type which determines how the data in the column is represented which calculations are available and how the data can be used in visualizations you’re already familiar with the different types of data in PowerBI including numeric types date and time types text and true or false once your data is loaded into a table you may identify missing data for example suppose you are working with a table of products consisting of two columns cost and sale price for the report you’re building you also need to display the profit per product sold since the data is not provided by the data source you can use a calculated column to derive the value required calculated columns use a data analysis expressions or DAX formula to create new values for each row in the table like in the previous example these calculated columns will often use values from existing columns to derive their values based on the example the formula to create a profit column would be profit equal sale price minus cost this is a simple example but DAX is a powerful expression language that you can use to create complex formulas to derive insights from your data now let’s take a moment to review how to identify a column’s data type transform the column and create a new calculated column in PowerBI load and open the sales data set in the Power Query Editor as you’ve previously learned PowerBI automatically determines the data type based on the first 1,000 rows of the data set however it is best practice to inspect the data type of each column before importing to do this select the first column in the main working view in the home tab of the ribbon menu the data type is specified as whole number inspect each column noting that all columns except the last one are set to the whole number data type the last column transaction date is set to date data type all types are correct except the sales amount column since a currency amount can have numbers after the decimal place you need to change this column’s data type to fixed decimal number to do this select the column then select the data type in the ribbon menu and select fixed decimal number in the drop-down note that this can also be done in the transform tab of the ribbon menu a prompt appears asking if you want to replace the existing change type step in the applied steps list or add a new step for this example select add new step a new change type step is added to the applied steps list now that the data types for each column are correct you need to add a new calculated column the data set is missing the sale price per unit which is calculated as the sales amount divided by the quantity to do this select the add column tab in the ribbon menu and then select custom column the custom column prompt appears for the new column name enter sales amount per unit next you need to complete the custom column formula powerbi provides a list of available columns on the right side of the prompt first select sales amount and select insert this adds the sales amount column to the DAX formula in the custom column formula type space then forward slash and then space forward slash is the division operator in DAX then select the quantity column in the available columns list and select insert on the bottom left of the prompt note that PowerBI has detected no DAX syntax errors then select okay power Query has now added the calculated column to the table select the column to inspect its data type the column has been created as an any type change the column to a fixed decimal type and the data set is now ready in the home tab on the ribbon menu select close and apply to begin importing the data into PowerBI as you work with large data sets consider how correct data types and calculated columns can help optimize the visualization of your data saving calculation time during visualization will improve the user experience and drive engagement with the reports you are building as you begin working with multiple data sources keeping track of the different queries can grow in complexity very quickly this is where PowerBI’s query pane and reference queries become crucial to a data analyst in this video you’ll learn about the query pane and how to effectively manage queries using it in PowerBI when you connect to a data source it creates a query in the query pane as you begin applying transformations these exist within the context of the query however if you are working with large data you may need to apply multiple transformations inserting data into tables at different stages doing this with a single query can become difficult to maintain very quickly this is where duplicate and reference queries come in in the query pane you can duplicate a query to create a copy and perform different transformations on it from the original query this allows you to transform data into different formats and insert it into different tables for example let’s say you have a sales data set that contains the following columns sales date item quantity shipment address and shipment country you need to build a table for sales and a table for countries the sales table can be imported from the data set but unfortunately you don’t have a separate countries data set so you need to build a table from the sales data set in this scenario you can duplicate the query rename it to countries and apply the necessary transformations to remove all columns except shipment country remove duplicates and import the data into a country’s table you now have a table containing all countries that sales have shipped to in this scenario duplicate queries make sense as you have two completely different sets of transformations and resulting tables if there are common transformations this creates an issue for maintainability let’s work through an example where duplicate queries could create problems again let’s say you have a sales data set that contains the following columns: sales date item quantity shipment address and shipment country you need to build a table for sales and a table for countries however in both tables you need to rename the shipment address column to address and shipment country column to country if you duplicate the query you will need to apply this transformation in both queries and if you need to update this transformation later you will need to do it in both queries well this is a simple example if you had a series of more complex transformations maintaining these in two different queries could easily result in mistakes and human error this is where reference queries are important to use reference queries allow you to use another query as the base of a query using the previous example you can apply the column rename transformations in one query and then create two new queries which reference the first query to perform the subsequent operations to create the sales and country tables now if you update anything in the first query the dependent queries will be automatically updated this reduces the complexity and effort of maintaining queries minimizing the risk of human error it also increases the efficiency of PowerBI as PowerBI can pipeline results from the first query as input to the dependent queries instead of repeating transformations multiple times on multiple queries when importing very large data sets efficient queries can be the difference between a few minutes and a few hours of importing data duplicate and reference queries require much consideration when working in PowerBI identifying when efficiency and maintainability are needed is an important skill to develop as you progress in your career as a data analyst and can help you perform effectively in your role as you work with multiple data sources you’ll discover that the data is often disjointed and needs to be combined and transformed into a data model that is suitable for analysis in this video you’ll explore how merge and append queries in PowerBI can combine multiple data sources into single tables suitable for visualization and analysis in later stages of the data analysis process it is common to encounter data that is broken down into multiple files or data sources for example sales data might be stored in one Excel file per month or perhaps sales data was originally stored in Excel files but later moved to a SQL database however to effectively analyze this data you require it to be contained in a single table in PowerBI fortunately the Power Query Editor contains the append queries feature which allows you to append multiple sources into a single table using the earlier example let’s say you have one Excel file containing sales for January the file contains the columns sales date product name and sales amount you then have a SQL database containing a table with sales for February with the same columns as the Excel file using an append query you can combine the data from these two data sources into a single table containing sales for both January and February but what happens if the columns are different suppose that the SQL table contains an additional column named discount when the append query executes it will insert null values in discount column for rows that originate from the Excel file append queries works well when the columns in the data source are well aligned and the desired resulting table should match the format of the data sources however you may encounter more complex scenarios requiring the merging of data from different sources this is where merge queries comes in let’s say you have a table of customers named customers from a customer relationship management or CRM system you then have a table of sales orders from a SQL database named sales you want to prepare a single table containing the most common cities where orders are delivered to to do this you’ll need to merge the tables from the two data sources using a merge query to merge two tables you need to tell the merge query which type of join you would like to use the join type informs PowerBI how to merge the two tables a join requires that there is a common column between the two tables in our previous example the sales table contains a unique customer ID which is present in the customers table this is known as the join key once the join key is determined the join type must be chosen powerbi supports the following join types left outer right outer full outer inner join left anti-join and right anti- join let’s explore each join type and the way it combines data from multiple tables based on matching criteria to understand the join types picture two tables one of the left side named sales and one of the right side named customers the sales table contains the columns sales ID customer ID and sales amount the customers column contains the customer ID country and name columns the customer ID column in both tables will act as the join key with a left outer join the resulting table will contain all rows and columns from the left table merged with all matching rows and columns from the right table this results in a table with the column sales ID customer ID sales amount country and name if the sales table has a customer ID that does not exist in the customers table the name and country columns for that row will contain null values in a right outer join the resulting table will contain all rows and columns from the right table merged with all matching rows and columns from the left table this results in a table with the columns sales ID customer ID sales amount country and name if the sales table contains customer ids that are not present in the customer’s table these rows are excluded from the results a full outer join simply merges all rows and columns from both tables into the resulting table if the sales table contains rows that do not match the customer’s table null values will be inserted for the country and name country columns if the customer table contains rows that do not match the sales table null values are inserted for the sales ID and sales amount columns in an inner join the resulting table only contains the matching rows from both left and right tables a left anti-join will keep rows from the left table that do not have matching rows in the right table note that this will still include columns from the right table but since there is no match in the right table every row will have a null value in these columns a right anti-join will keep rows from the right table which do not have matching rows in the left table again note that this will still include columns from the left table but will have null values for these columns in each row merge and append queries are valuable tools in your data analysis toolkit they allow you to combine tables from multiple data sources into a format that aids rather than hinders the data analysis process as you continue through the data analysis process designing a schema to represent your data is a key step before diving into the analysis itself this video will explore table relationships and how to identify appropriate keys for establishing relationships a table relationship is how two tables are connected to each other let’s say you have two tables sales and products the sales table contains the following columns sales ID sales amount and product ID the products table contains the columns product ID product name and product category in the products table the product ID column is what’s known as a primary key each value in the product ID column is unique that is if one row has the ID of 11 no other rows in that table will have that ID therefore a primary key uniquely identifies a row in the table in the sales table the product ID column is what’s known as a foreign key it’s not the primary key of the table but instead it establishes a relationship to the products table this means that each row in the sales table is associated with a specific row in the products table if a row in the sales table has a value of 11 in the product ID column it is therefore associated with the row in the product table which has a primary key of 11 for primary and foreign keys the whole number data type is most commonly used however there are scenarios where a non-numeric identifier may be used for example if you are analyzing countrybased data you could use the two-letter standard identifier for each country such as US for United States DE for Germany and so on now that you know how to establish a relationship between two tables the next important aspect is the cardality of the relationship in PowerBI there are three types of cardality one many to one or one to many and many to many to explain these cardalities let’s say that you have two tables table A and table B a onetoone relationship would mean that each row in table A is directly related to only one row in table B and vice versa for example if table A contained countries and table B contained capital cities the relationship would be one to one as each country has only one capital and each capital belongs to only one country a many to one relationship would mean that multiple rows in table A can be related to a single row in table B the relationship from table B to table A is a one to many relationship that is each row in table B is related to multiple rows in table A our earlier sales and products example was an example of a many to one relationship multiple rows in the sales table are associated with one product in the products table a many to many relationship would mean that each row in table A is related to many rows in table B and each row in table B is related to many rows in table A for example if you had a table of books and a table of authors a book can be written by multiple authors and an author can write multiple books establishing relationships is an important aspect of building a schema for your data model you will learn more about schemas and data modeling later table relationships are an important consideration when modeling your data in PowerBI using incorrect relationships or cardality can lead to wrong insights and results in the data analysis process as a data analyst it is your responsibility to ensure correctness in the data model so that a successful analysis outcome can be achieved congratulations on completing the first part of the Microsoft PL300 exam preparation and practice course designed to help you achieve your PL300 certification you’ve discovered much about the PL300 exam and honed your data preparation skills and knowledge within Microsoft PowerBI to ensure your success let’s recap some key takeaways and insights you’ve covered so far you began with an overview of the course and how it will prepare you for your certification journey you explored the syllabus course structure and helpful tips for success you delved into all things Microsoft certification as part of your exam preparation you identified key knowledge and skills measured in this course’s mock exam and the PL300 exam learning how to plan your study time effectively the steps to register and schedule the procedur exam were outlined offering a clear road map to taking the exam you also discovered more about the administration of the PL300 exam so you know what to expect you explored testing strategies and the advantages of practice assessments and mock exams you also had the opportunity to discuss exam preparation with your fellow learners armed with more knowledge about the PL300 exam you moved on to reviewing exam content focusing on data preparation in Microsoft PowerBI you began by revisiting the practicalities of getting data from various sources you learned the importance of choosing the right data sources and were reminded of PowerBI’s extensive range of connectors you were guided through connecting to an Excel data source and loading data via the Power Query Editor and you explored configuring data source settings you also explored the difference between local and shared data sets the pros and cons of import direct query and dual modes and choosing different storage modes you gained handson experience setting up and configuring a data set reviewing the advanced query capabilities of Power Query and using query parameters in Power Query expanded your toolkit you covered connecting to a data flow recapping data flows and creating them in a workspace you also explored the difference between data flows and Microsoft data versse enriching your expertise then you focused on the critical task of profiling and cleaning data you covered evaluating data data statistics and column properties reviewing why data evaluation is crucial Power Query’s profiling capabilities and different evaluation methods through an interactive activity you practiced analyzing a data set for anomalies and statistical irregularities preparing you for real world scenarios as a PowerBI data analyst you also explore data inconsistencies unexpected or null values and data quality issues you may encounter as a PowerBI data analyst as well as resolving data import errors next you explored the transforming and loading data you reviewed creating and transforming columns understanding the importance of selecting appropriate column data types and how to transform columns and create calculated columns in Power Query you brushed up on shaping and transforming tables and applying query steps to shape the data exploring reference queries you recaped when to use reference or duplicate queries you also unpacked the differences between merge and append queries and explored the different types of joins finally you reviewed how table relationships work identifying appropriate keys for relationships and configuring data loading for queries in a PowerBI project you now have detailed insight into what taking the PL300 exam entails and have boosted your skills and knowledge in data preparation with PowerBI and that’s not just good for the exam it’ll also contribute to your success in the world of data analytics previously you covered how to establish table relationships building on this you will explore how to design a schema that contains facts and dimensions when deciding on the data schema you plan to use for your analysis the most common schema types are star and snowflake schemas you may recall that in these schemas data is broken down into fact and dimension tables fact tables represent a business processes measurements metrics or facts they can contain several repeated values for example one product can appear multiple times in multiple rows sold to different customers on different dates these values are used to create aggregations during visualizations dimension tables store contextual data or descriptive attributes about the facts these tables are connected to the fact table via key columns you can use dimension tables to group or filter data in the fact table during visualization in Microsoft PowerBI in the context of an Adventure Works data set with sales and product tables the sales table is the fact table as it contains transactional information about the sales process the product table is the dimension table as it contains the contextual information the product sold for each sale in the star schema the most common data model a single fact table is typically related to one or more dimension tables the snowflake schema further normalizes the dimension tables for example the product table is broken down into product category and product subcategory tables based on category ID and subcategory ID now let’s revisit how to create and configure a star schema in PowerBI launch PowerBI desktop and load the data from the Excel workbook containing Adventure Work sales data the data set contains four data tables one fact table the sales table and three dimension tables these are product region salesperson navigate to the model view where you can create and configure the data model and build a star schema once you load the data PowerBI auto detects the relationships between the data tables based on the key columns you can disable this function from options and settings to create and control the nature of relationships between your data models you can establish the relationship between the fact and the dimension table in two ways to build a star schema remember in a star schema the fact table is at the center of the star the first method is simply dragging the key column from the fact table to the dimension table in the current data set drag the product key column from the product table and drop it on the product key column in the sales table if there are no duplicate values in the product key column of the product table PowerBI automatically establishes a one to many relationship with a single cross filter direction repeat the same process for region and salesperson tables to relate these dimension tables to the sales fact table let’s delete the relationships to explore the second way to build the star schema right click on the connector line and select delete the relationship select manage relationships from the home ribbon a manage relationship dialogue box appears on screen here you can either select autodetect or new with the autodetect selection PowerBI identifies the key columns and establishes relationships in your data similar to when you load data into the PowerBI data model for the current exercise let’s select new a create relationship dialogue box opens select tables cardality and cross filter direction for all data model tables one at a time your star schema is ready to use for your analysis and visualizations practically in a star schema dimension tables are typically positioned above the fact table to give it a waterfall-like structure these dimension tables are used for filtering the fact table meaning the typical direction of the filter is like the flow of water from the waterfall in this video you explored how to build and configure the star schema from the adventure works data set data modeling is a key skill set that you need to master in your journey to become a successful PowerBI analyst and succeed in the Microsoft PL300 exam role-playing dimensions enable data to function dynamically and facilitate better informed decision-making this involves assuming the perspective of your data to play multiple roles and uncover insights that might remain hidden to the untrained eye in this video you’ll recap roleplaying dimensions and the use relationship function in Microsoft PowerBI in business intelligence a role- playinging dimension is a single dimension that can be used for different purposes in the same data model using an adventure works example you might have a date dimension table that connects to various fact tables like sales purchases and inventory this date dimension could play distinct roles like acting as order date when examining sales data purchase date when working with purchases or inventory check date for inventory related analyses previously you encountered a practical scenario involving role-playing dimensions a single sales table that contained multiple date related fields like order date shipping date and delivery date in this case the date dimension table in your model can be related to the sales fact table via multiple relationships to accommodate the different date roles such as new sales shipping dates and receipt dates however remember that only one relationship can be active at a time and the remaining relationships must be inactive you can switch the active relationship manually from the manage relationship in the PowerBI model view continuing with the previous example you would need to import Adventure Works sales data into PowerBI desktop to implement the roleplaying dimension and start building the relationships between the date dimension and the sales fact table the date dimension table is the roleplaying dimension in this scenario and is used for the entire analysis and visualization in PowerBI in a realworld environment you often need to analyze data and present information from a distinct perspective for example Adventure Works might need information about its sales values based on shipping or delivery dates currently the data model contains only one date dimension which is role-playing one way to achieve this is to duplicate the date dimension and rename it shipping date although this is not a practical approach fortunately PowerBI’s formula language DAX provides the solution with its use relationship function creating a measure using the DAX use relationship function temporarily switches the inactive relationship to active let’s break down the DAX formula to create a measure that calculates sales values based on shipping date the code is defining a new measure or calculation called total sales orders shipped in this formula the calculate function alters the filter context of the entire measure within the calculate function it uses the sum function to sum up the sales amount column of the sales table as the default relationship between the sales table and the date table is based on the order date column each DAX calculation is based on the relationship between the tables the use relationship function in DAX overrides this relationship and establishes a temporary relationship between the date column of the date table and the shipping date column of the sales table this inactive relationship becomes active only during the current calculation when using the use relationship function there are some essential points to consider you can only use use relationship within DAX functions that take filter as an argument for example calculate calculate table and total YTD when rowle security is defined for a data table you cannot use the use relationship function otherwise PowerBI will return an error you must first define relationships in your data model because the use relationship function uses existing relationships the column used as the argument in the formula must be part of the relationship if not an error message will display on screen you can nest up to 10 use relationship functions in a single expression lastly in a onetoone relationship use relationship can only activate a relationship in one direction meaning filter propagation will be in one direction only to activate birectional filter propagation you need to use two use relationship functions within the same expression mastering creating custom measures within your data model using a use relationship function and implementing role-playing dimensions are two methods you can use to handle the inactive relationship between data models these skills will not only help you to succeed in your Microsoft PL300 exam but will be valuable in practice as a PowerBI data analyst by now you have an idea of evaluation context and how it works in DAX calculations all DAX calculations compute measures under row and filter context calculate along with its companion calculate table is the only DAX function that can alter the filter context during a DAX calculation in this video you’ll revise how to use calculate to manipulate filters at Adventure Works the management team wants to analyze granular levels of sales data for example suppose the sales manager needs information about the sales of mountain bikes in Europe only a product specialist is interested in the performance of a specific color product that the company recently launched and the United States Countrywide manager wants to filter out the sales amount for the newly hired salesperson all this granular information is easy to compute using DAX measures in PowerBI you can filter the entire sales measure for a specific color product a particular region a salesperson and so on using calculate this will change the filter context of the measure from all to the filtered arguments let’s examine the syntax of calculate and how it impacts the filter context of the calculations in a DAX formula that calculates the total sales of red products the DAX code uses the calculate function and specifies a filter condition where the product table’s color column is equal to red when you use this measure in a matrix or table visual the filter over product color is added to the already existing filter placed by the matrix itself on the month column in the first column the month is the filter context filtering sales for each month the total sales measure computes the sales amount for each month for all products this time adding product color equals red as an additional filter context in this syntax a condition is used to apply the filter over product color however in the DAX engine filter arguments of calculate are tables so the same calculations can be achieved by a formula where the DAX engine converts the previous shorter syntax of calculate to a longer syntax let’s explore this behavior from another perspective if you visualize the total sales by color in a matrix the filter context is filtering the product color the presence of the all function in the longer expression means the outer filter over product color is ignored and replaced by the new filter introduced by calculate in the matrix the sales values for the red products are repeated in all the rows for each row the filter introduced by the matrix is the corresponding color and the red product sales imposes a new filter forcing red to be visible this means the new filter introduced by calculate overrides the existing filter so the sales values are computed within the filter context that filters only red products let’s say the European sales manager of Adventure Works needs the sales amount of red products in Europe only you need to introduce another filter argument within the calculate expression this expression applies two filter functions to the overall filter context of the calculation namely the product color filter to include only red products as in the previous example and takes the region groups as an additional filter to specify Europe as the region the measure presents the sales of red color products in Europe for various months likewise you can perform further granular analysis to compute the sales amount for individual categories product salespersons resellers of the company and so on from the examples you have learned the calculate only modifies the outer filter context by applying new filters this is done by either overriding the existing filter or by combining new filters with the existing ones the evaluation context and calculate function are the foundation of the DAX language making these fundamental skills any PowerBI analyst should master to pass the PL300 exam and to handle realworld analytical challenges previously you learned that multiple data tables constitute a data model for instance a star or snowflake schema a relationship exists between the data tables why does this relationship exist a model relationship propagates filters applied to one column of the table to another model
table a filter can only propagate if there is a relationship path to follow which may involve multiple model tables this video will cover the cardality types and cross- filter directions that exist between the data tables in Microsoft PowerBI in a model relationship two columns are involved from two different tables one from the from side and one from the two side of the relationship both these columns must be of the same data type at its core cardality defines the nature of the connection between two data tables it tells you how many values in one table correspond to how many values in another each relationship must have at least two data tables a from side and to side of the relationship the column on the from side of the relationship must contain unique values while the two side column can have duplicate values powerbi supports four types of cardality these are one one to many many to one and many to many when you establish relationships between tables by dragging the key column from one table to another PowerBI automatically detects and sets the cardality type by sending queries to investigate which columns contain unique values however sometimes PowerBI’s autodetected cardality is not correct therefore it is recommended to check the cardality type before starting analysis and visualization now let’s start by reviewing the onetoone relationship one type of cardality supported in PowerBI a onetoone cardality means both related columns contain unique values this is not a common type of relationship in data modeling consider an example where Adventure Works has two dimension tables product and product category each table has a skew or stock keeping unit column all fields in these columns contain unique values a onetoone relationship exists between these two tables based on the skew column because it’s common to both this means that when skew filters the product category table the product table will be filtered for products associated with the skew next are the one to many and many to one cardality types these two types are essentially the same where each value in one table column is related to multiple values in another it is also the most common type of cardality in PowerBI data models it ensures slicing and dicing data allowing for drill down analyses to uncover granular insights for example in an adventure works data set the sales table also the fact table is related to the region table or the dimension table both tables have a sales territory key column which establishes a one to many relationship between the tables in the region table the sales territory key field contains a unique value in each row as each region only exists once in the table each region can have multiple sales so their sales territory key may be repeated in multiple rows of the sales table a many to many relationship means both related columns can contain duplicate values this type of relationship is used when designing a complex data model typically it’s also used to relate two dimension tables or two fact tables for example consider the relationship between a financial corporation’s customers and the various financial products they hold a customer can hold many financial products and each financial product can be held by many customers a many to many relationship supports the duplicate customer ID data in both tables now that you’ve covered the cardality types in PowerBI let’s delve into how these cardonality types influence the cross filter direction you may recall that cross filter direction refers to the direction of filter propagation between two related model tables it dictates how data from one table influences the data in another table enabling relational analysis without resorting to complex queries or manual data consolidation single cross filter direction means the filter propagates unidirectionally from one table to the other within the relationship and both means the filter can propagate in both directions a relationship that filters in both directions is commonly described as birectional the cross- filter direction is dependent on cardality type onetoone relationships support only both cross filter direction one to many and many to one relationships support both types of cross filter directions many to many relationships can have a single cross filter direction where table A filters table B or table B filters table A or both of these single cross filter directions simultaneously although you can set and configure cross filter direction in PowerBI desktop’s model view in real world scenarios it’s often necessary to answer business questions that require changing the direction of filter propagation manually adjusting the cross filter direction to meet these analytical requirements is not practically feasible dax provides the solution with its cross filter function with the cross filter function you can change the cross filter direction for a specific measure while maintaining the original settings the syntax of the cross filter function takes three arguments let’s examine this syntax briefly in the first argument table one name refers to the name of the first table and column name one refers to an existing column within that table usually representing the many side of the relationship to be used similarly in the second argument table two name refers to the name of the second table and column name two refers to an existing column within that table this time usually representing the one side of the relationship to be used finally filter direction represents the cross- filter direction to be used you can define this as none single or both cross filter directions in the expression both cardality and cross- filter direction are the key analytical concepts in data modeling and analysis as the businesses continue to rely on datadriven decision-making mastering key skills in data modeling and DAX will set you on a path to becoming a functional and influential analyst you have just imported a data set for analysis and upon careful investigation you’ve realized that some information required to address business questions is missing in the data set creating calculated columns to add the missing information into your data tables is a concept you’ve learned before and will be covered briefly in this video calculated columns are custom data columns that are created within a Microsoft PowerBI data model using data analysis expressions or DAX language unlike standard columns that store data directly from imported data sets calculated columns contain formulas that drive values from existing data once you add a calculated column to your data model by defining a DAX expression you can use this column to generate any report and visualization just like the standard columns calculated columns are stored in the data model level and therefore consume memory so you have to be careful not to use too many calculated columns the standard columns of a data model are populated with the imported data model whereas you need to define a DAX expression to populate a calculated column from the existing data the data can be taken from multiple columns and tables of the data model that you must define in the DAX script remember calculated columns can be created from the report view data view or model view of PowerBI desktop and are based on the data you have already loaded into your data model for instance if you have a customer data table with two distinct columns containing information about the first and last names of the customer and you want to combine these two columns into a single column containing the full name of the customer you can use a DAX expression to concatenate the two columns into a single calculated column one of the most common examples of populating a data table with calculated columns is creating a date dimension table previously you populated a date table with various calculated columns like year month name month number and so on now let’s briefly recap the DAX syntax for defining calculated columns the syntax starts with the name of your calculated column followed by an equal operator then write the names of the tables to be referenced in single quotation marks and their respective column names in square brackets include a relevant arithmetic operator or any other expression for example at Adventure Works you are creating a sales report based on geographical information in the geography table both city and state information are available in separate columns displaying only the city name in a visualization might create ambiguity because of the same city name in multiple regions of the globe you can solve this by creating a calculated column using the following DAX code to create a new column in your geography table city and state and an equal operator region in single quotation marks followed by region then city in square brackets the concatenation operator followed by state in square brackets you also learned that if you want to include data from two different tables of the model you first need to make sure the tables have appropriate relationships and secondly you need to use the related DAX function in your formula let’s now recap the benefits of using calculated columns the first benefit is that it enhances data transformation calculated columns help you transform raw data into meaningful information for instance you can convert currency values calculate percentages and so on the next benefit is dynamic and interactive reports you can use calculated columns to introduce slicers and filters to make your report interactive and dynamic another benefit is consistency by embedding calculated columns within the data model you can ensure consistency in your reports changes in source data reflect instantly in calculated columns thereby reducing the risk of errors the last benefit of calculated columns is complex analysis whether it’s timebased calculations statistical analysis or forecasting calculated columns with the power of DAX allow you to tackle intricate data challenges calculated columns are indispensable tools in PowerBI offering a means to shape and analyze data effectively they enhance your data model by introducing new information based on an already loaded data set that allows you to reveal the hidden insights of your data the key lies in mastering the art of crafting the calculated columns using DAX to extract valuable information as a data analyst you receive data from different sources you clean and transform the data and build an effective data model for accurate and effective analysis to ensure an accurate and effective analysis you need to put on DAX magnifiers to see the hidden information in your data data analysis expressions DAX can be used to build calculated tables calculated columns and measures measures are of special significance as they do not take space from your PowerBI memory and are not stored in the model the measures are executed dynamically and can thereby integrate any filter context you apply while writing the script measures in PowerBI are the calculations that summarize aggregate or perform complex calculations on data the calculations can range from simple sums to intricate analyses with the use of these measures you can go beyond basic data visualization as they allow you to drive insights make data back decisions and unearth patterns and trends within your data set that at first glance are not noticeable you can create measures in PowerBI in two ways quick measures and custom measures using DAX in a previous lesson you covered how to create quick measures in PowerBI for Adventure Works timebased analysis to recap briefly PowerBI supports the following types of calculations in quick measures this average per category calculation lets you create the average variance min and max for each category you can apply some fundamental filters in this category of calculations powerbi allows you to create some basic time intelligence calculations like year-toate or YTD monthtoate or MTD and yearover-year or Y with this calculation category you can calculate the running total or total for each category basic mathematical operations are used like addition and subtraction to create quick measures simple concatenation can be done for your measures although you can create a handful of quick measures in PowerBI to get some quick insights the real analytical power of measures lies within the DAX logic dax allows you to write complex logic in the form of formulas and expressions custom measures refers to userdefined calculations or metrics created using DAX to generate insights about the data through aggregations calculations time intelligence functions and so on for example suppose Adventure Works needs to analyze its sales and profit data for each product category in sales region you can compute DAX measures to calculate total sales total profit and profit margin percentage separately these measures can be visualized in your report and you can integrate any filter the company needs to evaluate the total profit and profit margin for each product category and region as mentioned earlier measures compute the values on the go for example when you apply the filter for the bikes category the profit measure will use the product category bikes as the filter during the calculation and only display the profit margin values for the bikes category this way you can help Adventure Works generate the insights needed let’s explore the DAX syntax to create simple measures for sales profit and profit margins that you can use to address Adventure Works needs for sales create a measure called sales then add the sum x function after the equals operator in your first parameter reference the quantity column from the sales table and multiply it with the unit price column from the sales table to calculate profit create a measure called profit and after the equal operator subtract the total cost measure from the total sales measure you can use a measure inside other measures as in the profit measure both total sales and total cost are pre-calculated measures used to compute the next total profit measure next for profit margin you’ll start by creating a measure called profit margin and after the equal operator divide the profit measure previously created with the total sales measure make sure to format the measure as a percentage so the measure will display the percentage profit in your visualization remember to format the measure appropriately for example profit and sales measures can be formatted as currency with two decimal places while profit margin measures need to be formatted as a percentage with two decimal places measures created with DAX provide a way to summarize calculate and compare data across various dimensions based on specific criteria and business requirements measures serve as a microscope to see and discover the hidden message of your data mastering DAX is the key skill for any data analyst and you will receive a considerable number of questions about DAX in your Microsoft PL300 exam time is the dimension that virtually underpins all data analysis and for this reason time intelligence functions hold a position of paramount importance time intelligence functions are specialized functions designed to work with date and time data enabling users to perform advanced temporal analysis and gain deeper insight into historical data previously you cover the theoretical foundations of time intelligence functions and gain significant hands-on experience in creating them to summarize and compare data over time in this video you’ll recap the important benefits of time intelligence functions and how you can implement them in the aggregation and comparison of data values let’s start with the benefits of time intelligence functions the temporal comparison function makes it easy to compare data across different time periods you can create measures using DAX to compute yearoveryear or quarter overquarter trends which allow you to track growth seasonality and performance the next benefit of time intelligence functions is that they allow you to compute moving averages moving averages are a valuable tool for smoothing out fluctuations in data and identifying trends and patterns over time this is particularly important in scenarios where noisy or erratic data can be a challenge with time intelligence DAX functions you can compute moving averages to enhance your data model and analysis time intelligence functions facilitate the creation of cumulative totals which help in understanding the progression of values over time these measures are crucial for tracking key metrics such as cumulative revenue profit or customer acquisition time intelligence functions also facilitate the creation of periodtoate calculations to simplify the process of calculating values from the beginning of a time period to a specific date this is a valuable set of DAX measures to compute metrics like year-toate and month-to-ate values parallel period functions make it straightforward to compare data with previous or future periods which is vital for identifying trends and seasonality and making datadriven decisions with the benefits of time intelligence functions refreshed it’s time to recap a few important time intelligence DAX functions the first important time intelligence DAX function is total year-to- date let’s say for example that Adventure Works wants to compute the real-time sales performance of its various product categories you can calculate year-to-ate from the sales table’s total sales column or measure the DAX expression to compute YTD is a measure called sales year-to-ate followed by the total year-to-ate function after the equal operator in your first parameter reference the total sales column from the sales table and aggregate the values using sum in the second parameter reference the order date column from the sales table the date in square brackets represents the date column of the date hierarchy powerbi IntelliSense provides the option to select other fields of the date hierarchy such as year or month but to create time intelligence measures you need to select the date one of the main product categories of Adventure Works is bikes the company wants to evaluate the sales trends of bikes over the summer months you can use the dates between DAX time intelligence function to compute the measure summer sales after the measure is executed you can add the bikes category as an additional filter to the measure to answer management’s question the DAX code for the measure should be a measure called summer sales followed by the calculate and sum functions to compute the values of the total sales column of the sales table then insert the dates between function which takes the order date column from the sales table as a date reference finally include the starting date and another date referencing the end date now let’s say the marketing executive of Adventure Works wants to evaluate the impact of her recent marketing campaign the original time for the campaign was 3 months and after a month its impact should be evaluated you can create a DAX time intelligence measure using the dates in period function to compute a measure for last month’s sales create a measure called last month sale followed by the equal operator and the calculate and sum functions to compute the values of the total sales column of the sales table next add the dates in period function which takes the order date column from the sales table as a date reference this is followed by the today function that takes today’s data as the starting time 30 represents the number of intervals finally day represents the unit of time adventure works CEO wants a sidebyside comparison of the company’s sales for the current and the previous year this will provide her with insights into the necessary improvements to sales and marketing strategies you can create a measure using the same period last year dax’s time intelligence function as follows a measure called revenue previous year then define var as the variable for the previous year’s revenue followed by the equal operator and calculate which computes the previous year’s revenue by filtering the revenue measure based on same period last year finally the return function displays the value of the entire expression a sales forecast is a vital component of an analysis and adventure works sales executive wants a report based on historical sales values that predicts the future growth of the company in terms of revenue and profitability you can use the date ad function in DAX to either compare the current period sales with the previous period or to predict the future period period here refers to year quarter or month for instance to compare the current month sales with the previous one the DAX script should be a measure called sales comparison followed by an equal sign then calculate computes the measure by filtering the revenue measure followed by date add which takes the order date column from the sales table as a date reference one represents the number of intervals the negative sign indicates that the intervals are back in time this is followed by month representing the unit of time you can modify the code to predict the sales for a future period by changing month to any other time period like year or quarter time intelligence DAX functions in PowerBI are indispensable for analyzing historical trends forecasting future outcomes and understanding the impact of time on your data these measures uncover the insights hidden in your raw data you need to master this skill to excel as a PowerBI modeler pass your Microsoft PL300 exam and become a certified PowerBI analyst as datadriven businesses are evolving so are the business analytical tools microsoft PowerBI stands out as a formidable business intelligence ecosystem offering profound insights through its rich array of features central to the effectiveness of PowerBI are measures which serve as building blocks for data calculations and visualizations previously you covered measures in detail in this video you’ll recap the three main types of measures with scenarios measures are essential for performing quantitative analysis and deriving meaningful insights from the data they provide a way to summarize calculate and compare data across various dimensions based on specific criteria and business requirements measures can be categorized into three types: additive semi-additive and non-additive let’s recap each of these types of measures additive measures are the workh horses of data analysis and provide the easiest summation additive measures behave as you expect they can be summed up or aggregated across various dimensions without losing their meaning adventure Works has a sales analysis report that displays the sales amount and quantity sold for individual transactions each transaction is then tracked with a specific customer region product category and date as a data analyst you can create simple additive measures to sum up the attributes across all given dimensions this will help the adventure works team visualize total sales and total quantity by product category region salespersons and time of course the next type is semi-additive measures these measures introduce a layer of complexity they can be summed across some dimensions but not all and the crux of the matter is time think of inventory on hand as a simple example while it is meaningful to sum the inventory by product or warehouse it makes no sense to sum by time semi-additive measures are often seen in scenarios where time plays a crucial role you can handle these using DAX in PowerBI by specifying which dimensions are suitable for summation and which are not this dynamic flexibility makes it possible to create insightful reports while leveraging the power of DAX in PowerBI let’s explore an inventory balance example if a warehouse has 35 mountain bikes in stock at the end of September and 62 mountain bikes at the end of October it is not accurate to say the warehouse had 97 mountain bikes for the two months together you will handle these measures using DAX functions like last date last non-blank and others you’ll review later finally let’s cover non-additive measures these measures lead you to advanced analytics non-addeditive measures defy straightforward summation across any dimension consider for example the profit margin measure while it is tempting to sum profit margin across products or time periods the results do not make sense you cannot add percentages in this manner you need to perform complex calculations to handle non-additive measures like percentages or ratios to produce meaningful summation dax functions like average X sum X and divide provide you with the toolkit to work with non-additive data thereby allowing you to craft sophisticated calculations that provide valuable insights let’s delve a bit deeper into the profit margin example profit margin is a percentage that represents the profitability of a business and is calculated by dividing the profit by revenue for example let’s say Adventure Works has four product categories and the profit margin of the individual product categories are 9% for bikes 5.5% for accessories 10% for components and 2% for clothes if you sum up the profit margin of these product categories you’ll get a total profit margin of 26.5% however this result is incorrect because it does not reflect the true overall profit margin of Adventure Works you need to employ other DAX functions to compute these types of complex calculations the skill to distinguish and handle additive semi-additive and non-additive measures is the key to generating accurate and actionable insights out of your data the use of appropriate DAX functions from its rich library empowers you to compute each type of measure with precision and to reveal the story hidden within your raw data as a data analyst you import data from disperate sources to your data model the imported data however may not contain the information you need to visualize the key to any analytical work is to reveal hidden insights trends and opportunities you may need to add tables to your data model to accomplish this in this video you’ll explore the types of calculated tables and scenarios where creating these tables is necessary at Adventure Works the executive management team needs answers to specific business questions based on a specific data set after careful investigation you realize the information required can be visualized based on the provided data set but it may require more time and resources a quick way to accomplish the task is to create additional tables in the data model using DAX calculations say for example the sales table contains several columns but you only need to present the summary or the date table is missing from the data model and you need to perform timebased calculations or you also want to perform some analysis but keep the original table intact for other analytical needs all these are scenarios where you must create calculated tables previously you’ve learned that cloned tables are the exact copy of any existing table or data model clone tables are important when you need to manipulate data without affecting the original table for example Adventure Works wants to analyze sales data without altering the original sales table as they want to keep it as a reference you can simply create a clone version of the sales table by writing the following DAX expression as clone table name equals all original table name or more specifically as sales cloned equals all sales you can also create calculated tables using DAX expressions by taking data from multiple sources some examples of calculated tables include combining specific data fields from the sales and product tables to compare various product categories and associated sales values normalizing the dimension table for instance the product table contains categories and subcategories with information you need to separate from the product table this you typically do by creating a snowflake dimension creating a common date dimension table for a data model using DAX to perform advanced time intelligence calculations the last example of a calculated table is combining two tables with the same structure while keeping the original tables unaltered for example suppose you received two different tables with the same structure for Adventure Works customers one for Eastern States customers and one for Western States customers and you need to combine them into a single customers table you can also use measure to create calculated tables in PowerBI for example consider the scenario where you’ve created a measure sales for Adventure Works this measure displays all sales across countries you can use this measure to create a calculated table displaying the individual sales by each country using the following DAX expression country sales is the name of the new calculated table sales in single quotes is the name of the original sales table sales in square brackets is the DAX measure used to create a calculated table total sales in double quotes is the name of a new column added to the calculated table creating calculated tables from pre-calculated measures is especially useful when you want to create a summary table from large data sets or when you want to create a table with data that does not exist in the original tables this can enhance data analysis and visualization capabilities in PowerBI now let’s explore the syntax of a few common DAX table functions you can use the add columns function to add calculated columns to a given table or table expression here is the syntax for using the add column function type add columns and within the parenthesis specify the table name from which you want to retrieve data follow this with the name of the new column enclosed in double quotes and then provide the DAX expression for the calculation you can add more column names and expressions as needed but these additional pairs are optional the summarize function returns a summary table for the requested totals over a set of groups the DAX syntax for summarize is as follows type summarize and inside the parenthesis first input the name of the table you wish to summarize next include the names of the columns to group by each enclosed in double quotes you can also add new column names in double quotes followed by their respective DAX expressions for calculated values adding these additional columns and expressions is not mandatory but can be done based on your data analysis requirements filters returns the values that are directly applied as filters to column name with filters inside the parenthesis simply specify the name of the table or column for which you want to retrieve the current filters applied in the context top n returns the top n rows of the specified table for top n within the parentheses start by specifying the number of top items to return follow this with the name of the table from which to retrieve these top items conclude by indicating the column to sort by and optionally the order of sorting ascending or descending and lastly union creates a union or join table from a pair of tables when using union inside the parenthesis list the tables you wish to combine ensure each table name is separated by a comma the tables should have the same number of columns and corresponding columns should have compatible data types by using DAX to generate calculated columns you can combine data from various multiple tables into a single table that opens a whole new door of analysis in practice you will encounter situations where creating calculated tables is the only solution to certain data challenges the skills you’ve gained will help you tackle these real world analytical tasks efficiently data is often like a complex puzzle with pieces scattered across various dimensions microsoft PowerBI offers a way to unravel this mystery by creating a data hierarchy hierarchies provide a structured way to organize and visualize data allowing users to uncover hidden insights and tell a compelling story adventure Works a multinational company sells its products across the globe the product department heads need not only an overview of the sales but also require a deeper level of understanding of the location of customers and the category and subcategory of products sold you can provide this information by creating a hierarchical visualization of the data powerbi provides a way to display information where managers can drill down to view the granular details about customers and products in PowerBI a data hierarchy comprises interconnected fields from the data set organized in a way to present data elements in ranked order it represents a structured relationship between data attributes typically organized from an overview level to the most granular the hierarchical structure simplifies data exploration and analysis by allowing users to focus on specific aspects of the data at different levels for instance in a sales data set you might have a hierarchy that starts with year drills down to quarter then month and finally day in certain cases you can also drill down to hourly details product geography and organizational hierarchies are some other examples of data hierarchies in PowerBI in a hierarchical structure the first level sometimes called the parent level is ranked over the other sometimes referred to as the child level this way report users can drill down from the parent level presenting the highest level of information to the lower levels in an order powerbi allows a maximum of five levels to be added to a hierarchy using a hierarchical structure to create your visualization enhances the user experience in understanding the data and provides a more comprehensive analysis common visualizations that can be used to visualize hierarchies include bar or column charts line charts heat maps and map visuals powerbi provides several options to use a hierarchy in visualizations for example you can enable inline hierarchy labels to sort data by hierarchy levels you can use the path DAX function to add a column for the entire path length this is important when you are working with an organizational hierarchy you can also create DAX measures to determine the path length of the hierarchy which helps you in determining the shortest and the longest path now let’s explore how you can create a data hierarchy in PowerBI to help Adventure Works analyze granular data launch PowerBI desktop and load the data from the Excel workbook containing Adventure Works sales data the data set contains two data tables a fact internet sales and a geography dimension table the geography dimension table of the model contains geographical information therefore it is advisable to generate a geographical hierarchy the first step is to format the location-based data for an appropriate data category to do this select the country field and then select country from the data category drop-own list now format state province name city and postcode as state or province city and postal code a globe icon appears before the field name which tells PowerBI that this is geographical information let’s visualize the sales data by geography in the report view of PowerBI desktop to do this select the column chart from the visualizations pane and bring the sales amount field from the sales table to the yaxis well of the visual in the x-axis a geographical hierarchy is needed to display the sales data at various levels of locations bring the country state or province and city fields to the x-axis in the same order a set of arrows appears in the top right corner of the visual indicating the drill down functionality to turn on the drill down select the second down arrow if you hover the cursor over any data point for example the United States a drill down icon displays on the tool tip to go to the next level of the hierarchy select the drill down icon in our example the next hierarchy level is states from here you can either drill up or down to the next level alternatively you can create a hierarchy in the data pane select the country field from the data pane and select more options which is represented by three dots a drop-own list appears where you have to select create hierarchy a new country hierarchy field appears in the geography table with country as the highest level of the hierarchy you can now add related fields to the newly created hierarchy one at a time to do this select state or province and from the drop-own option select add to hierarchy next you need to select the hierarchy where you want to add the field in the current project there is only one hierarchy available country hierarchy select the country hierarchy and the field is added as the second level repeat the process for city and postal code you can test the country hierarchy by creating a new visual remember to format your reports using the appropriate font style and colors data hierarchies are indispensable tools for effective and granular data analysis and reporting in PowerBI they provide structure and context to your data making it easier to navigate and drive trends and let your audience gain a deeper understanding of the information at hand in fast-paced analytics where every business is turning into a datadriven organization performance is everything businesses rely on business analytics tools such as Microsoft PowerBI to turn vast amounts of data into actionable insights but what happens when too many users interact with your reports and you need to optimize the speed and efficiency of your reports and dashboards the performance analyzer helps you evaluate the performance of various elements of your PowerBI reports and dashboards adventure Works uses PowerBI as a business intelligence tool to create stunning reports and visualizations however as the data sets grow with the growth of the company and reports become more complex there is a need to make sure the reports perform optimally you can implement PowerBI’s performance analyzer to evaluate the performance of individual report elements such as visuals and DAX measures you may recall that the performance analyzer is a built-in tool of PowerBI that allows users to diagnose and optimize the performance of their reports and dashboards it provides insights into query execution time data model performance and visual rendering enabling analysts to pinpoint bottlenecks and fine-tune the creative work slow responding reports and dashboards hinder productivity and may lead to customer dissatisfaction with the performance analyzer you can identify and rectify slow performing report components not only is speed critical but efficiency also matters by identifying and optimizing inefficient elements of your reports you can reduce resource consumption and enhance user experience a healthy data model is the foundation of your analytical work the performance analyzer offers insights into your data model performance helping you to maintain and enhance it the tool does not stop at query diagnostics it also helps to analyze visual renderings this means you can identify the problematic and slow rendering visuals and optimize them for faster loading now let’s review how to use the performance analyzer you need to launch your PowerBI report and access the performance analyzer from the view ribbon of the report view upon selection the performance analyzer displays on the right side of the report canvas the performance analyzer records the processing time required to update or refresh each report element for instance when a user interacts with a slicer to modify the visual a query is sent to the underlying data model and visuals are updated according to the interaction you need to select start recording to start recording with the performance analyzer the performance analyzer inspects and collects performance measures in real time each time you interact with a report element the performance analyzer displays performance results in its pane once you finish recording select stop and the performance analyzer will display information about queries data models and visuals in a userfriendly interface the information log contains the time spent completing the following tasks dax query if your report has DAX calculations the duration between the query sent to the data and the results retrieved is displayed in the pane visual display the time needed by a visual to display on the report canvas which also includes the time to retrieve web data other this is the time the visual requires for preparing queries waiting for other visuals to complete or performing other background processing evaluated parameters if your report visual contains field parameters the time spent on these will be displayed in this category this is in preview mode the performance analyzer records duration in milliseconds and the values indicate the difference between the start and end of any operation once you stop the recording you can save the results onto your local computer now you can identify areas that need optimization and make necessary adjustments to your DAX logic visual elements and data model to improve overall performance having reviewed how to use the performance analyzer let’s briefly explore some of its real life applications when working with large data sets the performance analyzer helps you optimize the reports to ensure they remain responsive in the case of complex data models this tool assists you in maintaining efficient performance in addition you can use the performance analyzer to fine-tune reports visuals elements and queries for faster performance where you have many report users the performance analyzer in PowerBI is your handy tool for faster and more efficient yet visually appealing reports and dashboards to succeed both in Microsoft PL300 and as an efficient data analyst you need to master the skill of diagnosing issues through the performance analyzer and optimizing your reports accordingly in the dynamic landscape of data the sheer volume of data itself is not a threat to meaningful analysis the key lies in how you handle the data transform it and create visually appealing and analytically insightful reports but often the amount of effort you put into creating a masterpiece doesn’t perform according to expectations due to the slow responsiveness of the visuals and queries this highlights the significance of performance optimization which is equally important as creating reports and dashboards in this video you’ll review how to improve report performance via cardality and summarization in Microsoft PowerBI imagine Adventure Works Microsoft PowerBI reports meticulously designed to dissect sales trends monitor inventory levels and analyze customer behavior are encountering a challenge with a colossal volume of transactional data streaming daily the reports are performing sluggishly you may recall that you can improve performance by reducing data although the PowerBI engine effectively handles extensive data minimizing the volume of data loaded into your data model is still crucial this is especially important when working with larger data volumes or anticipating substantial data growth over time there are many reasons to minimize the data volume loaded into the PowerBI model including your current PowerBI capacity may not support the larger volumes of data for instance PowerBI shared capacity can host a model maximum of 1 GBTE in size smaller data models can reduce resource contention by using fewer resources like memory and processing power increasing efficiency loading more models for a longer period helps reduce the eviction rate meaning the data is removed from memory less frequently this can result in faster queries as the data sets do not need to be reloaded into memory smaller data models also tend to refresh more efficiently resulting in decreased time to generate and deliver reports with up-to-date data or lower report latency finally fewer rows in a data table can lead to faster calculation and improve query performance powerbi supports many techniques to reduce the data loaded to the PowerBI data model in this video you will review two methods reducing cardality and aggregation or summarization let’s begin with reducing cardality previously you learned about the type of cardality between data tables throughout the development of the data model you either establish or modify the relationship between the tables you need to ensure the data types of the fields participating in the relationship establishment are the same you cannot create a functional relationship where the data types of the columns are different for example the column has a key column that might be set to a text data type if the column contains only the numeric values you must change the data type to integer and whole numbers to decimal numbers which performs better than the text data type in the PowerBI model changing the decimal number data type to a fixed decimal number also improves the performance as you learned in the previous DAX lessons when you create a DAX calculation in your data model the default data type is decimal number or general this means the results of the calculation display unlimited places after the decimal which hinders optimal performance you need to define the distinct data type with specified decimal places for best performance changing to fixed decimal places reduces storage requirements enhancing model performance the next technique is reducing data via aggregations aggregation refers to summarizing large volumes of data into more manageable summary tables to improve query performance by condensing detailed information into simpler higher level values consider an example where you have a large data set containing a record of each transaction for reporting you’re analyzing only the yearly or monthly sales or sales by region you can create aggregated tables that are imported to the data model in the current example you can generate aggregated tables from the sales table grouped by region or month according to your requirements this pre-calculated aggregation can be imported to the memory of PowerBI and will be more efficient in querying daily analysis powerbi also supports three storage modes to handle large data sets where you can define the storage modes of data tables for example a large fact table with millions of rows can be set to direct query while smaller tables can be imported to the model for improved performance aggregations offer several benefits that can help you improve model performance if you are handling a vast data set aggregations provide a faster and optimized query performance they assist you in analyzing the data and revealing insights without importing the entire data set into the model if users are experiencing a slower refresh time of the reports in PowerBI you can create aggregations to help speed up the refresh process the smaller size of aggregated tables imported to memory reduces the refresh time enabling a better user experience lastly suppose your company is anticipating a growth in sales volume by expanding its operations to new regions or adding new products to its inventory you can leverage PowerBI to create and manage aggregations as a proactive measure to futureproof the solution enabling a smooth scaleup optimization of your data model in PowerBI is not just a technical endeavor it is a strategic imperative for organizations and an analytical challenge for you as an analyst powerbi’s performance optimization unlocks a new door of analysis ensuring that every decision is not just datadriven but empowered by the speed and efficiency necessary to thrive congratulations on completing the data modeling section of this course a prerequisite to analyzing data and creating reports and dashboards in Microsoft PowerBI let’s recap the key takeaways you began with a journey into designing data models starting with a recap of schema design principles you reviewed the star and snowflake design the two major types of schemas used in PowerBI and worked through a hands-on activity building a star schema for adventure works by understanding the fact and the dimension tables you explored how to handle the inactive relationships between two data tables by implementing a role-playing dimension and using the DAX user relationship function as DAX and the evaluation context are fundamental to data analysis in PowerBI you recaped using the calculate function to alter the filter context of your calculations you also explored cardality the nature of the relationship between data tables types of cardalities and different cross filter directions in PowerBI you can either select single or both cross- filter directions determining the filter propagation in one or both directions of the related tables next you moved on to creating model calculations using DAX you recaped calculated columns the custom data columns you create in your data model using DAX you gained a detailed overview of conceptual foundations and practical skills related to creating and managing measures using a library of DAX functions measures hold the hidden information in your raw data empowering users to gain meaningful insights you reviewed sum sum x and calculate functions to compute aggregation measures which are the most common calculation used for analysis in any datadriven business you also explored implementing time intelligence measures as the time dimension is the foundation of any business analysis requiring historical analysis and future predictions dax offers a rich library of time intelligence functions to aggregate and compare data over time such as dates YTD and total YTD by using time intelligence functions you can compute things like moving averages temporal analysis and cumulative totals to gain insight into the overall performance and growth of the organization you also recap types of measures including additive measures like total sales or total cost non-additive measures for example profit and margin and semi-additive measures such as inventory level and current account balance you gained hands-on insight into replacing an implicit measure with an explicit one and creating a semi-additive measure after that your focus shifted to implementing a data model you started by identifying the need for calculated tables such as when a data model lacks a common date dimension table and how to create them in PowerBI you gained a solid understanding of DAX functions that you can use to create and manipulate tables in PowerBI you then explored creating hierarchies including date product and geographical hierarchies creating a hierarchy is a significant feature of PowerBI allowing you to create a hierarchical structure to analyze the overview and granular details of data within the same visual by using drill down functionality further you explored how you can add a hierarchy to slicers in addition to the standard PowerBI visuals you reviewed PowerBI’s Q&A feature which uses natural language processing to answer business specific and userdefined questions in visual form this feature is significant in the real world datadriven environment by making it possible for individuals regardless of technical expertise or department to use and gain insights into the data from your reports and dashboards you learned that PowerBI allows you to teach Q&A to customize the review questions synonyms and relationships to help PowerBI better understand your business needs finally you focused on optimizing model performance this began with a review of PowerBI’s performance analyzer a robust diagnostic tool within the PowerBI ecosystem that allows you to monitor and evaluate the performance of your report visuals data model health and DAX queries you can use the information the performance analyzer provides to optimize slow responding report components and enhance the user experience you explored improving report performance by choosing optimal data types and summarizing data you learned that PowerBI offers several techniques to reduce data size and volume which is important for avoiding slower reports reducing cardality and creating aggregated tables are the two most important techniques you can employ as data reduction strategies to enhance model performance in PowerBI building and managing a healthy and functional data model is the key to performing any analytical work in PowerBI and gaining meaningful insights from your data understanding the schema DAX logic and performance optimization can help you become a certified PowerBI analyst via the Microsoft PL300 exam as well as handle complex realworld data challenges visualizations act as a bridge between raw data and actionable insights microsoft PowerBI offers a wide array of visualization options for reports empowering analysts to create compelling data narratives in this video you’ll explore the analytical background of visuals in PowerBI to help you identify and implement the appropriate visual to address the business need the management of Adventure Works requested a comprehensive sales report for the past year the challenge is to select the right visuals that align with the data and the analysis objectives ensuring clear and insightful presentation of the sales performance powerbi features a broad spectrum of visualizations each tailored for specific data representation needs the visualizations in PowerBI can be broadly categorized into general purpose visuals and specific purpose visuals general purpose visuals include visuals like tables and KPI cards that are versatile and can be employed across various analysis scenarios specific purpose visuals include a range of visualizations each designed to cater to specific analytical needs like time series and geospatial analysis among others the general purpose visuals in PowerBI are tables and matrices which effectively display data in a structured tabular format allowing for easy comparison and analysis across multiple dimensions card KPIs or key performance indicators which are instrumental in highlighting critical metrics immediately enabling decision makers to quickly grasp the performance indicators that are crucial for their business objectives and lastly slicers which act as interactive filters allowing users to filter the data being displayed dynamically thus enabling a focused analysis powerbi offers numerous visuals each tailored for specific types of analysis used daily in modern enterprises the key to effective data visualization lies in aligning the visual with the analysis goal thus enabling a clear insightful and engaging data narrative let’s explore the various categories of analysis specific visualizations and the PowerBI visuals most suited for each time series analysis is a method to analyze timeordered data to discern the structure or functionalities underlying them it is an essential analysis in forecasting monitoring and anomaly detection the optimal charts for time series analysis are line charts and area charts line charts are the ideal and most common way of visualizing a time series analysis while area charts are suitable for tracking quantity over time while emphasizing the magnitude the next analysis type categorical analysis deals with data that can be segregated into multiple categories but have no inherent order or priority categorical analysis helps you to understand the distribution and relation of data across different categories the optimal charts for categorical analysis are bar and column charts and pie and donut charts bar and column charts are effective for comparing the magnitude of categories and easily identifying the differences among them pie and donut charts are best for representing the proportions of categories especially when dealing with a small number of categories to prevent visual clutter correlation analysis aims to find a relationship between two or more variables understanding correlations is foundational for prediction causation analysis and trend discernment the optimal charts for correlation analysis are scatter charts and bubble charts scatter charts are suitable for spotting relationships between two variables and understanding the strength and direction of the relationship bubble charts extend scatter charts by adding a dimension through bubble size allowing for an additional layer of analysis the next type of analysis is distribution analysis this type of analysis observes how values of a variable are spread or clustered over a range it’s crucial for statistical analysis allowing comprehension of data variability and central tendencies distribution analysis is suitable for spotting relationships between two variables and understanding the strength and direction of the relationship next there’s part to whole analysis this type of analysis examines how individual parts contribute to the aggregate it’s a widely used analysis in understanding composition analyzing contribution and comparing individuals to the total waterfall charts are the most widely used for partto-ole analysis as it’s highly effective in showing the cumulative effect of sequential positive and negative values the last type of analysis is geospatial analysis geospatial analysis examines data in terms of geographical or spatial relationships it’s instrumental in finding patterns understanding spatial distributions and making geographically informed decisions powerbi offers a variety of different map visuals including shape maps cororoplath or filled maps and arcgis maps shape and corropath or filled maps support external geographical files to draw a map arcgis maps are rich in map visualization features the array of visualizations in PowerBI provides a powerful tool set for analyst to convey data narratives effectively the right choice of visualization based on the analysis need is crucial mastering the art of selecting the right visual in PowerBI is a valuable skill that significantly augments the data storytelling proess of analysts to ensure Microsoft PowerBI visuals are of a professional standard it is important to explore both general and visual formatting settings in this video you’ll explore the available formatting options in PowerBI and how to implement formatting options lucas is tasked with enhancing an Adventure Works sales report with two visualizations let’s help Lucas explore all general visual and conditional formatting techniques in PowerBI launch the sales categorical analysis PowerBI file in this report two commonly used categorical analysis visualizations have been used column and pie charts lucas is tasked to investigate all available formatting and configuration options that could enhance this report select the column chart and navigate to the visualizations pane select the format visual tab this is where the formatting options for every visual reside the formatting options are split into two categories visual and general visual contains chart specific settings and general contains settings shared by all visualizations even the text box and shape visualizations share these settings let’s select the column chart and general options again to view them in detail the properties section is used to adjust the size position or padding of the visual it’s helpful when slight adjustments are necessary like moving the visual to the right the title section focuses on formatting the title of the visualization and provides numerous setting options like font size color background color alignment subtitles and even a divider lastly the effects section includes settings to format the visualization background visual borders and shadows when you navigate to the visual formatting settings the column chart specific settings appear here you can view settings for both axes modifying their range of values font or axis title you can even change the y-axis to logarithmic to display the results on a different scale when using disabled settings like legend and small multiples make sure that fields are using the respective visual slots the next settings allow you to add grid lines on your visual a zoom slider to magnify specific axis ranges modify the color of your columns and add data labels when you select the table visual note that the visual settings are adjusted to fit this visualization here you have style presets to easily modify the table some grid options as well as options to change the appearance of cell values column headers and the total finally to add conditional formatting to your chart you can enable it on your table visual columns by selecting any field and then selecting conditional formatting in PowerBI you can format the background and font colors you can also add data bars icons or even links to web URLs selecting a font color for example the conditional formatting window appears here you can format the font color of the table visualization this formatting can be conditional based on a custom rule that you can apply the specific value of any field in the data set or even a gradient based on a value powerbi keeps adding conditional formatting on various visualization aspects for example select the column chart navigate to the columns field and expand it a button with a function symbol appears on the right of the color field this indicates that conditional formatting can be applied to the columns dynamically altering the color based on specific criteria when you select this button the conditional formatting window appears indicating that these visualization columns can be formatted based on specific rules field values or with a gradient color just like for the table in this video you learned how to explore all the available formatting options in PowerBI and implement formatting options navigating through large data sets to find important insights is a common task in data analysis microsoft PowerBI helps ease this task with its robust slicing and filtering features in this video you’ll explore the available slicing and filtering options available in PowerBI these features are essential for data analysis projects making it easier for users to focus on specific data subsets and uncover meaningful insights in their reports the management team at Adventure Works requested interactivity to be added to the sales categorical analysis report enabling them to dynamically apply filtering in the report the ability to shift through extensive data sets focusing on specific data points is important when building business intelligence reports slicing and filtering for this reason is an essential tool for a PowerBI analyst facilitating interactivity in reports that offer a dynamic and engaging data analysis experience let’s explore slicing and filtering in PowerBI in more detail to identify the three main methods of filter applications slicers the filter pane and visual filters the first way of slicing and filtering a report is by using slicers slices are visualizations that act as filters enabling a user to make selections that filter data within reports to add a slicer to the sales categorical analysis report select the slicer icon on the visualizations pane and adjust it by dragging its edges drag date into the field box the slicer visualization automatically identifies the field as a date field and selects the slicer setting style between the second way of slicing and filtering a report is through the filters pane the filters pane is a central location where users can apply and manage filters to their reports at three different levels visual page or report level visual level filters apply to a single visual page level filters apply to all visuals on a page and all pages or report level filters apply to all visuals within a report add country region to the filters on the page section and select Canada this will immediately filter the report to display only the data for the table rows with Canada in the country region field an important aspect of the filters pane is the hide and lock features it provides to the right of the filter you just added a lock filter button is visible this feature prevents report users from changing this filter the hide filter button hides the filter and prevents users from knowing that a filter is applied finally the third method of filtering is through visualization filters visual filters are a direct method of filtering allowing users to interact with the visuals on a report to filter the data for instance selecting the blue color on the tree map will filter the rest of the report based on the selected segment this feature is what makes PowerBI stand out as a highly interactive business intelligence tool as all page visualizations are constantly interacting with each other with a click of a button understanding slicing and filtering is key to unlocking the full capabilities of PowerBI they not only simplify the process of creating interactive reports and focusing on specific data segments but also empower data analysts to quickly identify valuable insights imagine effortlessly navigating through vast oceans of data in Microsoft PowerBI just like a seasoned captain navigating a ship through turbulent waters with page navigation tools you can unlock your report’s full potential for you and report users in this lesson you will cover the core features related to navigation and sorting you will learn about how page navigation effectively streamlines the flow and readability of multi-page reports effectively utilizing bookmarks capturing and sharing specific reports and states exploring the sorting functionalities in PowerBI to visually organize data enhancing clarity impact and insights lucas is a data analyst with Adventure Works and has been tasked with enhancing the interactivity and user experience of the company’s sales categorical analysis report in PowerBI as this report is crucial for monthly sales meetings the report requires navigation improvements to help the sales team navigate data more efficiently and gain quicker insights lucas’ objectives are to streamline the report’s navigation across multiple pages create bookmarks for key data points to enhance presentations and apply sorting techniques for clearer data visualization page navigation in PowerBI is a feature used to create multi-page reports that are userfriendly and easy to navigate it allows users to move between different pages of a report and is essential for organizing information logically across multiple pages the implementation of page navigation in PowerBI involves setting up interactive elements like buttons or links that users can select to move to different report pages it provides a guided experience beyond clicking on tabs as it directs users through the report in a structured userfriendly way especially in complex reports page navigation is integral for assisting users through a report’s narrative especially in complex data sets or presentations there are several benefits to using page navigation in PowerBI reports they include an enhanced user experience these features collectively improve the navigation and understanding of reports making them more userfriendly and accessible for instance in a financial report the first page might provide an overall summary and subsequent pages delve into specific areas like revenue by region or departmental expenses all interconnected through intuitive page navigation the second benefit is dynamic data presentation bookmarks and page navigation enable dynamic storytelling with data allowing for interactive and engaging presentations for example in a market analysis report bookmarks can allow users to switch between different market segments time periods or product categories making the presentation interactive another benefit of page navigation is improved data organization sorting mechanisms help in structuring data effectively leading to better comprehension and quicker insights for example sorting mechanism can be applied to a sales table to organize data by revenue allowing users to quickly identify top performing products when utilizing page navigation it often leads to increased efficiency this is due to streamlining the process of exploring and analyzing large data sets saving time and effort for both report creators and viewers for instance bookmarks can be combined with sorting mechanisms creating different sorted views of a data set like sorting customers by purchase frequency or sales by region this allows for quick comparisons and analysis saving time for both report creators and viewers the final advantage to using page navigation tools is the flexibility in analysis navigation offers flexibility in how data is viewed and analyzed accommodating a variety of analytical approaches and styles bookmarks can be used to switch between different data filters or visualizations even on the same page accommodating various analytical approaches bookmarks in PowerBI are a powerful feature that can enhance report interactivity and storytelling bookmarks allow users to save specific views and states of a report enabling quick navigation to these points during presentations or analysis they are particularly useful in highlighting changes or comparisons in data over time creating bookmarks involves selecting and saving the current state of a report including filters slicers and the visibility of visuals where visualizations can be hidden or left in view in cases where specific report configurations and filters are used in a report they can be saved as bookmarks to easily navigate back to them without having to reconfigure the report these bookmarks can then be linked to buttons or other interactive elements allowing for a seamless transition between different views within the report sorting data in PowerBI reports is a fundamental feature that organizes data within visualizations making it easier to interpret and analyze it brings clarity to reports by arranging data in a logical order whether ascending descending or based on specific criteria sorting helps present data in a structured manner aiding in the quick identification of trends outliers or specific data points it’s essential for making reports more intuitive and insightful powerbi allows sorting of data in various visualizations like tables charts and graphs users can sort data based on different attributes such as alphabetical order numerical values or custom criteria to suit the specific needs of their analysis in this video you explored essential features in PowerBI that elevate the functionality and user experience of reports you learned how page navigation streamlines the flow of multi-page reports how bookmarks offer dynamic presentation capabilities and sorting mechanisms bring order and clarity to data visualizations these tools are invaluable for analysts like Lucas at Adventure Works as they make reports not only more interactive and engaging but also more insightful and easier to navigate by effectively utilizing these features PowerBI users can transform their reports into powerful tools for storytelling and data analysis driving more informed decision-making in Microsoft PowerBI the interactions between visuals in a report is a fundamental aspect that enhances data exploration and analysis this is due to the fact all visualizations can filter one another over the next few minutes you will discover how visuals utilize and share data and how they can be configured to interact with one another you will explore the key interaction types filter highlight and none and their impact on overall report dynamics understanding these interactions and how to choose between them depending on the specific business need in hand is crucial for creating cohesive and informative reports that allow users to delve into data with greater clarity and context there are three key topics you will learn about in this video specifically you will learn how to grasp the basics of visual interactions specifically how visualizations interact with a PowerBI report explore interaction types specifically filter and highlight and how they can be applied and lastly you will gain insights into the non-interaction setting and when it is appropriate to use it in a report lucas the data analyst at Adventure Works encounters a challenge with a report called sales categorical analysis the sales team has reported an issue where selecting a data point in a column chart unexpectedly wipes out the data in the tree map visualization realizing this is a visual interactions problem Lucas is tasked with troubleshooting and resolving it he discovers that the current setting is likely a filter interaction causing the column chart selections to overly restrict the data displayed in the tree map the way visualizations interact within a report is crucial for a comprehensive data analysis experience these interactions determine how selecting or hovering over data in one visual affects the data displayed in another there are three primary types of interactions filter highlight and none let’s start with filter interaction when you select a data point in one visual it acts as a filter for the other visuals in the report for example selecting a specific category in a bar chart will filter the data in all other visuals to show only data related to that category filter interactions are essential for drilling into specific subsets of data and analyzing them in the context of the whole report filter interactions provide a focused view allowing users to isolate and analyze specific data points across different visuals next is the highlight interaction instead of filtering out non- selected data the highlight interaction dims it maintaining the overall context selecting a data point in one visual will highlight related data in other visuals while dimming the rest a highlight is used when the context of the entire data set is required even while focusing on a specific section the highlight interaction helps to understand the relationship of one part to the whole providing a broader perspective of the data this option disables interaction between visuals where selecting a data point in one visual has no effect on others this interaction is useful when visuals are meant to function independently without influencing each other’s displayed data it is crucial for reports with visuals that represent different data dimensions or when independent data exploration is required understanding these interactions is necessary for effective report design in PowerBI by applying these interaction types you can create reports that not only present data in an organized manner but also offer intuitive and insightful data exploration experiences in the upcoming video let’s assist Lucas in configuring the interactions between the sales categorical analysis report let’s start by launching the sales categorical analysis report to identify the interactions between visualizations we know that the bike category contributes almost entirely to the total of sales amount which might prove to be an issue for interaction between visualizations selecting the bikes column of the column chart the tree map boxes are almost unchanged then selecting accessories and clothing categories you notice that those categories are such a small percentage that they are barely visible when filtered the reason this occurs is that there is a highlight interaction type from the column chart to tree map chart highlighting just the percentile of each category this makes it difficult for users to comprehend the filtering of the report so you need to modify the interaction to access the interactions between visualizations select any visualization for example the column chart the format tab will now appear on the ribbon select format and enable edit interactions this is an onoff button which is now enabled it shows the interactions of a selected visualization towards all other objects in the report having selected the column chart notice the icons above the tree map these are the three interaction options: filter highlight and none select filter to change the interaction type and press on the columns of the column chart to notice the modification the users can now clearly see the color of products with the most amount in sales for each category remember that it’s a good practice to always disable the edit interaction button when completing your modifications on interactions as it takes up a lot of memory and might reduce the performance of PowerBI desktop the strategic use of visual interactions in PowerBI filter highlight and none plays a pivotal role in crafting engaging and insightful data stories by understanding and applying these interaction types report designers can guide users through a more nuanced and comprehensive data exploration journey imagine you are a data analyst for Adventure Works creating multi-page reports and you have implemented slicers on some pages when you change a slicer on one page it doesn’t change on the others currently you are recreating the same filter over and over which can be tiring for you and with so many changes to implement any mistake will lead to poor user experience how can your workload be improved and lead to a better chance of a strong user experience in this video you will learn about the fundamentals of synced slicers in Microsoft PowerBI learning how to implement this feature and gain insights into the enhanced storytelling capabilities and improved user experience provided by synced slicers adventure Works wants to analyze their bicycle sales performance across multiple regions they’ve created a comprehensive PowerBI report with pages dedicated to sales data customer demographics and seasonal trends however a challenge arises in maintaining consistent analysis across these pages when users want to focus on specific regions or time frames this is where implementing synced slicers comes into play enabling a seamless unified view of data through the entire report project slicers serve as an effective method for narrowing down information enabling you to concentrate on a particular segment of the semantic model slicers provide the flexibility to choose precisely which values are shown in your PowerBI visuals there may be instances where you require a slicer to be active on a single page of your report while at other times applying the slicer across multiple pages might be more appropriate utilizing the sync slicers feature allows any selection made via slicer on one page to influence the visualizations across all the pages you’ve synchronized synced slicers are not just a cosmetic addition they are a functional necessity for creating cohesive and user-friendly reports here’s why they are essential first is navigation consistency synced slicers ensure that when a user selects one page it reflects across all other pages this consistency eliminates confusion and enhances the user’s ability to analyze data coherently the second necessity of sync slicers is time efficiency by avoiding the need to repeatedly set the same filters on each page synced slicers save time and streamline the data exploration process lastly is improved data storytelling in reports where data storytelling is crucial synced slicers help maintain the narrative flow they allow the story to unfold effortlessly across different pages without jarring interruptions or resets in filters now let’s explore how you can sync slicers across pages in PowerBI reports let’s get the slicers in sync for the current report the report is split into two pages the first page shows sales by product category and color and the second page details sales data for all products from the last two months at the top left corner of both pages there’s a slicer if you pick a country on the product category and color page it only changes the data on this page the details page hasn’t changed however if you activate slicer sync the same filter will apply to both pages here’s how to do it in the view tab of the ribbon select sync slicers this brings up the sync slicers pane on the right now select the slicer on the first page and in the sync slicers pane select the sync checkbox for both product category and color and details now whenever you select a country on the slicer in the product category and color page it’ll also update the details page with the same filter to check if it’s working properly I’ll select a country on the first page when I open the second page I notice that the selected country in the slicer remains as selected this is how you can quickly synchronize slicers on various pages in a PowerBI report the sync slicers feature in PowerBI is a critical tool for enhancing the coherence and usability of reports by allowing slicers to synchronize across multiple pages it ensures that filter selections are consistent thus providing a smoother and more intuitive experience for the user you are part of a team working on sales reports for the stakeholders at Adventure Works you’ve noticed that the way the designers arrange the visuals is causing confusion making it hard to spot related items as well as this there’s no consistency in how visuals have been named everyone’s been labeling them however they please which makes it even harder to locate the essential elements using the selection pane you can organize and group these visuals making everything much easier to manage and understand in this video you are going to learn how to name visuals group the related visuals and properly organize by layering them on top of one another grouping and layering visuals in Microsoft PowerBI simplifies report creation and management by organizing data in a user-friendly way enhancing the user experience through clear logical presentation the first step towards enhancing user experience in PowerBI is to clearly name your visuals this involves assigning each visual a name that is meaningful and relevant ensuring quick identification following this organizing the visuals in your report by grouping related visuals to create a report that is both well ststructured and userfriendly the next crucial aspect is layering these groups effectively this technique is about strategically arranging your data to guide the viewer’s attention ensuring that the most important information stands out first lastly the culmination of these skills is evident in the way you manage the visibility of various report elements the control over what and when information is displayed allows you to direct your audience’s focus to essential data significantly enhancing the overall experience in your PowerBI reports now let’s explore how this works in Microsoft PowerBI naming grouping and layering in PowerBI is done from the selection pane to open the selection pane go to view on the ribbon and select selection the selection pane will appear on the right side of the PowerBI desktop editor displaying all items on the current page you can select any name in this pane to identify which visual it refers to it’s important that you name these visuals properly to organize them in an appropriate way this is especially useful when you have many visuals on a page for example if I select text box it will highlight the report heading i can rename it as heading by doubleclicking the item and entering the updated name this can be done on any of these titles when I double click on any item it enables me to edit the name in this selection pane you can also change the layering of the items meaning you can rearrange the order in which visuals appear to better understand this select the insert tab on the ribbon select buttons and then blank from the listed options this will place a new button on the report page notice the new button item that now appeared in the selection pane i drag the new button next to the date slicer i select this button in the selection pane and using the up and down arrows I can change its order for example if I send it below the slicer it disappears from the report because it is under the slicer visual using this method you can bring any item to the front or send them back using the selection pane you can also group items from this pane let’s group the heading and the underline below this heading named shape select the shape item from the selection pane i then press the control key on the keyboard and select heading notice how these two items are now highlighted now I right click on either item select group and then group again this will create a new group of these two items to ungroup right click on this newly created group then select group and choose ungroup this way you can use the selection pane to change the item names group them and layer them on top of or below each other by grouping and layering visuals effectively you’re not just tidying things up you’re making the whole experience smoother and more intuitive for anyone seeing your reports use these techniques in your next PowerBI project to create reports that are not just visually appealing but also userfriendly and coherent in today’s fast-paced business environment the ability to access and analyze data on the go is increasingly important with a significant shift towards mobile device usage optimizing Microsoft PowerBI reports for mobile viewing becomes an asset for any organization this video highlights the importance of adjusting reports for mobile view and explores the capabilities of Microsoft PowerBI’s mobile layout view offering a strategic advantage in data accessibility by the end of this video you’ll be able to understand the significance of mobile optimized PowerBI reports explore the features and benefits of PowerBI’s mobile layout view and identify best practices for designing mobile friendly reports lucas a data analyst with Adventure Works is tasked with creating PowerBI reports that are easily accessible and readable on mobile devices his challenge is to ensure that these reports provide a seamless user experience maintaining readability and functionality across various mobile platforms lucas aims to make these reports not just accessible but also as informative as possible for his team who often rely on quick data insights while on the move the way users interact with data has fundamentally changed mobile devices with smaller screens and touch-based navigation require a different approach to data visualization compared to traditional desktop displays recognizing this shift PowerBI introduced a dedicated feature for the unique demands of mobile platforms the mobile layout view the PowerBI mobile layout view is a feature within PowerBI desktop that allows creators to design and customize reports specifically for mobile devices this view addresses the unique challenges posed by smaller screens and touch interfaces key aspects include mobile optimized layout this layout differs from the standard view focusing on simplicity and readability on mobile devices it allows users to rearrange visuals to fit a vertical layout which is more suitable for mobile devices interactivity and functionality despite the change in layout the mobile view retains the interactivity and functionality of the desktop reports users can still filter slice and interact with the data in meaningful ways customization and flexibility powerbi provides flexibility in designing these reports users can choose which visuals to include how to arrange them and even create different views for different devices consistency in data representation while the layout changes the data and its representation remain consistent with the desktop version this ensures that users get the same insights regardless of the device they use preview and testing powerbi allows creators to preview how their reports will look on various devices helping them make necessary adjustments before publishing let’s look at an example of adjusting the sales categorical analysis report for mobile navigation using PowerBI mobile layout view to access the PowerBI mobile layout view you select the phone screen button on the bottom left of the page using this button enables you to switch between the desktop and mobile layout views the mobile layout view appears on screen it features the mobile layout canvas a grid layout where you adjust the visualizations to fit any mobile screen the page visuals pane where all the reports visualizations are listed and visualizations where the format settings of any selected visual will appear to adjust the report for mobile platforms drag and drop any visualization from page visuals to the canvas such as the date slicer and the tree map fitting both to the screen you can use the visualizations pane to format the visualizations such as enabling data labels for the tree map chart these changes won’t reflect on the desktop layout view the sales categorical report will now appear with these configurations when launched through PowerBI mobile ensuring the seamless navigation of the report using any kind of mobile device when designing reports for mobile devices using PowerBI’s mobile layout view it’s important to be aware of certain considerations and limitations that can impact the user experience these include tool tips availability while the tool tips are not active in the mobile layout canvas during the design phase they become accessible to users when viewing the report through the PowerBI mobile app metric visuals interaction on the mobile layout canvas metric visuals are set to be non-interactive this means users cannot interact with these visuals in the same way they might in a desktop report slicer selections consistency slicer selections made in the mobile layout do not transfer when switching to the web layout conversely if you switch from the web layout back to the mobile layout the slicer selections will reflect those changes additionally when a report is published any slicer selections displayed will be those set in the web layout regardless of whether the report is viewed in a desktop or mobile optimized view optimizing PowerBI reports for mobile devices is a strategic step towards enhanced data accessibility and decision making in today’s mobile ccentric world this feature is instrumental in ensuring that valuable data insights are always at the fingertips of decision makers regardless of their location or the device they use have you ever noticed numbers in your data that seem unusual and just don’t seem to fit the data analysts in Adventure Works have in their recent sales report some unusual figures stand out and need investigation these odd numbers might be a coincidence or they might be indicators of hidden issues in the Adventure Works data or in the business as a whole they might also be clues that can lead the Adventure Works team to deeper business insights these odd numbers are referred to as anomalies and outliers in this video you will learn what anomalies and outliers in data are you will also discover how these odd figures can reveal deeper insights and information about your data and how you can use them to inform smarter business decisions the Microsoft PowerBI sales report prepared by the Adventure Works Analytics team shows a profit downturn for a month in the middle of the cycling season typically this is a time associated with peak sales profits rose in another month without a corresponding increase in sales volume the team needs to understand why these numbers are appearing to determine if any action needs to be taken let’s explore the terms anomalies and outliers and discover some examples of each anomalies are data points that occur outside the expected range of values and which cannot be explained by the base distribution base distribution is the normal pattern that data follows anomalies are often caused by invalid data outliers are data points significantly different from the rest of the data there are often values that deviate from the other values in a data set however outliers can be explained by the base distribution the main difference between an anomaly and an outlier is that an anomaly is often an error or a rare unexpected event whereas an outlier is an extreme but expected value that still belongs to the pattern of the data so how would you recognize an anomaly let’s step through some examples a sudden spike in website traffic that cannot be explained by any known marketing campaigns or events a sudden drop in sales for a product that has been consistently selling well a sudden increase in the number of errors in a system that has been running smoothly could also be an anomaly a customer who is aged 200 years old now let’s step through some examples of outliers a top student who scores 100% on a test while the class average score is 70% a house that is significantly larger and more expensive than the other houses in a neighborhood a stock that experiences a sudden price change that is not in line with the rest of the market a customer who is aged 99 let’s explore how to use a scatter chart visualization in PowerBI to identify anomalies and outliers in a data set this data set contains advertising spending and profits based on the same campaign in different media over several months it looks problem free but we can’t be sure until we process this data with some visuals like scatter charts to visually spot outliers and anomalies we’ve plotted this data set using a scatter chart on this report page placing campaign ID in the values advertising spend on the x-axis sales revenue on the y-axis and platform on the legend there are some data points which stand out in the scatter chart some of these data points demonstrate a slight variation while others diverge significantly these unusual data points might be anomalies or outliers the orange data points represent the social media campaigns the majority of them did well and the chart shows that when the advertising spend increased sales also increased the CO4 campaign is an exception to this however this will not be considered an anomaly because you know that the Adventure Works website was down on that day despite the ads continuing to run on social media because you can define a reason why C00004 performed badly you can define it as an outlier another campaign C006 didn’t perform well despite its high advertising spend this was a print media campaign and on further investigation you found that that type of media was not popular and this is why the C006 campaign failed the campaign is also considered an outlier because you can explain the reason why it varies so much from the other campaigns the online campaign C023 also stands out as different from the other data points in its category in this case the reason why this campaign has performed so differently has not yet been identified until you have the exact reason why this campaign performed exceptionally well you would consider this an anomaly and not an outlier anomalies and outliers in data are critical indicators of deviation from the norm while outliers can be explained within the context of existing data anomalies hint at underlying issues or exceptional occurrences that demand deeper analysis identifying these can lead to improved strategies and more informed decision-making processes in business operations orders at Adventure Works have increased recently as more of their customers are enjoying outdoor pursuits the data analysis team are kept busy analyzing data related to the large volume of orders being processed and shipped and creating reports to present the results their reports contain many of Microsoft PowerBI’s bright and colorful visuals it’s a large amount of data and the team wants to ensure that viewers of the report can quickly spot patterns and insights two PowerBI features grouping and bin will help them to create visuals that are concise organized and easier to draw conclusions from in this video we will explore what groups and bins are in PowerBI and how they can help you to organize your visuals to deliver information and insights more effectively as a data analyst at Adventure Works you’re part of the team creating a sales report which will provide a summary of the current order fulfillment situation your first task is to compare the number of items that have been shipped with those that have a status of processing or cancelled the management team particularly wants to be able to easily access information on shipped orders data grouping will allow you to group orders according to their status that will make the order fulfillment status more visible and make the data as a whole more coherent the management team also want to know the overall number of shipped orders in different value ranges the data bidding process will be invaluable for this it will enable you to organize the results based on the order value ranges and this in turn will allow the management team assess the pattern of which orders were more valuable let’s explore how the grouping and bin techniques work grouping refers to the process of combining data rows based on specific column values in PowerBI this technique allows you to create a new column that represents aggregated data the purpose of grouping is to simplify and streamline your data visualization by categorizing similar data points together you can group data related to product categories regions or customer segments making it easier to analyze and present summary information for instance you can group states into regions like East Coast West Coast and Central or you could group products by categories such as electronics clothing and home appliances to understand combined sales numbers bidding involves dividing a numeric column into ranges or bins bidding is useful when you want to analyze data in discrete intervals by categorizing numeric values into bins you can gain insights into the distribution of data and identify patterns for instance you could bin ages into ranges such as 1 to 18 19 to 30 31 to 45 and so on if you’re monitoring website performance you could bin website load times into categories like fast less than 1 second average 1 to 3 seconds slow 3 to 5 seconds and very slow 5 plus seconds to identify user experience issues let’s explore how you can use grouping and bin to help Adventure Works display the order status and the value range of the orders let’s begin by applying data grouping to a visual this clustered bar chart shows orders across multiple product regions it includes all shipped orders as well as orders that were cancelled or are still showing as processing let’s group those orders which have a status of canceled or processing to do that right click on the order status field in the legend well and select new group when the group pop-up appears press the control key on the keyboard and select cancelled and processing then select the group button and finally select okay the clustered bar chart updates with this new group data instantly now the orders with a status of canceled or processing are displayed in the same group and you can see the total value for these orders summed up together the management team asked you to display the orders in different value segments you can use the bin feature to achieve this create a new report page and add a clustered bar chart select the product region and order status fields from the data pane ensure that the product region is placed on the y-axis and order status on the x-axis ensure that the clustered bar chart visual on the report page is still selected open the filter pane drag the order status field from the data pane into the filter pane in the filter pane select the order status filter box and then select shipped from the drop-own checklist the visual updates to show only the shipped orders as requested by the management team they also wanted to have the orders displayed in order value ranges so let’s create bins to achieve this in the data pane right click on the order total field on the data pane and select new group from the shortcut menu in the new popup enter 5,000 as the bin size and select okay a new entry appears in the data pane called order total open parenthesis bins close parenthesis drag this new entry to the legend well now the data is properly binned you can hover on any bar to see how many orders are in each of these bins in this video you explored what the grouping and binning features are and how to apply them in your data set by using these two features to organize the results displayed in the PowerBI visual you made the visual clearer and more concise the use of grouping and binning in the chart visuals has enabled additional analysis to be implemented artificial intelligence commonly referred to as AI has revolutionized the world of data analysis and visualization making it easier for businesses to uncover insights and make informed decisions microsoft PowerBI Microsoft’s popular business analytics tool has embraced AI with a range of AI visuals that empower users to delve deeper into their data in this video you will explore three key AI visuals available in PowerBI key influencers decomposition trees and forecasts you will learn how these AI visuals are applied in PowerBI and how they are utilized by data analysts to improve the key factors behind business results gain a detailed overview of data breakdown and predict future trends the Adventure Works management team has noticed a concerning trend a significant drop in bicycle sales despite a surge in interest in outdoor activities they want to identify the reasons behind it the management team need to discover why the results for this product range are not as good as expected they also want to identify the product ranges that are performing well and predict if the current trends in sales will continue the data analysis team in Adventure Works can use AI visuals to provide this information they begin with the key influencers visual the key influencers visual helps users identify the factors that influence a particular outcome or metric in their data the visual uses machine learning to analyze and identify the factors that have the most impact on a selected outcome as the name suggests the key influencers visual examines potential influencers ranks them based on their impact and presents these insights in an interactive easy to understand format it helps business users to understand what drives specific results or why events occur by using the key influencers visual the data analysis team can identify the adventure works products and product categories that are not performing well they can also obtain key insights on how to reverse the current downward trend in bicycle sales key influencer visuals in Microsoft PowerBI offer many benefits first they help to identify causal factors key influencers help you pinpoint the variables or factors that have the most significant impact on your chosen outcome allowing you to make datadriven decisions second key influencer visuals offer intuitive visualization the visual representation of insights is easy to interpret making it accessible to both technical and non-technical users key influencers visuals also incorporate drill down capability you can drill down into specific features to gain deeper insights and an understanding of how different values within those features affect the outcome lastly there is a statistical significance with key influencer visuals the tool calculates statistical significance ensuring that the relationships it uncovers are robust and reliable the data analysis team uses another AI visual called a decomposition tree to help the management team optimize their product lines the decomposition tree visual is an AI powered visual in PowerBI that allows users to break down a measure into its underlying components a measure in PowerBI is an aggregated combined or calculated value the decomposition tree visual is particularly useful when you want to understand the factors contributing to a particular metric it offers a structured approach to dissecting data hierarchies and providing clarity in identifying the most influential components this type of information and insights can be crucial for optimizing strategies and resource allocation the management team at Adventure Works wants to gain a clear understanding of sales trends and the data analysis team uses the decomposition tree visual to provide information on how revenue breaks down by product decomposition trees in PowerBI offer many benefits they are ideal for breaking down complex measures into their underlying components making data more digestible and actionable a decomposition tree is a hierarchical visualization it allows users to explore the contribution of different factors at various levels of detail this visual also allows for interactive exploration users can drill down into each component for deeper insights and perform ad hoc analysis the tool calculates statistical significance ensuring that the relationships it uncovers are robust and reliable now that the management team at Adventure Works has a clearer idea of the factors influencing low sales in one product range and of the patterns and breakdown of their revenue they want to move on to forward planning their goal is to proactively adjust production plans with the appropriate models to stay ahead of the competition by capturing emerging markets and effectively meeting future customer demands the data analysis team can facilitate this by using AI features in PowerBI to forecast future bicycle demand trends the forecasting feature in PowerBI leverages AI to predict future values based on historical data this is vital for businesses that want to make datadriven predictions and anticipate future trends forecasting provides three important benefits forecasting enables you to predict future trends the forecasting tool helps organizations anticipate future values based on historical data aiding in proactive decision-making and planning another key benefit of using forecasting is scenario analysis users can explore different forecasting scenarios adjusting parameters to discover how changes impact future predictions lastly forecasting allows users to use datadriven planning businesses can use forecast to optimize inventory management resource allocation and budgeting microsoft PowerBI’s AI tools including key influencers decomposition trees and forecasting make complex data easy to understand they do this by analyzing patterns and trends in the data which assists businesses in planning and decision-m these tools turn complicated data into useful information helping companies respond to today’s needs prepare for the future and stay ahead in their fields some viewers of your report still have difficulty quickly absorbing the core data insights you’ve learned a lot about working with data in Microsoft PowerBI and you’ve created your reports according to best practices your reports use appropriate visualizations and they look great is there anything else you can do to help the viewers of your report focus on the key points yes you can use reference lines and error bars to insert further analytical visuals this video will explore the concept of reference lines in PowerBI and the application of different types of reference lines in data visualization you’ll also learn about error bars and use different types of error bars to represent data variability and uncertainty by the end of the video you should be able to recognize appropriate scenarios and visuals where you can effectively use reference lines and error bars renee Gonzalez is the marketing director at Adventure Works she asks you to enhance a Microsoft PowerBI sales report she wants to add an average reference line to display a clear sales performance benchmark she also wants to incorporate percentage error bars into a sales by product chart to give the sales managers a better understanding of sales fluctuations reference lines are used to highlight significant data points or trends these lines serve as benchmarks or guides to make data easier to interpret a reference line allows viewers to quickly identify key points like averages medians or specific thresholds they play a crucial role in highlighting deviations understanding distributions and setting performance targets there are several types of reference line in PowerBI choose the one that best interprets your data an average line marks the average value across a data set this is useful to compare individual data points against the overall average a median line indicates the median or middle value a feature that is especially helpful in skewed distributions percentile lines display a specific percentile giving a better understanding of the data spread a constant line or x-axis y-axis line represents a fixed value it is often used for benchmarks or targets min and max lines are used in charts to highlight the lowest and highest values in a data set providing a clear visual reference for understanding the range and distribution of the data and a trend line helps identify patterns or trends in data aiding in understanding data movements over time error bars are used to represent variability or uncertainty in data visualizations an error bar extends from a central point in a chart such as a specific line of a line chart or a bar of a bar chart the error bar visually demonstrates the potential range of values around a data point with the specific lower and higher bound highlighted in the tool tip this feature is particularly important in conveying precision reliability and potential errors in data in addition to displaying a range of values error bars also provide context and depth to the data points allowing for a more nuanced understanding of the data for instance in a financial report error bars can illustrate the potential fluctuation in revenue forecasts helping investment managers grasp the level of risk or uncertainty involved there are different error bar types choose the type you need depending on how they should be calculated and applied over the visualization the by field type of error bar allows you to specify a particular field in your data set to determine the range of the error bars it is useful when you have specific error values for each data point with by percentage the error bars use a percentage to calculate the error range this is particularly helpful when you want to display a consistent percentage error across all data points uh by percentile type will provide insight into the distribution of data points by displaying the range within a specific percentile for example a 25th to 75th percentile error bar indicates the interquartile range covering the middle 50% of data points these error bars help in understanding the central trend and spread of the data and the standard deviation type calculates the error range based on the standard deviation of your data it’s commonly used to indicate the variability of the data around the mean let’s discover how you can use the power of reference lines and error bars to add data insights in PowerBI the sales report contains two column charts the one on the left distributes the dollar sales amount over the customer country field and the other one distributes it over the product color field let’s explore how reference lines and error bars can help us interpret this data let’s start with the sales amount by customer country column chart select it and navigate to the visualizations pane then to the analytics pane component which is located below the icon of a chart in a magnifying glass the analytics pane has all the analytics metrics that PowerBI can apply to your visualization to add a horizontal line giving the average of the sales amount value select average line choose add line and turn on the data label section also expanding its options adjust the horizontal position to right so that the average value will be visible on the visualization and modify the style to be both so the users are clear about what’s being depicted with the reference line moving to the other sum of sales amount by product color visualization let’s add error bars to showcase the potential fluctuation of sales based on color select the visualization and navigate to its settings in the analytics pane once again error bars are at the bottom of the analytics expand this section and choose on for the options field box then directly below expand the type option to select an error bar type to be applied on the column chart select by percentage and modify the upper and lower bounds to be 5% the error bars are applied to your visualization you can hover over any column to display how the figures of any color will be modified based on a 5% increase or decrease in the sales amount this video highlighted the importance of reference lines and error bars in PowerBI both are key tools for enhancing data visualization reference lines aid in identifying and comparing key data points while error bars provide crucial insights into data variability and precision in summary reference lines serve as benchmarks or indicators helping to highlight key data points error bars offer a visual representation of the variability or uncertainty within the data adventure Works has streamlined its data analysis thanks to Microsoft PowerBI to keep making datadriven business decisions Adventure Works needs to be able to visualize performance tracking this is a crucial business metric for instance how is its customer satisfaction rating how close is it to the required goal and how can Adventure Works compare satisfaction ratings across different regions metrics and scorecards are the answer they are PowerBI tools that Adventure Works can use to track measure and report on key business goals and outcomes in this video you will explore the fundamentals of metrics and scorecards you’ll also learn how to create and customize metrics and discover how to build effective scorecards adventure Works needs a scorecard in PowerBI service to track the company’s ambitious sales target jamie the CEO wants a real time updating metric that accurately reflects the progress towards the sales goal this metric is the focal point of the scorecard which will also encompass other key performance indicators metrics in PowerBI are quantifiable measures that serve as key indicators of business performance essentially they are datadriven benchmarks used to track and assess the efficiency and success of an organization’s processes initiatives or strategies metrics in PowerBI are not just static numbers they are dynamic and interactive elements that update in real time reflecting the latest data the real-time tracking capability of metrics means that businesses can respond promptly to changes metrics can be customized to suit specific business needs such as tracking sales targets monitoring customer satisfaction levels or measuring operational efficiency scorecards in PowerBI are a step further in data visualization and analysis scorecards display a collection of related metrics on a single comprehensive dashboard providing a broad view of business performance this consolidated view is vital for managers and decision makers it encapsulates critical data points and trends in an easily digestible format that can reveal how business areas interconnect and impact each other scorecards and PowerBI are highly customizable organizations can tailor the information to align with their strategic objectives and key performance indicators or KPIs this includes the ability to set and track goals visualize progress and identify areas needing attention or improvement let’s create a scorecard with metrics for Adventure Works to track its sales amount target sign into PowerBI service with your credentials navigate to the left sidebar of the platform and locate the metrics icon select metrics to take you to the metrics page on the top right select plus new scorecard a new scorecard opens which you can start populating with metrics on the right of untitled scorecard select the edit pencil to rename the scorecard to adventure work sales goals all scorecards are saved in my workspace by default but you can move it to another workspace by selecting file and then move scorecard select the adventure work sales workspace to move the scorecard to and select continue the scorecard is now ready for the first metrics to create one select new metric name it sales amount goal and assign the admin account as the owner together with yourself on the current value field select set up to provide an actual figure from your data set instead of a manual number choose connect to data select the all reports tab and search for sales report select sales report then select next to move to the next step the report is previewed in the metrics window on the report there is a card visualization showcasing the total amount of sales select it to confirm the measure being used the current value as well as the filters and slicers affecting this value select connect to drive this measure onto your metric on the next field box final target input 30 million as the goal for the total sales amount a small box appears as you type the number aiding you in formatting the figure add a status on the metric which could be on track since the sales team is close to hitting the required goal let the start date be the default date given and assign a due date for the team to hit the target for instance this could be the end of the year all metric settings are now configured so you can select save to add the new metric to the scorecard the scorecard is now ready for users to access to share the scorecard and its metric goals with other Adventure Works members on the top menu of the scorecard select share and for instance select Renee the marketing manager to share the scorecard with her this video explored metrics and scorecards in Microsoft PowerBI illustrating their critical role in tracking and achieving business goals metrics in PowerBI provide quantifiable indicators that reflect the success of or progress to specific objectives scorecards give a comprehensive view combining multiple metrics into a holistic view of performance using these tools can empower organizations to align their strategies with datadriven insights ensuring that decisions are informed and goal oriented congratulations on completing visualizing and analyzing data in Microsoft PowerBI during these lessons you’ve gained insights into key data analysis concepts and tools in PowerBI and worked through practical activities for a deeper knowledge of these topics let’s recap what you learned and the key takeaways from each topic you began by learning more about the wide choice of visualizations available in PowerBI general purpose visualizations such as tables and matrices card KPIs and slicers are versatile as they can be used in a variety of analysis scenarios powerbi also offers many visuals that are tailored for specific types of analysis and this lesson explored which visualization is appropriate for specific analysis types for example categorical analysis is best displayed in bar and column charts or pie and donut charts scatter and bubble charts are more appropriate for correlation analysis histograms waterfall charts and maps were also discussed this lesson also examined the specific and general formatting settings that enhance the appeal and readability of visualizations in your reports modifying the size or position of visual elements or applying format changes such as font size and color to titles and data labels can add clarity and impact to visualizations you also learned about conditional formatting which can be used to dynamically highlight critical data points and add visual variety the slicing and filtering features in PowerBI allow you to dynamically adjust visuals and focus on specific data points slicers allow for intuitive selections and enable you to refine the data represented in all the visuals on a report page the filtering feature can be applied in the filter pane which manages filters at different levels visual level filters apply to a single visual page level filters apply to all visuals on a page and report level filters apply to all visuals within a report you also had an opportunity to learn about the tools in PowerBI that business users can use to export data for further analysis or presentation for example the analyze in Excel feature allows them to work with PowerBI data sets directly in Excel this offers a familiar environment for in-depth analysis and custom report creation another feature pageionated reports is ideal for creating print friendly formats these reports are designed for easy reading on paper or PDF and they can accommodate detailed data and complex layouts you then learned how to enhance reports for usability and storytelling this lesson began by exploring how smooth page navigation can improve readability and flow in multi-page reports the use of buttons or interactive links creates a seamless transition between different pages and guides users through the report’s narrative bookmarks captures specific report views and states enabling quick access during presentations and highlighting data changes over time sorting organizes data within visualizations making it easier to identify trends and insights the way that multiple visualizations within a PowerBI report interact with each other enhances data exploration and analysis filter interactions cause a change in one visual to filter data on another this refineses the display data based on the selection and allows users to isolate and analyze specific data points across different visuals another option highlight interactions does not filter out non- selected data instead it emphasizes selected data in connected visuals while the unselected data is dimmed and not filtered out this provides a clear view of how parts relate to the whole lastly there is an option none which completely disables the interaction between visuals doing this keeps the visuals independent without any interaction which can be useful for standalone data presentations you learned that syncing slicers in PowerBI reports improves the user experience with synchronized slicers a selection made on one page applies to all other pages this streamlined approach reduces confusion saves time and maintains the narrative flow you are also introduced to the selection pane where you can manage the report elements here you can clearly name individual visuals to ensure quick and easy identification you can use the selection pane to group visuals and provide structure to the report the selection pane also allows you to layer these groups this helps you to guide the report viewer through the data by controlling the order in which the visuals appear finally this lesson focused on how to adapt a report for mobile use the PowerBI mobile layout view it demonstrated how to modify the visual elements and layout for better readability and interaction on a smaller screen size in the final lesson you learned about the features in PowerBI which help you identify and analyze patterns and trends in your data it demonstrated how to recognize anomalies and outliers you were provided with examples of both and shown how to use scatter charts to identify them in PowerBI recognizing these types of discrepancies is essential for uncovering underlying issues or exceptional events and leads to smarter business decisions and strategy improvements the lesson continued with an explanation of grouping and binning in PowerBI grouping consolidates similar data points into categories which facilitates efficient summary visualizations bidding in contrast segments numeric data into ranges aiding in distribution analysis finally you learned about PowerBI’s AI tools which provide insights that can inform planning and decision-m key influencers to identify critical factors affecting outcomes decomposition trees to break down complex metrics and forecasting to predict future trends from historical data you should now have a powerful tool set in PowerBI for creating reports the first item in this tool set is the wide array of charts offered by PowerBI which you can use to convey insights features such as bookmarks grouping and layering visuals offer a way to create a smooth narrative for the viewer filtering and slicers help them to drill down to deeper insights techniques such as detecting outliers and anomalies data grouping and binning and using AI visuals provide a solid foundation for accurate data analysis in the world of data and reports having a centralized location where teams can work together is beneficial for all involved that’s where Microsoft PowerBI workspaces come in workspaces are more than simple folders they are special team rooms where analysts can add and share their charts reports and data in this video you will learn about what Microsoft PowerBI workspaces are and how they can benefit your work you will explore the different roles people can have in these workspaces and learn how these roles can make teamwork in PowerBI smooth and efficient at Adventure Works you are responsible for
creating and managing reports for a variety of teams the sales team requires regular updates on their performance metrics the marketing team tracks campaign results and the customer service department looks for feedback on user behavior each team creates its own set of data visualizations often leading to a collection of reports scattered across different platforms however using the PowerBI workspace feature you can set up workspaces for each of the sales marketing and product teams then each team will have its centralized room to create share and discuss their specific reports first let’s explore what PowerBI workspaces are powerbi workspaces are places to collaborate with colleagues and create collections of dashboards reports data sets and pageionated reports powerbi provides two types of workspaces personal and shared your personal workspace is a private area for individual tasks while shared workspaces are designed for team collaborations where members can jointly develop and fine-tune reports workspaces can contain a maximum of 1,000 data sets or 1,000 reports per data set workspace offers a feature called roles which helps to manage access control on these resources understanding and properly utilizing the roles within PowerBI workspaces is important to ensure effective collaboration and content management assigning the correct role to each user is vital to maintain data integrity security and efficient workflow powerbi offers four types of roles: admin member contributor and viewer let’s start with the admin role the most powerful role the admin has full control over the workspace including content creation member management and workspace settings adjustments they can add or remove members change roles and even delete the workspace next you have the member role members have the privilege to add modify and delete content in the workspace they can collaborate with others and share the workspace content but cannot change workspace level settings after the member role is the contributor this role is slightly more restricted than the member role contributors can add and modify content but cannot delete items from the workspace they also cannot share content with others lastly we have the viewer role the viewer role represents the most limited level of access within a workspace viewers are primarily consumers of content and their permissions are confined to viewing the materials available within the workspace they do not possess the right to modify or delete any content making them ideal for scenarios where readonly access is required having established your understanding of workspace roles let’s consider workspace role capabilities when an individual belongs to a user group they receive the role you have designated if a person is part of multiple user groups they inherit the highest level of permission from the roles they have been assigned in PowerBI service a user group refers to a collection of users who are grouped together based on certain criteria roles or purposes these groups can be leveraged for various functionalities including content sharing and permission management powerbi’s workspace offers a unique and powerful feature the ability to create template apps these are preset customizable structures that serve as a foundation for building specific data visualization applications once created they can be shared not just within the organization but also externally this external sharing capability enhances the utility of template apps rather than confining data visualizations and reports within organizational boundaries businesses can distribute these template apps to customers partners or other stakeholders the usefulness of these template apps lies in their flexibility when customers receive a template app they aren’t just locked into viewing static predefined data instead they can connect these templates to their own data sets now that you’ve learned about Microsoft PowerBI’s workspace tools you can explore ways to help your teams collaborate and use data more efficiently from setting roles that decide who can do what to offering readytouse templates it streamlines many tasks imagine you’re tasked with presenting multiple reports and data sets to teammates across various departments it will be convenient to bundle everything neatly together and offer it as a unified online package this not only simplifies your presentation process but also enhances accessibility for a wider audience this is precisely the type of solution that Microsoft PowerBI workspace apps look to provide streamlining and enhancing your data sharing capabilities in this video you are going to learn about PowerBI workspace apps what they offer and how to create and share them with your audience adventure Works faces a data sharing hurdle different departments need various PowerBI dashboards and reports to operate effectively the finance team requires sales data the marketing team are keen on customer insights and the supply chain team wants to view inventory levels sharing this data separately will be challenging this is where PowerBI workspace apps can assist you in generating these dashboards and reports using this feature the data analysis team can group related content into specific apps for instance all sales related reports and dashboards go into one app while customer insights go toward another these apps are then published to the appropriate teams ensuring everyone has access to the relevant information this improves workflow and efficiency for you and the data analysis team in PowerBI you can create official packaged content and then distribute it as an app these can be distributed to a wide audience such as an entire organization or to specific groups or people apps are created in workspaces you can choose a selection of reports dashboards and data sets from a workspace to distribute as an app you can then publish the finished app to large groups of people in your organization to create or update an app you need a PowerBI Pro or premium per user known as PPU license for app consumers there are two options the workspace for this app is not in a PowerBI premium capacity the workspace for this app is in a PowerBI premium capacity if the app is not in a PowerBI premium capacity all business users need PowerBI Pro or premium per user licenses to view your app if the workspace for the app is in a PowerBI premium capacity business users without PowerBI Pro or premium per user licenses in your organization can view app content however they can’t copy the reports or create reports based on the underlying data sets let’s consider how you create apps you can start the app publishing process when your workspace has content when you enter your workspace you will notice a create app button which will be your starting point you’ll be taken to the application settings area where you can set the name of your application add a description choose a logo and select the theme color for your application after that you can select which content you want to include in your app and you can sort content as you please once you are happy with the content selection you must select the audience for this application having created your app you must create and manage the audiences engaging with the app an app audience is the group of people you choose to share your app with in the audience tab there is a centralized place to decide who has access to your app and to what extent think of it as your control room where you can set up different audience groups for your app you might want to give access to everyone in your company or just want a specific group or certain individuals to have access with PowerBI apps you can create multiple audiences for your app and show or hide different content to each audience you can also set some advanced options like if your audience can share the data set or build new content with the data set in this app once you have the audience and the content they can engage with it is time to publish your app once the app is published it can be accessed by your intended audience you can come back to the app and update the settings and the published app will reflect the changes in a few minutes once the app is published it can be accessed via the URL or by searching for it from the app marketplace app consumers in PowerBI service and in PowerBI mobile apps only see the content based on the access permissions for their respective audience groups by default consumers see the all tab view which is a consolidated view showing all content that they have access to in this video you’ve learned about the process of setting up audiences in PowerBI deciding on the content visibility for each group and the steps to effectively publish and share your app microsoft PowerBI subscription and alert features enable users to remain informed about significant shifts in their data with data alerts users can establish notifications that activate when dashboard data surpasses predefined limits along with data alerts subscriptions ensure users consistently receive updates on their reports and dashboards in this video you will learn about Microsoft PowerBI subscription and alert features to keep you consistently informed about crucial data changes and how to utilize them effectively the newly appointed director of the strategic planning department at Adventure Works is eager to make a measurable impact with the recent launch of ebikes in Adventure Works it’s essential for the director to have a firm grasp on the daily sales figures however being new to the company’s PowerBI setup navigating through the PowerBI dashboards can be timeconuming to streamline this the business intelligence team establishes a PowerBI subscription focused on eBike sales metrics every day the director receives an email snapshot of the prior day sales enabling immediate datadriven strategic discussions powerbi subscription and alert features are tools that redefine the way businesses approach data analytics it is important to note that to activate subscriptions and alerts the content must reside in premium capacity or be tied to a premium per user license to support nearrealtime data flows data sets must be configured for scheduled refreshes or direct query connections with data alerts users can establish notifications that activate when dashboard data surpasses predefined limits along with data alerts subscriptions ensure users consistently receive updates on their reports and dashboards let’s first explore subscriptions with subscriptions timely delivery and tailored report dissemination becomes seamless eliminating a laborious manual process and ensuring that stakeholders are always informed there are many benefits of using subscriptions in Microsoft PowerBI with subscriptions you can schedule automatic delivery of reports on a recurring basis email or chat digests of key report pages to stakeholders set different schedules like daily weekly or monthly delivery customize data views with parameters and rowle security and eliminate the need to manually distribute reports users can set up to 24 subscriptions per report or dashboard with unique recipients times and frequencies for each subscription subscriptions can include a snapshot and link to the report or dashboard or a full attachment of the report or dashboard you can also create dynamic per recipient subscriptions which are designed to simplify distributing a personalized copy of a pageionated report to each recipient of an email subscription now let’s turn our attention to alerts alerts in PowerBI notify users when data meets defined conditions such as surpassing sales targets dropping below inventory thresholds or any other measurable value set within the system alerts shift from passive data monitoring to proactive and timely decision-making allowing businesses to harness real time data intelligence effectively the benefits of using alerts in PowerBI include getting realtime notifications when data meets thresholds responding quickly to insights instead of passive monitoring receiving dynamic metric alerts account for data variability ingestion alerts notifying you on data set refreshes getting push notifications via email mobile and Microsoft Teams chat and shifting from reactive to proactive data analytics with subscriptions and alerts microsoft PowerBI analysts can build out robust notification strategies ensuring stakeholders always have visibility into the data they care about this keeps them informed of critical metrics and enables proactive responses to data trends and anomalies in today’s datadriven world how can data analysts discern between trustworthy Microsoft PowerBI content that holds reliable information and content whose accuracy hasn’t been tested microsoft PowerBI’s features of promoting and certifying content hold the answer promoting and certifying content in PowerBI can elevate data credibility and can elevate the credibility of your data and ensure it is trusted as reliable content in this video you will learn about the differences between promoting and certifying PowerBI content their respective use cases and the implications of each method for content creators and consumers the marketing team at Adventure Works detects a noteworthy increase in sports bike sales in Europe after compiling the data a PowerBI report is generated highlighting the sales trends and key insights after compiling the data a PowerBI report is generated highlighting the sales trend and key insights recognizing its value the report is promoted within the European sales division and given its potential relevance to global strategies the upper management deems it fit for companywide sharing before its wider distribution the central PowerBI team thoroughly reviews the report ensuring it aligns with global standards once certified this report will be accessible across all regions its certification badge becomes an assurance of its precision and significance influencing strategic decisions throughout Adventure Works global operations promoting content in PowerBI is like giving it a stamp of approval when content is marked as promoted it signifies that it aligns with specific organizational benchmarks for accuracy and reliability however it is crucial to note that while it has met these preliminary checks it has not been subjected to an exhaustive vetting process when content like a report or data set is promoted it is made available for a wider audience to discover and consume promoted content appears in content packs and curated content lists in the PowerBI service promoting makes the content visible to more users but does not validate or endorse it any user with edit access to a workspace can promote content from it certifying content is more specific and detailed than promoting content it requires setting up a content certification policy and process with designated reviewers reviewers validate content to ensure it meets standards and best practices before officially certifying it certification offers a greater level of trust and validation when content is certified it means it has passed through a rigorous scrutiny process adhering to the standards set by the organization this is often a testament to its quality accuracy and overall trustworthiness there are four key aspects of certifying content in PowerBI they are review process expert validation of data quality and adherence to best practices governance implementing strict organization standards while certifying contents visibility certified content is marked with a badge for easy recognition trust indicates high level approval and reliability for all users in the organization when certifying content it requires admin setup of content certification policies certified status expires unless reertified within the policy period let’s explore the key differences between promoting and certifying content when it comes to level of trust promoted content signifies the content is trusted by the creator and might have undergone peer review certified content implies organizational approval often by a central team or authority indicating the highest level of trust with visibility promoted content appears in shared and recommended sections for end users certified content stands out with a distinct badge in the service ensuring users can instantly recognize its elevated status with regards to governance promoted content allows for decentralized governance where individuals or departments can decide the criteria certified content typically requires centralized governance with strict criteria that content must meet to achieve certification next we have the audience promoted content is ideal for departmental or team level sharing where the audience knows the creator and trusts their expertise certified content is best for organizationwide sharing where the audience might not be familiar with the creator but trust the centralized certification process lastly is the review process promotive content might involve peer reviews or departmental checks while certified content often involves strict review by experts or a central BI team including checks on data sources calculations and visualizations in this video you’ve learned about content promotion and certification in Microsoft PowerBI and the key distinctions between each process these two methods are vital for distinguishing trustworthy data and ensuring its credibility some of your data is in cloud-based storage but your other data sources are on premises do you have to move the on- premises data to the cloud to be able to combine and analyze all your data no microsoft PowerBI connects to many data sources microsoft PowerBI data gateways are used to connect PowerBI cloud-based data analysis technology and the data source on premises the gateway is responsible for creating the connection and passing data through in this video you will discover what PowerBI gateways are and how they can help organizations manage on premises data that will later be shared with different types of users adventure Works operates across North America Europe and Asia it uses its global data sources to analyze market trends to make smart business decisions effective decision-making depends on up-to-date reports based on the latest data that’s why the team needs a solution to synchronize the on- premises data sources like SQL Server Excel files and Microsoft Dynamics CRM with Microsoft PowerBI service with the gateway in place every morning when a regional manager logs in they get a dashboard showing not just their own store sales from their on premises sources but also data from other branches across the world despite originating from a server thousands of miles away the data is upto-date and ready for use managers can compare their sales with other regions identify trends and adjust their local strategies accordingly a PowerBI data gateway is an application that connects PowerBI cloud-based data analysis technology and on premises data sources such as SQL server databases or Excel spreadsheets it is required whenever PowerBI must access data that isn’t accessible directly over the internet gateways are responsible for creating the connection and passing data through and they can be installed on any server in the local domain running Windows Server 2012R2 or later there are three types of gateways available personal mode standard or on premises mode and virtual network data gateway with a personal mode gateway only one user connects to data sources and sources can’t be shared with others this mode can only be used with PowerBI and is ideal when one person creates reports and doesn’t need to share data sources the standard or on premises mode gateway allows multiple users to connect to multiple data sources that are secured by virtual networks this mode is well suited to complex scenarios in which multiple people access multiple data sources the virtual network data gateway facilitates secure connections for multiple users to various data sources protected by virtual networks as a Microsoft managed service it eliminates the need for manual installation the virtual network data gateway is particularly effective in handling intricate situations where numerous individuals need access to diverse data sources simultaneously who is the gateway for what type of user with personal mode individual analysts want to manage their own reports and sync personal data sources with the cloud whereas with the on premises mode admins set up the gateway and configure it the BI team uses the gateway to get up-to-date data for their reports what is the connection type you can use the personal mode to import data or schedule refresh the standard mode is used to grab refresh or run direct query how is the data managed each user handles their own data in personal mode in the standard mode the company manages data centrally for all users what is happening with data supervision can we oversee the data there is no supervision in personal mode users are on their own in the standard mode there’s a central system to watch over all the data the final factor to consider is compatibility personal mode works only with PowerBI the standard mode works with PowerBI various apps flows and more the gateway is responsible for creating the connection with PowerBI online service and syncing the local data let’s examine some of the gateway details the gateway is installed on a server in the local domain during installation credentials are stored in local and PowerBI services credentials entered for the data source in PowerBI are encrypted and then stored in the cloud only the gateway can decrypt the credentials the gateway controls access to the local data when an online tool wants data it asks the gateway the gateway checks asking and if they have permission grants access the gateway doesn’t store data it just connects and transfers when data in PowerBI needs updating the gateway passes the request to the local data once the data responds the gateway sends the updated info back to PowerBI one of the standout features of the gateway is the ability to set up scheduled refresh this means that at specified intervals the gateway will automatically fetch the latest data ensuring that online reports and dashboards are always updated finally let’s check some business use cases for PowerBI data gateways organizations with multiple locations or teams spread across different regions can face challenges in accessing a centralized data source the data gateway ensures all teams have uniform access to the same data source data can change rapidly for instance there can be continual updates in global markets for businesses to make informed decisions they need realtime access to data the gateway ensures that the data in online reports and analyses is always up to date a security consideration to remember is that when you use a data gateway direct connections to the online premises data sources are minimized only the gateway communicates with the data source providing an added layer of security all data transferred is encrypted and the established connection is outbound this reduces the risk of security vulnerabilities in this video you learned about Microsoft PowerBI gateways gateways help organizations keep databases and other data sources on their on- premises networks yet allow secure use of that on- premises data in cloud services organizations have a lot of data but not everyone needs to access all of it all the time and some data is sensitive in nature and access to it should be restricted rowle security or RLS is a powerful and exciting data governance capability in PowerBI that enables you to control access to the organization’s data at a granular level it allows you to restrict data visibility for different users or groups ensuring that each user can only access the data they are authorized to view in this video you will explore different types of role security and roles and how to configure them in PowerBI the BI team in Adventure Works is working on quarterly reports and forecasts as their data grows they often need to protect their reports and control access among teams in a report they want to grant certain teams access to specific visuals while restricting access to those visuals for others this security challenge led Adventure Works to implement rowle security rls allows them to precisely manage who can view data and particular visuals within a report providing a tailored and secure experience for each team rowle security controls the data viewable by users based on predefined roles and rules the role is like a group the user belongs to and the role or rules can be designed based on columns of the data set there are two types of rowle security static RLS and dynamic RLS static RLS is the rowle security method to use when you have a fixed set of users and roles like when you have some predefined roles like manager product lead customer marketing lead and so forth in your team you can create these types of roles and apply filters within PowerBI desktop using its rowle security editor static RLS is suitable when you have a small fixed list of users and a simple RLS logic in the report dynamic RLS is a flexible approach because it operates with the user attributes and conditions stored in the data itself it operates by using a centralized role assignment table containing user attributes like role assignments user ids and filter conditions relationships between this table and the primary data tables are established and DAX expressions are used to dynamically filter data based on the user’s role and attributes dynamic RLS is ideal for scenarios where user access is based on varying criteria such as region specific data access or complex role assignments whatever rowle security you create you must always test your configurations rigorously to guarantee accurate and secure data visibility across users testing might mean you just open your report as a specified user and check the data visibility in the modeling ribbon there is a choice called view as that will allow you to simulate a user login and check if the RLS is working as expected let’s create some static and dynamic RLS in the Adventure Works reports first let’s start with static RLS this is the Adventure Works world sales report on the modeling ribbon select manage roles and create a new role called manager Europe we want people in this role to view data from Europe only select the sales table select more options the three dots next to it and select the region field now in table filter DAX expression add this DAX expression open square bracket product region close square bracket equal to open double quotes Europe close double quotes and select save this DAX expression means that any user who belongs to the manager Europe role will only view sales data related to the Europe region to test if the static rowle settings are working properly return to the report view in your PowerBI editor and check if you can view sales data for every region now on the modeling ribbon select view as and check the manager Europe role and select okay this will immediately apply the RLS restrictions on the report and you get sales data for only the Europe region all other regional sales data is hidden you can exit this restricted view by selecting stop viewing since everything is working as expected publish the report to your workspace and add some users to the manager Europe role go to your workspace and select the data set named world sales report choose more options the three dots next to it from the drop-down select security in the role level security dialogue select the manager Europe role and add users to this role then select save with this static role security setup when users in this role view the world sales report they will be able to view sales data related to Europe but will be unable to view sales data from other regions for a more flexible filtering approach you can create dynamic row security return to your PowerBI editor to start applying dynamic RLS for example inside the PowerBI editor model view of your report you can have a table with all the regional managers email addresses and the product regions they belong to this table is related to the sales table using the product region field if you create a dynamic RLS when the managers view this report they will get only sales data related to their corresponding regions return to the modeling ribbon and select manage roles let’s delete the previously created manager Europe role and create a new one named managers this time select the sales table and add this DAX expression sales open square bracket product region close square bracket equal to lookup value open parenthesis managers open square bracket product region close square bracket comma managers open square bracket email close square bracket comma user principal name open parenthesis close parenthesis comma managers open square bracket product region close square bracket comma sales open square bracket product region close square bracket close parenthesis when finished select save this DAX expression checks the currently logged in user’s email against the manager table then filters the product region based on the product regions this user belongs to to test if the security settings are working properly return to the report view and check if you can view sales data for every region on the modeling ribbon select view as check the newly created manager role you also need to check the other user role and input one of the manager’s email addresses from the manager table notice how the report view changed and you are viewing sales data only for the regions assigned to this manager you can select stop viewing to return the report to the normal unfiltered view return to the home ribbon and publish this report to your workspace then open your workspace in the PowerBI service area and go to the security setting of your data set add as many users as you want to this new manager role the dynamic role security is active for this report so when users view the report based on their email address and assigned regions in the PowerBI data set they will view only relevant sales data this way users will have access to filtered data dynamically based on their email and product regions rowle security or RLS is a powerful feature in PowerBI to filter data based on various conditions and roles by establishing the right relationships and using appropriate DAX expressions PowerBI can filter data based on various conditions ensuring that each user sees only the data relevant to their specific permissions always test your RLS configurations rigorously to ensure users data visibility is accurate and secure team collaboration is crucial for proper data analysis the challenge presented by collaboration is to ensure the correct distribution of data within your organization discover how PowerBI’s robust permission management settings can help you maintain control over critical data sets at Adventure Works ensuring data integrity while enabling effective collaboration in this video we’ll explore aspects of permission management for data sets and workspace apps you work as a Microsoft PowerBI data analyst at Adventure Works and there are occasions when you need to share certain data sets with your colleagues your colleagues can either reshare these data sets or create new reports based on them however some of these data sets hold significant importance for the organization and even though they are shared among users you do not want anyone to modify the data set in addition to standard sharing there are times when you also need to share all items in a specific workspace with other users or teams as workspace apps nevertheless you still require precise control over some of these items like reports or data sets ensuring that various teams can only access relevant items the Microsoft PowerBI service offers various permission management settings for data sets and workspace apps which can be incredibly helpful in this context let’s quickly review some key terms data sets are the core collections of data that you work with in PowerBI often representing various aspects of your organization’s data workspace apps in PowerBI allow you to share entire workspaces including data sets dashboards and reports ia workspace app is a full data package that can be shared with specific users or teams ensuring a comprehensive sharing experience now to briefly review the topic of permissions with data set level permissions PowerBI service enables you to assign specific permissions to data sets while sharing you can ensure that although colleagues can access and utilize the data they cannot make changes to it this ensures the sanctity of vital data sets then there is workspace apps permissions in some cases you need to share all files within a particular workspace with other users or teams using workspace apps with PowerBI’s permission management you can maintain granular control over who sees which reports this means different teams can access only the reports that are relevant to their needs keeping your data organized and secure to check how many workspaces reports or dashboards are affected by a data set you can perform what is known as impact analysis to do this you go to your workspace and hover on a data set then select the more options three dots next to it and select show lineage this opens the lineage view for your workspace items where you can view which items are connected to each other on the right side of the screen it also shows the impacted workspaces reports and dashboards for this data set you can always perform impact analysis by selecting show impact across workspaces under each data set to exit lineage view on the top right corner in your workspace you select source view this will take you back to the previous list view where you can view all the items in this workspace as a list let’s experiment with permissions in PowerBI service to begin open your workspace to set permissions for a data set select more options the three dots next to the data set and select manage permissions from here you can add users to your data sets at the top select add user in this grant people access dialogue you can type the username or email address and then select the appropriate permission level using the check boxes for example if you don’t want this user to make any changes to this data set uncheck the allow recipients to modify this data set checkbox once added all users will be shown in this permission view you can make further changes by selecting more options the three dots next to a user and removing or granting permissions you can also fine-tune permissions for your new or existing workspace apps we have already discussed how to create an app and select an audience in previous lessons let’s discover how to update the audience for an existing workspace app open your workspace and at the top select update app select the audience tab here you can fine-tune all the settings related to the audience for an app on the right side in edit audience you can modify the current audience for example currently this app is shared with all users in the entire organization you can change it to some specific users by selecting specific users or groups and then typing their name and selecting update app alternatively you can select new audience and choose other users with different permissions for example you may want to share it with some other user but this time you want to allow them to share the data set among the users in this audience group you can select advanced settings then check allow people to share the data set in this app audience you can also select allow people to build content with the data set in this app audience just in case you want to allow the creation of new reports based on this data set to complete select update app and select update again on the confirmation popup and finally closing the published popup that is a demonstration of how you can manage permissions for a specific data set or for workspace apps inside your PowerBI service area powerbi’s permission management settings offer a robust framework for maintaining data integrity while facilitating effective collaboration at organizations like Adventure Works whether you’re safeguarding critical data sets or sharing workspaces these tools help you to apply access control to your data congratulations on reaching the end of these lessons in deploying and maintaining assets you explored creating monitoring connecting to and maintaining workspaces data sets and dashboards in Microsoft PowerBI let’s recap what you’ve learned so far you began the first lesson by exploring the concept of a workspace you learned that a workspace is a specialized area in PowerBI that holds important assets like data sets reports and dashboards its advantages are that it helps to organize assets for easy management provides security through access control only permitted users can access workspaces enables collaboration teams can use workspaces to build reports and allows analysts to update or modify data quickly when creating a new workspace you must consider workspace roles workspace roles determine who can perform each task viewers can view content but can’t modify it contributors can add and modify content members can alter content and add new members admins have full control over the Workspace assets and its members during this lesson you learned how to share Workspace assets as an app creating an app requires a PowerBI Pro or premium per user license the technical process of creating apps in PowerBI was outlined beginning with selecting create app in the workspace leading to an application settings area where one can name the app add a description set a logo and choose a theme color content can be selected and sorted for inclusion in the app which is followed by selecting and managing the audience powerbi allows the creation of multiple audience groups for an app enabling tailored access and content visibility you also learned how to manage assets in a workspace you can import assets directly into a workspace by uploading them or publishing them from your PowerBI desktop when the changes are made you can always publish them again which will update the previously published reports and data sets in addition you learned about setting up subscriptions and alerts in PowerBI service which allows users to receive regular updates and notifications based on data changes these tools enhance user engagement by automating the distribution of insights and ensuring timely awareness of critical metrics the lesson continued by exploring the steps required to promote and certify contents in PowerBI promoting and certifying are crucial for establishing trust and standardizing data quality across the organization thereby enabling users to identify and rely on the most accurate and relevant business intelligence assets the lesson ended with a detailed guideline on various global options for files within PowerBI such as data load and report visualization knowing how to configure these settings is important because it allows for more tailored and efficient data processing enhances visual representation and ensures a more seamless and intuitive user experience the next lesson started with the concepts of a data gateway and how it can help PowerBI data analysts and organizations a data gateway serves as a bridge between PowerBI’s cloud services and on premises data sources such as SQL databases or Excel files whether you are a data analyst working on your own or working for an organization you can sync your data with data sets hosted in PowerBI service using these data gateways and always keep these data sets up to date by setting up schedule refresh there are three types of data gateway personal mode is for single user use and this is suitable for individual report creators the standard mode also known as on premises mode supports multiple users and data sources and it’s used for complex access scenarios lastly the virtual network data gateway allows multiple users to connect to various data sources within virtual networks without any installation managed by Microsoft this lesson also discussed details of rowle security or RLS in PowerBI service a feature that allows for more granular control over access to data rls enables creators to define permissions on data rows so that users will only view data relevant to them enhancing both security and user experience this is particularly useful in organizational scenarios where data access needs to be restricted based on user roles or departments ensuring that sensitive information remains confidential while still providing valuable insights to authorized personnel finally this lesson covered the management of permissions for data sets and workspace applications effective permission management enables selective sharing of data sets and workspace apps allowing the designated individuals to access the data sets and create reports from these data sets the workspace audience management tools allow for sharing with the entire organization or customizing access for users additionally impact analysis tools are available to determine the connectivity and potential effects on workspaces reports and dashboards when there are updates to a data set you’ve reached the end of our summary on deploying and maintaining assets keep practicing your practical skills with sample data sets reports and dashboards and remember you can always revisit any item in the course to revise a topic by playing a video viewing a document or engaging with an activity best of luck with your studies the Microsoft PL300 exam is a professional certification in Microsoft PowerBI for aspiring analysts the exam tests your knowledge and skills in the technical and business requirements of data modeling analysis and visualization in PowerBI in this video you’ll discover the recommended strategy to maximize your chances of passing the exam PL300 Microsoft PowerBI data analyst a successful exam with a good grade is achievable if you are well prepared and practice some basic strategies one of the best ways to prepare is to take a practice test before the exam this way you can monitor your progress and identify the areas requiring more study or attention you have taken knowledge checks graded quizzes and completed exercises throughout this course these are designed to help you monitor your progress while preparing for the real exam you’ll be able to complete the PL300 mock exam a little later focusing on topics and key skills measured in the proctored exam the topics include preparing the data modeling the data visualizing and analyzing the data and deploying and maintaining assets during this program you have covered the skills measured in the PL300 exam and gained significant hands-on experience using the realworld data set of Adventure Works now it’s time to practice what you’ve learned the PL300 mock exam is based on a similar style and format to the proctored exam you can revisit any lesson to revise a concept if you need to review anything this practice exam is intended to provide an overview of the style wording and difficulty of the questions that you are likely to experience on this exam these questions may differ from those you could encounter in the exam and the practice exam is not illustrative of the length of the official exam or its complexity for example you may encounter additional question types such as drag and drop build list order and case studies you’ll also encounter exhibit and active screen questions like drop-own menus option boxes and complete a statement these questions are examples to provide insight into what to expect on the exam and help you determine if additional preparation is required review some possible exam formats and question types from the Microsoft documentation to get a feel for an exam in the reading preparing for the exam you can access Microsoft’s exam sandbox environment which was created to demo the interface that hosts exams to protect exam security Microsoft does not specify exam formats or question types before the exam microsoft continually introduces innovative testing technologies and question types and reserves the right to incorporate either into exams at any time without advanced notice in the mock exam you’ll have 150 minutes to complete the final practice exam which consists of 50 questions on completion of the exam you’ll be presented with your overall score and the questions you answered correctly once you’ve completed the PL300 mock exam it’s time to focus on the real exam a good exam strategy for the PL300 exam can be summarized with a checklist of what to do on the test day when test day arrives you should follow these tips to prepare ensure that you are well rested and nourished eat a meal or a snack and try not to drink too much water so you don’t need the bathroom during the exam give yourself enough time to get set up the last thing you want is to feel hurried or be late for the exam remember to bring your current governmentissued ID which must match the name on your Microsoft certification profile use your phone to capture the required headshot and ID if you’re unsure and require more details check the official documentation from Microsoft and Pearson View you’ll find links to these resources in the reading preparing for the exam the PL300 is a closedbook exam meaning you cannot bring any study or exam materials to the examination a score of 700 or greater is required to pass when it comes to answering the exam questions you can use these strategies keep calm and read the entire question before checking the answer options if multiple answer options exist try eliminating those you know are incorrect by using this process of elimination you can cross off all the incorrect answers read every answer option before choosing a final answer don’t rush and pick the first answer if you’re having difficulty with a question move on and return after you’ve answered all the questions you know try not to spend too much time on only one question ensure that you have enough time to attempt all the questions before checking them at the end you may be unable to change some of your answers so ensure you answer questions correctly avoid second-guessing yourself and changing your answer this can often be counterproductive you can complete the PL300 mock exam later focusing on the topics and key concepts this exam does not employ negative marking if you’re unsure of a question try making the best educated guess possible the important thing to always remember is that a successful blend of preparation test strategy and exam technique will help you maximize your chances of obtaining certification best of luck on a brisk Monday morning you step into your office ready to tackle the terrain of data as a seasoned PowerBI specialist your manager stops by your desk her expression a mix of excitement and anticipation she places a challenge before you i need you to explore Microsoft Copilot in Bing a powerful artificial intelligence or AI tool it’s designed to revolutionize problem solving and enhance productivity i believe it’s quite transformative and I want your insights on it as you switch on your computer the weight of opportunity settles in your mind races with possibilities could co-pilot streamline the development process and uncover new insights that haven’t been considered yet instead of reacting to market changes now there’s an opportunity to proactively shape them it’s more than just analyzing data it’s stepping into the future of generative AI microsoft Copilot is a powerful AI tool that enhances how users interact with data and digital content across various platforms with its design deeply integrated into Microsoft’s ecosystem including Bing and Microsoft Edge C-Pilot serves as an everyday AI companion that simplifies tasks boosts productivity and enhances creative processes c-pilot is accessible directly through the Bing website or the Microsoft Edge browser it employs advanced AI to provide a dynamic interaction model where you can ask questions generate content and receive detailed answers directly related to the task they are performing this is useful in scenarios like getting suggestions on generating a color palette from a company logo understanding and troubleshooting data analysis expressions also known as DAX formula or even answering specific contextual questions about improving a report interface in the everchanging digital landscape proficiency with advanced tools like Copilot is crucial for adapting swiftly to new technologies and maintaining a competitive edge now that you know what Microsoft C-Pilot is let’s explore its core capabilities and features c-pilot transforms traditional search capabilities by providing comprehensive contextaware responses to complex queries whether you’re asking for the benefits of using direct query or wanting travel advice on attending a data conference Copilot generates textbased answers images additional links and more delivering a rich detailed response copilot excels in creating text for a variety of needs including drafting emails writing user manuals and generating creative content like marketing posts this feature allows users to input prompts and Copilot crafts the necessary text in seconds tailored to the desired tone and format integrated with Dell E3 technology the designer feature in Copilot enables users to generate images on demand this tool is accessible directly through the Bing interface and creates visual content ranging from social media posts to custom event invitations copilot extends its functionality to the edge browser offering insights within the sidebar additional information links and suggestions enrich the browsing experience helping to discover new content and access relevant data quickly copilot supports various multimodal interactions which means it can handle tasks combining different data input and output types such as text and images this enhances the flexibility and depth of user interactions with the tool having covered Microsoft Copilot’s vast capabilities and features in Bing let’s explore how its varied modes adapt to an individual’s needs these modes creative balanced and precise enhance the experience by shaping the AI’s responses to fluently match the context of queries creative mode is suitable for tasks requiring a high degree of creativity such as composing poetry and images or crafting engaging narratives it enhances responses with stylistic elements like word play providing more elaborate and detailed communication for instance creative mode can be used in the retail industry to develop unique marketing campaigns that captivate customers consider a clothing brand wanting to launch a new line using creative mode they can generate inventive product descriptions engaging storytelling around the brand’s journey and eye-catching promotional materials that differentiate their offerings from competitors and attract more customers balanced mode is the default configuration providing a compromise between creative mode’s detailed expressiveness and precise mode succinct nature it aims to deliver factually correct responses yet includes a slight creative twist to enhance engagement this mode is well suited for regular inquiries that require clear and accurate information but are enriched by a creative element to maintain interest and readability in the manufacturing sector balanced mode can be used to write user manuals that are not only informative and precise but also easy to understand and engaging this helps ensure that technical documentation while accurate is also accessible to users enhancing customer satisfaction and reducing errors in product use precise mode focuses on delivering brief and accurate responses when precision and conciseness are critical this mode ensures that responses are direct and to the point concentrating solely on factual content without additional creative additions it is ideal for straightforward questions where timely and accurate information is needed or when a concise summary is required to quickly grasp the essential facts for example precise mode is essential for developers and data professionals when troubleshooting complex formulas this mode provides straightforward accurate responses that help individuals quickly understand errors in their code or apply the best techniques to optimize their queries without sifting through irrelevant information by harnessing the power of Microsoft Copilot you embark upon infinite digital possibilities with each query you explore and insight you uncover you’re not only keeping up with new age technology you begin driving it as a data analyst your agenda consists of creating a series of PowerBI reports that accurately capture the company’s performance over the past quarter you have gathered the necessary data and spent hours planning the data flow however as you explore the data set you encounter familiar roadblocks some of the formulas in your reports are returning errors disrupting the flow of your analysis moreover ensuring the aesthetics of the reports align with your company’s theme is proving to be more time consuming than anticipated you often find yourself pondering the hours spent each week on similar tasks time that could otherwise be directed towards deeper analysis that could propel the company forward the potential of integrating C-pilot with PowerBI becomes apparent in moments like these as a data analyst your daily work is fraught with challenges that can perplex even the most experienced professionals in the field each step presents obstacles from data collection to report delivery one of the primary issues data analysts face regularly is formula errors these errors can range from simple syntax mistakes to more complex logical problems that can skew the analysis and lead to incorrect conclusions such issues not only delay the reporting process but also jeopardize the accuracy and reliability of the information presented to decision makers maintaining consistency in color usage that reflects the company’s theme across all reports requires meticulous attention to detail and in-depth knowledge of branding guidelines these design challenges often consume a substantial amount of time and can divert one’s focus from core analytical responsibilities copilot paired with PowerBI transforms the way you navigate these challenges you can ask C-Pilot questions about techniques to improve your reports interface or instruct it to troubleshoot data analysis expressions or DAX formulas for instance you might say “Explain this DAX formula and why it results in an error then Copilot immediately interprets your request and generates the relevant explanation and corrected DAX formula without you manually troubleshooting it moreover Copilot’s machine learning or ML aspect continuously learns from the data it processes and its interactions with you this enables Copilot to become more adapted understanding your specific needs over time for example imagine you are working on a series of financial reports and Copilot has resolved DAX errors for these formulas earlier in the chat session copilot then recognizes these patterns in your query history and personalizes future interactions to ensure the chat context remains relevant this saves you time by reducing the need to copy and paste formulas repeatedly and helps ensure accuracy in your analysis by minimizing the potential for errors now that you understand how Copilot leverages cutting edge artificial intelligence technologies let’s explore the advantages this powerful tool offers for data analysts these features not only enhance the efficiency of workflows but also elevate the quality and impact of reports c-pilot excels in troubleshooting and optimizing DAX formulas which are central to data manipulation and analysis in PowerBI if you’re struggling with a formula’s performance or accuracy C-Pilot provides suggestions for optimization it can also explain the logic behind DAX functions in simple terms making it easier for you to understand and effectively use them in your reports from an aesthetic standpoint Copilot can analyze images of your current reports or even suggest improvements to the layout for example if you upload an image of a report you’re currently working on Copilot can analyze the placement of elements and suggest a more streamlined or visually appealing arrangement that enhances readability and viewer engagement when you upload an image representing a company’s branding like a logo or marketing material Copilot can analyze the colors and generate a color palette that matches the branding this feature ensures that all reports maintain a consistent visual style that aligns with a company’s identity enhancing the professional quality of your presentations copilot can also serve as a creative assistant by generating images that inspire the design of your reports for example if you need to create a report on sustainability C-Pilot can generate images that evoke themes of sustainability you can use these images as a reference to design your own report visuals ensuring your reports are not only informative but also aesthetically aligned with the topic it is clear that C-Pilot is not just a tool but an assistant that brings out the best in your analysis efforts remember every report you create every DAX formula you solve and every insight you derive contributes to the decision that drives the company forward as you continue to leverage the power of PowerBI redefine the boundaries of what you can achieve with data and let C-pilot guide you to a new horizon of possibilities it’s early Monday morning and your manager has assigned you a critical task whereby you must develop a report for the upcoming quarterly review your manager expects the report to embody the company’s new logo and color scheme to add to the challenge the task now is not only to present data but to do so in a way that reflects the company’s updated brand identity feeling the weight of this responsibility you take a deep breath sip your coffee and get to work you are confident you can complete this task well with your trusty ally Microsoft Copilot when designing a report matching colors to a company’s logo and branding isn’t just about aesthetics but also about communication and consistency using artificial intelligence or AI assisted tools like Microsoft Copilot enables you to easily integrate a new color pallet aligning your report with the updated company branding this AIdriven approach enhances productivity by automating the once time-consuming task of manual caller matching so let’s unpack how you can achieve this first open Microsoft Edge and select the C-Pilot icon next to the search bar this access point is part of Microsoft’s integrated experience merging the functionalities of Bing and Copilot ensure that you are signed in with your Microsoft account you’ll be prompted to create an account if you don’t have an account once signed in select the more creative button to activate creative mode creative mode is recommended for highly creative tasks like developing unique concepts or exploring artistic elements such as images now focus towards the bottom left of the interface next to ask me anything and select add an image followed by upload from this device next in the file explorer navigate to the location where the logo image is saved select the image file and confirm the selection by selecting the open button to upload it the selected image then begins to upload to Copilot type the instructions in the text box depending on what you need Copilot to do with the image in this instance let’s create a color palette by inputting generate a color palette based on this logo upon selecting the submit button Copilot uses its AI technology to analyze the uploaded logo image it examines the logo’s colors and uses algorithms designed to identify and extract predominant and accent colors based on the analysis Copilot presents the color palette in hex codes which is the standard for color representation if the initial palette isn’t satisfactory or lacks some colors you can modify your prompt to specify your needs further for instance if the company branding includes the color blue which wasn’t present in the logo you can amend your prompt to include shades of blue in the palette with your generated color palette it’s time to integrate these colors into your PowerBI report open the report and select the view tab now select the themes drop-down to expand the theme gallery upon selecting customize current theme input the hex codes provided by Copilot via the drop- down buttons for each color setting such as first level and second level these hex codes represent the colors identified from the logo after inputting the new colors select apply to update the report with a new theme there you have it you can now confidently use Microsoft Copilot to enhance your report design you achieved maximized productivity and reduced the time you spent on the task remember partnering with an AI tool such as Microsoft Copilot makes managing complex tasks and deadlines easier so enjoy the journey as you embrace and explore its powerful capabilities as a senior data analyst you’ve spent weeks crafting a PowerBI dashboard for the company’s quarterly review however as you run through the last data validations a series of errors cascade through critical data analysis expressions or DAX formulas these aren’t simple fixes they involve complex nested if statements within calculate functions that you had previously tested in this critical moment you recall the Microsoft Copilot in Bing is the solution you need in this video you’ll discover the importance of mastering DAX for data manipulation and analysis in PowerBI and learn how Copilot can be a valuable tool for addressing formula issues mastering DAX is essential to turn complex data into compelling business insights however even the most skilled data analysts can encounter errors when navigating through its syntax and functionalities understanding these common issues can help you write more robust and efficient DAX code let’s explore these and how to resolve them using Microsoft Copilot in Bing when applied over large data sets the filter function can be computationally expensive and slows report performance for instance imagine using filter to identify all sales transactions above a certain value across the sales database the row iterative nature of filter would examine each transaction individually causing delays in loading the report here Copilot can help optimize the formulas to enhance performance and assist in correcting any logical errors by refining the filter criteria let’s examine how to achieve this begin by opening your PowerBI desktop report and navigate to the table containing the filter formula you intend to refine next select the formula bar where the filter statement is displayed now copy the contents from the field with your formula copied launch Microsoft Edge and select the Copilot icon in the sidebar to access the integrated C-pilot in Bing upon loading Copilot select the more precise button that activates precise mode locate the ask me anything text box and paste the slow filter formula providing Copilot with context now type the specific query for assistance on a new line in the same prompt window in this instance to optimize performance you can type “How can I optimize this filter function to improve performance when handling large data sets?” Select the submit button to send the query to Copilot once you press submit Copilot processes your input using its artificial intelligence commonly referred to as AI capabilities once you have a revised filter formula and are satisfied copy this directly from the copilot interface by selecting the copy button upon navigating to your PowerBI report select the table where you want to apply the updated formula then select the formula bar and paste the updated formula make sure to replace the old formula completely to avoid conflicts or errors select enter to commit the formula in PowerBI and observe how it executes one of the most powerful yet tricky aspects of calculate is its ability to modify the filter context of a calculation suppose you want to use calculate to sum sales for all countries but as a result it returns total sales for only the United States microsoft Copilot in Bing can help guide you through the correct structuring of calculate formulas suggest how to perform dynamic aggregations and even detect and suggest fixes to syntax errors in the ask me anything text box paste the calculate formula you need to troubleshoot on a new line in the same prompt window type how can I modify this calculate formula to sum sales for all countries once you select the submit button Copilot returns an explanation and a corrected calculate formula with a requested context after reviewing the initial results you can ask some additional questions to deepen your understanding or refine your formula further for instance can you suggest ways to avoid common syntax errors in this calculate formula this followup empowers you to grasp common mistakes and learn best practices in writing DAX formulas once you are satisfied with the response from Copilot select the copy button finally paste the results in Microsoft PowerBI to assess whether the suggestions improve the formula’s functionality deeply nested if statements can become difficult to manage and troubleshoot imagine using nested if statements to categorize sales into different classes based on the column amount the complexity of checking multiple conditions can easily lead to mistakes and logic copilot can simplify this by suggesting straightforward alternatives or helping restructure these nested conditions into manageable components now in the ask me anything text box paste the if formula that requires troubleshooting on a new line in the same prompt window enter can you suggest a simpler alternative to this nested if statement for better manageability upon selecting the submit button Copilot generates suggestions to simplify or improve the efficiency after reviewing the feedback provided by Copilot select the copy button finally navigate to PowerBI desktop paste the revised if statement into the formula bar and select enter to apply the formula as your journey through mastering DAX comes to a close reflect on the transformative power of blending AI with your analytical skills as you move forward equipped with the knowledge of DAX and the support of AI remember that each challenge overcome is not just a step toward progression but a leap toward mastering PowerBI congratulations on completing the Microsoft PL300 exam preparation and practice course your dedication has given you the skills and tools for success when writing the Microsoft PL300 exam you have now achieved all the PowerBI milestones in this program this course gave you opportunities to practice your exam technique and refresh your knowledge of all the key areas assessed in the Microsoft PL300 exam you tested your knowledge in a series of practice exams mapped to all the main topics covered in the Microsoft PL300 exam to help you prepare for certification success you also got tips and tricks testing strategies useful resources and information on how to sign up for the Microsoft PL300 proctored exam now that you have successfully completed this professional certificate you are ready to schedule the Microsoft PL300 exam through Pearson View through a mix of videos readings and exercises you’ve learned about the expectations for the learning content by starting with an introduction to the course following this you were provided with information about the Microsoft certification here you explored an introduction to preparing for the exam how to prepare for the procedurate examination how the exam is administered topics covered in the PL300 exam and testing strategy next you reviewed what you learned about getting data from data sources here you revisited how to identify and connect to a data source using a shared data set or local data set direct query import and dual mode parameter values how to set up a data flow how to connect to a data flow the Microsoft data versse and how to get data from data sources you then investigated how to profile and clean data this included consolidating your knowledge of evaluating data data statistics and column properties how to resolve inconsistencies and data quality issues and an indepth dive into profiling and cleaning data after that you explored the process of transforming and loading data where you covered how to create and transform columns identify when to use reference queries how to merge and append queries table relationships and an in-depth view of transforming and loading data next you explored modeling data where you revised key concepts related to modeling data in PowerBI here you reviewed designing data models where you learned about how to design a schema implement role playing dimensions use calculate to manipulate filters and configure cardality and cross filter direction next you explored how to create model calculations using DAX this is where you explored calculated columns and single aggregation measures as well as how to implement time intelligence measures you also reviewed the differences between additive semi-additive and non-additive measures later you reviewed how to implement a data model this is where you explored calculated tables and data hierarchies you also covered how to optimize model performance this included reviewing important topics like using the performance analyzer and how to improve performance via cardality and summarization you reviewed data visualization and analysis techniques in PowerBI to help you prepare for the PL300 exam in this section you revisited the process of report creation this included reviewing important topics like using appropriate visualizations configuring and formatting visualizations applying slicing and filtering and exporting and printing reports you re-examined how to enhance reports for better usability and storytelling this included reviewing report navigation and sorting interactions between visuals sync slicers group and layer visuals by using the selection pane and how to design reports for mobile devices following that you explored how to identify patterns and trends you revisited how to detect outliers and anomalies grouping and binning data AI visuals reference lines and error bars and scorecards and metrics you then moved on to deploying and maintaining assets this is where you revised creating and managing workspaces and assets you reviewed key concepts such as workspaces and workspace roles workspace apps how to publish import or update assets in a workspace subscriptions and data alerts how to promote or certify PowerBI content and global options for files next you reviewed how to manage data sets this section provided you with a summary of data gateways rowle security and granting access to data sets to round off your learning you took a mock exam that has been set up in a similar style to the industry recognized Microsoft PL300 exam by passing the exam you’ll become a Microsoft certified PowerBI data analyst it will also help you to start or expand a career in this role this globally recognized certification is industry endorsed evidence of your technical skills and knowledge the exam measures your ability to perform the following tasks prepare data for analysis model data visualize and analyze data and deploy and maintain assets to complete the exam you should be familiar with Power Query and the process of writing expressions using data analysis expressions or DAX you’ve done a great job so far and you should be proud of your progress the experience you’ve gained will showcase your willingness to learn your motivation and your capability to potential employers it’s been a pleasure to embark on this journey of discovery with you best of luck in the future the Microsoft PowerBI Analyst program is an excellent resource to start your career whether you’re a beginner or a seasoned professional looking to improve your skills data is the driving force behind this everchanging modern world shaping and developing industries and society it has transformed the way institutions operate from banks and hospitals to schools and supermarkets and for businesses data is everything it informs decisions and helps create value for customers content streaming services analyze data to decide what content to promote social media services analyze data to determine what products their customers are interested in and your local supermarket gathers and analyzes data to ensure the products you want are available the result of having all this data is that professional analysts are required to process and sort it to gain the insights that drive both the business and social worlds are you intrigued by this career field and wondering how to get started let’s meet two other students who have just begun their careers in entry- levelvel positions discover how and why they’ve chosen to embark upon career paths in this field with Microsoft and Corsera lucas a recent information technology graduate is currently searching for his first IT job he is eager to secure a position in the IT sector that offers good earning potential and a quick career progression he wants to work full-time in data analysis as he feels this career would offer both benefits during his degree he found working with and analyzing cloud-based data to be the most enjoyable element hence his focus on this career path lucas currently works shifts in a warehouse environment so he will need the flexibility of self-paced learning his earnings are low so he wants to achieve the qualification using the same basic laptop he relied upon as a student despite being a beginner Lucas has already mapped out his career and certification path and has enrolled in the Microsoft PowerBI analyst program he plans to apply for an entry- levelvel position as a data analyst once he has successfully completed the program and passed the PL300 exam as a data analyst he will inspect data identify key business insights for new business opportunities and help solve business problems amelia has been working as an administrative assistant in sales and marketing since leaving high school now that a few years have passed she is ready to embark upon a new career path in her current role Amelia has seen PowerBI reports and dashboards created by colleagues and shared with the team she was impressed at how the information was used to shape and focus the sales campaigns this sparked an interest in a career in data analysis amelia’s job requires her to work long hours so the ability to structure her own learning path is vital she also has a long commute so would like to access e-learning through her smartphone or tablet pursuing the PowerBI analyst qualification will showcase her dedication and help her apply for more senior roles in the department in the short term amelia doesn’t have a scientific background but she finds IT concepts logical and easy to understand so she’s embarking on the Microsoft PowerBI analyst program as it doesn’t assume a pre-existing high level of technical knowledge in the long term she hopes to secure an entry-level role as a PowerBI analyst as a PowerBI analyst she will be responsible for building data models creating data assets like reports and dashboards and ensuring data requirements are met you may be in a similar position to Lucas and Amelia and possess an interest in this exciting field of data analysis like them you can begin your career in this field by enrolling in the Microsoft PowerBI analyst program this will be the start of your new adventure good luck with your learning journey
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The provided text introduces an extensive Excel course designed for data analytics beginners. It outlines a curriculum that starts with fundamental spreadsheet skills and progresses to advanced analytical features like pivot tables and Power Query, culminating in two portfolio-worthy projects analyzing real-world data. The course emphasizes hands-on learning through exercises and practice problems, highlighting Excel’s widespread use in the data analysis and business analysis fields. Furthermore, the instructor uses practical examples, such as analyzing job market data and salary information, to illustrate Excel’s capabilities.
Data Analysis with Excel Study Guide
Quiz
Describe two methods for accessing the course workbooks provided with this material.
What are course perks mentioned in the source, and is purchasing them required to complete the data analysis learning?
What role will the learner assume for the data analysis exercises in this course, and what is the primary data source for these exercises?
What is the main dataset used throughout the majority of the course, and what key information does it contain?
According to the source, what is a recommended first step for learners when they encounter difficulties or errors during the course?
What are the minimum Excel version requirements for completing all chapters of this data analysis course on a Windows machine? What limitation exists for Mac users?
Briefly explain the difference between a worksheet and a workbook in Microsoft Excel, as described in the “Spreadsheets Intro” chapter.
What is the ribbon in Microsoft Excel, and where is it located within the application interface?
Explain the core functionality of the COUNT function in Excel and how it differs from the COUNTIF function.
Describe the purpose of logical functions like IF, AND, and OR in Excel data analysis.
Quiz Answer Key
Learners can access the workbooks by either navigating through the numbered chapter folders and downloading individual lesson workbooks (using the three dots and “Download” option) or by downloading the entire repository as a ZIP file by clicking on “Code” and then “Download ZIP.”
The course perks are practice problems and course notes that provide additional opportunities to reinforce learning. Purchasing these perks is not a requirement to complete the data analysis learning, but it helps support the course creator.
The learner will take on the role of a job seeker exploring top-paying data science roles and related skills. The primary data source for this exploration is data collected from the app datnerd.tech, specifically a dataset of job postings from 2023.
The main dataset is located in the “data sets” folder and is named “data job salary.” It includes over 30,000 job postings from 2023 and contains information such as company name, salary, and location.
The source recommends using a chatbot like ChatGPT, Gemini, or Claude to get immediate assistance with errors by providing the error message. It advises against solely relying on the comment section for help.
For Windows, any version of Excel from 2010 or later, including Microsoft 365 and Microsoft Office Home and Student, is sufficient. Mac users with Excel installed directly on their operating system will not be able to complete the advanced chapters on Power Query and Power Pivot, as well as the project.
A worksheet (or sheet) is a single tab within Excel where data is entered and manipulated in cells. A workbook is the entire Excel file, which can contain one or multiple worksheets.
The ribbon is located at the top of the Excel interface and contains various tabs (like File, Home, Insert, etc.) that provide access to a wide range of Excel functionalities and features.
The COUNT function counts the number of cells within a selected range that contain numerical values. The COUNTIF function counts cells within a range that meet a specific condition or criteria defined by the user.
Logical functions are used to perform conditional analysis in Excel. IF allows for different outcomes based on whether a condition is true or false. AND requires multiple conditions to be true. OR requires at least one of multiple conditions to be true.
Essay Format Questions
Discuss the importance of exploratory data analysis (EDA) in the context of this course’s job seeker scenario. How can the math and statistical functions covered in the course be applied to gain meaningful insights from the job posting dataset?
Explain the concept of “what-if” analysis in Excel and describe the three tools covered in the source material (Scenario Manager, Goal Seek, and Solver). Illustrate with potential examples how a job seeker could utilize each of these tools in their career planning.
Describe the functionality and benefits of using data tables in Excel for “what-if” analysis. How can one-input and two-input data tables help a job seeker evaluate different potential outcomes related to salary and career growth?
Discuss the advantages of using Power Query for importing and transforming data, particularly when dealing with large datasets or multiple data sources. How could a job seeker leverage Power Query to consolidate and prepare job market information for analysis?
Explain the purpose and basic concepts of DAX (Data Analysis Expressions) within the context of Excel’s data model. How can DAX measures be used to perform more complex calculations and comparisons on the job posting data, such as analyzing median salaries across different countries or skill sets?
Glossary of Key Terms
Workbook: An electronic spreadsheet file created in Microsoft Excel, containing one or more worksheets.
Worksheet (Sheet): A single page within an Excel workbook where data is organized in rows and columns.
Cell: The intersection of a row and a column in a worksheet, where data can be entered.
Ribbon: The main command bar at the top of the Excel window, organized into tabs containing various functions and features.
Function: A predefined formula in Excel that performs calculations on specific values (arguments) in a particular order or structure.
Formula: An expression that calculates the value of a cell. It can contain numbers, operators, cell references, and functions.
Data Set: A collection of related data points, often organized in a table format with rows representing individual records and columns representing attributes.
Repository (Repo): A storage location for code and files, often used in version control systems like Git and platforms like GitHub.
Commit (Git): A snapshot of the changes made to a repository at a specific point in time, with a descriptive message.
Push (Git): The process of uploading local repository content to a remote repository (e.g., on GitHub).
Pull (Git): The process of downloading changes from a remote repository to a local repository.
Markdown: A lightweight markup language with plain text formatting syntax, commonly used for creating formatted text in readmes and other documents.
Dashboard: A visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance.
KPI (Key Performance Indicator): A measurable value that demonstrates how effectively a company is achieving key business objectives.
Data Validation: A feature in Excel used to control the type of data or the values that users enter into a cell.
Conditional Formatting: A feature in Excel that allows you to automatically apply formatting to cells based on specific rules or criteria.
Pivot Table: A powerful tool in Excel used to summarize and analyze large amounts of data.
Power Query: A data transformation and data preparation engine available in Excel (also known as Get & Transform Data).
M Language: The formula language used by Power Query to perform data transformations.
Data Model (Excel): An integrated collection of tables, relationships between them, and calculations, used for data analysis with tools like Power Pivot.
DAX (Data Analysis Expressions): A formula language used in Power BI, Power Pivot in Excel, and SQL Server Analysis Services Tabular models for performing calculations and data analysis.
Measure (DAX): A calculation formula defined in a data model, used to quantify and summarize data based on the current context of a pivot table or other visualization.
Calculated Column (DAX): A new column added to a table in a data model, with values determined by a DAX expression evaluated row by row.
Filter (DAX): A condition applied to a DAX calculation to restrict the data being evaluated.
Briefing Document: Review of Excel Data Analysis Course Sources
This briefing document summarizes the main themes, important ideas, and key facts presented in the provided excerpts from the “01.pdf” source, which appears to be related to an online course on data analysis using Microsoft Excel.
Main Themes
Structured Learning Resources: The course provides structured learning through chapters and lessons, accompanied by downloadable workbooks, data sheets (for advanced topics), practice problems (for course perk purchasers), and course notes.
Practical Data Analysis Focus: The course centers around a practical data analysis project where learners take on the role of a job seeker exploring top-paying data roles and required skills. This utilizes real-world (or realistic) data from the instructor’s app, datnerd.tech, containing information on job titles, locations, skills, and salaries.
Accessibility and Flexibility: While offering supplementary materials like practice problems and course notes for purchase, the core learning (video lessons and workbooks) appears to be accessible without these. The course also encourages learners to adapt the exercises to their own interests (job titles, countries).
Troubleshooting and Support Strategies: The course proactively addresses the likelihood of learners encountering difficulties and recommends using AI chatbots like ChatGPT, Gemini, or Claude for immediate assistance rather than relying solely on the course’s comment section.
Version Compatibility and Setup: The course emphasizes the importance of having a compatible version of Microsoft Excel installed, detailing which versions (Windows and Mac, including Microsoft 365) are suitable for different parts of the course, particularly noting limitations for Mac users regarding Power Query and Power Pivot in the advanced chapters.
Introduction to Excel Fundamentals: The initial chapters cover basic spreadsheet concepts like worksheets, workbooks, and the Excel ribbon, designed for beginners with little to no prior experience with the software.
Core Excel Functionality for Data Analysis: The course delves into essential Excel functionalities for data analysis, including formulas, various types of functions (logical, math, statistical, text, lookup), data manipulation, data visualization (charts), “what-if” analysis tools (Scenario Manager, Goal Seek, Solver, Data Tables), and data transformation using Power Query.
Leveraging Data Analysis ToolPak: The course introduces and utilizes the Data Analysis ToolPak for more advanced statistical analysis and charting (e.g., histograms, descriptive statistics, ranking and percentile).
Data Modeling and DAX: The course covers the creation of a data model in Excel and introduces Data Analysis Expressions (DAX) for creating measures and calculated columns to perform more complex analysis, particularly focusing on aggregation, statistics, and filtering within pivot tables.
Power Query for Data Import and Transformation: The course demonstrates how to use Power Query to import data from various sources (including large datasets) and perform transformations, including merging queries, appending data, and unpivoting columns.
Dashboard Creation and Sharing: The course culminates in building an interactive dashboard in Excel, emphasizing formatting, conditional formatting, and the use of slicers for filtering. It also covers methods for sharing projects, specifically recommending GitHub for version control and showcasing work.
Introduction to VBA and Macros: The course briefly touches upon the use of Visual Basic for Applications (VBA) to automate tasks within Excel through macros.
Most Important Ideas and Facts (with Quotes)
Course Structure and Resources: The course is organized into eight chapters with lessons, workbooks, and supplementary materials.
“by chapter along with the lesson in addition to that resources folder you can see numbered here we have each of those eight chapters and if we navigate into something like spreadsheets intro we have a workbook for each one of the lessons”
“inside the workbooks I provide a blank template for you to go through and actually fill in”
“as we move into the advanced chapters they’re going to have something like the data sheet or you’re going to use the data from the data sheets in order to do different operations”
“after going through a lesson I then have practice problems for those that purchase the course perks to go through”
“the other perk that you’ll receive with those practice problems are the course notes these break down the concepts in a similar format of all the different chapters and lesson”
Practical Project: Job Seeker Analysis: The core project involves analyzing job data to identify top-paying roles and skills.
“what are we actually going to be covering in this data analysis that we’re going to be doing inside of excel well you’re going to be taking the role of a job Seeker in exploring what are some of the top paying roles along with skills of data nerds”
“For this we’re going to use the data from my app dat nerd. Tech that is collected to this point up to 3 million jobs it tells based on a job title and also on a location what are the top skills and it not only tells us the salary of these skills for a particular job but also the salaries of the jobs themselves.”
Main Dataset: The primary dataset for the course contains a significant number of job postings.
“Now the main data set we’re going to be using for the majority of this course is this one here inside the data sets folder of data job salary all this data set includes over 30,000 job postings from 2023 and it includes a wealth of information such as company name salary and location”
Flexibility in Analysis: Learners are encouraged to personalize the analysis.
“as I’m going to be doing it from the perspective of a data analyst which is their top job in the data set but as shown here there’s a lot of different other job titles that you can check out and use as well so feel free to deviate additionally I’ll be primarily focusing on the United States but there’s a lot of different countries in there as well so feel free to plug in your home country and analyze this instead”
Recommended Help Strategy: Utilizing AI chatbots for troubleshooting is advised.
“I don’t recommend just jumping into the comment section and waiting for somebody to help you out instead I recommend using a chat bot like chat GPT in it you can provide whatever era you’re seeing and it will help you out and guide you along the way on what to do and there’s other great options as well such as gemini or even Claude so feel free to use whichever one you’re most comfortable with”
Downloading Course Files: Learners need to download the GitHub repository.
“all right if you haven’t done so already it’s your turn now to go in and download that GitHub repo with all the different workbooks needed for this course”
Excel Version Compatibility: Specific Excel versions are required for certain advanced features.
“if you have the Mac version or Mac operating system and Excel is installed directly on that operating system you’re not going to be able to complete the Advanced chapter specifically on power query and on power pivot along with the project and it’s similar as well for Microsoft 365 online as you won’t also be able to complete the Advanced Data analysis section.”
“if you’re running Excel on a Windows machine either through Microsoft 365 Microsoft Office at home and student or even an older version of excel up to about 2010 you’re going to be fine with completing all the different course content”
Basic Spreadsheet Vocabulary: The course introduces fundamental Excel terms.
“for this lesson we’re going to be focusing on worksheets and that is basically as you can see this tab here called sheet one that is how to manipulate these different cells within this worksheet or also known as a sheet in the next lesson we’re going to be going into workbooks so workbooks basically captures either one sheet like this one sheet one if I add another one sheet two so it encapsulates multiple different sheets within this program of Excel and then finally in the third lesson of this chapter we’re going to be moving into the ribbon which is up here at the top and has a bunch of different functionality to extend into those spreadsheets along with using this file tab up here that has a whole bunch of features within it as well”
Purpose of the Spreadsheets Intro Chapter: This section is for beginners.
“now this chapter was designed for those that may not have experience with using Microsoft Excel before so if you don’t fall in that category as in you’ve used excel in your job and you’re pretty familiar with all those different features I just shown you can feel free to skip this chapter and then move into the next one on functions along with all those different practice problems”
File Tab Functionality: The File tab contains various options including saving, printing, and account management.
“Beyond save as we also have things like print which I really don’t find myself doing that too often should be sending an electronic version export if I wanted a pdf version of something and then finally close as well same thing as this x up here just a x out of it and there’s two more areas down here that I want to call out and that’s a count and that allows you to actually see behind the scenes of what going on with your Microsoft account and this is generic to all the different Microsoft products that you have”
Ribbon Tabs: The ribbon contains various tabs with different functionalities.
“we’re going to be getting into the ribbon inside of Excel and better understanding what are all the different tabs and what are the capabilities by doing some simple exercises”
Introduction to Formulas: The course covers the basics of creating formulas in Excel.
“so let’s get into understanding the basics about formulas by calculating these different counts and especially counts around whether any of these jobs meet our goals for this I know I want to use a count function”
Using Functions (e.g., COUNTIF): The course teaches how to use built-in Excel functions.
“specifically I have these different counts right here and I’m going to scroll over this count if right here and it’s going to provide me a description it says Hey counts the number of cells Within range that meet the given condition and that’s what we want to do we want to meet a condition of a certain amount of experience”
Common Formula Errors: The course acknowledges and addresses potential errors in formulas.
“frequently you’re going to run into errors with your formulas let’s say I wanted to divide one by zero not a good thing that we need to do anyway I’m going to get this error right now you can notice it because it has this green check on the upper left hand corner but also it starts with this hashtag and it’s saying hey you have a divide by zero error”
Logical Functions (e.g., IF): The course covers the use of logical functions for conditional analysis.
“now that we have the basics down on formulas and also functions we’re going to be moving into one of the most important typ of functions to know logical ones the most popular of these are an if condition basically looking at something and then providing a response based on it”
“we can use an if statement in order to clarify this so I can specifically call out with an if statement saying if it has The Logical test that we want to actually evaluate so I’m going to put in P3 in this case as it’s going to return true or false and then from there the next value in there is value if true which what do we want to return if it is true well that our goal is met and then if it’s not met we want to have well not met”
Nested IF Statements: The course explains the concept of nested IF functions.
“we’re going to do one approach first and it’s called a nested if statement and it’s not really the approach I’m going to recommend but it’s something that you should be aware of”
AND and OR Functions: The course introduces the use of AND and OR functions for combining logical conditions.
“instead I like using the functions of and and or and…”
IFS Function: The IFS function is presented as an alternative for multiple conditions.
“so S functions are one of the more complex functions to work with so you do need some practice with this like for those that purchased course practice problems you have some now to go into and actually try this out manipulate and better understand how to work with this”
Math and Statistical Functions (e.g., COUNT, SUM, AVERAGE, MIN, MAX, COUNTIFS): These are essential for exploratory data analysis (EDA).
“in this lesson we’re going to be using math functions and also some statistical functions in order to perform Eda or exploratory data analysis on our job posting data set and for this we’re going to be focusing on the five major functions of count sum average and also Min and Max and we’re not only going to focus on the core versions such as just count but also the if an ifs version so they have multiple different versions that we’re going to get to”
Standard Deviation and Quartiles: These statistical measures are covered for understanding data distribution.
“we’ll find that one standard deviation from something like the average has in this case right here 34,000 so if we went above and below the average by one standard deviation around 68% which is a heck a lot of data is within this one standard deviation”
“but what if we wanted to be more precise about finding say something like where does 50% of the data actually fall well we can use quartiles”
Text Functions (e.g., LEFT, RIGHT, MID, FIND, TEXTJOIN, TEXTSPLIT, SUBSTITUTE): These are important for manipulating text data.
“now we’re going to be diving into text functions and these are essential for cleaning and transforming text data”
Date and Time Functions: The course covers functions for working with date and time values.
“now we’re going to be shifting into date and time functions which are essential whenever you’re working with time series data”
Data Visualization (Charts): Creating charts is a key aspect of the course.
“now we’re going to be shifting into the world of data visualization specifically focusing on the different types of charts available in Excel and how to customize them to best represent your data”
“What-If” Analysis (Scenario Manager, Goal Seek, Solver, Data Tables): These tools are introduced for exploring different scenarios and solving problems.
“Now we’re going to be shifting into the world of what if analysis and Excel provides a suite of tools that allow you to explore different scenarios and understand the potential impact of changes to your data and we’re going to be covering four major tools in this section of scenario manager goal seek solver and data tables”
Data Analysis ToolPak (Histogram, Descriptive Statistics, Rank/Percentile): This add-in provides additional analytical capabilities.
“now we’re going to be diving into the data analysis tool pack and this is a free add-in for Excel that provides a range of statistical and analytical tools that can help you perform more advanced analysis without needing to write complex formulas”
Getting External Data (Power Query): Power Query is used for importing and transforming data from various sources.
“Now we’re going to be shifting into the world of getting external data into Excel using a powerful tool called power query”
Data Modeling and Relationships: Creating a data model and defining relationships between tables is covered.
“Now we’re going to be diving into the world of data modeling in Excel and this involves creating relationships between different tables in your data to allow for more powerful and flexible analysis”
DAX (Data Analysis Expressions): DAX is introduced for creating custom calculations in the data model.
“welcome to this lesson on Dax or data analytical Expressions we’ve used it a few times before in the previous lesson but now we’re going to go much more in depth and actually understanding the basics of it”
Sharing Projects (GitHub): GitHub is recommended for sharing and version control.
“in this video and the next video which are the last two videos of this entire course they’re going to be focused on how to actually go through and share your projects in my recommended way specifically we’re going to be sharing this on GitHub”
This briefing document provides a comprehensive overview of the content and key aspects of the Excel data analysis course based on the provided source excerpts. It highlights the structured approach, practical focus, and the wide range of Excel features covered to equip learners with data analysis skills.
Excel for Data Analysis: Course FAQs
Excel for Data Analysis FAQ
1. What kind of files are provided in the course, and how do I access them? The course provides workbooks for each lesson within each of the eight chapters. These workbooks often include a blank template for you to fill in and, in the advanced chapters, data sheets to be used for exercises. To access these files, you can download the entire repository as a zip file by clicking on the “Code” button and then “Download ZIP”. Once downloaded, you’ll need to unzip the file, and you’ll find folders corresponding to each chapter, containing the relevant workbooks. Alternatively, individual files can be downloaded by navigating to them, clicking the three dots next to the file, and selecting “Download”.
2. Are there any practice materials or additional resources offered with the course? Yes, for those who purchase the course perks, there are practice problems available for each lesson, broken down by chapter and lesson. These problems allow you to apply what you’ve learned. Additionally, course notes are provided, which offer a written breakdown of the concepts covered in the videos, following the same chapter and lesson structure. It’s important to note that purchasing these perks is not required to complete the course.
3. What real-world scenario and data will be used for the data analysis exercises? Throughout the course, you will take on the role of a job seeker exploring top-paying data-related roles and the skills associated with them. The primary data source for this analysis is data collected from datnerd.tech, which includes information on over 3 million job postings. The main dataset used for the majority of the course, located in the “data sets” folder and named “data job salary,” contains over 30,000 job postings from 2023, including details like company name, salary, and location. While examples will primarily focus on the perspective of a data analyst in the United States, you are encouraged to explore other job titles and countries present in the data.
4. What are the system requirements for using Excel with this course, particularly for the advanced sections? For the majority of the course content, if you are running Excel on a Windows machine with Microsoft 365, Microsoft Office (Home and Student), or even an older version back to 2010, you should be able to follow along without issues. However, if you are using the Mac version of Excel installed directly on macOS or Microsoft 365 online, you will not be able to complete the advanced chapters that specifically cover Power Query and Power Pivot, as well as the final project. These features have limitations or are not available on these platforms.
5. What fundamental concepts of spreadsheets and Excel will be covered in the “Spreadsheets Intro” chapter? The “Spreadsheets Intro” chapter is designed for beginners who may not have prior experience with Microsoft Excel. It covers essential vocabulary and concepts, including: * Worksheets: Understanding and manipulating individual sheets within an Excel file. * Workbooks: Recognizing that workbooks are containers that can hold one or more worksheets. * The Ribbon and File Tab: Navigating and understanding the functionality found in the ribbon at the top of the Excel interface and the features available within the File tab.
6. How can I get help or troubleshoot issues I encounter while working through the course material? Instead of solely relying on the comment section for help, it is highly recommended to use a chatbot like ChatGPT, Gemini, or Claude. You can provide the specific error message or describe the problem you are facing to these AI tools, and they can offer guidance and solutions. This method is suggested as a more efficient way to get immediate assistance and understand how to resolve issues as you progress through the course.
7. What are some of the key function categories that will be covered in the data analysis section of the course? The course will cover a range of Excel functions essential for data analysis. These include: * Basic Formulas and Functions: Understanding the fundamentals of creating formulas and using built-in functions. * Logical Functions (e.g., IF, AND, OR): Evaluating conditions and returning different values based on whether those conditions are true or false. * Math and Statistical Functions (e.g., COUNT, SUM, AVERAGE, MIN, MAX, STDEV, QUARTILE): Performing calculations and analyzing data distributions. * Text Functions (e.g., LEFT, RIGHT, MID, FIND, TEXTJOIN, TEXTSPLIT): Manipulating and extracting information from text strings. * Lookup and Reference Functions (e.g., VLOOKUP, XLOOKUP): Searching for and retrieving data from different parts of a spreadsheet. * Date and Time Functions (e.g., DATE, TODAY, MONTH, YEAR): Working with and analyzing date and time data.
8. What tools and techniques will be taught for visualizing data in Excel? The course will cover various methods for creating effective data visualizations in Excel. These include: * Basic Chart Types: Creating column charts, line charts, pie charts, and more to represent data visually. * Customizing Charts: Modifying chart elements such as titles, axis labels, legends, and data labels. * Trendlines: Adding trendlines to charts to identify patterns and directions in the data. * Histograms: Using histograms to understand the distribution of data. * Map Charts: Visualizing geographical data on interactive maps. * Pivot Charts: Creating dynamic charts that are linked to pivot tables for interactive analysis. * Conditional Formatting: Applying visual cues like data bars, color scales, and icon sets to highlight patterns and trends in data.
Mastering Excel for Data Analytics
Based on the provided source, “01.pdf”, Excel is presented as a highly popular spreadsheet tool for data analytics, estimated to have over 1 billion users worldwide. For “data nerds,” it is considered one of the most popular skills for data analysts, second only to SQL, and the same holds true for business analysts.
The course outlined in the source aims to take individuals with no prior analytics or spreadsheet experience and guide them to master Excel for data analytics. The curriculum is structured into basic and advanced chapters, broken down into 10 to 20-minute lessons with exercises and practice problems to facilitate learning by doing.
The basic chapters focus on building a foundational understanding of Excel, including:
Getting familiar with the different versions of Excel and installing it.
Learning how to manipulate spreadsheets.
Practicing data analysis using formulas and functions.
Visualizing data using common charts.
Performing statistical analysis.
Building an interactive dashboard to predict salary based on job and location as a portfolio project.
The advanced chapters delve into more sophisticated analytical features:
Pivot tables are highlighted as a “secret weapon” for quickly analyzing data. The course covers how to make, manipulate, and read pivot tables, including advanced features like grouping and aggregation.
Power Query is described as a powerful tool (like “washing down a couple caffeine pills with a shot of espresso”) for connecting to various data sets and performing ETL (Extract, Transform, Load) operations to ingest and clean data efficiently. The course covers connecting to different data sources, cleaning data using the Power Query Editor, and automating ETL processes.
Power Pivot is likened to “putting your spreadsheets on steroids,” enabling data modeling on datasets larger than Excel’s typical row limit (over a million rows). Combined with DAX (Data Analysis Expressions), it allows for supercharged and advanced calculations. The course covers enabling Power Pivot, data modeling, creating relationships between tables, and utilizing DAX for measures and calculations.
The course also touches upon other relevant Excel features for data analytics:
Charts: The course emphasizes Excel’s capabilities for creating and customizing various types of charts, including line charts, pie charts, bar/column charts, scatter plots, map charts, histograms, and box and whisker charts. Pivot charts, which are linked to pivot tables, are also covered.
Tables: The course covers using tables, slicers, and custom formulas to analyze data.
Conditional formatting is mentioned as a way to highlight cells based on specific rules.
Data validation is taught for standardizing inputs and preventing errors in dashboards.
The course briefly mentions add-ins like Solver and Analysis ToolPak for forecasting and statistical analysis.
While VBA (Visual Basic for Applications) is acknowledged, the course opts to focus on Python instead for task automation. Python in Excel is mentioned as a newer feature, though its usefulness is contingent on knowing Python.
Copilot (AI chat bots) within Excel is noted, but the course advises against relying on it as the primary method for learning.
The instructor emphasizes open-sourcing education, making the course and all necessary content freely available on GitHub. The GitHub repository contains Excel workbooks and datasets needed for the course and projects. For those seeking additional support, “supporter resources” are offered for purchase, providing guided practice problems, a community forum, step-by-step instructions, and a certificate of completion.
The source also provides a brief history of spreadsheets, starting from ancient Babylon and tracing the evolution to paper spreadsheets and finally to modern spreadsheet software like VisiCalc, Lotus 1-2-3, and ultimately Microsoft Excel, which has dominated the market since its launch in 1985. The continuous addition of features like pivot tables, VBA, Power Query, and Power Pivot has solidified Excel’s position.
The course culminates in two portfolio projects: predicting one salary based on job and location (after the basics) and analyzing the data science job market (after the advanced chapters), both designed to showcase practical data analysis skills in Excel. The source also touches on sharing these projects via platforms like OneDrive and LinkedIn. While GitHub is mentioned for project storage and collaboration, detailed instructions are reserved for after the second project.
In summary, the source “01.pdf” portrays Excel as a powerful and widely used tool for data analytics, suitable for beginners and capable of handling complex analytical tasks through its array of features like functions, charts, tables, pivot tables, Power Query, Power Pivot, and DAX. The described course provides a comprehensive pathway to mastering these capabilities through a structured, hands-on learning approach with practical projects.
Excel for Data Analytics: A Beginner’s Course
The source “01.pdf” introduces a full course tutorial on Excel for data analytics designed for beginners with no prior analytics or spreadsheet experience. The instructor aims to provide the knowledge and skills necessary to master Excel for data analysis through a hands-on learning approach.
The course is structured into two main parts: basic chapters and advanced chapters, with lessons broken down into 10 to 20-minute segments. Each lesson includes exercises to facilitate learning by doing and practice problems to reinforce newly acquired skills.
The basic chapters focus on building a strong foundation in Excel and data analysis, covering topics such as:
Understanding different versions of Excel and the installation process.
Learning how to manipulate spreadsheets.
Practicing data analysis using formulas and functions, including IF, math, statistical, lookup, text, and date/time functions.
Visualizing data using common charts like line charts, pie charts, bar/column charts, scatter plots, map charts, histograms, and box and whisker charts.
Performing statistical analysis.
Building an interactive dashboard to predict salary based on job and location as the first portfolio project.
The advanced chapters delve into more sophisticated analytical features within Excel:
Pivot tables, described as a “secret weapon” for quick data analysis, covering their creation, manipulation, advanced features like grouping and aggregation, and pivot charts.
Power Query (originally “Get and Transform”), likened to a powerful tool for ETL (Extract, Transform, Load) processes. This section covers connecting to various data sets, cleaning data using the Power Query Editor, and automating ETL workflows.
Power Pivot, referred to as “putting your spreadsheets on steroids,” enabling data modeling on datasets exceeding Excel’s row limit. This is combined with DAX (Data Analysis Expressions) for advanced calculations.
The course also touches on other relevant Excel functionalities for data analytics:
Tables and slicers for data analysis.
Conditional formatting for highlighting data based on rules.
Data validation for standardizing inputs in dashboards.
Add-ins like Solver and Analysis ToolPak for forecasting and statistical analysis.
The instructor explains the decision to focus on Python for task automation instead of VBA (Visual Basic for Applications), considering VBA outdated. While Python in Excel and Copilot (AI chatbots) are mentioned, they are not the primary focus for learning Excel analytics in this course. The instructor advises against relying heavily on AI chatbots for learning.
A key aspect of the course is its commitment to open-sourcing education. All course content, including Excel workbooks and datasets needed for lessons and projects, is freely available on a GitHub repository. The instructor provides instructions on how to download these materials. For users seeking additional support, “supporter resources” are available for purchase, offering guided practice problems, a community forum, step-by-step instructions, and a certificate of completion.
The course includes two portfolio projects designed to showcase practical data analysis skills in Excel. The first project, built after the basic chapters, involves predicting one salary based on job and location. The second, more advanced project focuses on analyzing the data science job market. The course also briefly covers sharing these projects via platforms like OneDrive and LinkedIn, with more detailed guidance on using GitHub for project sharing provided towards the end of the course.
In essence, this “Excel for Data Analytics” course aims to be a comprehensive guide for individuals new to data analytics, leveraging the widespread accessibility and powerful features of Microsoft Excel to build essential data analysis skills through a structured, hands-on, and project-based learning experience.
Excel Spreadsheet Basics: An Introduction
Based on the source “01.pdf”, understanding spreadsheet basics is the starting point for mastering Excel for data analytics. The course begins with the basic chapters to build a foundational knowledge of how to use a spreadsheet.
Here are some of the key spreadsheet basics discussed in the source:
Worksheets: A worksheet is a single tab within an Excel file, also referred to as a sheet. It’s where you manipulate individual cells.
Workbooks: A workbook encompasses one or more worksheets. It’s the entire Excel file that can contain multiple sheets.
Cells: Spreadsheets are organized into cells, which are intersections of rows and columns.
Rows: Rows are labeled with numbers (1, 2, 3, and so on) and extend down to over a million.
Columns: Columns are labeled with letters (A, B, C, …, Z, AA, AB, …, XFD).
Cell Referencing: Each cell is referenced by its column letter followed by its row number (e.g., B2, C7). This cell name is also displayed next to the formula bar.
Data Entry: You can enter data directly into a selected cell or via the formula bar. Pressing Enter typically moves to the next cell down.
Data Types: Excel recognizes different data types, such as numerical values, text, and Boolean values (TRUE/FALSE), which it may format accordingly (e.g., TRUE becoming uppercase). Dates are also a specific data type.
Autofill: Excel has an autofill feature that allows you to quickly populate cells with sequential data, repeated values, or patterns by dragging the lower right-hand corner of a selected cell or range. This works for numerical sequences (like 1, 2, 3), text, and even dates, although date autofill increments by one day by default.
Manipulating Cells: Basic manipulation includes selecting cells, entering data, and deleting content. To delete content from multiple selected cells, you might need to use the Delete key (or Function + Delete on some Macs).
The Ribbon: The ribbon is located at the top of the Excel window and contains a variety of tabs (like Home, Insert, Data) with different functionalities for working with spreadsheets. The File tab also contains a menu with options like Save and Open. The ribbon can be temporarily hidden by double-clicking on any of the tabs.
Zoom: You can adjust the zoom level of the worksheet, typically found in the bottom right corner of the Excel window.
The course emphasizes getting familiar with these fundamental aspects of Excel as a crucial first step in learning data analysis. The “Spreadsheets Intro” chapter is specifically designed for individuals with no prior experience in using Microsoft Excel.
Advanced Excel for Data Analysis
Based on the source “01.pdf”, the “Excel for Data Analytics – Full Course for Beginners” delves into several advanced Excel features in its second half, designed to ramp up your learnings after establishing the fundamentals. These features focus on more sophisticated analytical capabilities and handling larger or more complex datasets.
Here’s a discussion of these advanced Excel features:
Pivot Tables: The course highlights pivot tables as a “secret weapon” for quick data analysis. They enable you to efficiently summarize and analyze large amounts of data by pivoting and aggregating it based on different criteria. The course covers creating, manipulating, and using advanced features of pivot tables, such as grouping and aggregation, as well as creating pivot charts. Pivot tables allow for easy pivoting and aggregation of data based on chosen values, and they automatically update when the underlying data changes, without the need to readjust formulas. You can also filter data within pivot tables using dropdowns and by dragging fields into the filters area.
Power Query (Get & Transform): This feature is described as a powerful tool for ETL (Extract, Transform, Load) processes. Power Query allows you to connect to various data sets from different sources, including files, databases, and online sources like web pages. It provides the Power Query Editor, a dedicated interface for cleaning and transforming data through a series of steps. These steps can automate data cleaning workflows, ensuring reproducibility and handling potential errors in copy-pasting data. Power Query is particularly useful for ingesting and cleaning large datasets efficiently, potentially exceeding Excel’s row limit by loading data into the data model.
Power Pivot: Referred to as “putting your spreadsheets on steroids,” Power Pivot enables data modeling on datasets larger than Excel’s traditional row limit (over a million rows). It allows you to create relationships between different tables of data, similar to a database, even if they originate from different sources. Combined with DAX (Data Analysis Expressions), Power Pivot allows for creating advanced calculations, measures, and KPIs (Key Performance Indicators) that go beyond standard Excel formulas. DAX functions, while similar to Excel functions in concept, allow for more complex and powerful analysis within the context of a data model.
Add-ins: The course also touches on the use of add-ins to extend Excel’s analytical capabilities. Specifically mentioned are:
Solver: Used for optimization problems, such as finding the optimal solution given certain constraints (discussed in the context of negotiating job offers).
Analysis ToolPak: Provides a range of tools for statistical and engineering analysis, including descriptive statistics, histograms, ranking, percentiles, moving averages, regression, and sampling.
Other Advanced Features for Data Analysis and Presentation:
Tables and Slicers: Tables enhance data management and analysis, and slicers provide interactive filtering capabilities for tables and pivot tables. Slicers allow for visual and easy filtering of data subsets.
Conditional Formatting: This feature allows you to highlight data based on specific rules, making patterns and outliers more easily identifiable. It can be used to visually represent data trends and insights.
Data Validation: Used to standardize data inputs, particularly important when building interactive dashboards to ensure data integrity. It can restrict the type of data entered into cells, providing dropdown lists or other input constraints.
Workbook Protection: Advanced feature used to protect dashboards and prevent unintended modifications by others. This includes locking cells, hiding sheets, and password-protecting the workbook structure.
The course consciously chooses to focus on Python for task automation instead of VBA, considering VBA outdated. While Python in Excel and Copilot (AI chatbots) are mentioned as recent additions to Excel, they are not the primary focus for learning Excel analytics in this course, with a caution against relying heavily on AI for learning.
Finally, the course covers sharing your data analysis projects, initially through platforms like OneDrive for a quick method. However, it emphasizes using GitHub for a more robust and professional way to share projects, including all associated files and a detailed explanation of the analysis using markdown in a README file. This approach is recommended because online versions of Excel (like OneDrive’s Excel) may not fully support advanced features like Power Query and Power Pivot, limiting the interactivity of shared workbooks.
In summary, the advanced features covered in this course aim to equip learners with the skills to perform more in-depth data analysis, handle larger and more diverse datasets, automate data-related tasks, and effectively present and share their analytical findings using Microsoft Excel.
Excel Data Visualization with Charts
Based on the source “01.pdf”, data visualization is a powerful tool within Excel that allows you to understand and communicate insights from data more effectively. Microsoft refers to all types of visualizations as charts. The source emphasizes that charts can reveal characteristics and patterns in data that might be difficult to discern by looking at the raw data itself.
Why Use Charts?
Charts are powerful because they make it easier to spot trends, identify the highest and lowest values, and understand the magnitude of differences within your data. Visual representations can quickly convey information that would take longer to extract from tables of numbers.
Basic Chart Types:
The source introduces several fundamental chart types:
Line charts are typically used for time series data to show trends over time and how data points are connected.
Pie charts are suitable for showing proportions of different parts of a whole.
Bar and column charts are used for comparing values across different categories. The source notes that bar charts can be preferable when labels are long, as they are displayed horizontally.
Creating Charts:
The process of creating charts in Excel generally involves:
Selecting the data you want to visualize.
Going to the “Insert” tab on the ribbon.
Exploring the “Recommended Charts” option, which often provides good starting points based on your data.
Alternatively, you can go to the “All Charts” tab to have more control over the specific chart type you want to use.
Once a chart is inserted, new tabs (“Chart Design” and “Format”) appear, allowing for further customization.
Customizing Charts:
Excel offers a wide range of options to customize charts:
You can add or remove chart elements such as axes, axis titles, chart titles, data labels, and trend lines using the “+” icon next to the chart or the “Add Chart Element” dropdown in the “Chart Design” tab.
Chart titles should be compelling and ideally provide a snippet of information or ask a question that the reader should take away from the chart.
Axis titles are important for clarity, especially for the y-axis to indicate what the numerical values represent.
Data labels can be added to show the exact values on the chart, although care should be taken to avoid clutter.
You can adjust the look and feel of the chart using the “Chart Design” tab, including quick layouts and chart styles.
The “Format Data Series” options allow you to change colors, add markers, and adjust other visual aspects of the data series.
You can move charts to new sheets for better organization.
Advanced Chart Types:
Beyond the basics, the source discusses more advanced chart types for specific analytical purposes:
Scatter plots and map charts are mentioned as more advanced charts. Map charts are particularly useful for visualizing geographical data.
Histograms and box and whisker charts are crucial for understanding the statistical distributions of data. Histograms show the count of values within different ranges. Box and whisker charts display the median, quartiles, and potential outliers.
Sparklines are small charts that can be inserted directly into individual cells to show quick trends in the data.
Interactive Visualizations:
The source highlights the use of slicers to make dashboards and charts more interactive. Slicers provide buttons that allow users to easily filter the data being displayed in associated tables and charts. Timelines serve a similar purpose for filtering date-related data in pivot charts.
Pivot Charts:
Pivot charts are directly linked to pivot tables and provide a visual way to analyze the summarized data in a pivot table. Changes made to the pivot table will automatically update the pivot chart. You can insert slicers and timelines directly from the “PivotChart Analyze” tab to filter the data displayed in the pivot chart.
Combo Charts:
Excel allows you to create combo charts that combine different chart types (e.g., columns and lines) to visualize different aspects of your data simultaneously. This can be useful for showing relationships between different metrics, such as salary and skill count.
Best Practices:
The source implicitly and explicitly suggests several best practices for data visualization:
Choose the right chart type for your data and the message you want to convey. For instance, using a line chart for chronological data and a bar chart for comparing categories.
Ensure your charts are easy to read and understand, with clear titles and labels.
Avoid sensory overload by not including too much information or unnecessary elements in a single chart.
Format data labels and axes for better readability, such as using abbreviations for large numbers (e.g., using “k” for thousands).
Consider the order of data in bar charts to facilitate comparison (e.g., sorting from high to low).
In conclusion, data visualization is a critical aspect of data analysis in Excel, enabling you to explore, understand, and communicate your findings through a variety of customizable charts and interactive elements. Understanding the different chart types and their appropriate uses, along with mastering customization options, are fundamental skills highlighted in the source.
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