Creating Reports and Visualizations with Excel and Power BI

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:

1. 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.
2. 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.

1. 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.
2. 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.
3. 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.
4. 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.

1. 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.

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

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