Power BI Dashboard Fundamentals

This extensive guide explores Power BI, a business intelligence tool, offering a comprehensive look at its interface and core functionalities. It walks users through report creation, beginning with understanding the canvas, ribbon, and panes for filters, visualizations, and data. The text progresses to data importation from various sources, data cleaning using Power Query Editor, and dashboard construction with diverse visualizations like bar charts, column charts, and scatter plots. Furthermore, it covers advanced topics such as DAX (Data Analysis Expressions) for complex calculations, creating data models with fact and dimensional tables, and using parameters for interactive dashboards. The guide concludes with advice on sharing dashboards and best practices for effective data presentation.

Power BI Desktop: Interface and Fundamentals

The Power BI interface, primarily referring to the Power BI Desktop application, is designed for data analysis and dashboard creation, drawing inspiration from car dashboards for quick insights. It has a distinct layout and terminology compared to tools like Excel.

Key components of the Power BI interface include:

• The Ribbon The ribbon is located at the top of the Power BI Desktop application, similar to other Microsoft products. It contains various tabs like Home, Insert, Modeling, View, Optimize, and Help, each offering different functionalities.
• Home Tab: Primarily used for getting and editing data queries, connecting to various data sources like Excel workbooks, SQL Server, text files, and the internet. It also includes options to transform data, which opens the Power Query Editor, and to refresh queries.
• Insert Tab: Allows users to insert new visuals, text boxes, shapes, and buttons into a report.
• Modeling Tab: Used for creating measures, calculated columns, tables, and parameters, often utilizing the DAX language. It also includes options for managing relationships between tables.
• View Tab: Enables changes to the report’s appearance, such as color themes (e.g., dark mode, light theme) and layout options. It also controls the visibility of various panes.
• Optimize Tab: Contains tools like the Performance Analyzer to inspect and identify bottlenecks in report loading or cross-highlighting.
• Help Tab: Provides access to help resources, though external chatbots like Gemini or ChatGPT are often recommended for more practical assistance.
• Views: Located on the left-hand side, Power BI Desktop offers several views:
• Report View: This is the primary area where users build their dashboards.
• Table View: Allows users to view and inspect their loaded data in a tabular format, similar to a spreadsheet. It also enables formatting of data types and decimal places for columns.
• Model View: Displays the data model, showing all loaded tables and the relationships between them. This view is crucial for understanding how different tables interact.
• DAX Query View: A newer view that allows users to write and execute DAX queries to analyze data and define measures. It can also generate column statistics.
• Panes: Located on the right-hand side, these provide interactive elements for report creation and data manipulation:
• Filters Pane: Used to apply filters to visuals, specific pages, or all pages in a report.
• Visualizations Pane: This is where users select different chart types (e.g., bar charts, line charts, pie charts, maps) and configure their properties, including axes, legends, and field wells. It also allows for formatting visuals, adding analytics features like trend lines, and toggling data labels.
• Data Pane: Displays the data model, showing tables, columns, and measures that can be dragged into visuals.
• Other Panes: Includes Bookmark Pane, Selection Pane, Performance Analyzer, and Sync Slicers, which are covered in more advanced lessons.
• Canvas: The central area of the report view where dashboards are built and visuals are placed. Unlike Excel’s “worksheets,” Power BI reports consist of multiple “pages”.

Initial Setup and Terminology Differences: Power BI Desktop is available for free from the Microsoft Store. Upon opening, users can start with a blank report. The application may prompt users about features like dark mode, though the source recommends the light theme for tutorials due to contrast. Power BI refers to its files as “reports” and the individual tabs within a report as “pages,” differentiating them from Excel’s “workbooks” and “sheets”.

Interaction and Navigation: Users interact with the interface by selecting visuals, dragging fields between panes, and utilizing the various options on the ribbon. Navigation between pages can be done through page tabs at the bottom or by implementing buttons and bookmarks for more dynamic interaction.

The Power BI Service, a cloud-based platform, complements the Desktop application by allowing users to publish and share dashboards with co-workers or to the web, ensuring a single source of truth for data. However, advanced sharing features in the Power BI Service often require a Power BI Pro license.The Power BI interface, primarily referring to the Power BI Desktop application, is designed for data analysis and dashboard creation, drawing inspiration from car dashboards for quick insights. It has a distinct layout and terminology compared to tools like Excel.

Key components of the Power BI interface include:

• The Ribbon: Located at the top of the Power BI Desktop application, similar to other Microsoft products, it contains various tabs like Home, Insert, Modeling, View, Optimize, and Help, each offering different functionalities.
• Home Tab: Primarily used for getting and editing data queries, connecting to various data sources like Excel workbooks, SQL Server, text files, and the internet. It also includes options to transform data, which opens the Power Query Editor, and to refresh queries.
• Insert Tab: Allows users to insert new visuals, text boxes, shapes, and buttons into a report.
• Modeling Tab: Used for creating measures, calculated columns, tables, and parameters, often utilizing the DAX language. It also includes options for managing relationships between tables.
• View Tab: Enables changes to the report’s appearance, such as color themes (e.g., dark mode, light theme) and layout options. It also controls the visibility of various panes.
• Optimize Tab: Contains tools like the Performance Analyzer to inspect and identify bottlenecks in report loading or cross-highlighting.
• Help Tab: Provides access to help resources, though external chatbots like Gemini or ChatGPT are often recommended for more practical assistance.
• Views: Located on the left-hand side, Power BI Desktop offers several views:
• Report View: This is the primary area where users build their dashboards.
• Table View: Allows users to view and inspect their loaded data in a tabular format, similar to a spreadsheet. It also enables formatting of data types and decimal places for columns.
• Model View: Displays the data model, showing all loaded tables and the relationships between them. This view is crucial for understanding how different tables interact.
• DAX Query View: A newer view that allows users to write and execute DAX queries to analyze data and define measures. It can also generate column statistics.
• Panes: Located on the right-hand side, these provide interactive elements for report creation and data manipulation:
• Filters Pane: Used to apply filters to visuals, specific pages, or all pages in a report.
• Visualizations Pane: This is where users select different chart types (e.g., bar charts, line charts, pie charts, maps) and configure their properties, including axes, legends, and field wells. It also allows for formatting visuals, adding analytics features like trend lines, and toggling data labels.
• Data Pane: Displays the data model, showing tables, columns, and measures that can be dragged into visuals.
• Other Panes: Includes Bookmark Pane, Selection Pane, Performance Analyzer, and Sync Slicers, which are covered in more advanced lessons.
• Canvas: The central area of the report view where dashboards are built and visuals are placed. Unlike Excel’s “worksheets,” Power BI reports consist of multiple “pages”.

Initial Setup and Terminology Differences: Power BI Desktop is available for free from the Microsoft Store. Upon opening, users can start with a blank report. The application may prompt users about features like dark mode, though the source recommends the light theme for tutorials due to contrast. Power BI refers to its files as “reports” and the individual tabs within a report as “pages,” differentiating them from Excel’s “workbooks” and “sheets”.

Interaction and Navigation: Users interact with the interface by selecting visuals, dragging fields between panes, and utilizing the various options on the ribbon. Navigation between pages can be done through page tabs at the bottom or by implementing buttons and bookmarks for more dynamic interaction.

The Power BI Service, a cloud-based platform, complements the Desktop application by allowing users to publish and share dashboards with co-workers or to the web, ensuring a single source of truth for data. However, advanced sharing features in the Power BI Service often require a Power BI Pro license.

Power BI: Power Query and DAX for Data Mastery

Data manipulation in Power BI is a crucial process, primarily handled through two powerful tools: Power Query for data extraction, transformation, and loading (ETL), and DAX (Data Analysis Expressions) for creating calculated data within the data model.

Data Manipulation with Power Query

Power Query is described as an ETL tool that allows users to extract data from various sources, transform it, and then load it into Power BI for visualization. It provides a graphical user interface (GUI) for performing these transformations without extensive coding, though it operates on a specialized language called M.

Accessing Power Query Editor: The Power Query Editor can be accessed from the “Home” tab in Power BI Desktop by selecting “Transform data”. This opens a separate window with its own ribbon, data view area, queries pane, and query settings pane.

Key Functionalities and Interface:

1. Connecting to Data Sources: Power Query supports hundreds of data sources, categorized broadly into files (Excel, CSV, PDF, text), databases (SQL Server, BigQuery), cloud services (Salesforce, Snowflake), and web sources. Users can directly import data or choose to “Transform data” to open the Power Query Editor first.

• Folder Connections: A common use case is combining multiple files (e.g., monthly Excel sheets) from a single folder into one table. This can be done by connecting to a “Folder” source and then using the “Combine and Load” or “Combine and Transform Data” options.
• Web Sources: Data from web pages, particularly tables, can be easily imported by pasting the URL.
• Database Connections: Power Query can connect to various databases, requiring credentials and allowing for optional SQL statements to extract specific subsets of data. When connecting to databases, users choose between “Import mode” (loads all data into the Power BI file, faster performance, larger file size) and “Direct Query” (data remains in the source, smaller file size, slower performance, limited DAX functionality). The source recommends using “Import mode” if possible for better performance and full functionality.

1. Power Query Editor Interface and Analysis:

• Ribbon Tabs: The editor has tabs like “Home,” “Transform,” and “Add Column,” each offering different functionalities.
• Queries Pane: Lists all loaded queries (tables).
• Applied Steps: This pane on the right tracks every transformation applied to the data. Users can review, modify, or delete steps, allowing for iterative and non-destructive data cleaning. Each step generates M language code.
• Formula Bar: Displays the M language code for the currently selected step.
• Data View Area: Shows a preview of the data after the applied transformations.
• Column Profiling (View Tab): The “View” tab offers features like “Column Profile,” “Column Distribution,” and “Column Quality” to inspect data, identify unique/distinct values, errors, and empty cells. This helps in understanding data quality and guiding transformations. Column profiling can be set to the top 1,000 rows or the entire data set.

1. Common Data Transformations in Power Query:

• Data Type Conversion: Easily change data types (e.g., text to date/time, whole number to decimal). The editor asks if you want to replace the current step or add a new one.
• Removing/Choosing Columns: Users can remove unnecessary columns or select specific columns to keep using “Remove Columns” or “Choose Columns”.
• Replacing Values: Replace specific text or characters within a column (e.g., removing prefixes like “via” or cleaning up extraneous spaces).
• Trimming/Formatting Text: “Format” options allow for changing case (uppercase, lowercase), and “Trim” removes leading and trailing whitespace.
• Splitting Columns: Columns can be split by a delimiter into new columns or into new rows, which is particularly useful for handling multi-valued fields within a single cell.
• Unpivoting Columns: Transforms columns into attribute-value pairs, useful when data is in a “pivot table” format and needs to be normalized.
• Adding Custom Columns: Create new columns based on existing ones using formulas or conditional logic.
• Standard Transformations (Add Column Tab): Perform mathematical operations like multiplication (e.g., calculating yearly salary from hourly pay).
• Column from Example: Users provide examples of the desired output, and Power Query infers the M language code to generate the new column. This can be more intuitive for complex text manipulations or bucketing.
• Conditional Columns: Create new columns based on “if-then-else” logic, similar to Excel’s IF function.
• Custom Column (M Language): For more complex scenarios, users can write M language code directly to define new columns. AI chatbots like ChatGPT or Gemini can assist in generating this M language code.
• Appending Queries: Combines rows from multiple tables with similar structures (same columns) by stacking them on top of each other. This is useful for consolidating data from different periods or sources.
• Merging Queries: Combines columns from two or more tables based on matching values in common columns, similar to SQL joins. Different “Join Kinds” determine which rows are included (e.g., Left Outer, Right Outer, Inner, Full Outer, Left Anti, Right Anti). This is crucial for building star schemas by linking fact tables to dimensional tables.
• Grouping Data (“Group By”): Aggregates data based on one or more columns, allowing for calculations like counts or sums for distinct groups, similar to pivot tables in Excel.

1. M Language: The underlying functional programming language that powers Power Query. Every action taken in the GUI translates into M code, which can be viewed and edited in the “Advanced Editor”. Understanding M can help with troubleshooting and advanced transformations. AI chatbots are recommended for assistance with M language queries.

Data Manipulation with DAX (Data Analysis Expressions)

DAX is a formula language used after data is loaded into the Power BI data model. Unlike Power Query which focuses on data preparation, DAX focuses on creating new calculations and enriching the data model.

Key Functionalities:

1. Calculated Columns: New columns added directly to a table in the data model using DAX formulas. These calculations are performed during data import or refresh and are stored as part of the model. While possible, Power Query’s custom columns are generally preferred for efficiency and better compression.

• Examples include creating an adjusted salary column or a combined yearly/hourly salary column.

1. Calculated Tables: Entire new tables created using DAX formulas. This is useful for creating lookup tables (e.g., a distinct list of job titles) or date dimension tables.

• The CALENDAR and CALENDARAUTO functions are specifically mentioned for creating date tables. The ADDCOLUMNS function can be used to add columns like year, month, or weekday name to a calculated table.

1. Explicit Measures: Unlike implicit measures (automatically generated by dragging fields), explicit measures are explicitly defined using DAX formulas. They are highly recommended for complex calculations, ensuring reusability, and maintaining a “single source of truth” for calculations across a report. Measures are calculated at “query runtime” (when a visualization is built) and are not stored in the table directly.

• Examples include Job Count, Median Yearly Salary, Skill Count, and Skills per Job.
• DIVIDE function: A safer way to perform division, handling divide-by-zero errors.
• CALCULATE function: One of the most powerful DAX functions, allowing expressions to be evaluated within a modified filter context. This is crucial for overriding or modifying existing filters and contexts.
• ALL and ALLSELECTED functions: Used within CALCULATE to remove filters from a table or selected columns/rows, respectively, enabling calculations against totals or specific subsets.

1. Parameters: While parameters are a user-facing feature, they rely on DAX to define their behavior.

• Field Parameters: Allow users to dynamically switch the columns or measures displayed in a visual via a slicer. These parameters are created based on selected fields and generate DAX code.
• Numeric Parameters (“What-if” Parameters): Enable users to input a numeric value (via a slider or field) that can then be used in DAX measures to perform “what-if” analysis (e.g., adjusting tax rates for take-home pay).

Context in DAX: Understanding DAX requires comprehending “context,” which dictates how calculations are evaluated. There are three types, with precedence from highest to lowest:

• Filter Context: Explicitly modified using DAX functions like CALCULATE.
• Query Context: Determined by visual selections, relationships, and cross-filtering.
• Row Context: Operates at an individual row level, typically seen in calculated columns.

Best Practices and Considerations

• Power Query for Cleaning, DAX for Calculations: Generally, it is recommended to perform extensive data cleaning and transformations in Power Query before loading data into the model, as it leads to better compression, smaller file sizes, and faster data model operations. DAX is best used for creating measures and calculated fields that enrich the analysis after the data is loaded.
• Star Schema: Organizing data into fact and dimensional tables (star schema) is a recommended practice for efficient data modeling and analysis, especially when dealing with complex relationships like multiple skills per job posting.
• Measure Organization: Store all explicit measures in a dedicated “measures” table for better organization and ease of access.
• Commenting DAX: Use comments (single-line // or multi-line /* */) to document DAX measures, improving readability and maintainability.
• Data Size: Be mindful of file size implications, especially when importing large datasets or creating many calculated columns, as this can affect performance and sharing capabilities.

Power BI Data Visualization: A Comprehensive Guide

Data visualization in Power BI is a core functionality that allows users to translate raw data into insightful, interactive reports and dashboards. It is a critical skill for data and business analysts, enabling them to communicate data-driven insights effectively.

Power BI Desktop and Its Interface for Visualization

The primary tool for creating visualizations is Power BI Desktop, a free application. When building reports, users interact with several key components:

• Ribbon: Located at the top, it contains various tabs like “Home,” “Insert,” “Modeling,” “View,” “Optimize,” and “Help,” which offer tools for data manipulation and visualization.
• Views: Power BI Desktop offers different views:
• Report View: This is the central canvas where dashboards are built by adding and arranging visuals. Pages within a report are analogous to worksheets in Excel.
• Table View: Allows users to inspect and verify the loaded data, view all values, and perform basic formatting like changing data types or currency formats.
• Model View: Displays the data model, including tables, columns, measures, and, crucially, relationships between tables. This view helps in understanding how different tables interact.
• DAX Query View: A newer feature that allows users to write and execute DAX queries to evaluate measures or view column statistics. It can assist in troubleshooting DAX formulas.
• Panes: Located on the right-hand side, these panes are essential for building and refining visuals:
• Filters Pane: Used to apply filters at the visual, page, or all-page level, controlling which data is displayed.
• Visualizations Pane: Contains a gallery of available chart types and options to format selected visuals.
• Data Pane: Shows the data model, listing all loaded tables, their columns, and measures, allowing users to drag fields into visual wells.
• Bookmark Pane: Manages bookmarks, which capture specific states of a report page (filters, visible visuals).
• Selection Pane: Controls the visibility and order of elements on the canvas, useful for managing layers in design.
• Performance Analyzer: Helps identify bottlenecks and slow-performing visuals by recording the time taken for interactions.
• Sync Slicers Pane: Manages the synchronization of slicer selections across different report pages.
• Canvas: The central area where visuals are added, arranged, and interacted with.

Chart Types and Their Applications

Power BI offers a wide range of built-in visuals, and understanding when to use each is crucial.

1. Column and Bar Charts:

• Stacked Bar/Column Chart: Compares values across categories, with segments of bars/columns representing proportions of a whole.
• Clustered Bar/Column Chart: Compares values across multiple categories side-by-side.
• 100% Stacked Bar/Column Chart: Similar to stacked charts but shows the proportion of each segment relative to 100%, useful for visualizing percentages.
• Often used for showing distributions or comparisons of categorical data, like “what are top data jobs” or “what are the type of data jobs”. Columns go vertically, bars horizontally.

1. Line and Area Charts:

• Line Chart: Ideal for showing trends over time, such as “what is the trend of jobs in 2024”. Trend lines can be added for further analysis.
• Stacked Area Chart: Shows trends over time while also indicating the composition of a total, useful for breaking down categories over time.
• 100% Stacked Area Chart: Displays the proportion of categories over time, emphasizing their relative contribution to a total.
• Combo Chart (Line and Stacked Column/Clustered Column Chart): Combines columns and lines to compare different measures, like yearly vs. hourly median salary.

1. Pie and Donut Charts:

• Represent proportions of a whole.
• Donut Charts: Similar to pie charts but with a hole in the middle.
• Recommended for use with only “two to three values” to maintain readability. Examples include “what portion of postings don’t mention a degree” or “what portion of job postings are work from home”.

1. Tree Maps:

• Display hierarchical data as a set of nested rectangles. The size of the rectangle corresponds to the value.
• Good for showing breakdowns and can be used to filter other visuals when clicked. Example: “what are the type of data jobs” (e.g., full-time, contractor).

1. Scatter Plots:

• Show the relationship between two numerical values, revealing trends or correlations.
• Example: “hourly versus yearly salary of data jobs”. Trend lines can be added.

1. Maps:

• Map Visual: Displays geographical data as dots or bubbles on a map, with bubble size often representing a measure like job count. Can include legends for categorical breakdowns (e.g., degree mentioned). Requires enabling in security settings.
• Filled Map: Colors regions on a map based on a measure or category. The source finds it “most useless” due to limited insights and distinct colors for all values.
• ArcGIS for Power BI Map: Offers advanced mapping capabilities, allowing for color-coding based on values. However, sharing reports with this visual requires an ArcGIS subscription.

1. Uncommon Charts:

• Ribbon Chart: Shows rank over time, with ribbons connecting values. Can be visually cluttered with too many categories.
• Waterfall Chart: Illustrates how an initial value is affected by a series of positive and negative changes, common in finance. Requires specific data formatting.
• Funnel Chart: Visualizes stages in a sequential process, showing conversion rates or progression.

1. Tables and Matrices:

• Table: Displays data in rows and columns, similar to a spreadsheet. Useful for showing detailed information and allowing users to export data.
• Matrix: Functions like an Excel pivot table, allowing for hierarchical aggregation and drill-down capabilities.
• Both support Conditional Formatting (background color, font color, data bars, icons, web URLs) to highlight patterns.
• Sparklines can be added to matrices to show trends within individual cells.

1. Cards:

• Display single key metrics or KPIs, typically placed prominently at the top of a dashboard.
• Card (original): Simple display of a single value.
• Card (new): Preferred due to its ability to display multiple values in a more intuitive layout and title placement.
• Gauge Card: Visualizes a single value against a target or range, showing progress or performance (e.g., median salary with min/max/average).
• Multi-row Card: Displays multiple values across several rows, useful for listing several key figures.
• KPI Card: Shows a key performance indicator, often with a trend line and color-coding (green/red) based on performance against a target.

Interactive Elements

Power BI enhances interactivity through:

• Slicers: Allow users to filter data dynamically by making selections.
• Styles: Vertical list, tile buttons, or dropdown.
• Selection: Single select (radio buttons) or multi-select (holding Ctrl/Cmd). “Show select all” option can be enabled.
• Types: Can be used for categorical data (e.g., job title), numerical ranges (e.g., salary), or date ranges (e.g., “between” dates, “relative date/time”).
• Search: Can be enabled for large lists of values.
• Sync Slicers: Allows a single slicer’s selection to apply across multiple report pages.
• Buttons: Can be configured to perform various actions.
• Page Navigation: Navigate to different report pages.
• Q&A Button: Provides a tool tip to guide users on how to interact (e.g., “press control while clicking a button”).
• Clear All Slicers: Resets all slicers on a page or report, providing an intuitive way to clear filters.
• Apply All Slicers: Delays filtering until the button is clicked, useful for large datasets to improve performance.
• Bookmark Actions: Activate specific bookmarks.
• Bookmarks: Capture the current state of a report page, including applied filters, visible visuals, and visual properties. They allow users to quickly switch between different views or hide/show elements.
• Can be set to preserve data (filters) or display (visual visibility) properties.
• Drill Through: Enables users to navigate from one report page to another, passing filter context based on a selected data point. For example, clicking on a job title in one report can show a detailed view for only that job title on a drill-through page. A “back arrow” button is automatically added for navigation.

Formatting and Design Principles

Effective visualization in Power BI extends beyond just selecting chart types to thoughtful design and formatting.

• Titles and Labels: Descriptive titles and clear labels are crucial for guiding the user’s understanding.
• Coloring: Use color palettes consistently and strategically to draw attention to key insights. Avoid excessive or distracting colors. Dark mode themes are an option.
• Font and Size: Adjust font sizes for readability.
• Decimal Places and Display Units: Format numerical values appropriately (e.g., currency, thousands).
• Gridlines: Often removed to reduce visual clutter.
• Tooltips: Enhance interactivity by displaying additional information when hovering over data points.
• Borders and Shadows: Can be used to group related visuals and add visual appeal.
• Backgrounds: Can be made transparent for visuals to sit on custom backgrounds.
• Edit Interactions: Control how visuals interact with each other when filtered or highlighted.
• Dashboard Design Best Practices:Problem-solving and Audience Focus: Always design with a clear problem and target audience in mind.
• Simplicity: Keep designs simple and avoid overwhelming users with too many visuals or colors.
• Symmetry and Layout: Symmetrical layouts, often with KPIs at the top and related visuals below, can improve intuitiveness.
• Visual Cues: Use background shapes or grouping to create visual cues that associate related visuals and parameters.
• Performance Analyzer: A tool to check the loading times of visuals and identify bottlenecks in report performance.

Overall, data visualization in Power BI is a comprehensive process that involves selecting appropriate visuals, applying detailed formatting, and incorporating interactive elements, all while adhering to best practices for effective dashboard design.

DAX: Power BI’s Calculation Engine

DAX (Data Analysis Expressions) is a powerful formula language used in Power BI for performing calculations on data that has already been loaded into the data model. It is distinct from M language, which is a programming language used in Power Query for data manipulation and transformation before data is loaded into Power BI.

Purpose and Usage of DAX DAX allows users to add calculations to their data models, enabling more in-depth analysis and dynamic reporting. It is not exclusive to Power BI and can also be used in other Microsoft tools like Microsoft Excel, Microsoft Fabric, SQL Server Analysis Services, and Azure Analysis Services. DAX is particularly effective for performing calculations on large datasets.

Comparison with Excel Functions DAX functions share a similar syntax with Excel functions, but they operate differently. While Excel functions typically operate on a single cell or a range of cells, DAX can perform calculations on single rows, entire columns, or even whole tables. For instance, the SUM function in DAX is similar to Excel’s SUM, but in DAX, you typically insert a column name rather than a cell or range.

Comparison with M Language DAX is a formula language (like SUM, AVERAGE), whereas M language is a more verbose programming language. Functions and structures in DAX are not interchangeable with those in M language; for example, concatenating text in DAX uses TEXTCOMBINE instead of a direct concatenation symbol as might be seen in M language.

Types of DAX Functions and Their Applications DAX offers a wide range of functions categorized into:

• Aggregation Functions: Such as AVERAGE, COUNT, MAX, MIN, and SUM.
• Date and Time Functions: Including those for extracting day, minute, or month, and functions like CALENDAR and CALENDARAUTO for creating date tables.
• Logical Functions: For operations like IF, AND, or OR statements.
• Math and Trig Functions: For mathematical calculations.

DAX can be applied in Power BI using four primary methods:

1. Calculated Columns:

• Calculated columns add new columns to an existing table in the data model.
• They are computed immediately upon data import and are visible in both the data and report views.
• Example: Creating a salary hour adjusted V2 column by multiplying salary hour average by 2080 (40 hours/week * 52 weeks/year). Another example is salary year and hour V2 which selects a value from either salary year average or salary hour adjusted V2 if the first is null.
• Recommendation: While possible, it is generally recommended to perform data transformations and create new columns in Power Query using custom columns instead of DAX calculated columns. Power Query processes data before loading, leading to more efficient compression, smaller file sizes, and quicker data model operations. It also keeps all data cleaning in one centralized place.

1. Calculated Tables:

• Calculated tables create entirely new tables within the data model based on DAX expressions.
• They are useful for creating lookup tables (e.g., job title dim using the DISTINCT function to get unique job titles) or date tables.
• Example: Date Dimensional Table: A date dim table can be created using CALENDAR (specifying start and end dates) or CALENDARAUTO (which automatically detects dates from the model). Additional columns like year, month number, month name, weekday name, week number, and weekday number can be added using functions like YEAR, MONTH, FORMAT, and WEEKNUM.
• Date tables can be marked as such in Power BI to enable automatic date-related functionalities. Sorting columns (e.g., weekday name by weekday number) helps ensure correct visual order.
• Recommendation: Similar to calculated columns, creating and cleaning tables is often more beneficial to do in Power Query.

1. Explicit Measures:

• Measures are dynamic calculations that are not computed until they are queried (e.g., when a visual is built). They are not visible in the table view.
• They provide a “single source of truth” for calculations across different reports, preventing inconsistencies that can arise from implicit measures (where aggregation is chosen directly in a visual).
• Creation: Measures are defined with a name followed by an equals sign and a DAX formula (e.g., Job Count = COUNTROWS(‘Job Postings Fact’)).
• Organization: Best practice is to create a dedicated table (e.g., _Measures) to store all explicit measures, improving organization.
• Examples:Job Count: Calculates the total number of job postings using COUNTROWS.
• Median Yearly Salary: Calculates the median yearly salary using the MEDIAN function. Measures can be pre-formatted (e.g., currency, decimal places).
• Skill Count: Counts the total number of skills for job postings using COUNTROWS(‘Skills Job Dim’).
• Skills Per Job: Calculates the ratio of Skill Count to Job Count using the DIVIDE function for safe division.
• Job Percent: Calculates the percentage likelihood of a skill being in a job posting, demonstrating the CALCULATE and ALLSELECTED functions to manage filter context.
• Median Yearly Take-Home Pay: Uses a numeric parameter to deduct a user-defined tax rate from the median yearly salary.
• Commentation: Measures should be commented using // for single-line comments or /* … */ for multi-line comments to document their purpose and logic.

1. Parameters (using DAX):

• Parameters allow end-users to dynamically change inputs in a chart without needing to modify the underlying DAX code.
• Field Parameters:Enable users to dynamically switch between different columns or measures on an axis of a visual.
• Example: A select category parameter can let users switch the Y-axis of a chart between Job Title, Country, Skills, or Company. A select measure parameter can switch between Median Yearly Salary and Job Count on the X-axis.
• Numeric Parameters:Allow for “what-if” analysis by providing a slider or input field for numerical values.
• Example: A select deduction rate parameter allows users to adjust a tax rate (e.g., from 0% to 50%) to see its impact on “take-home pay” calculations.

Context in DAX Understanding evaluation contexts is crucial for complex DAX calculations:

• Row Context (Lowest Precedence): Refers to the current row a calculation is being applied to. Calculations in calculated columns typically operate at the row context level. The RELATEDTABLE function can be used to count related rows for the current row context.
• Query Context: Determines which rows from a table are included in a calculation based on visual selections, relationships, slicers, and cross-filtering. This is an abstract context derived from the visual itself.
• Filter Context (Highest Precedence): Applied on top of query and row contexts. It can explicitly modify the calculation environment, overriding other contexts. The CALCULATE function is a powerful tool used to explicitly modify filter context. The ALL and ALLSELECTED functions can remove existing filters from columns or tables within a CALCULATE expression.

DAX Query View The DAX query view in Power BI Desktop allows users to write and execute DAX queries to evaluate measures or view column statistics. It can also be used to define and evaluate measures, and even update the data model. While it requires some DAX knowledge, it can be assisted by quick queries for basic evaluations.

Learning and Troubleshooting DAX For learning and troubleshooting DAX, the source recommends consulting official DAX documentation and utilizing AI chatbots like Google Gemini or ChatGPT, which can provide step-by-step instructions and code for DAX formulas. Additional courses on DAX are also recommended for deeper learning.

Power BI Dashboard Design and Sharing Guide

Dashboard creation, particularly using Power BI, involves a structured approach that prioritizes understanding the user’s needs, careful planning, and effective utilization of Power BI’s features for data visualization and interaction.

What is a Dashboard? Analytical dashboards are inspired by car dashboards, providing users with quick insights at a glance. They consolidate key information and visuals to help users understand data and identify patterns or anomalies efficiently.

Tools for Dashboard Creation Power BI Desktop is a free and popular business intelligence tool specifically designed for creating dashboards. While Excel can be used to build dashboards, it comes with limitations regarding data manipulation, formula complexity for interactive elements, and sharing, which Power BI aims to solve. Power BI is noted as the second most popular BI tool and is gaining popularity over competitors like Tableau.

Power BI Ecosystem for Dashboard Creation and Sharing The Power BI ecosystem consists primarily of two parts:

• Power BI Desktop (App): This is the application where dashboards are built. It’s free to install and allows users to load data, build reports (which contain multiple pages, unlike Excel’s worksheets), and design visualizations.
• Power BI Service: This is a cloud-based platform accessible via an internet browser, designed for sharing dashboards. Dashboards published to the Power BI Service can be accessed by co-workers within shared workspaces, or even published to the web for public access if the data is not confidential. While there is a free option, it is very limited; a Power BI Pro license (paid) is often needed for sharing and collaboration. Microsoft Fabric is also an umbrella platform that consolidates various data tools, including Power BI.

Best Practices for Dashboard Design To create effective dashboards that users will actually utilize, consider the following:

• Define the Problem and Audience: Always ask: “What problem are we trying to solve with this dashboard?” and “Who are we designing this dashboard for?”. Dashboards are ineffective if they don’t address the specific concerns or problems of the end consumer.
• Simplicity and Clarity: Avoid overwhelming dashboards with too many visuals or distracting colors. Simple color palettes help guide the user’s eye to important information.
• Key Performance Indicators (KPIs): Place cards displaying key metrics (KPIs) prominently at the top of the dashboard, as they provide immediate value and draw attention.
• Symmetry and Layout: A symmetrical layout, often with KPIs at the top and equally spaced graphs below, can improve readability and intuitiveness. Visual cues like backgrounds and boxes can group related elements and draw attention.
• Interactivity: Incorporate features that allow users to interact with the data, such as slicers, buttons, and drill-through options.

Planning and Rough Drafting Before building, it’s recommended to sketch out a rough design of the dashboard, or at least rough draft it within Power BI itself. This allows for early feedback from stakeholders and helps ensure the design aligns with the intended purpose.

Steps in Dashboard Creation (Power BI Desktop)

1. Start a New Page: Create a dedicated page for your dashboard.
2. Add a Title: Insert a text box for the dashboard title, formatting it appropriately for size and boldness.

• Insert Slicers:Slicers enable users to interactively filter data.
• Types include vertical list, tile, and dropdown.
• Enable search functionality for long lists.
• Allow multi-select (default with Ctrl/Cmd) or enforce single-select.
• The “Show select all” option is useful.
• Date and numeric slicers (between, before, after, relative) can be added, though some date slicer types may have known bugs.
• Slicers can be synchronized across multiple pages using the “Sync slicers” pane.
• A “Clear all slicers” button can be added for user convenience, often styled with visual cues like shadows and rounded corners. An “Apply all slicers” button can be useful for very large datasets to control refresh performance.
• Add Cards (KPIs):Use card visuals (e.g., “Card (new)”) to display single, prominent data points like “Job Count,” “Median Yearly Salary,” or “Skills Per Job”.
• New card visuals can display multiple fields.
• Format callout values, labels, and remove borders as needed.
• Other card types like Gauge cards (showing min, max, target values) and Multi-row cards are available. KPI cards show a value with a trend and color-coding based on goals.
• Insert Charts/Visualizations:Choose appropriate chart types (e.g., bar charts for comparison, line charts for trends over time, scatter plots for relationships, tree maps for hierarchical breakdown).
• Formatting: Adjust axes (labels, values, ranges), legends, titles, and data labels for clarity.
• Conditional Formatting: Use data bars, background colors, or icons to highlight specific values based on conditions. This helps draw the user’s attention.
• Trend Lines: Add trend lines to visualize patterns in data, especially in line charts or scatter plots.
• Matrices and Tables: These are useful for displaying detailed data and can include conditional formatting and sparklines (mini-charts within cells) for quick trends.

1. Implement Drill-through: This advanced feature allows users to right-click on a visual and navigate to a separate, detailed page filtered by their selection. A dedicated button can also be created for drill-through.

• Use Parameters:Field Parameters: Allow end-users to dynamically switch columns or measures displayed in a visual (e.g., changing a chart’s axis from “Job Title” to “Country” or “Skill”).
• Numeric Parameters: Enable “what-if” analysis by allowing users to adjust numerical inputs (e.g., a tax deduction rate) via a slider, which then affects calculations in visuals.

1. Add Backgrounds and Organize Visually: Insert shapes (e.g., rounded rectangles) behind visuals to create visual groupings and a cohesive design. Set visual backgrounds to transparent to reveal these background shapes.
2. Hide Header Icons: Turn off header icons on visuals by making their transparency 100% to clean up the design.
3. Save Frequently: Power BI Desktop does not have an autosave feature, so frequent saving is crucial to prevent data loss.

Data Preparation for Dashboards Effective dashboards rely on well-prepared data.

• Power Query (M Language): Used for Extract, Transform, Load (ETL) operations before data is loaded into the Power BI data model. It’s recommended for data cleaning, shaping, and creating new columns or tables (e.g., combining data from multiple files in a folder, unpivoting data, cleaning text). Power Query transformations lead to more efficient data compression and smaller file sizes.
• DAX (Data Analysis Expressions): A formula language used after data is loaded into the data model to add calculations. It is used for creating calculated columns, calculated tables, and explicit measures. While calculated columns and tables can be created with DAX, it’s generally recommended to do data transformations in Power Query for better performance and organization.
• Explicit Measures: Dynamic calculations that are computed at query runtime (e.g., when a visual is built), providing a “single source of truth” for consistent calculations across reports. They are preferred over implicit measures (automatic aggregations) for complexity and control. Measures can be organized in a dedicated table and thoroughly commented for documentation.
• Context in DAX: Understanding row context (individual row calculation), query context (visual/filter selection), and filter context (explicit modification, highest precedence) is crucial for complex DAX calculations.

Sharing Dashboards After creation, dashboards can be shared in several ways:

• Power BI File (.pbix): The dashboard file can be directly shared, but the recipient needs Power BI Desktop to open it, and version control can be an issue.
• Power BI Service: Publishing to the Power BI Service allows for centralized access, sharing with specific groups (workspaces), and embedding reports (e.g., into websites). Admin settings may be required to enable features like “Publish to Web”.
• GitHub: An online repository to store project files, including the Power BI file and a “readme” document that explains the project, showcases skills, and can link directly to the interactive dashboard in the Power BI Service. This method allows for version control and provides a professional portfolio for showcasing work.
• LinkedIn: Projects hosted on platforms like GitHub or the Power BI Service can be linked and showcased on LinkedIn profiles, or shared directly via posts, to gain visibility and potential career opportunities.

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

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