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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Make smarter business decisions with Microsoft PowerBI
Microsoft PowerBI For Dummies gives you the foundation you need to use this powerful software platform to manage your data and gather insight for making better decisions. With PowerBI, you can visualize, model, and interpret large datasets, quickly revealing insights that once took weeks to dig out. This book takes you through the basics of getting your data ready, preparing your analysis, and creating reports. Then, you'll dig into more advanced features using DAX—the PowerBI programming language—even if you have no prior programming experience. This edition covers the latest updates to the software, including AI integration, enhanced customization, and improved real-time collaboration tools.
- Transform raw data into meaningful information and make data-driven decisions
- Import datasets and create dashboards, visualizations, and reports
- Use the Copilot AI assistant to speed up workflows and simplify tasks
- Integrate PowerBI with other Microsoft services like Excel and Azure
Businesses of all sizes can use PowerBI to manage their data. With Microsoft PowerBI For Dummies, you can quickly and easily set up PowerBI, learn the fundamentals, and handle complex reporting.
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Veröffentlichungsjahr: 2025
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Microsoft Power BI For Dummies®
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Introduction
About This Book
Foolish Assumptions
What’s New in This Edition
Icons Used in This Book
Beyond the Book
Where to Go From Here
Part 1: Put Your BI Thinking Caps On
Chapter 1: A Crash Course in Data Analytics Terms: Power BI Style
What Is Data, Really?
Looking Under the Power BI Hood
Knowing Your Power BI Terminology
Chapter 2: The Who, How, and What of Power BI
Highlighting the Who of Power BI
Understanding How Data Comes to Life
Identifying Your Who and What
Examining the Various Types of Data Analytics
Taking a Look at the Big Picture
Chapter 3: Oh, the Choices: Power BI Versions
Why Power BI Rather Than Excel?
Power BI Products in a Nutshell
Building a Decision Framework
Planning Your Power BI Governance and Licensing Strategy
Chapter 4: Power BI: The Highlights
Power BI Desktop: Connecting, Transforming, and Visualizing Data
Power BI Service: Sharing Your Work Far and Wide
Part 2: It’s Time to Have a Data Party
Chapter 5: Preparing Data Sources
Getting Data from the Source
Managing Data Source Settings
Working with Shared versus Local Datasets
Accessing Data via Storage Modes and Modalities
Considering the Query
Chapter 6: Getting Data from Dynamic Sources
Getting Data from Microsoft-Based File Systems
Working with Relational and Non-Relational Data Sources
Importing Data from a Nonrelational Data Source
Importing JSON File Data into Power BI
Importing Data from Online Sources
Discovering Modern Semantic Modeling in Power BI and Microsoft Fabric
Dealing with Modes for Dynamic Data
Fixing Data Import Errors
Chapter 7: Cleansing, Transforming, and Loading Your Data
Engaging Your Detective Skills to Hunt Down Anomalies and Inconsistencies
Stepping through the Data Lifecycle
Evaluating and Transforming Column Data Types
Configuring Queries for Data Loading
Resolving Errors During Data Import
Curating Data Using Microsoft Copilot for Power BI
Part 3: The Art and Science of Power BI
Chapter 8: Crafting the Semantic Model
An Introduction to Semantic Models
Dealing with Table and Column Properties
Managing Cardinality and Direction
Chapter 9: Designing and Deploying Semantic Models
Creating a Semantic Model Masterpiece
Managing Relationships
Arranging Data
Working with Extended Semantic Models
Publishing Data Models
Chapter 10: Perfecting the Semantic Model
Matching Queries with Capacity
Deleting Unnecessary Columns and Rows
Swapping Numeric Columns with Measures and Variables
Reducing Cardinality
Reducing Queries
Converting to a Composite Model
Creating and Managing Aggregations
Chapter 11: Visualizing Data
Looking at Report Fundamentals and Visualizations
Dealing with Table-Based and Complex Visualizations
Creating Charts with the Q&A Feature
Adding Smart Narratives for Busy People
Chapter 12: Pumping Out Reports
Formatting and Configuring Report Visualizations
Filtering and Sorting
Configuring the Report Page
Refreshing Data
Chapter 13: Diving into Dashboarding
Creating a New Dashboard
Enriching Your Dashboard with Content
Pinning Reports
Customizing with Themes
Working with Dashboard Layouts
Setting Alerts and Establishing Subscriptions
Subscribing to Reports and Dashboards
Chapter 14: Sharing and the Power BI Workspace
Working Together in a Workspace
Creating and Configuring Apps
Slicing and Dicing Data
Troubleshooting the Use of Data Lineage
Datasets, Dataflows, and Lineage
Defending Your Data Turf
Part 4: Oh, No! There’s a Power BI Programming Language!
Chapter 15: Digging into DAX
Discovering DAX
Dealing with Data Types
Operating with Operators
Making a Statement
Ensuring Compatibility
Chapter 16: Digging Deeper into DAX
Working with DAX Parameters and Naming Conventions
Using Formulas and Functions
Working with Variables
Writing DAX Formulas
Best Practices for DAX Coding and Debugging in Power BI
Part 5: Enhancing Your Power BI Experience
Chapter 17: Making Your Data Shine
Establishing a Schedule
Protecting the Data Fortress
Sharing the Data Love
Refreshing Data in Baby Steps
Treating Data Like Gold
Configuring for Big Data
Governing Your Power BI Data
Chapter 18: Extending the Power BI Experience
Linking Power Platform and Power BI
Powering Up with Power Apps
Integrating OneDrive, SharePoint, and Teams with Power BI
Embedding Power BI Reports in SharePoint Online
Unleashing Dynamics 365 for Data Analytics
Collaboration with Microsoft Teams and Power BI
Chapter 19: Enhancing Power BI with Fabric and Copilot
Knowing When to Introduce Fabric and Copilot
Getting to Know Fabric for Power BI
Preparing and Managing Data with Fabric
Getting Smarter with Copilot
Best Practices and Pitfalls When Using Fabric and Copilot with Power BI
Part 6: The Part of Tens
Chapter 20: Ten Ways to Optimize DAX Using Power BI
Focusing on Logic
Formatting Your Code
Keeping the Structure Simple (KISS)
Staying Clear of Certain Functions
Making Your Measures Meaningful
Filtering with a Purpose
Transforming Data Purposefully
Playing Hide-and-Seek with Your Columns
Optimizing for Large Models
Rinse, Repeat, Recycle
Chapter 21: Ten Ways to Make Accessible and User-Friendly Reports
Navigating with the Keyboard
Having a Screen Reader As Your Companion
Standing Out with Contrast
Recognizing Size Matters (with Focus Mode)
Switching between Data Tables and Visualizations
A Little Extra Text Goes a Long Way
Setting Rank and Tab Order
It’s All About Titles and Labels
Leaving Your Markers
Keeping with a Theme
Chapter 22: Ten Useful Power BI Resources
Microsoft Learn
Microsoft Documentation
Power BI Social Forums
Power BI Video Channel
Definitive News Source
Podcast
Learning Platforms for Power BI Skills
Programming Language Tutorials
GitHub Repositories and Sample Templates
Annual Events and Conferences
Index
About the Author
Connect with Dummies
End User License Agreement
List of Tables
Chapter 2
TABLE 2-1 Types of Analytics Produced in Power BI
Chapter 3
TABLE 3-1 Power BI License Types
TABLE 3-2 Comparison of Power BI Licensing Options
Chapter 4
TABLE 4-1 Comparison of Power BI Refresh Types
Chapter 5
TABLE 5-1 Comparing Data Connectivity Modes
Chapter 6
TABLE 6-2 Common Data Errors and Fixes
Chapter 7
TABLE 7-1 Join Types
TABLE 7-2 Fuzzy Matching Options
Chapter 8
TABLE 8-1 Comparison of Schema Types
TABLE 8-2 Dimensional Data Approaches
TABLE 8-3 Table Relationship Types
Chapter 9
TABLE 9-1 Sections of the Power BI Model View Home Ribbon
Chapter 10
TABLE 10-1 Cardinality and Direction
Chapter 15
TABLE 15-1 DAX Data Types
TABLE 15-2 Arithmetic Operators
TABLE 15-3 Comparison Operators
TABLE 15-4 Logical Operators
TABLE 15-5 Text Operator
TABLE 15-6 Operator Order
TABLE 15-7 Statements
Chapter 16
TABLE 16-1 Parameter Naming
TABLE 16-2 Elements of DAX Formulas
TABLE 16-3 Calculated Columns versus Measures
Chapter 18
TABLE 18-1 Comparing OneDrive, SharePoint, and Teams
Chapter 19
TABLE 19-1 Contrasting Power BI and Fabric Workspaces
TABLE 19-2 Storage Options in Fabric
TABLE 19-3 Cleansing and Transformation Activities in Microsoft Fabric
TABLE 19-4 Comparing Dataflows, Lakehouse, and Direct Lake Mode
TABLE 19-5 Using Fabric and Copilot with Power BI
List of Illustrations
Chapter 1
FIGURE 1-1: An example of structured data.
FIGURE 1-2: My Workspace in Power BI Service.
FIGURE 1-3: A sample Power BI report.
FIGURE 1-4: The Reports tab in Power BI Desktop.
FIGURE 1-5: Locating your dashboards.
FIGURE 1-6: The Navigation pane.
Chapter 2
FIGURE 2-1: A prototype data lifecycle for an organization using Power BI.
Chapter 4
FIGURE 4-1: Power BI Desktop navigation.
FIGURE 4-2: Getting data from the Power BI Ribbon.
FIGURE 4-3: Accessing a data source.
FIGURE 4-4: A data model in the Model Viewer.
FIGURE 4-5: A sample dashboard that aggregates many visual sources.
FIGURE 4-6: Publishing items using the Power BI Desktop File menu.
FIGURE 4-7: Publishing items using the Power BI Desktop Ribbon.
Chapter 5
FIGURE 5-1: Finding the CSV File Connector in Power BI Desktop.
FIGURE 5-2: Selecting data in the Navigator.
FIGURE 5-3: Your data, loaded into the Power Query Editor.
FIGURE 5-4: Selecting Folder from Get Data.
FIGURE 5-5: Files from a folder load into Power BI.
FIGURE 5-6: Using the Applied Steps area to update the data source settings.
FIGURE 5-7: The Data Source Settings button.
FIGURE 5-8: Accessing a Power BI semantic model.
FIGURE 5-9: Connecting to a shared dataset in Power BI Service.
FIGURE 5-10: Start and Stop query diagnostics.
Chapter 6
FIGURE 6-1: OneDrive file path.
FIGURE 6-2: SharePoint Folder path.
FIGURE 6-3: Azure SQL database location.
FIGURE 6-4: Entering credentials for a SQL-based relational database.
FIGURE 6-5: Selecting the authentication method to connect.
FIGURE 6-6: Selecting the tables from the Navigator for import.
FIGURE 6-7: Selecting the Cosmos DB data source.
FIGURE 6-8: Connecting to the Cosmos DB, a Microsoft NoSQL database.
FIGURE 6-9: Connecting to an online service in Power BI Desktop.
FIGURE 6-10: Common interface to enter login credentials for online services.
FIGURE 6-11: Changing modes in Properties pane.
Chapter 7
FIGURE 7-1: Addressing column quality issues.
FIGURE 7-2: A look at column distribution.
FIGURE 7-3: Data preview options in the Power Query Editor.
FIGURE 7-4: Data preview of the column profile and column quality.
FIGURE 7-5: Replace Values menu option.
FIGURE 7-6: Replacing values.
FIGURE 7-7: An error, as presented in Power Query.
FIGURE 7-8: The Merge Columns option.
FIGURE 7-9: Columns that have been merged.
FIGURE 7-10: The available data types.
FIGURE 7-11: Changing the data type.
FIGURE 7-12: Inserting a step.
FIGURE 7-13: Merging datasets using Merge Query.
FIGURE 7-14: Removing queries.
FIGURE 7-15: Using Copilot to identify quality issues and recommendations.
FIGURE 7-16: Using Copilot to identify quality issues and recommendations.
FIGURE 7-17: Optimizing queries with Microsoft Copilot.
Chapter 8
FIGURE 8-1: A flat schema.
FIGURE 8-2: Facts versus dimensions.
FIGURE 8-3: A star schema prototype.
FIGURE 8-4: A snowflake schema.
FIGURE 8-5: A denormalized schema.
FIGURE 8-6: A hybrid schema.
FIGURE 8-7: Calculation capabilities on the Modeling tab.
FIGURE 8-8: Some Quick Measure options.
FIGURE 8-9: The completed quick measure for the sum of running totals calculati...
FIGURE 8-10: Populating New Measure on the Formula bar.
FIGURE 8-11: Flattening a hierarchy.
FIGURE 8-12: Example of a multi-level hierarchy.
FIGURE 8-13: Table property selection.
FIGURE 8-14: Column properties.
FIGURE 8-15: Editing relationships.
FIGURE 8-16: A cross-filter direction example.
Chapter 9
FIGURE 9-1: The Table View tab.
FIGURE 9-2: The Model View tab.
FIGURE 9-3: The Home Ribbon in Model view.
FIGURE 9-4: The Power Query Editor Ribbon.
FIGURE 9-5: The Navigator window in Table view.
FIGURE 9-6: Using the Column Tools tab to change the data type.
FIGURE 9-7: A list of data type options.
FIGURE 9-8: Numeric formatting options.
FIGURE 9-9: The Summarization options on the Column Tools tab.
FIGURE 9-10: The Data Categories options.
FIGURE 9-11: The Enter Data button.
FIGURE 9-12: Creating a table.
FIGURE 9-13: Updating the table name in Model view.
FIGURE 9-14: Deleting a table from the model.
FIGURE 9-15: Asking whether you’re sure.
FIGURE 9-16: Clicking the Gear to open the Source under Applied Steps.
FIGURE 9-17: Before modifications were made to the source file and then refresh...
FIGURE 9-18: Refreshed dataset based on changes made in the source file.
FIGURE 9-19: The New Relationship interface.
FIGURE 9-20: Grouping by capabilities.
FIGURE 9-21: Hiding data.
FIGURE 9-22: The Publish button for deploying the data model and reports to Pow...
Chapter 10
FIGURE 10-1: Query reduction options.
FIGURE 10-2: The Choose Columns icon on the Ribbon Home tab.
FIGURE 10-3: Selecting columns to aggregate.
FIGURE 10-4: Aggregated columns in the Power Query Editor.
FIGURE 10-5: The Group By dialog box and its interface in Power Query.
FIGURE 10-6: Setting up the Group By feature to count rows for each funding age...
FIGURE 10-7: The aggregated column.
FIGURE 10-8: The updated Data Model view.
FIGURE 10-9: Manage aggregations from the Data pane.
FIGURE 10-10: Managing aggregations.
Chapter 11
FIGURE 11-1: The Report view button.
FIGURE 11-2: Overview of Report view in Power BI.
FIGURE 11-3: The Visualizations pane.
FIGURE 11-4: The Data pane.
FIGURE 11-5: Filtering data based on a category.
FIGURE 11-6: Setting up filtering conditions with quantitative data.
FIGURE 11-7: A stacked bar chart.
FIGURE 11-8: Using multiple dimensions in a stacked bar chart.
FIGURE 11-9: A stacked column chart.
FIGURE 11-10: Using multiple dimensions in a stacked column chart.
FIGURE 11-11: A clustered bar chart.
FIGURE 11-12: A clustered column chart.
FIGURE 11-13: A 100% stacked bar chart.
FIGURE 11-14: A 100% stacked column chart.
FIGURE 11-15: A line chart.
FIGURE 11-16: An area chart.
FIGURE 11-17: A line chart and a stacked column chart.
FIGURE 11-18: A ribbon chart.
FIGURE 11-19: A waterfall chart.
FIGURE 11-20: A funnel chart.
FIGURE 11-21: Scatterplot.
FIGURE 11-22: A pie chart.
FIGURE 11-23: A donut chart.
FIGURE 11-24: A treemap.
FIGURE 11-25: A map.
FIGURE 11-26: A filled map.
FIGURE 11-27: Using a gauge.
FIGURE 11-28: A card visual.
FIGURE 11-29: A multi-card visual.
FIGURE 11-30: A KPI example.
FIGURE 11-31: A slicer tied to a table.
FIGURE 11-32: Table visualization.
FIGURE 11-33: A matrix.
FIGURE 11-34: A decomposition tree.
FIGURE 11-35: Working with key influencers.
FIGURE 11-36: Prescribed questions and answers.
FIGURE 11-37: Self-created questions and answers.
FIGURE 11-38: Smart Narratives.
Chapter 12
FIGURE 12-1: Formatting features found in the Visualizations pane.
FIGURE 12-2: The General settings for formatting a visualization.
FIGURE 12-3: Configuring the legend.
FIGURE 12-4: The Data Colors and Data Labels options.
FIGURE 12-5: The title settings.
FIGURE 12-6: Setting the background color.
FIGURE 12-7: Integration of lock aspect, background, and border.
FIGURE 12-8: Visual Header configuration options.
FIGURE 12-9: The Conditional Formatting button.
FIGURE 12-10: The Conditional Formatting interface.
FIGURE 12-11: Configuring fields for a specific visuals example.
FIGURE 12-12: Your Filtering menu options.
FIGURE 12-13: Configuring a report page.
FIGURE 12-14: Saving dataset and visualizations in the report to a workspace.
FIGURE 12-15: Creating an updated version of a reporting package for Power BI S...
FIGURE 12-16: The Recent menu under the Home button in Power BI Service.
FIGURE 12-17: Selecting an item from the selected Workspace in Power BI Service...
FIGURE 12-18: Your export choices.
FIGURE 12-19: Create a new paginated report.
FIGURE 12-20: The Create Power BI Paginated Report button.
FIGURE 12-21: The Report Builder interface.
FIGURE 12-22: Switching from Editing to Viewing mode in the Power BI Paginated ...
FIGURE 12-23: Exporting a Report as a PDF file.
Chapter 13
FIGURE 13-1: Creating a dashboard.
FIGURE 13-2: Naming a new dashboard.
FIGURE 13-3: A blank dashboard canvas.
FIGURE 13-4: Accessing the Add a Tile menu.
FIGURE 13-5: Selecting a tile type.
FIGURE 13-6: Configuring a tile.
FIGURE 13-7: Customizing a content tile on the dashboard canvas.
FIGURE 13-8: The Pin button.
FIGURE 13-9: Opting for a new or existing dashboard.
FIGURE 13-10: A finished dashboard with tiles.
FIGURE 13-11: Choosing the Dashboard Theme option.
FIGURE 13-12: Choosing a prebuilt theme.
FIGURE 13-13: Customizing a theme.
FIGURE 13-14: The web layout for a dashboard.
FIGURE 13-15: A dashboard’s mobile layout.
FIGURE 13-16: The Set Alert button.
FIGURE 13-17: The Alerts pane.
FIGURE 13-18: Change conditions for alerts.
FIGURE 13-19: Becomes conditions for alerts.
FIGURE 13-20: Active alert in the Alerts pane.
FIGURE 13-21: Alert notification in Microsoft Teams.
FIGURE 13-22: The Subscription button.
FIGURE 13-23: Configuring a subscription.
FIGURE 13-24: Turning subscription on/off and testing a subscription.
Chapter 14
FIGURE 14-1: A list of workspace apps.
FIGURE 14-2: The My Workspace interface.
FIGURE 14-3: The content of a workspace in Power BI.
FIGURE 14-4: The navigation menu in Power BI Service.
FIGURE 14-5: The +New Workspace button.
FIGURE 14-6: Configuring the standard features of a workspace.
FIGURE 14-7: Configuring the advanced features of a workspace.
FIGURE 14-8: Assigning workspace access.
FIGURE 14-9: Adding new content to an app workspace.
FIGURE 14-10: Granting permissions as part of the app creation process.
FIGURE 14-11: The Dashboard menu under Workspaces.
FIGURE 14-12: The Report menu under Workspaces.
FIGURE 14-13: The Download prompt for the Excel add-on.
FIGURE 14-14: The Quick Insights feature.
FIGURE 14-15: A usage metrics report.
FIGURE 14-16: Gaining access to data lineage.
FIGURE 14-17: An example of data lineage.
FIGURE 14-18: Example of a Dataset card.
FIGURE 14-19: A Report card.
FIGURE 14-20: A Dashboard card.
FIGURE 14-21: Arrows between each asset in a workspace.
FIGURE 14-22: Drilling down into a Dataset card.
FIGURE 14-23: Showing the impact of an action across a workspace.
FIGURE 14-24: Enabling sensitivity labels in Power BI.
Chapter 15
FIGURE 15-1: A syntax example.
FIGURE 15-2: A row context example.
FIGURE 15-3: A filter context example.
FIGURE 15-4: DAX calculation options under Table tools.
FIGURE 15-5: A calculated measure in the DAX editor.
FIGURE 15-6: A calculated measure added to the Fields pane.
FIGURE 15-7: Calculated columns in Report view.
FIGURE 15-8: Creating a calculated table from Table view.
FIGURE 15-9: DAX data types.
FIGURE 15-10: Power Query data types.
Chapter 16
FIGURE 16-1: A sample function.
FIGURE 16-2: The Data pane, showing a calculated column.
FIGURE 16-3: The table output.
FIGURE 16-4: Setting a column’s calculation type.
FIGURE 16-5: Power BI automatically recognizes a modified format type (currency...
FIGURE 16-6: DAX editor query testing.
Chapter 17
FIGURE 17-1: The Schedule Refresh interface.
FIGURE 17-2: Downloading a data gateway.
FIGURE 17-3: Data gateway options.
FIGURE 17-4: Modifying data gateways.
FIGURE 17-5: Configuring security roles for group membership in Power BI Deskto...
FIGURE 17-6: Managing workspace security.
FIGURE 17-7: Configuring workspace security.
FIGURE 17-8: RLS error.
FIGURE 17-9: Assigning users and groups to a semantic model in Power BI Service...
FIGURE 17-10: Managing parameters.
FIGURE 17-11: Filtering rows using the RangeStart and RangeEnd parameters.
FIGURE 17-12: Setting up an incremental refresh.
FIGURE 17-13: Configuring endorsements.
FIGURE 17-14: Endorsements listed in a Power BI workspace.
FIGURE 17-15: Large Semantic Model Storage Format configuration.
Chapter 18
FIGURE 18-1: Data connector options in Power Platform.
FIGURE 18-2: Placing a Power App in a Power BI Report.
FIGURE 18-3: Select an existing app or create a new one within the Power BI Rep...
FIGURE 18-4: Saving the App within Power Apps.
FIGURE 18-5: Power App Integrated within Power BI Report.
FIGURE 18-6: Saving a PBIX File to OneDrive.
FIGURE 18-7: Choosing between a SharePoint folder and SharePoint list.
FIGURE 18-8: Accessing the Power BI Embed functionality for SharePoint.
FIGURE 18-9: Adding a web part in SharePoint.
FIGURE 18-10: Locating the Power BI web part.
FIGURE 18-11: Adding the report to the SharePoint page.
FIGURE 18-12: Copying the link into the URL bar to present the Power BI report.
FIGURE 18-13: Complete integration of Power BI within the SharePoint page.
FIGURE 18-14: Configuring your Power Automate Flow with Power BI.
FIGURE 18-15: Accessing the Power Automate Visualization.
FIGURE 18-16: Configuring the Power Automate Visualization.
FIGURE 18-17: Selecting the Power Automate template to support the dataset.
FIGURE 18-18: A Run Flow trigger button.
FIGURE 18-19: The menu for selecting a Dynamics 365 Instance.
FIGURE 18-20: Integrating Power BI into Teams Configuration.
FIGURE 18-21: Selecting Power BI from the Teams App Gallery.
FIGURE 18-22: A report integrated into Teams from Power BI Service.
Chapter 19
FIGURE 19-1: Microsoft Fabric capabilities.
FIGURE 19-2: Creating a new dataflow.
FIGURE 19-3: Removing a column by using the Fabric Power Query Editor.
FIGURE 19-4: An active dataflow in the Fabric My Workspace.
FIGURE 19-5: Changing a numeric value to a text value in the Fabric Power Query...
FIGURE 19-6: Evaluating changes made in the Query Settings pane.
FIGURE 19-7: Accessing schedule for a Dataflow.
FIGURE 19-8: Creating a Dataflow schedule.
FIGURE 19-9: Connecting to Lakehouse data in the OneLake storage catalog.
FIGURE 19-10: Simplifying the data schema with Copilot.
FIGURE 19-11: Selecting queries to validate model changes.
FIGURE 19-12: Applying conversational instructions into the semantic model.
FIGURE 19-13: DAX Formula created based on a prompt.
FIGURE 19-14: Giving Copilot complete control of the report and narrative exper...
FIGURE 19-15: Report output based on a conversational dialogue with Copilot.
FIGURE 19-16: Conversation and response in the Copilot agent window.
FIGURE 19-17: A text-based summarization of a Copilot-generated report.
Chapter 21
FIGURE 21-1: Accessing accessible theme options under the View tab.
FIGURE 21-2: Changing the focus.
FIGURE 21-3: Data table output.
FIGURE 21-4: Adding alt text.
FIGURE 21-5: Setting the tab order.
FIGURE 21-6: Representative titles and labels for a visual.
FIGURE 21-7: Title and labels that are highly configured.
FIGURE 21-8: Title and labels with minimum configuration.
FIGURE 21-9: Working with markers.
FIGURE 21-10: Configuring markers in the Visualizations pane.
Guide
Cover
Table of Contents
Title Page
Copyright
Begin Reading
Index
About the Author
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Microsoft® Power BI For Dummies®, 2nd Edition
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Introduction
Data isn’t just everywhere — it is everything. In today’s connected world, every click, swipe, purchase, and interaction generates data. The challenge? Organizations drown in their data because they struggle to transform raw information into the actionable intelligence required to drive positive business outcomes.
Enter the era of democratized analytics, where business intelligence isn’t just a buzzword in your IT department or unique to the data science expert. Today, every employee handles data, from frontline workers to C-suite executives. The expectation is that employees — folks like you and me — explore, understand, and act on that data.
Transforming raw data to produce valuable insights requires that you do more than use “just another tool” that sits on the proverbial shelf, waiting to be used ad hoc. Sure, spreadsheets served their purpose last decade (and still do today), but modern businesses require a platform that can handle massive datasets, provide real-time analysis, integrate artificial intelligence, and enable collaboration across teams and departments. The question is not whether your organization will need sophisticated business intelligence tools; it’s how quickly you will acquire and begin to use them.
Microsoft Power BI has evolved far beyond its humble beginnings as a SQL Server reporting extension tool. What started in 2010 as a bunch of Excel add-ins has matured into a comprehensive analytics ecosystem that sits at the heart of Microsoft Power Platform alongside Power Apps, Power Automate, and Power Pages. As you see in this book, Power BI uses the newest available AI capabilities and can connect to hundreds of other data platforms, not just those owned by Microsoft. Power BI also provides enterprise-grade security and governance features for all users, from individual users to global organizations.
Whether this is the first time you are noodling around building a dashboard or architecting an enterprise solution that requires real-time analytics, you can be assured that Power BI will transform how you think and act with data. It bridges the gap between the raw information and strategic decision-making. Users throughout an organization can use Power BI to create stunning visualizations, discover hidden patterns, and share insights that propel business outcomes.
About This Book
I wrote this book for anyone interested in business analytics who wants to find out how they can apply the general capabilities of the Power BI platform to their work. It doesn't matter whether you’re a novice or a power user — you’ll benefit from reading this book.
The second edition of Microsoft Power BI For Dummies reflects the dramatic evolution of both the Power BI user experience and the broader analytics landscape. This edition incorporates and expands on the fundamental concepts introduced in the first edition. You get more information about intelligent automation, artificial intelligence, data warehousing tools, and methods for managing scalable datasets. You also discover the expanded collaboration possibilities that didn’t exist just a few years back.
This book is intended for the following business roles:
Business analyst:
As a business analyst, you’re tasked with many responsibilities. Maybe you're the requirements-gathering expert, the configuration guru, the designer, or even the quasi-developer. This book can serve as a valuable resource for many of the critical tasks you may encounter in the field. The reality is that your role will expand with the use of Power BI. You’ll be expected to be part detective, part designer, and part strategist. For you, this book serves as a practical framework to tackle real-world analytics challenges, from gathering requirements to delivering solutions.
Data professional:
Data is complex — make no mistake about it. This book doesn't help you tackle the formulas behind the scenes or tell you how to construct and programmatically code many sophisticated reports, dashboards, visuals, and KPIs. It does, however, help you understand the foundational activities across the Power BI platform if this is your first foray into using Microsoft's business intelligence (BI) platform. You'll be able to ingest data quickly, conduct data analysis, and build relatively sophisticated reports after reading this book. You now operate in an ecosystem where technical skills must blend with business acumen. This book won’t make you a world-renowned DAX expert or a Power BI master ninja overnight, but it provides you with the foundational knowledge needed to architect your scalable solutions, implement best practices, and communicate effectively to business stakeholders, not just technical wizards.
Developer:
This book isn't specifically for you, but you can find plenty of tips, tricks, and techniques you can learn throughout the book. Power BI is a collection of products that require users to understand several fundamental programming languages, including DAX and SQL. This book scratches the surface of these topics ever so slightly. Take a look at the chapters on DAX in
Part 4
if you want an introduction or a refresher. As you learn more about the integration between Microsoft Fabric, pipelines, and the various data lake options, you can dabble with Power BI’s expanded API capabilities, custom visualizations, and embedded analytics to support application integration. Remember, this book is not meant to be a technical deep-dive; it highlights the integration patterns and opportunities to help build your solution.
IT professional:
If you’re a cloud expert, systems engineer, or database professional, or you fill another IT role, this book doesn't provide you with all the technical answers you’re looking for. Instead, the book serves as a starting point if you want to leap into the world of Microsoft enterprise business intelligence. Today, you must strike a balance between user empowerment and governance, security, and scalability requirements. This book addresses operational aspects of Power BI deployment such as tenant management, data source connectivity, and the security considerations — the basics that allow you to sleep at night, knowing your organization’s data is safe and sound.
Manager or executive:
Often, the deliverables created in Power BI are built for managers and executives. Power BI now offers hundreds of data connectors for data extraction, report development, visualization support, and dashboard creation. Under your guidance, these deliverables are created by analysts, developers, and data professionals. Reading
Microsoft Power BI For Dummies,
2nd Edition, can help you better understand the art of the possible. In your role, you act on data as both a strategic asset and a practical tool to help move the organization forward. With Power BI, you can accelerate data analysis, integrate with countless data sources to get a 360-degree picture of your data universe, and guide meaningful conversations with teams and customers.
Foolish Assumptions
I've written this book for users who want to learn about the critical features across the three Power BI platforms: Desktop, Service, and Mobile. This book isn’t intended to be a crash course for certification or a deep dive into administration or coding for Power BI. You can find other books for these purposes.
Power BI is a pretty big application, as you can probably already tell. Microsoft assumes that its interfaces are relatively simple to figure out, and that most users can create reports and dashboards without too much trouble. Here’s the truth: The interfaces can be overwhelming, depending on which product you’re using. Just to keep users on their toes, a great deal of bells and whistles appear across each Power BI platform.
Now I need to forewarn you here about what you should expect from time to time in this book. You’ll be able to utilize the free features provided by Microsoft to learn Power BI for most exercises. That said, Power BI Service, Microsoft Fabric, and Copilot do require premium licenses; therefore, you’ll need to spend money or have your system administrator provide you access. If you begin to scratch your head and wonder why you can’t access the feature, you’ve probably run into a premium roadblock.
Since the last edition, the Power BI user experience has become more intuitive, and the platform has incorporated hundreds of new features, meaning that there is a bit more to learn. This new edition helps you know about the key features and updates, as well as how to use such features as a business professional rather than a data scientist.
Throughout this book, I point you directly to the Microsoft Power BI website, when appropriate, where you can find resources to dig a bit deeper from time to time, on technical capabilities you may need to know about.
Because Power BI is made up of many components, I’ve made some assumptions about your configuration for this book as you follow along on the journey:
You have downloaded a copy of Power BI Desktop.
Some things in life are free, and this is one of them. Microsoft provides its users with the Desktop client for free. The Desktop client is designed to enable users to build data models, reports, and dashboards for personal use. That's where it ends, though. You do need an online account to share and collaborate. About half of the steps listed in this book can be completed using the Desktop client. Power BI serves as your primary development environment, as the Desktop is where individual productivity begins. Power BI Service, on the other hand, is meant for online collaboration and sharing.
You have at least signed up for a Power BI and Fabric Trial account, but preferably, you have a Power BI Pro account (at a minimum).
A free account allows basic functionality so that you can test the exercises in this book, but if you intend to collaborate or share work, you need to get a Power BI Pro license (at a minimum). With the online companion, you can distribute your outputs in read-only format. If you want others to edit and manipulate the data, you have to pay for the Pro or Premium per-user version. The larger your dataset, the more likely you will want the upgrade, anyway.
You have access to the Internet.
Even with the Desktop client, you need an Internet connection to access datasets from the Internet. Power BI Desktop also heavily relies on Internet connectivity for the advanced features covered in this book’s second edition.
You have a meaningful dataset.
Meaningful
generally means at a minimum hundreds, if not thousands of records with at least 10 – 20 columns. In the example used throughout the book, you are provided a dataset with 3000+ rows and 275 columns. I point you to open-source datasets on sites such as
Kaggle.com
so that you can truly appreciate the power available in Power BI.
What’s New in This Edition
Beyond advancements Microsoft introduced to the Power BI user experience, which means that many features were moved to other locations within the platform, Power BI now incorporates artificial intelligence capabilities, new data preparation tools, extensible visualization options, and hundreds of new integration possibilities. In Microsoft Power BI For Dummies, 2nd Edition, you see how to deploy Power BI based on real-world experiences and become more familiar with the latest best practices.
Icons Used in This Book
Throughout Microsoft Power BI For Dummies, you see some icons along the way. Here’s what they mean:
Tips highlight shortcuts or essential suggestions for doing things quicker, faster, and more efficiently in Power BI.
If you see the Remember icon, pay particular attention because these gotchas can make Power BI a bit difficult to understand. Don't worry, though — I'll help you find a workaround.
Technical Stuff is a way for you to consider exploring the inner workings of Power BI and perhaps how it integrates with other applications in a bit more detail. That means there may be a configuration to a data source that has a nuance or an advanced reporting feature that may help shape your data a smidgen. These items are here to help you on a case-by-case basis.
This icon points to useful content available to you out there on the World Wide Web.
Do not take warnings as a sign of panic. They appear once in a while, though, to make you aware of a common issue or product challenge many users face. Again, do not fret!
Beyond the Book
In addition to the content you’re reading in this book, you have access to a free Power BI Cheat Sheet that can give you a hand when it comes to creating compelling dashboards, valuable reports, and structured DAX code. To find the Cheat Sheet, go to www.dummies.com and enter Microsoft Power BI For Dummies in the Search box.
You can find the dataset used in this book on Kaggle.com, located at https://www.kaggle.com/jackhymanpowerbi/datasets.
Where to Go From Here
This book is designed as both a road map and a reference. For beginners, the chapters build step by step, from foundational concepts (Part 1) to data modeling (Part 2), visualization (Part 3), programming (Part 4), and advanced features like Microsoft Fabric and Copilot (Part 5). But each part also stands on its own, so you can jump directly to the topics that matter most to your work without losing context.
As you continue your Power BI journey, remember that the platform is constantly evolving. Fabric adds scalability and governance, and Copilot accelerates analysis with AI-driven insights. The key is to continue experimenting, applying these concepts to your own data, and leveraging the Power BI community. Whether you follow the book in order or chart your own path, you now have the tools to move from beginner to confident practitioner and beyond.
Part 1
Put Your BI Thinking Caps On
IN THIS PART …
Understand the three types of data.
Learn key Power BI terminology.
Explore the Power BI licensing model.
Master the fundamentals needed to transform your organization's data into meaningful insights.
Chapter 1
A Crash Course in Data Analytics Terms: Power BI Style
IN THIS CHAPTER
Figuring out the different types of data Power BI can handle
Understanding your options for business intelligence tooling
Familiarizing yourself with Power BI terminology
Data is everywhere — literally. From the moment you awaken until the time you sleep, some system somewhere collects data on your behalf. Even as you sleep, data is being generated that correlates to some aspect of your life. What is done with this data is often the proverbial million-dollar question. Does the data make sense? Does it have any structure? Is the dataset so voluminous that finding what you’re looking for is like finding a needle in a haystack? Or is it more like you can’t even find what you need unless you have a special tool to help you navigate?
I answer that last question with an emphatic yes, and that’s where data analytics and business intelligence join the party. And let’s be honest: The party can be overwhelming if data is consistently generating something on your behalf.
Dealing with data isn’t always a chore — data can be fun to explore as well. Sometimes it’s easy to figure out precisely what is needed to solve a problem, but at other times you need to put on your Sherlock Holmes deerstalker cap. Why? Because the data you’re working with may lack structure and meaning. Of course, you’re bound to take up tools to help you play the role of detective, evaluator, designer, and curator.
In this chapter, I discuss the different types of data you may encounter along your journey. I review the key terminology that you should become familiar with upfront. Don’t worry: It’s not like you need to memorize a dictionary. You learn a few key concepts to give you a head start in Power BI and business intelligence. Are you ready to go?
What Is Data, Really?
Ask a hundred people in a room what the definition of data is and you may receive one hundred different answers. Why is that? Because, in the world of business, data means a lot of different things to a lot of different people. So, here’s try to get a streamlined response. Data contains facts. Sometimes, the facts make sense; sometimes, they’re meaningless unless you add a bit of context.
The facts can sometimes be quantities, characters, symbols, or a combination of sorts that come together when collecting information. The information allows people — and more importantly, businesses — to make sense of the facts that, unless brought together, make absolutely no sense whatsoever.
When you have an information system full of business data, you also must have a set of unique data identifiers you can use so that, when searched, it’s easy to make sense of the data in the form of a transaction. Examples of transactions may include the number of jobs completed, inquiries processed, income received, and expenses incurred.
The list can go on and on. To gain insight into business interactions and conduct analyses, your information system must have relevant and timely data that is of the highest quality.
Data isn’t the same as information. Data is the raw facts. That means you should think of data in terms of the individual fields or columns of data you may find in a relational database or perhaps the loose document (tagged with some descriptors called metadata) stored in a document repository. On their own, these items are unlikely to make much sense to you or to a business. And that’s perfectly okay — sometimes. Information is the collective body of all those data parts, that results in the factoids making logical sense.
Working with structured data
Have you ever opened a database or spreadsheet and noticed that data is bound to specific columns or rows? For example, would you ever find a United States zip code containing letters of the alphabet? Or, perhaps when you think of a first name, middle initial, and last name, you notice that you always find letters in those specific fields. Another example is when you’re limited to the number of characters you can input into a field. Think of Y as Yes; N is for No. Anything else is irrelevant.
What I’m describing here is called structured data. When you evaluate structured data, you notice that it conforms to a tabular format, meaning that each column and row must maintain an interrelationship. Because each column has a representative name that adheres to a predefined data model, your ability to analyze the data should be straightforward. This embodies your classic relational data system.
If you’re using Power BI, you notice that structured data conform to a formal specification of tables with rows and columns, commonly referred to as a data schema. In Figure 1-1, you find an example of structured data as it appears in a Microsoft Excel spreadsheet.
FIGURE 1-1: An example of structured data.
Whether you’re using Power BI for personal analysis, educational purposes, or business support, the most accessible data sources for BI tools are structured. Platforms that offer robust structured data options include Microsoft SQL Server, Microsoft Azure SQL Server, Microsoft Access, Azure Table Storage, Oracle, IBM DB2, MySQL, PostgreSQL, Microsoft Excel, and Google Sheets.
Looking at unstructured data
Unstructured data is ambiguous, having no rhyme, reason, or consistency whatsoever. Pretend that you’re looking at a batch of photos or videos. Are there explicit data points that one can associate with a video or photo? Perhaps, because the file itself may consist of a structure and be made of some metadata. However, the byproduct itself — the represented depiction — is unique. The data isn’t replicable; therefore, it’s unstructured. That’s why video, audio, photos, text files, and social media posts are considered unstructured data.
Adding semi-structured data to the mix
Semi-structured data does have some formality, but it isn’t stored in a relational system, and it has no set format. Fields containing the data are by no means neatly organized into strategically placed tables, rows, or columns. Instead, semi-structured data contains tags that make the data easier to organize and manage in some form of hierarchy. Nonrelational data systems or NoSQL databases are best associated with semi-structured data, in which the programmatic code, often serialized, is driven by the technical requirements. There is no hard-and-fast coding practice.
Common examples of semi-structured data include JSON files from web APIs, XML documents, and data from cloud applications. Power BI can work with these formats, making it possible to analyze data from modern web services and applications alongside your traditional databases and spreadsheets. This flexibility makes Power BI particularly valuable in today’s business environment, where data comes from many different sources and formats.
Looking Under the Power BI Hood
The Power BI platform combines cloud-based apps and services to help you organize, collect, manage, and analyze your data. Whether you’re working with a few hundred rows or millions of records, Power BI can handle datasets of various sizes and reveal patterns and trends that may not be obvious from the raw data. Unlike a tool such as Microsoft Excel, Power BI can simultaneously connect to and evaluate multiple data sources. The sources don’t need to be structured like a spreadsheet, either. They can include unstructured and semi-structured data. After connecting to these data sources and processing them, Power BI helps you create visually compelling outputs such as charts, graphics, reports, dashboards, and KPIs (key performance indicators).
As you see throughout this book, Power BI isn’t just a single application. It has desktop, online, and mobile components.
Across the Power BI platform, you are certain at some point to encounter one (or more) of the following products:
Power Query:
A data connection tool you can use to transform, combine, and enhance data across several data sources
Power Pivot:
A data modeling tool
Power BI Visualizations:
Built-in tools for creating interactive charts, graphs, maps, and other visuals
Power Q&A:
An AI-powered engine that allows you to ask questions about your data and receive responses using plain language.
Copilot in Power BI:
An AI assistant that helps create reports, write DAX formulas, and answer questions about your data
Power BI Desktop:
A free application that brings together data connection, modeling, and visualization capabilities in a single interface
Power BI Service:
A cloud-based platform for collaboration, sharing, and distributing reports and dashboards with others
In the following sections, I explore each product’s core functionality.
Posing questions with Power Query
Power Query is the capability that allows Power BI to create data connections and transform data among many different data sources, from databases like SQL Server and Oracle to cloud services, spreadsheets, and web APIs. Power Query also serves as your trusty helper to clean and shape (or transform) your raw data before analysis, handling tasks like removing empty rows, changing data types, and combining information from multiple sources. The transformed data can then be used in your Power BI reports and dashboards, enabling you to work more easily with information that may otherwise be messy or difficult to analyze. For the data analyst and engineer, these tools are invaluable.
Power Pivot is the Power BI data modeling tool that helps you create relationships between data sources, craft models of varying complexities, and build calculations for your analysis. With Power Pivot, you can connect tables from various sources and define how they relate, which enables you to analyze data across multiple datasets. For example, a sales manager can link customer records from a CRM system with revenue data from an ERP system to quickly see which customer segments are driving the most profit. Power Pivot uses a formula language called DAX (Data Analysis Expressions) to create custom calculations, but don’t worry — Power BI includes many built-in calculations, so you can get started without writing formulas. I cover DAX in Chapters 15 and 16.
Mastering Power BI Visualizations
Power BI visualizations help you see your data in a way that makes sense. For example, instead of scanning a long list of numbers, a bar chart can quickly show which department spends the most on travel. A map can display where most service requests come from so that you can spot regional patterns right away.
The key is that these visuals are interactive. You can click, filter, and drill into the details to answer specific questions. Later in this book, I walk you through the complete set of chart types available in Power BI and talk about when each one works best.
Interpreting data with Power Q&A
One of the biggest challenges for many users is data interpretation. Say, for example, that you’ve built this incredible data model using Power Pivot. Your data sample is significant in size, meaning that you need some way to make sense of all the data you’ve deployed in the model. But is the model useful? Possibly not, unless you have a way to interpret the data. That’s why Microsoft created Power Q&A, an AI-powered feature that allows you to ask questions about your data using plain English and get instant visual answers.
Power Q&A comes with AI assistance to help you ask better questions by suggesting queries based on your data. For example, you may type, “How many customers purchased red shirts in our Chicago store last month?” From there, Power Q&A can suggest follow-up questions such as “Which day of the week had the most sales?” or “How does this compare to last year?” Of course, this only works if your dataset includes those details. Power Q&A can’t generate insights from data you don’t have. When your data model is well-designed, these AI-driven prompts can guide you toward insights you may not think to ask on your own.
Guiding the way with Copilot
At first glance, Q&A and Copilot may seem to be the same thing, seeing as though both are AI assistants. Here’s how they differ: Copilot in Power BI is an AI assistant that helps you learn and use Power BI itself, whereas Power Q&A allows you to explore and analyze your data. Although Power Q&A helps you discover what’s in your data by asking questions like “How many red shirts sold last month,” Copilot enables you to figure out how to work with Power BI as a tool. For example, if you need to create a calculation, Copilot can write DAX formulas when you describe what you want to calculate. It can also suggest the best visualization types for your data and even help create entire reports based on your requirements. Whether you’re wondering “How do I calculate year-over-year growth” or “What’s the best chart to show regional sales trends,” Copilot provides step-by-step guidance to help you accomplish your goals. This makes it particularly valuable for beginners who know what they want to achieve but aren’t sure how to use Power BI’s features to get there.
Power BI Desktop
Power BI Desktop is a free application that combines Power Query, Power Pivot, Power BI Visualizations, and Power Q&A. Using Power BI Desktop, you can complete all your business intelligence activities under one umbrella — from connecting to data sources and building models to creating reports and dashboards. Power BI Desktop is where you do all your development work on your local computer. Microsoft updates Power BI Desktop monthly with new features and improvements, so you can always access the latest capabilities.
Power BI Service
Accessible at https://app.powerbi.com from any device with an Internet connection, Power BI Service allows you to publish the work you create in Power BI Desktop and share it with colleagues across your company.
Power BI Service is the cloud platform where your reports and dashboards come to life for your organization. You can access it at https://app.powerbi.com from any device with an Internet connection, but you’ll also need an organizational or educational email account to sign in. This means personal email addresses (like Gmail, Yahoo, or Outlook.com) won’t work for publishing and sharing reports in Power BI Service.
Once you’re set up, you can publish the reports you’ve built in Power BI Desktop, share them securely with colleagues, and even schedule automatic refreshes so your data stays up to date. Administrators can manage permissions to ensure that the right people see the right information, keeping your data protected.
Your Power BI license determines which sharing, refresh, and collaboration features you can access.
