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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Reveal the insights behind your company's data with Microsoft Power BI Microsoft Power BI allows intuitive access to data that can power intelligent business decisions and insightful strategies. The question is, do you have the Power BI skills to make your organization's numbers spill their secrets? In Microsoft Power BI For Dummies, expert lecturer, consultant, and author Jack Hyman delivers a start-to-finish guide to applying the Power BI platform to your own firm's data. You'll discover how to start exploring your data sources, build data models, visualize your results, and create compelling reports that motivate decisive action. * Tackle the basics of Microsoft Power BI and, when you're done with that, move on to advanced functions like accessing data with DAX and app integrations * Guide your organization's direction and decisions with rock-solid conclusions based on real-world data * Impress your bosses and confidently lead your direct reports with exciting insights drawn from Power BI's useful visualization tools It's one thing for your company to have data at its disposal. It's another thing entirely to know what to do with it. Microsoft Power BI For Dummies is the straightforward blueprint you need to apply one of the most powerful business intelligence tools on the market to your firm's existing data.
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Veröffentlichungsjahr: 2022
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Microsoft® Power BI For Dummies®
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Copyright © 2022 by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Control Number: 2021952556
ISBN: 978-1-119-82487-9 (pbk); 978-1-119-82488-6 (ebk); 978-1-119-82489-3 (ebk)
Microsoft® Power BI For Dummies®
To view this book's Cheat Sheet, simply go to www.dummies.com and search for “Microsoft Power BI For Dummies Cheat Sheet” in the Search box.
Table of Contents
Cover
Title Page
Copyright
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
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
Business Intelligence (BI): The Definition
Chapter 2: The Who, How, and What of Power BI
Highlighting the Who of Power BI
Understanding How Data Comes to Life
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 versus Excel?
Power BI Products in a Nutshell
Examining the Details of the Licensing Options
On the Road with Power BI Mobile
Working with Power BI Report Server
Linking Power BI and Azure
Chapter 4: Power BI: The Highlights
Power BI Desktop: A Top-Down View
Services: 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
Storage Modes
Considering the Query
Exporting Power BI Desktop Files and Leveraging XMLA
Chapter 6: Getting Data from Dynamic Sources
Getting Data from Microsoft-Based File Systems
Working with Relational Data Sources
Importing Data from a Nonrelational Data Source
Importing JSON File Data into Power BI
Importing Data from Online Sources
Creating Data Source Combos
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
Tweaking Power Query's M Code
Configuring Queries for Data Loading
Resolving Errors During Data Import
Part 3: The Art and Science of Power BI
Chapter 8: Crafting the Data Model
An Introduction to Data Models
Dealing with Table and Column Properties
Managing Cardinality and Direction
Data Granularity
Chapter 9: Designing and Deploying Data Models
Creating a Data Model Masterpiece
Managing Relationships
Arranging Data
Working with Extended Data Models
Publishing Data Models
Chapter 10: Perfecting the Data Model
Matching Queries with Capacity
Chapter 11: Visualizing Data
Looking at Report Fundamentals and Visualizations
Dealing with Table-Based and Complex Visualizations
Dabbling in Data Science
Questions and Answers
Chapter 12: Pumping Out Reports
Formatting and Configuring Report Visualizations
Filtering and Sorting
Configuring the Report Page
Refreshing Data
Chapter 13: Diving into Dashboarding
Configuring Dashboards
Creating a New Dashboard
Enriching Your Dashboard with Content
Pinning Reports
Customizing with Themes
Working with Dashboard Layouts
Integrating Q&A
Setting Alerts
Part 4: Oh, No! There’s a Power BI Programming Language!
Chapter 14: Digging Into DAX
Discovering DAX
Dealing with Data Types
Operating with Operators
Making a Statement
Ensuring Compatibility
Chapter 15: Fun with DAX Functions
Working with DAX Parameters and Naming Conventions
Using Formulas and Functions
Chapter 16: Digging Deeper into DAX
Working with Variables
Writing DAX Formulas
Best Practices for DAX Coding and Debugging in Power BI
Chapter 17: 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 5: Enhancing Your Power BI Experience
Chapter 18: 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
Chapter 19: Extending the Power BI Experience
Linking Power Platform and Power BI
Powering Up with Power Apps
Integrating OneDrive and Power BI
Collaboration, SharePoint, and Power BI
Automating Workflows with Power BI
Unleashing Dynamics 365 for Data Analytics
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
Using All Those Fabulous Functions
Rinse, Repeat, Recycle
Chapter 21: Ten Ways to Make Compelling Reports Accessible and User-Friendly
Navigating 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
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 Desktop, Common, Service Features
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 7
TABLE 7-1 Join Types
TABLE 7-2 Fuzzy Matching Options
Chapter 8
TABLE 8-1: Dimensional Data Approaches
TABLE 8-2 Cardinality and Relationship Description
Chapter 9
TABLE 9-1 Buttons On the Power BI Model View Home Ribbon
Chapter 10
TABLE 10-1 Cardinality and Direction
Chapter 14
TABLE 14-1 DAX Data Types
TABLE 14-2 Arithmetic Operators
TABLE 14-3 Comparison Operator
TABLE 14-4 Logical Operators
TABLE 14-5 Text Operator
TABLE 14-6 Operator Order
TABLE 14-7 Statements
Chapter 15
TABLE 15-1 Parameter Naming
TABLE 15-2 Aggregate Functions
TABLE 15-3 Date-and-Time Functions
TABLE 15-4 Filter Functions
TABLE 15-5 Financial Functions
TABLE 15-6 Information Functions
TABLE 15-7 Logical Functions
TABLE 15-8 Mathematical and Trigonometric Functions
TABLE 15-9 Other Functions
TABLE 15-10 Parent-Child Functions
TABLE 15-11 Relationship Functions
TABLE 15-12 Statistical Functions
TABLE 15-13 Table Manipulation Functions
TABLE 15-14 Text Functions
TABLE 15-15 Time Intelligence Functions
Chapter 16
TABLE 16-1 Formulas In Depth
TABLE 16-2 Calculated Column versus Measures
Chapter 19
TABLE 19-1 Power BI Mobile Support
List of Illustrations
Chapter 1
FIGURE 1-1: An example of structured data.
FIGURE 1-2: My Workspace in Power BI Services.
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 3
FIGURE 3-1: The Power BI Desktop.
FIGURE 3-2: The Power BI Pro user experience.
FIGURE 3-3: An example of Power BI Mobile output.
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 using the Data Navigation icon and landing ...
FIGURE 4-4: Example of a data 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.
FIGURE 4-8: Reports imported to the workspace.
FIGURE 4-9: Accessing reports directly.
FIGURE 4-10: Access app from Apps menu in Power BI.
FIGURE 4-11: Drill down from the Power BI dashboard for a report.
FIGURE 4-12: Architecture of a dashboard.
Chapter 5
FIGURE 5-1: Finding the Excel Data 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: Power BI datasets navigation.
FIGURE 5-9: Connecting to a shared dataset in Power BI Services.
FIGURE 5-10: Start and Stop query diagnostics.
FIGURE 5-11: The step process for query diagnostics.
FIGURE 5-12: Premium capacity configuration for XMLA.
Chapter 6
FIGURE 6-1: OneDrive file path.
FIGURE 6-2: SharePoint Folder path.
FIGURE 6-3: Azure SQL database location.
FIGURE 6-4: Entry of credentials for 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: Representative query data from Azure SQL Server.
FIGURE 6-8: Selecting the Cosmos DB data source.
FIGURE 6-9: Connecting to the Cosmos DB, a Microsoft NoSQL database.
FIGURE 6-10: JSON file, transformed by the Power Query Editor.
FIGURE 6-11: Modifying a JSON file using the Power Query Editor.
FIGURE 6-12: Connecting to an online service in Power BI Desktop.
FIGURE 6-13: Interface to authenticate with Online Services.
FIGURE 6-14: Data displayed in the Navigator pane within the Power Query Editor...
FIGURE 6-15: Accessing Analysis Services.
FIGURE 6-16: The Azure Analysis Services connectivity interface.
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: Look for the 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: An example of merged columns.
FIGURE 7-14: Adding a column.
FIGURE 7-15: The expanded Merged Columns example.
FIGURE 7-16: The Advanced Editor button on the Ribbon's Home tab.
FIGURE 7-17: Getting to the Advanced Editor via the Queries pane.
FIGURE 7-18: The M Query Editor interface.
FIGURE 7-19: Enabling the Formula bar.
FIGURE 7-20: Removing queries.
FIGURE 7-21: Close & Apply in the Power Query Editor.
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: The Quick Measure icon on the Ribbon's Home tab.
FIGURE 8-6: Some Quick Measures options.
FIGURE 8-7: Populating New Measure on the Formula bar.
FIGURE 8-8: Flattening a hierarchy.
FIGURE 8-9: Example of a multi-level hierarchy.
FIGURE 8-10: Table property selection.
FIGURE 8-11: Column properties.
FIGURE 8-12: Editing relationships.
FIGURE 8-13: A cross-filter direction example.
FIGURE 8-14: Granularity of NAICS ID, StartDate, and EndDate in the Award List ...
Chapter 9
FIGURE 9-1: The Data 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 Data 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: Before and After views for column removal.
FIGURE 9-17: The Gear icon under Applied Steps.
FIGURE 9-18: The modified table with new row and changed data.
FIGURE 9-19: Before a change in the Products.xlsx file.
FIGURE 9-20: Seeing the changes made in the Products.xlsx file.
FIGURE 9-21: The Create Relationship interface.
FIGURE 9-22: Grouping by capabilities.
FIGURE 9-23: Hiding data.
FIGURE 9-24: Combining content and joins in Report view.
FIGURE 9-25: Aggregate of two data sources using joins.
FIGURE 9-26: The Publish button for deploying the data model and reports to Pow...
Chapter 10
FIGURE 10-1: Your query reduction options.
FIGURE 10-2: The Choose Columns icon on the Ribbon's Home tab.
FIGURE 10-3: Selecting columns to aggregate.
FIGURE 10-4: Aggregated columns in Power Query Editor.
FIGURE 10-5: The Group By icon on the Ribbon's Home tab.
FIGURE 10-6: Adding a new sum column using Group By.
FIGURE 10-7: The Aggregated column.
FIGURE 10-8: The updated data model view.
FIGURE 10-9: Manage aggregations accessible from the Data View tab.
FIGURE 10-10: Managing aggregations.
Chapter 11
FIGURE 11-1: The Report View icon.
FIGURE 11-2: Overview of Report view in Power BI.
FIGURE 11-3: The Visualizations pane.
FIGURE 11-4: The Fields pane.
FIGURE 11-5: Filtering data based on a category.
FIGURE 11-6: Setting up filtering conditions with quantitative data.
FIGURE 11-7: The Filter on This Page and Filter on All Page options.
FIGURE 11-8: A Stacked Bar chart.
FIGURE 11-9: Using multiple dimensions in a Stacked Bar chart.
FIGURE 11-10: A Stacked Column chart.
FIGURE 11-11: Using multiple dimensions in a Stacked Column chart.
FIGURE 11-12: A Clustered Bar chart.
FIGURE 11-13: A Clustered Column chart.
FIGURE 11-14: A 100% Stacked Bar chart.
FIGURE 11-15: A 100% Stacked Column chart.
FIGURE 11-16: A Line chart.
FIGURE 11-17: An Area chart.
FIGURE 11-18: A Line chart and a Stacked Column chart.
FIGURE 11-19: A Ribbon chart.
FIGURE 11-20: A Waterfall chart.
FIGURE 11-21: A Funnel chart.
FIGURE 11-22: Scatterplot.
FIGURE 11-23: A Pie chart.
FIGURE 11-24: A Donut chart.
FIGURE 11-25: A treemap.
FIGURE 11-26: A Map example.
FIGURE 11-27: A Filled Map example.
FIGURE 11-28: Using a gauge.
FIGURE 11-29: A Card example.
FIGURE 11-30: A Multi-card example.
FIGURE 11-31: A KPI example.
FIGURE 11-32: A slicer example.
FIGURE 11-33: Table visualization.
FIGURE 11-34: A Matrix example.
FIGURE 11-35: A decomposition tree.
FIGURE 11-36: Working with key influencers.
FIGURE 11-37: Selecting the R or Python scripting editor.
FIGURE 11-38: Enabling script visuals.
FIGURE 11-39: A placeholder for creating R or Python script.
FIGURE 11-40: Prescribed questions and answers.
FIGURE 11-41: Self-created questions and answers.
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 in Power BI Services.
FIGURE 12-17: Selecting an item from the My Workspace menu in Power BI Services...
FIGURE 12-18: Your export choices.
FIGURE 12-19: The Paginated Report menu.
FIGURE 12-20: The Report Builder Wizard screen.
FIGURE 12-21: The Report Builder interface.
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 icon.
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: Posing a question.
FIGURE 13-17: A Q&A example.
FIGURE 13-18: Adding alerts.
FIGURE 13-19: Configuring alerts.
FIGURE 13-20: Managing alerts.
Chapter 14
FIGURE 14-1: A syntax example.
FIGURE 14-2: A row context example.
FIGURE 14-3: A filter context example.
FIGURE 14-4: DAX calculation options.
FIGURE 14-5: A calculated measure in the DAX editor.
FIGURE 14-6: A calculated measure added to Fields pane.
FIGURE 14-7: The Quick Measures dialog box.
FIGURE 14-8: Accessing the New Quick Measure option via the Fields pane.
FIGURE 14-9: Calculated columns in Report view.
FIGURE 14-10: Creating a calculated table from the Data view.
FIGURE 14-11: DAX data types.
FIGURE 14-12: Power Query data types.
Chapter 15
FIGURE 15-1: A sample function.
Chapter 16
FIGURE 16-1: The Fields pane, showing a calculated column.
FIGURE 16-2: The table output.
FIGURE 16-3: Setting a column's calculation type.
FIGURE 16-4: Bad data representation.
FIGURE 16-5: Simple measure formation on the Ribbon.
Chapter 17
FIGURE 17-1: A list of workspace apps.
FIGURE 17-2: The My Workspace interface.
FIGURE 17-3: The content of a workspace in Power BI.
FIGURE 17-4: The navigation menu in Power BI Services.
FIGURE 17-5: The Create a Workspace button.
FIGURE 17-6: Configuring the standard features of a workspace.
FIGURE 17-7: Configuring the advanced features of a workspace.
FIGURE 17-8: Assigning workspace access.
FIGURE 17-9: Adding new content to a workspace.
FIGURE 17-10: Creating an app.
FIGURE 17-11: The Dashboard menu under Workspaces.
FIGURE 17-12: The Report menu under Workspaces.
FIGURE 17-13: The Download prompt for the Excel add-on.
FIGURE 17-14: The Quick Insights feature.
FIGURE 17-15: A usage metrics report.
FIGURE 17-16: Gaining access to data lineage.
FIGURE 17-17: An example of data lineage.
FIGURE 17-18: Example of a Dataset card.
FIGURE 17-19: A Report card.
FIGURE 17-20: A Dashboard card.
FIGURE 17-21: Arrows between each asset in a workspace.
FIGURE 17-22: Drilling down into a Dataset card.
FIGURE 17-23: Showing the impact of an action across a workspace.
FIGURE 17-24: Enabling sensitivity labels in Power BI.
FIGURE 17-25: The Sensitivity Label drop-down menu.
FIGURE 17-26: Sensitivity labels in a workspace.
Chapter 18
FIGURE 18-1: The Scheduled Refresh pane.
FIGURE 18-2: Downloading a data gateway.
FIGURE 18-3: Data gateway options.
FIGURE 18-4: Modifying data gateways.
FIGURE 18-5: Configuring roles for group membership in Power BI Desktop.
FIGURE 18-6: An RLS error message.
FIGURE 18-7: Row level security in Power BI Services.
FIGURE 18-8: Setting app permissions.
FIGURE 18-9: Managing parameters.
FIGURE 18-10: Filtering rows using the RangeStart and RangeEnd parameters.
FIGURE 18-11: Setting up an incremental refresh.
FIGURE 18-12: Configuring endorsements.
FIGURE 18-13: Endorsements listed in a Power BI workspace.
FIGURE 18-14: Configuring large dataset storage formats in dataset settings.
FIGURE 18-15: Configuring a large dataset for a workspace.
Chapter 19
FIGURE 19-1: Data connector options in Power Platform.
FIGURE 19-2: A list of admin centers in Microsoft 365.
FIGURE 19-3: Options for your Power Platform Admin Center.
FIGURE 19-4: The Power Apps Data field.
FIGURE 19-5: Creating or selecting a Power Apps environment.
FIGURE 19-6: A newly created Power App.
FIGURE 19-7: An example of Power BI and Power Apps integration.
FIGURE 19-8: Select either Open in App or Open in Browser.
FIGURE 19-9: The Info option to Copy Path.
FIGURE 19-10: The Get Data from Web option.
FIGURE 19-11: The basic option to add the URL.
FIGURE 19-12: Configuring Power BI for SharePoint Online.
FIGURE 19-13: Embedded link for SharePoint Online.
FIGURE 19-14: Adding a web part on a Modern SharePoint web page.
FIGURE 19-15: A web part catalog.
FIGURE 19-16: Power BI web part initialization.
FIGURE 19-17: The Power BI Report Configuration panel.
FIGURE 19-18: A Power BI report embedded into SharePoint Online.
FIGURE 19-19: Accessing Power Automate from the Microsoft 365 console.
FIGURE 19-20: Searching for Power BI on the Power Automate landing page.
FIGURE 19-21: The Power Automate prebuilt Power BI templates.
FIGURE 19-22: The Update an Excel Table from Power BI workflow template.
FIGURE 19-23: Configuring accounts.
FIGURE 19-24: Mapping fields for Power Automate Flow.
FIGURE 19-25: The Test and Save options.
FIGURE 19-26: Adding Power Automate Visual to the Power BI canvas.
FIGURE 19-27: Creating a SharePoint item for a List from Power BI.
FIGURE 19-28: Mapping fields for Power BI SharePoint Flow.
FIGURE 19-29: A Power Automate button for Power BI attached to a report.
FIGURE 19-30: The menu for selecting a Dynamics 365 Instance.
Chapter 21
FIGURE 21-1: A high contrast theme in Power BI.
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.
FIGURE 21-11: Report theme options.
Guide
Cover
Title Page
Copyright
Table of Contents
Begin Reading
Index
About the Author
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Introduction
Data is everywhere — no matter where you go, and no matter what you do, someone is gathering data around you. The tools and techniques utilized to evaluate data have undoubtedly matured over the past decade or two. Less than a decade ago, for example, the lowly spreadsheet was considered an adequate tool to collect, measure, and calculate results — even for somewhat complex datasets. Not anymore! The modern organization accumulates data at such a rapid pace that more sophisticated approaches beyond spreadsheets have become the new normal. Some might even call the spreadsheet a dinosaur.
Welcome to the generation of business intelligence. And what does business intelligence require, you ask? Consider querying data sources, reporting, caching data, and visualizing data as being just the tip of the iceberg. Ask yourself this question: If you had to address your organization's needs, what would they be? Would taking structured, unstructured, and semistructured data and making sense of it be part of your organizational requirements? Perhaps developing robust business analytics outputs for executive consumption? Or, is the mandate from the leadership the delivery of complex reports, visualizations, dashboards, and key performance indicators? If you’re shaking your head right now and whispering all the above, you are not alone.
This is what enterprises today, large and small, expect. And with Microsoft Power BI, part of the Power Platform, you can deliver a highly sophisticated level of business intelligence to your organization, accomplishing each of these business objectives with little effort.
Power BI was initially conceived as part of the SQL Server Reporting Team back in 2010. Then, Power BI made its way into the Office 365 suite in September 2013 as an advanced analytics product. Power BI was built around Microsoft Excel core add-ins: Power Query, Power Pivot, and Power View. Along the way, Microsoft added a few artificial intelligence features, such as the Q&A Engine, enterprise-level data connectors, and security options via the Power BI Gateway. The product became so popular with the enterprise business community that, in July of 2015, Power BI was separated from the Office family, becoming its own product line. Finally, in late 2019, Power BI merged with other Microsoft products to form the Power Platform family, which consists of Power Apps (mobile), Power Automate (workflow), and Power BI (business intelligence).
Whether you’re using Power BI as a stand-alone application to turn your data sources into interactive insights or integrating Power BI with applications such as Power Apps, SharePoint, or Dynamics 365, Power BI allows users to visualize and discover what is truly essential in their vast data resources. Users can share data at scale with ease. Depending on your role, you can create, view, or share data using the Power BI Desktop, the cloud-based Service, or the mobile app. The Power BI platform is designed to let users create, share, and consume business insights that effectively serve you and your team.
About This Book
This book is intended for anyone interested in business analytics, focusing as it does on the general platform capabilities across the Power BI platform. It doesn't matter whether you’re a novice or a power user — you'll definitely benefit from reading this book. I'm thinking especially of 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 be used as a resource for many of the critical tasks you may encounter in the field.
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 quickly ingest data, conduct data analysis, and build relatively sophisticated reports after reading this book.
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. In this book, you can see that the surface is scratched ever so slightly in covering these topics. Take a look at the chapters on DAX in
Part 4
if you want an introduction or a refresher.
IT professional:
Whether 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, this is a starting point if you want to take a leap into the world of Microsoft enterprise business intelligence.
Manager or executive:
Often, the deliverables created in Power BI are built for managers and executives. Power BI has over 70 data connectors available for data extractions, report development, visualization support, and dashboard creation. Under your guidance, these deliverables are created by analysts, developers, and data professionals. Therefore, reading
Microsoft Power BI For Dummies
may help you better understand the art of the possible.
Foolish Assumptions
Power BI is a pretty big application, as you can probably already tell. Microsoft assumes that its interfaces are relatively simple for users to create reports and dashboards. Here's the truth: Some users find that it can be overwhelming, depending on which product you’re using. Admittedly, lots of bells and whistles appear across each platform. As the author, I've written the book for users wanting to learn about those critical features across the three Power BI platforms: Desktop, Services, 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 specific books on the market for these purposes.
Throughout this book, though, 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 the Power BI Desktop.
Some things in life are free, and this is one of them. Microsoft actually provides the Desktop client to its users for free! The Desktop client is intended to build the enduser data models, reports, and dashboards for personal consumption. That's where it ends, though. You do need an online account to share and collaborate. About half the steps lists in this book can be completed using the Desktop client.
You have at least signed up for a Power BI Free Services account, but preferably have a Power BI Pro account
. If you want to share and collaborate with others, you need a Pro account. Otherwise, the Free online account will do for now. The purpose of the online companion is to distribute your outputs in read-only format, if you want. Suppose that you want others to edit and manipulate the data. In that case, there's no getting around paying for the Pro or Premium per User version. Also, the larger your dataset, the more likely you will want the upgrade.
You have access to the Internet
: This may sound a bit obvious. Even with the Desktop client, an Internet connection is required in order to access datasets from the Internet.
You have a meaningful dataset
: What does
meaningful
mean? I've created a sample dataset that can be downloaded for you from
www.dummies.com
to follow throughout the book. However, suppose that you want to use your own data. In that case, a meaningful dataset includes at least 300 to 400 records containing a minimum of five or six columns’ worth of data.
Icons Used in This Book
Throughout Microsoft Power BI For Dummies, you see some icons along the way. Here’s what they mean:
Tips point out shortcuts or essential suggestions on 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 a bit more. 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. You also have access to a complete dataset that can be imported into your instance of Power BI Desktop or Services. The dataset is helpful because it can be used across all exercises throughout the book. To find the Cheat Sheet, go to www.dummies.com and enter Power BI For Dummies in the Search box. For the dataset I've prepared for you, go to www.dummies.com/go/mspowerbifd.
Part 1
Put Your BI Thinking Caps On
IN THIS PART …
Get introduced to the types of data used in enterprise BI solutions.
Identify the roles, responsibilities, and products produced by BI professionals.
Discover the licensing options and core features available with Power BI.
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 64-million-dollar question. Does the data make sense? Does it have any sort of 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’d 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, let'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 might 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.
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 would 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 any video, audio, photo, or text file is considered unstructured data.
Adding semistructured data to the mix
Semistructured 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, semistructured data contains tags that make the data easier to organize in some form of hierarchy. Nonrelational data systems or NoSQL databases are best associated with semistructured data, where the programmatic code, often serialized, is driven by the technical requirements. There is no hard-and-fast coding practice.
For the business intelligence developer utilizing semistructured languages, serialized programming practices can assist in writing sophisticated code. Whether the goal is to write data to a file, send a data snippet to another system, or parse the data to be translatable for structured consumption, semistructured data does have the potential for business intelligence systems. If the serialized language can communicate and speak the same language, a semistructured dataset has great potential.
Looking Under the Power BI Hood
Power BI is a product that brings together many smaller, cloud-based apps and services with a specific objective: to organize, collect, manage, and analyze big datasets. Big data is a concept where the business and data analyst will evaluate extremely large datasets, which may reveal patterns and trends relating to human behaviors and interactions not easily identifiable without the use of specific tools. A typical big data collection is often expressed in millions of records. Unlike a tool such as Microsoft Excel, Power BI can evaluate many data sources and millions of records simultaneously. The sources don’t need to be structured using a spreadsheet, either. They can include unstructured and semistructured data.
After pulling these many data sources together and processing them, Power BI can help you come up with visually compelling outputs in the form of charts, graphics, reports, dashboards, and KPI's.
As you've already read, Power BI isn't just a single source application. It has desktop, online, and mobile components.
Across the Power BI platforms, 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 View
: A data visualization tool you can use to generate interactive charts, graphs, maps, and visuals
Power Map
: A visualization tool for creating 3D map renderings
Power Q&A
: An artificial intelligence engine that allows you to ask questions and receive responses using plain language
Power BI Desktop
: A free, all-in-one solution that brings together all the apps described in this list into a single graphical user interface.
Power BI Services
: A cloud-based user experience to collaborate and distribute products such as reports with others
In the following few sections, I help you take a deeper dive into each product's core functionality.
Posing questions with Power Query
Before Power BI became its own product line, it was originally an advanced query and data manipulation add-in for Excel, circa 2010. It wasn’t until around 2013 that Microsoft began to test Power BI as its own product line, with the formal launch of Power BI Desktop and Services in July 2015. One of the justifications for the switch to a dedicated product was the need for a more robust query editor. With the Excel editor, it was a single data source, whereas with Power BI's Power Query you can extract data from numerous data sources as well as read data from relational sources such as SQL Server Enterprise, Azure SQL Server, Oracle, MySQL, DB2, and a host of other platforms. If you’re looking to extract data from unstructured, semistructured, or application sources — such as CSV files, text files, Excel files, Word documents, SharePoint document libraries, Microsoft Exchange Server, Dynamics 365, or Outlook — Power Query makes that possible as well. And, if you have access to API services that map to specific data fields on platforms such as LinkedIn, Facebook, or Twitter, you can use Power Query to mine those platforms as well.
Whatever you have Power Query do, the procedure is always pretty much the same: It transforms the data you specify (using a graphical user interface as needed) by adding columns, rows, data types, date and time, text fields, and appropriate operators. Power Query manages this transformation by taking an extensive dataset which is nothing more than a bunch of raw data (often disorganized and confusing to you, of course) and then creates some business sense by organizing it into tables, columns, and rows for consumption. The product produced by the Power Query output in the Editor can then be transferred to either a portable file such as Excel or something more robust, such as a Power Pivot model.
Working behind the Power Query scenes is a formula language called M. Although M never shows its face as part of the graphical user interface, it’s definitely there and doing its job. I briefly tackle M in several upcoming chapters so that you can see how the mechanics work as you transform data quickly across structured, semistructured, and unstructured datasets in Power BI.
Modeling with Power Pivot
Power BI's data modeling tool is called Power Pivot. With it, you can create models such as star schemas, calculated measures, and columns and build complex diagrams. Power Pivot leverages another programming language called the Data Analysis eXpression Language — or DAX, for short. DAX is a formula-based language used for data analysis purposes. You soon discover that, as a language, it’s chock-full of useful functions, so stay tuned.
Visualizing with Power View
The visualization engine of Power BI is Power View. The idea here is to connect to data sources, fetch and transform that data for analysis, and then have Power View present the output using one of its many visualization options. Power View gives users the ability to filter data for individual variables or an entire report. Users can slice data at the variable level or even break out elements in Power View to focus like a laser on data that may be considered anomalous.
Mapping data with Power Map
Sometimes, visualizing data requires a bit more than a Bar chart or a table. Perhaps you need a map that integrates geospatial coordinates with 3D requirements. Suppose that you’re looking to add dimensionality to your data — perhaps with the help of heat maps, by gauging the height and width of a column, or basing the color used on a statistical reference. In that case, you definitely want to consider Power BI's Power Map feature set. Another feature built into Power Map is the use of geospatial capabilities using Microsoft Bing, Microsoft’s external search engine technology that includes capabilities for mapping locations. A user can highlight data using geocoordinate latitude and longitudinal data as granular as an address or as global as a country.
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. Now what? Your data sample is often pretty significant in terms of size, which means that you need some way to make sense of all the data you've deployed in the model. That's why Microsoft created a natural language engine, a way to interpret text, numbers, and even speech so that users can query the data model directly.
Power Q&A works directly in conjunction with Power View.
A classic example of a situation where Power Q&A can be enormously helpful would involve determining how many users have purchased a specific item at a given store location. If you want to drill down further, you could analyze a whole set of metrics — asking whether the item comes in several colors or sizes, for example, or specifying which day of the week saw the most items sold. The possibilities are endless as long as you've built your data model to accommodate the questions.
Power BI Desktop
All these Power BI platforms are great ideas, but the truly stupendous idea was bundling together Power Query, Power Pivot, Power View, and Power Q&A to form Power BI Desktop. Using Power BI Desktop, you can complete all your business intelligence activities under a single umbrella. You can also develop BI and data analysis activities far more easily. Finally, Microsoft updates Power BI Desktop features monthly, so you can always be on the BI cutting edge.
Power BI Services
Over time, the product name for Power BI Services has evolved. When the product was in beta, it was called Power BI Website. Nowadays, you often hear the product referred to as Power BI Online or Power BI Services. Whatever you call it, it functions as the Software as a Service companion to Power BI. Accessible at https://app.powerbi.com, Power BI Services allows users to collaborate and share their dashboards, reports, and datasets with other users from a single location.
The version of Power BI you have licensed dictates your ability to share and ingest data.
Knowing Your Power BI Terminology
Whether Microsoft or another vendor creates it, every product you come across has its own terminology. It may seem like a foreign language, but if you visit a vendor’s website and do a simple search, you’re sure to find a glossary that spells out what all these mysterious terms mean.
Microsoft, unsurprisingly, has its own glossary for Power BI as well. (Those folks refer to terminology as concepts, for reasons clear only to them.). Before you proceed any further on your Power BI journey, let's establish the lay of the land. In Microsoft Power BI-speak, some concepts resonate across vendors no matter who you are. For example, all vendors have reports and dashboards as critical concepts. Now, do all other vendors adopt Microsoft’s practice and call dataflows a type of workflow? Not quite. They all have their names for these specific features, although all such features generally work the same way.
Microsoft has done a pretty good job of trying to stick with mainstream names for critical concepts. Nevertheless, some of the more advanced product features specific to AI/machine learning and security adopt the rarefied lingo of Microsoft products such as Azure Active Directory or Azure Machine Learning.
Capacities
What's the first thing you think about when it comes to data? Is it the type, or is it the quantity? Or do you consider both? With Power BI, the first concept you must be familiar with is capacities, which are central to Power BI. Why, you ask? Capacities are the sum total of resources needed in order for you to complete any project you may create in Power BI. Resources include the storage, processor, and memory required to host and deliver the Power BI projects.
There are two types of capacity: shared and dedicated. A shared capacity allows you to share resources with other Microsoft endusers. Dedicated capacities fully commit resources to you alone. Whereas shared capacity is available for both free and paying Power BI users, dedicated capacity requires a Power BI premium subscription.
Workspaces
Workspaces are a means of collaborating and sharing content with colleagues. Whether it’s personal or intended for collaboration, any workspace you create is created on capacities. Think of a workspace as a container that allows you to manage the entire lifecycle of dashboards, reports, workbooks, datasets, and dataflows in the Power BI Services environment. (Figure 1-2 shows a My Workspace, a particular example of a Power BI workspace.)
The My Workspace isn’t the only type of workspace available. You also have the option to collaborate. If you want to collaborate, you have no choice but to upgrade to a Power BI Pro or Premium plan. Features that come with collaboration include the ability to create and publish Power BI-based dashboards, reports, workbooks, datasets, and apps with a team.
FIGURE 1-2: My Workspace in Power BI Services.
Looking to upload the work you've created using Power BI Desktop? Or perhaps you need to manipulate the work online without collaborating with anyone? If the answer to either question is yes, My Workspace is all that is necessary. You only require the use of the Power BI Online Free License. As soon as you want to collaborate with others, you need to upgrade to a paid Pro or Premium subscription.
So now you know that your work is stored in a workspace. Next question: What happens with the data in that workspace? The answer is twofold: There is what you see as the user, and then there’s what goes on behind the scenes as part of the data transformation process. Let's start with the behind-the-scenes activities first.
A dataflow is a collection of tables that collects the datasets imported into Power BI. After the tables are created and managed in your workspace as part of Power BI Services, you can add, edit, and delete data within a dataflow. The data refresh can occur using a predefined schedule as well. Keep in mind that Power BI uses an Azure data lake, a way to store the extremely large volumes of data necessary for Power BI to evaluate, process, and analyze data rapidly. The Azure Data Lake also helps with cleaning and transforming data quickly when the datasets are voluminous in size.
Unlike a dataflow (which, you may remember, is a collection of tables), a dataset should be treated as a single asset in your collection of data sources. Think of a dataset as a subset of data. When used with dataflows, the dataset is mapped to a managed Azure data lake. It likely includes some or all of the data in the data lake. The granularity of the data varies greatly, depending on the speed and scale of the dataset available.
The analyst or developer can extract the data when building their desired output, such as a report. Sometimes, there may be a desire for multiple datasets, in which
