Power BI: Data Analysis and Reporting

This extensive guide explores Microsoft Power BI, a powerful business intelligence tool, covering its multifaceted capabilities from data import and transformation to advanced analytics and reporting. It details how to manage data sets, including refreshing and securing them, and explains the creation of calculated tables and columns using DAX functions. The content further extends to building interactive reports and dashboards, incorporating visualizations, slicers, and conditional formatting, and explores AI visuals like Q&A and key influencers. Finally, the source addresses workspace management, content sharing, app distribution, and paginated reports, emphasizing performance optimization and accessibility throughout.

Power BI Reports: Creation, Features, and Distribution

Power BI reports are a core feature of Microsoft Power BI, designed to compile and visualize data to provide insights and tell compelling stories.

Creation and Environment Most of the work involved in creating Power BI reports is done within the Power BI Desktop application, which is a robust tool allowing connection to over 80 data sources, data transformation, analysis, shaping, modeling, and creation of calculations, visualizations, and reports. Reports can also be created and edited directly in the cloud-based Power BI service, which offers an interface very similar to the Desktop’s Report View. Report design can begin from scratch or be based on existing reports. Reports are structured with multiple “pages,” analogous to Excel sheet tabs, allowing users to add, delete, duplicate, or hide them.

Key Components and Features Power BI reports incorporate various elements and functionalities:

• Visualizations Reports are built around interactive data visualizations, such as pie charts, clustered column charts, stacked bar charts, multi-row cards, tables, scatter charts, and line charts.
• Panes The Fields pane displays tables and their associated fields, which are dragged onto the “canvas” area to build visuals. The Visualizations pane allows users to select the type of visual and contains specific “fields wells” where data fields are placed for the selected visual. The Format well (represented by a paint roller icon) is used for extensive formatting options, including titles, background colors, shadow effects, and data labels, which vary based on the visualization type. The Filters pane enables the application of visual-level, page-level, or report-level filters, including filtering by fields not directly used in the visualization.
• Tooltips When hovering over elements in a visualization, tooltips display the associated values and percentages, and additional pertinent fields can be added to them.
• Slicers These are visual filters that can be synchronized across multiple report pages and can be configured as dropdowns for space efficiency.
• Hierarchies Fields can be grouped into hierarchies (e.g., Region > State > City), enabling “drill-down” and “drill-up” functionality on visualizations to explore data at different levels of detail.
• Conditional Formatting This feature allows applying color scales or other visual cues to fields within table or matrix visualizations based on their values, providing quick visual insights into data ranges.
• Bookmarks Users can save specific filtered states of a report as bookmarks for quick navigation to frequently used views.
• AI Visuals and Analytics Power BI reports support advanced analytics features and AI visuals, including:
• Q&A: Allows users to ask natural language questions about their data, generating visualizations and insights.
• Key Influencers: Identifies factors that influence a key metric’s increase or decrease.
• Forecasting: Built into line charts, it enables predicting future values based on historical data, with configurable confidence intervals and seasonality.
• Animated Scatter Charts: Can display data trends over time by animating changes based on a “play axis” (e.g., year).
• Analyze Feature: Available on every report page, it can analyze data distribution and highlight factors causing changes.
• Accessibility Features Power BI reports include various accessibility options:
• Built-in (no configuration required): Keyboard navigation, screen reader compatibility (adopting Windows high contrast colors), Focus mode (to enlarge a visual), and the ability to show an underlying data table for a visual.
• Configurable Features: Alt text for visuals (for screen readers), proper tab order for keyboard navigation, and the application of report themes, such as “colorblind safe” themes.
• Custom Visualizations Users can add custom visualizations from the Power BI AppSource store, and these can be “pinned” to the visualizations pane for use across multiple Power BI files. An example provided is the “Violin Plot” for visualizing data distribution.

Types of Reports: Standard vs. Paginated Power BI differentiates between standard Power BI reports and paginated reports:

• Standard Power BI Reports: These are the typical interactive reports created in Power BI Desktop. If a table visualization within a standard report spans multiple pages, only the visible portion on the screen will print or export. Their icon in the service is typically a blue chart icon.
• Paginated Reports: These are specifically designed for printing and publishing, often across multiple pages. They ensure that all data in a table, even if it spans many pages, will be fully printed or exported (e.g., to PDF, PowerPoint, Word, Excel). Paginated reports are only available in premium workspaces and must be authored using a separate application called Power BI Report Builder. In Report Builder, report titles (in text boxes) and column headings can be configured to repeat on every page, although the report title repetition may only apply when viewing online, not when printed or exported. They can also include “built-in fields” for dynamic information like execution time or page numbers. Paginated reports are not intended to be consumed directly on a dashboard but can be viewed within the Power BI service. Their icon in the service looks like a page with a folded upper right corner.

Data Interaction and Optimization Reports are built on underlying “datasets”. Changes to the source data (e.g., Excel files, Access databases, web data) require a “refresh” in Power BI Desktop. If the report is published to the Power BI service, and the source data is stored locally, it must be republished to reflect changes. However, if the source data is cloud-stored (e.g., OneDrive, SharePoint), the dataset in the service can be refreshed directly, and the reports will then update. Parameters, set up in Power Query Editor, can be used in reports to dynamically filter data, and their values can be changed directly within the Power BI service for text or decimal number data types. Scheduled refreshes can be configured for datasets in the service, except for those linked to locally stored Excel files.

Report performance can be optimized by applying the most restrictive filters, limiting the number of visuals on a single page, and evaluating the performance of custom visuals. Data modeling practices, such as breaking down large tables into fact and dimension tables, also enhance performance. Using DAX “measures” (virtual calculations) instead of calculated columns or tables can reduce file size, as measures only calculate when added to a visual. Furthermore, using “variables” in DAX functions can improve performance, readability, and simplify debugging of complex calculations. For very large or frequently changing data, “Direct Query” can be used to connect directly to the source repository without importing data into Power BI, significantly reducing file size and ensuring real-time data updates.

Publishing and Distribution Once created, reports are published from Power BI Desktop to the Power BI service. In the service, reports can be shared with specific users or groups, with options to control whether recipients can re-share the report or build new content using the underlying dataset. Permissions can be managed to specify access levels like read, re-share, or build content. Reports can also be copied to different workspaces, and access to these workspaces can then be granted based on defined roles (Admin, Member, Contributor, Viewer). A powerful distribution method is to publish reports and dashboards as part of a Power BI “app,” which can be automatically installed for authorized users. Paginated reports specifically must be published to a premium workspace.

DAX: The Language of Power BI Data Analysis

Data Analysis Expressions, or DAX, is the powerful formula language used within Microsoft Power BI to create calculations and perform dynamic data analysis. It is a core component for data analysts working in Power BI, enabling them to connect to and transform data, create interactive visualizations, and uncover important insights.

What is DAX? DAX is a collection of functions, operators, and constants that can be used in a formula or expression to calculate and return one or more values. While it shares some similarities with basic Excel functions, its structure is distinct. A key concept in DAX is “context,” which allows the results of a formula to dynamically change to reflect the current row or cell selection and any related data.

Where DAX is Used in Power BI: DAX is primarily utilized within the Power BI Desktop application, especially in the context of data modeling. It’s used to create various calculated elements that enhance the underlying data model:

• Calculated Tables: These are new tables added to the data model, whose contents are defined by a DAX expression. For example, the DISTINCT function can be used to create a one-column table containing only the unique order IDs from another table. Calculated tables become a permanent part of your dataset.
• Calculated Columns: These are new columns added to existing tables in the data model, with each row’s value derived from a DAX formula. An example is calculating the “days to ship” by finding the difference between Order Date and Ship Date using the DATEDIFF function. Calculated columns also become part of your dataset.
• Measures: Unlike calculated columns and tables, measures are “virtual calculations” that do not become part of your dataset directly. Instead, they calculate values only when they are added to a report visualization. This characteristic makes measures beneficial for optimizing file size. There are two variations of measures:
• Quick Measures: These are templates that allow users to select common calculations (e.g., average sales per category) through a dialog box, and Power BI Desktop automatically generates the complex DAX formula.
• Measures from Scratch: Users can write DAX formulas directly to create custom measures, such as calculating “average sales per product category” using functions like CALCULATE and ALLSELECTED.

Key DAX Functions and Concepts Mentioned:

• Aggregations: DAX is used to perform aggregations on data.
• Time Intelligence Functions: These functions enable powerful time-based calculations (e.g., year-over-year growth). They require a “date table” in the data model, which can be created using functions like CALENDAR (needs explicit start and end dates) or CALENDARAUTO (scans the model for earliest and latest dates). The ADDCOLUMNS function can then be nested with CALENDARAUTO to add additional date-related columns like Year, Quarter, Month Name, and Month Number using functions like YEAR, QUARTER, FORMAT, and MONTH. Once created, the date table must be explicitly “marked as date table” and related to other tables for time intelligence functions to work correctly. Functions like ENDOFMONTH and ENDOFQUARTER can also be used to add calculated columns for month or quarter endings.
• DISTINCT: Used to return a table with one column containing only the unique values from a specified column.
• DATEDIFF: Calculates the difference between two dates in a specified interval (e.g., days, months, years).
• RANKX: Assigns a rank to each row in a table based on an expression. It has options to determine the order (ascending/descending) and how ties are handled (e.g., SKIP by default, or DENSE to avoid skipping numbers).
• CALCULATE: A highly versatile and frequently used function that evaluates an expression in a context modified by filters.
• ALLSELECTED: Used within CALCULATE to return all rows in a table or values in a column, ignoring filters applied inside the query (like from the filters pane) but retaining filters applied from outside the query (like from a slicer visual).
• Variables (VAR and RETURN): Variables allow storing the result of an expression as a named variable, which can then be referenced in other parts of the DAX formula. This improves performance by preventing recalculation, enhances readability, simplifies debugging, and reduces the complexity of compound expressions. Variables are declared using the VAR keyword and their scope is limited to the measure or calculated column they are defined within. The RETURN keyword specifies what the measure or calculated column will ultimately output using the declared variables.
• Comments: Double forward slashes (//) can be used to add comments to DAX formulas, improving readability and aiding in understanding the calculation’s purpose.

DAX and Row-Level Security (RLS) DAX expressions are also fundamental for implementing Row-Level Security (RLS) in Power BI. Filters defined within roles in the Power BI Desktop application use simple DAX expressions (e.g., [Region] = “East”) to restrict data access at the row level for assigned users.

Overall, DAX is an essential language for anyone looking to perform advanced data analysis, create sophisticated calculations, and build robust data models and reports within Power BI.

Power BI Data Management: A Comprehensive Guide

Data management within Power BI is a comprehensive process that involves acquiring, cleaning, transforming, modeling, storing, securing, and distributing data to enable insightful analysis and reporting. It is a critical responsibility of a data analyst working within the Power BI application.

Here are the key aspects of data management as discussed in the sources:

1. Connecting and Importing Data Power BI Desktop allows users to identify and retrieve data from various data sources, supporting over 80 different options. There are two primary options for getting data into Power BI:

• Connecting (Direct Query): This method connects directly to the original source repository (e.g., SQL Server, Azure Analysis Services) without importing any data into Power BI. When visualizations are created or refreshed, queries are sent to the underlying data source to retrieve the necessary data. This approach is beneficial for very large datasets or frequently changing data, as it optimizes file size and ensures reports reflect the latest data in real time. With Direct Query, Power BI Desktop only has Report View and Modeling View, as no data is actually imported.
• Importing Data: This involves loading all or a large subset of data from a data source directly into Power BI Desktop. This creates a data set within Power BI, which is the data once it’s brought into the application, as opposed to the external data source. Importing is necessary for certain data types, such as Power Pivot or Power View data models from Excel, to ensure the underlying data model is fully accessible for analysis.

2. Data Transformation and Cleaning Before data can be effectively analyzed, it often needs to be cleaned and transformed. This process, also known as data shaping, ensures the data is in the proper condition for loading into the model. Power Query Editor, accessible via the “Transform data” button in Power BI Desktop, is the primary tool for these operations. Key transformation steps include:

• Profiling Data: Understanding the condition, size, and shape of the data, and identifying anomalies. Power Query Editor offers profiling tools like “column quality,” “column distribution,” and “column profile” for in-depth assessment.
• Renaming: Renaming columns or tables for clarity (e.g., changing “users” to “managers” or “column one” to “region”).
• Removing Columns: Decreasing the dataset size by removing unnecessary columns.
• Promoting Headers: Setting the first row of data as column headers.
• Duplicating and Replacing: Duplicating columns and replacing values within them to create new, useful fields (e.g., creating “manager first” and “manager last” names from an original “manager” column).
• Filtering and Sorting: Applying filters to narrow down data to specific criteria (e.g., filtering for particular states) and sorting data in ascending or descending order.
• Merging Queries: Combining information from multiple tables into a new query (table) based on common fields (e.g., merging orders and managers tables by “region”). Different join types are available for merging, such as “left outer,” “right outer,” “full outer,” and “inner” joins. All changes made in Power Query Editor must be “Closed and Applied” to update the data model in Power BI Desktop. Power Query Editor also tracks “applied steps,” allowing users to undo transformations if mistakes are made.

3. Data Modeling Data modeling involves designing and developing the structure of data for proper performance and scalability, making the data as accurate and intentional as possible. Key aspects of data modeling include:

• Calculations using DAX: Data Analysis Expressions (DAX) is the formula language used in Power BI to create calculations and perform dynamic data analysis. DAX formulas use functions, operators, and constants to return values, and DAX “context” allows results to change dynamically based on selections.
• Calculated Tables: New tables created from DAX expressions (e.g., using DISTINCT to get unique order IDs).
• Calculated Columns: New columns added to existing tables, with values derived from DAX formulas (e.g., “days to ship” using DATEDIFF, or sales rankings using RANKX).
• Measures: “Virtual calculations” that do not become part of the dataset but calculate values only when added to a report visualization. This helps optimize file size. Measures can be created as “Quick Measures” (templates) or “from scratch” using functions like CALCULATE and ALLSELECTED.
• Table Types (Fact and Dimension Tables): A best practice for optimizing performance is to break down large tables. Fact tables hold numeric data for aggregation (e.g., sales, profits), while dimension tables hold descriptive information used to slice and dice data (e.g., customer or product information). Dimension tables require a unique identifier called a “key field” or “primary key”.
• Relationships and Cardinality: Power BI can auto-detect existing relationships when data is imported from databases or Power Pivot files. Users can also create relationships manually in “Model View” by dragging common fields between tables. Cardinality defines the nature of the relationship: one-to-one, one-to-many (most common), many-to-one, or many-to-many.
• Hierarchies: A container for grouping related fields, allowing for drill-down and drill-up functionality in visualizations (e.g., a “region hierarchy” containing region, state, and city).
• Date Tables: Essential for using time intelligence functions in DAX (e.g., year-over-year growth). Date tables can be generated automatically using CALENDARAUTO (scans data for earliest/latest dates) or specified with CALENDAR (requires start/end dates). Additional columns (year, quarter, month name, month number) can be added using ADDCOLUMNS. A created date table must be explicitly “marked as date table” and related to other tables for time intelligence functions to work.
• Column Categorization: Categorizing location fields (e.g., region, state, city, postal code) for mapping purposes, which enhances location-based visualizations.

4. Data Storage and Performance Optimization Efficient data management is crucial for optimal performance, especially with large datasets.

• File Storage Location: The location of source Excel files (locally stored vs. cloud-stored like OneDrive or SharePoint) significantly impacts data refresh capabilities and performance. Cloud-stored files allow for automatic refreshes in the Power BI service, while locally stored ones do not.
• DAX Variables: Using variables in DAX formulas can improve performance by storing the result of an expression once, preventing recalculation if the variable is referenced multiple times. Variables also enhance readability, simplify debugging, and reduce complexity.
• Report Design Optimization: Limiting the number of visuals on a single report page and applying restrictive filters can optimize report performance.

5. Data Refresh Keeping the data up-to-date is a core data management task.

• On-Demand Refresh: Manually clicking the refresh button in Power BI Desktop or Power BI Service to update data.
• Scheduled Refresh: For cloud-stored datasets, refreshes can be scheduled at set frequencies (daily or weekly, multiple times a day). This is not possible for locally stored Excel files in the service.
• Parameters: Parameters can be set up in Power Query Editor to store reusable values, offering flexibility to dynamically change query output (e.g., filtering a report by region). These parameters can then be changed directly in the Power BI Service settings for the dataset, provided their data type is text or decimal number.

6. Data Security and Governance Controlling who can access and manage data is paramount.

• Row-Level Security (RLS): RLS restricts data access for specific users at the row level, meaning users only see the data they are authorized for. Filters are defined using simple DAX expressions within “roles” in Power BI Desktop, and users are assigned to these roles in the Power BI Service.
• Workspaces: Workspaces are organizational containers for Power BI assets like datasets, reports, and dashboards. Access to workspaces can be managed by assigning different roles to users: Admin, Member, Contributor, and Viewer, each with varying capabilities for managing and interacting with content.
• Sharing Content: Dashboards and reports can be shared with individual users or groups, with options to control whether recipients can reshare or build content with the underlying dataset. Reports can also be copied to other workspaces to grant specific access.
• Publishing Apps: An app can be configured to include multiple dashboards and reports from a workspace, serving as a consolidated distribution method for content. Permissions for app access can be set for entire organizations or specific individuals/groups.

7. Data Profiling and Quality Understanding the quality and characteristics of data is an ongoing part of data management. Power Query Editor’s data profiling tools provide insights into column quality (valid, errors, empty percentages), distribution (distinct vs. unique values), and column statistics (count, min, max, mean, standard deviation).

In essence, effective data management in Power BI encompasses a continuous cycle of data preparation, modeling, optimization, refreshing, and securing, all working together to deliver reliable and insightful data to end-users.

Power BI Report Visualization and Management

Report visualization in Power BI is a fundamental aspect of data analysis, focusing on the design, creation, and enhancement of visual representations of data to tell a compelling story and derive insights. A data analyst’s role involves creating interactive data visualizations and uncovering important insights.

Here’s a comprehensive discussion of report visualization within Power BI:

1. Core Concepts of Report Design Report design involves selecting the correct visuals, designing page layouts, and applying critical functionality. There are various methods for designing a report, including drawing out designs on paper, using previously created reports as a basis, or designing from scratch. Reports are primarily created in Power BI Desktop, but can also be edited and created in the Power BI Service.

2. The Report View Interface The default view when you first open Power BI Desktop or load data is the Report View, which features a blank area called the “canvas” where visuals are built. On the right side, there are three panes: Filters, Visualizations, and Fields. Page tabs, similar to Excel sheet tabs, allow adding, deleting, duplicating, and hiding pages.

3. Types of Report Visualizations Power BI offers a host of options for choosing visualizations.

• Pie Chart:
• Displays regular sales units and markdown sales units.
• Automatically shows values and percentages as tooltips when hovering over slices; additional relevant fields can be added to tooltips.
• Allows toggling on/off legends and detail labels (often used to avoid redundancy).
• Titles can be customized for clarity (e.g., “Regular Sales and Markdown Sales Units”) and aligned.
• Background colors and shadow effects can be applied for visual appeal.
• Clustered Column Chart:
• Used to display data such as this year’s sales, last year’s sales, and total sales variance, often by categories like “Chain”.
• Supports “small multiples” for breaking down data by additional fields (e.g., “Territory”), but this can lead to scrolling issues if too much data is present.
• Data labels can be enabled to show the value of each column.
• Background colors and shadow effects can be applied.
• Card Visualization:
• Used to show a single aggregate value, such as total sales.
• Key Performance Indicator (KPI) Visualization:
• A critical indicator of progress toward an intended result.
• Requires an “indicator” (e.g., total units this year), a “trend axis” (e.g., fiscal month), and “target goals” (e.g., total units last year).
• Cannot be sorted after it’s converted to a KPI visualization; sorting must be done on the initial chart before conversion.
• The shaded area represents the goal, showing the value, goal, and percentage difference.
• Tile alerts can be set on card, KPI, and gauge visualizations to notify users when data changes above or below set limits.
• Table Visualization:
• Can display various fields in a tabular format, such as customer segment by sales.
• Useful for conditional formatting.
• Slicer Visualization:
• A visual filter that allows users to interactively filter report data.
• Can be configured as a list with checkboxes or a dropdown, which is particularly useful for long lists of items.
• Formatting options include background color and borders.
• “Sync slicers” feature allows a single slicer to filter visuals across multiple report pages.
• Multi-Row Card Visualization:
• Displays multiple fields in a card format, useful for focusing on specific entities.
• Can be given borders and background colors for visual interest.
• Stacked Bar Chart:
• Can be used to show categories like technology and experience. Often used in conjunction with drill-through features.
• Histogram:
• A custom visual that needs to be added from AppSource.
• Represents data points in ranges or “bins” (e.g., salary ranges) to make data more understandable.
• Uses “values” (e.g., monthly salary) and “frequency” (e.g., count of employees) fields.
• Can be pinned to the visualizations pane for use across multiple Power BI files.
• Animated Scatter Chart:
• Allows visualization of data over time using a “play axis” (e.g., year) to animate the chart’s progression.
• Line Chart (with Forecasting):
• The only built-in visual that allows for forecasting.
• Uses the “analytics well” to configure forecast settings like forecast length (points/years), ignoring last periods, confidence interval (e.g., 95% or 75%), and seasonality (e.g., 5-year cycle).
• Forecasting data is typically represented by a distinct line and a shaded “confidence band”.
• Q&A Visual (AI Visual):
• Allows users to ask questions about their data in natural language (e.g., “show defect quantity supplies and total defect quantities”).
• Available in both Power BI Desktop and Service.
• Can display series of “tiles” with suggested questions based on the data.
• Results can be pinned to a dashboard.
• Key Influencers Visual (AI Visual):
• Identifies the factors that influence a metric to increase or decrease (e.g., what influences sum of Revenue to increase).
• Uses “analyze,” “explained by,” and “optionally expand by” fields.
• Quick Insights (AI Feature):
• Power BI analyzes all data in a dataset and provides a wide variety of visualizations and textual insights.
• Insights can be pinned to dashboards.
• Violin Plot (Custom AI Visual):
• An advanced analytics visualization used to visualize the distribution of data.
• Requires “sampling,” “measure data,” and optional “category” fields.
• Shows median (white line) and mean (circle) values within the violin shape.

4. Advanced Report Functionality

• Filtering: Visualizations can be filtered by fields not directly used in the visual, using Visual-level, Page-level, or Report-level filters.
• Sorting: Visualizations can be sorted in ascending or descending order based on various fields.
• Grouping: Data fields (e.g., segments) can be grouped together within a visualization for different analytical perspectives. Grouped fields appear in the legend.
• Binning: Similar to grouping but performed on numeric fields (e.g., years). It groups data points into defined ranges or “bins”.
• Drill Down/Up: Requires a hierarchy (a container for related fields like region, state, city) within the visualization.
• Allows users to navigate through different levels of detail (e.g., from region to state to city).
• The “expand all down one level in the hierarchy” button combines levels.
• Analyze Feature: Available by right-clicking on a visualization, it provides analytical insights into data distribution changes based on various filters. Insights can be added to the report page.
• Conditional Formatting: Applies visual formatting (e.g., background color) to fields in table or matrix visualizations based on conditions (e.g., color scale based on minimum, center, and maximum values).
• Bookmarks: Allows users to save specific filtered or sorted views of a report page for quick access.
• Focus Mode: Expands a selected visualization to fill the entire canvas, allowing users to focus solely on that visual.
• Show Data Table: A built-in feature that displays the underlying data for a visualization in a tabular format.

5. Accessibility Features Designing for accessibility is an important topic in report creation.

• Built-in (No Configuration): Includes keyboard navigation, screen reader compatibility, high contrast color application from Windows, and focus mode.
• Configurable:Alt Text: Provides a detailed text description for screen readers, preventing generic descriptions.
• Tab Order: Ensures correct navigation sequence for keyboard users. This can be configured in the “Selection” pane under the “View” tab.
• Themes: Apply consistent design elements (colors, fonts). Built-in themes include “colorblind safe.” Custom themes can also be created, or background colors/images can be applied. Themes in the Power BI Service are fewer but include “dark” and “colorblind friendly”.

6. Managing Reports and Dashboards

• Publishing: Reports created in Power BI Desktop can be published to the Power BI Service. This publishes both the report and its underlying dataset.
• Sharing: Reports and dashboards can be shared with individuals or groups via email, with options to control resharing and content building with the underlying dataset. Permissions can be managed to revoke or modify access.
• Copying Reports: Reports can be copied to other workspaces, allowing different access permissions to the copied version.
• Workspaces: Organizational containers for Power BI assets. Access is managed by assigning roles (Admin, Member, Contributor, Viewer), each with specific capabilities.
• Apps: An app can be configured to include multiple dashboards and reports from a workspace, serving as a consolidated distribution method. Permissions for app access can be set for the entire organization or specific users/groups.
• Dashboards vs. Reports: Dashboards are created in the Power BI Service (not Desktop) and can contain pinned report visualizations, videos, text boxes, audio files, and web content. Unlike reports, dashboards cannot be sorted once created, and individual visuals are pinned to them rather than the entire report page (for streaming data).
• Mobile View: Dashboards can be configured for mobile layout, allowing users to select and arrange tiles for optimal viewing on phones or tablets.
• Usage Metrics: Reports showing how dashboards are being used (views, unique viewers, shares, platforms) are available.
• Featured/Favorite Dashboards: A single dashboard can be set as “featured” to open by default in the service. Multiple dashboards can be marked as “favorites” for easier navigation.

7. Paginated Reports These are a distinct type of report designed primarily for printing and publishing, ensuring that all data in a table displays even if it spans multiple pages (unlike regular Power BI reports that only print what’s on screen).

• Creation Tool: Paginated reports are created using Power BI Report Builder, a separate application.
• Workspaces: Can only be published to “premium workspaces”.
• Data Source: Connect to data sets in the Power BI Service (e.g., Supplier Quality Analysis data set).
• Design: Allows designing multi-page layouts, defining data sources/datasets, and arranging fields (e.g., vendor, total defect quantity, total downtime minutes).
• Repeating Elements: Report titles (text box objects) can be configured to repeat on every page for online consumption, while column headings repeat for both online consumption and printing/exporting.
• Publishing & Exporting: Once created, they are published to the Power BI Service. They can be exported to various formats like PDF, PowerPoint, Word, and Excel. Print preview shows how they will look when printed.
• Editing: Paginated reports can be edited directly from the Power BI Service, which re-launches Report Builder.

8. Report Performance Optimization Optimization techniques for reports include applying restrictive filters to visualizations, limiting the number of visuals on a single report page, and evaluating the performance of custom visuals.

Power BI Workspace Collaboration: Management and Sharing

Workspace collaboration in Power BI focuses on how users can create, manage, and share content effectively within the Power BI Service. It involves organizing Power BI assets, assigning roles to users, and distributing content to a wider audience.

Here’s a detailed discussion of workspace collaboration:

1. What are Workspaces? Workspaces are organizational containers within the Power BI Service designed to hold various Power BI assets. These assets can include reports, dashboards, and datasets. The default workspace for a user with a free Power BI license is “My Workspace”.

2. Creating and Managing Workspaces Users can create new workspaces. When creating a workspace, you provide a name (e.g., “streaming data set”) and an optional description (e.g., “contains all components of our streaming data”).

3. Workspace Roles and Permissions Access to a workspace is managed by assigning specific roles to users. There are four primary roles available:

• Admin: Has the most extensive capabilities, including publishing and unpublishing apps, sharing items and apps, managing data set permissions, and adding members with lower permissions.
• Member: Can add members with lower permissions, publish, unpublish, and change app permissions.
• Contributor: Has more limited permissions compared to Admins and Members, only able to perform certain actions if allowed.
• Viewer: Has the most restricted access, generally only able to view content if allowed.

When assigning roles, you enter an email address, choose their permission level, and then add them. Permissions can also be changed or removed from the actions ellipsis next to each user.

4. Sharing Content within Workspaces

• Sharing Dashboards:
• Dashboards can be shared by entering individual email addresses or groups.
• By default, recipients can re-share the dashboard and build content with its associated dataset, but these options can be unchecked to restrict permissions.
• An optional email notification can be sent.
• Permissions can be managed (e.g., revoking re-share privilege or total access) via the “Manage permissions” option. Shared content, including reports and underlying datasets, is listed.
• Sharing Reports:
• Reports can be shared similarly to dashboards, by entering names, email addresses, or groups.
• Sharing options include sending a link for people in the organization to view and share, or specifying people with existing access.
• Permissions can be controlled to allow or disallow re-sharing the report or building content with its associated data.
• Links can also be copied for distribution via Outlook or Teams. Permissions can be managed and modified after sharing.
• Copying Reports to Other Workspaces:
• A report can be copied to another workspace, which allows for setting different access permissions for the copied version. After copying, you can navigate to the new workspace to manage its access.
• Dataset Roles and Row-Level Security (RLS):
• Data set roles are assigned in the Power BI Service.
• RLS is configured in Power BI Desktop to restrict data access at the row level for specific users by defining filters within roles.
• However, RLS does not restrict data access for members of a workspace, as they already have access to the datasets within that workspace.
• Users are assigned to these roles in the Power BI Service.

5. Distributing Content via Apps An app can be configured to include multiple dashboards and reports from a workspace, offering a consolidated method for content distribution.

• When creating an app, you can choose which dashboards and reports from the workspace to include.
• Apps have settings for navigation, adding a logo, choosing a theme color, and contact information.
• Permissions for app access can be set for the entire organization or for specific individuals or groups.
• Users can be allowed to connect to the app’s underlying datasets, make copies of reports, and even share the app itself.
• Apps can also be configured to install automatically for permitted users.

6. Premium Workspaces Paginated reports, which are designed for printing and publishing, can only be published to “premium workspaces”. A regular workspace can be converted to a premium workspace (e.g., “Premium per user” or “Premium per capacity”) via workspace settings. This change in workspace access means that only users with corresponding premium licenses will be able to access that workspace.

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

Affiliate Disclosure: This blog may contain affiliate links, which means I may earn a small commission if you click on the link and make a purchase. This comes at no additional cost to you. I only recommend products or services that I believe will add value to my readers. Your support helps keep this blog running and allows me to continue providing you with quality content. Thank you for your support!