Businesses need to manage their payroll processes in an efficient manner which helps them achieve compliance standards and maintain accurate records and keep their employees satisfied. Implementing a new payroll system requires specialized knowledge because it involves complicated procedures.
The payroll process represents a critical yet time-intensive task that growing companies must manage. Small teams face difficulties with payroll work because it requires them to calculate employee wages and handle deduction management and tax compliance.
Organizations now need to manage their personnel operations as they need to manage their financial and operational activities. Organizations are continuously seeking methods to enhance employee productivity while they optimize their human resource activities and develop a better workforce.
As organizations expand their operations, they encounter difficulties in handling employee management and payroll responsibilities and compliance requirements and workforce planning activities. Businesses today need a unified solution that simplifies human resources operations while improving productivity and decision making.
Payroll is one of the most critical functions in a business. A tiny mistake can have a ripple effect on employee trust, compliance, and cash flow. This is precisely the reason why Managed Payroll is considered a wise choice for companies that look for precision without operational tension.
When you think about how to manage a workforce today, many people assume it finishes with HR policies or payroll cycles. However, managing people today is really about how well we allow our systems to help employees and make decisions — this leads to the overall success of your employee’s growth.
Modern-day businesses need intelligent, quick, and well-connected systems for managing their staff, salary and performance. This is the point where Dayforce features have a significant influence. By creating a single solution for human capital management, they integrate HR, payroll, and talent processes of companies in real-time.
Author: Kay Unkroth - Principal Program Manager
Author: Kay Unkroth - Principal Program Manager
Power BI’s latest update introduces an enhancement to how users interact with reports with the general availability of modern visual tooltips. All Power BI reports—from Power BI Desktop to Power BI reports in the web, in the mobile app, in Teams, and embedded in any website—now use the updated visual tooltips, making report interactions easier with the built-in actions footer and report creation faster with tooltip styling and colors coming from the report theme. By default, visual tooltips show the details of the visual's data point report consumers hover over, such as the name and value. Let's look at how these updated tooltip features improve both the visual tooltip creation and consumer experiences of Power BI reports. 1. Drill actions directly in tooltips One of the standout features is the Actions footer. Users can perform drill down, drill up, and drill through actions directly from the tooltip. This removes the need to right-click or use the visual header. Hovering over a bar in a chart lets you drill down into that data point or drill through to related report pages. This approach streamlines workflows and makes data exploration more intuitive. Screenshot_of_the_modern_tooltip_showing_the_Actions_footer_with_Drill_down_andActions footer of a modern tooltip showing the Drill down and Drill through options. Report authors can enable or disable the Actions footer when editing any report using the Format pane. Screenshot_of_format_pane_showing_Actions_group_highlightedCustomize Actions footer in the Format pane. 2. Theme-based styling and customization Modern visual tooltips adopt your report’s theme colors for a consistent and professional look across all visuals. You can customize tooltips further when editing a report, not only by using the Format pane, adjusting colors, fonts, and transparency to match your brand or reporting needs, but also when customizing the report theme from the View ribbon > Themes dropdown > Customize current theme option or importing a custom theme to your report using Browse for theme. Screenshot_of_Customize_theme_dialog_with_the_Tooltip_tab_selectedFormat tooltip colors within the Customize theme dialog. Customizing the theme updates tooltips in all existing visuals, not modified individually, and applies to new visuals you create in the report. Modifying the visual individually using the Format pane only updates the style for that visual without impacting other visuals or new visuals. 3. No impact to existing reports Previously created reports continue to have the same tooltip experience until the report author edits them to use the new styling and actions footer. New reports: All tooltips use the modern experience by default, including updated styling from the theme and with the Actions footer enabled. Existing reports: Tooltips remain as they were before this update. To adopt the updated tooltips, select Reset to default in the Format pane on any visual to update all visuals in the report. Try out the updated visual tooltips in your Power BI reports today! More interactive and accessible, tooltips help report consumers discover insights faster and with less effort. Custom styling streamlines professional report creation, and the actions footer puts next steps right where users are interacting with the data to take their analysis to the next level. To learn more, check out the detailed documentation at: Create Modern Visual Tooltips.
Getting your data job done just got easier with composite semantic models, mixing Direct Lake tables with import tables, now available in public preview. Direct Lake on OneLake table storage mode already could mix tables from other Fabric data sources, such as lakehouses, warehouses, SQL databases in Fabric, and mirrored databases. And with this update, now that flexibility is extended much further with the ability to add in import tables from any data source, from 100s of connectors in Power Query online. The import tables added can also be from the same source as your Direct Lake tables. A small dimension table changed from Direct Lake to import can have a calculated column or the hierarchy usable in Analyze in Excel. Semantic link labs have a function to convert in place. Refer to the Convert a table in Direct Lake mode to import mode code example. A_screenshot_of_Power_BI_web_modeling_showing_tables_in_Direct_Lake_storage_modeA screenshot of Power BI web modeling showing tables in Direct Lake storage mode and import storage mode in the same composite model with options to add more tables. Regular relationships with Direct Lake and import composite models keep the performance of reports as expected. This is an improvement over the traditional DirectQuery and import composite models only supporting limited relationships. Create the Direct Lake and import composite models using Power BI experiences in the web. Power BI Desktop live editing is also available, but without the ability to choose different tables from the OneLake or transform options on import tables. This update also comes with more options to create a new semantic model with Direct Lake on OneLake tables. A_screenshot_of_the_create_page_in_the_Power_BI_service_showing_the_OneLake_cataA screenshot of the create page in the Power BI service showing the OneLake catalog tile used to create semantic models from scratch with Direct Lake tables. On the main Fabric portal page: From the 'Create' button in the left navigation, choose the OneLake catalog tile and select a Fabric item with delta tables, then select 'Connect'. On the workspace page: From the 'New item' button choose 'Semantic model', then select the 'OneLake catalog tile' and a Fabric item with delta tables and 'Connect'. On the Lakehouse page: In the ribbon, choose 'New semantic model'. In addition to the existing path from Power BI Desktop. In Power BI Desktop: In the ribbon, select the 'OneLake catalog' then choose a Fabric item with delta tables and 'Connect'. These all lead to the semantic model with Direct Lake tables dialog. A_screenshot_of_the_semantic_model_creation_dialog_when_creating_with_Direct_LakA screenshot of the semantic model creation dialog when creating with Direct Lake tables. There are several ways in web modeling to create a composite model with Direct Lake and import tables. Web modeling includes 4 new buttons when editing any semantic model. A_screenshot_of_Power_BI_web_modeling_with_the_Get_data_OneLake_catalog_TransforA screenshot of Power BI web modeling with the Get data, OneLake catalog, Transform data, and Refresh buttons added to the ribbon. Adding Direct Lake tables to import or import tables to Direct Lake tables is as easy as clicking a button. On an existing Power BI semantic model with tables in Direct Lake on OneLake storage mode: In the ribbon, select the 'OneLake catalog' then choose a Fabric item with delta tables and Connect. On an existing Power BI semantic model with tables in import storage mode: In the ribbon, select 'Get data' or 'Transform data' then choose any source add a table in import mode. You can continue to use Edit tables to change out tables used from Direct Lake sources and continue to use Power Query online through 'Transform data' to edit and bring in new tables in import storage mode. Refresh is also now available to update import tables data and reframe Direct Lake tables and perform a schema sync on all tables to get latest column information from the respective data sources. You can continue semantic modeling in the web, or you can switch to Power BI Desktop and continue to live edit the semantic model there. A_screenshot_of_Power_BI_web_modeling_showing_how_to_Edit_in_Desktop_from_the_EdA screenshot of Power BI web modeling showing how to Edit in Desktop from the Editing drop down. Semantic modeling can include these tasks and many more to get your data ready for reporting. Rename tables and columns, and add descriptions, for easier report creation. Add relationships between tables upfront. Add measures to aggregate data columns and perform data analysis such as % of total or % change. Add calculated columns and tables to extend the data already available, including a common date table. Organize the columns by hiding columns not needed for reporting, grouping in display folders, and adding hierarchies. Add row-level security roles, calculation groups, sort by on columns, and more. In web or live editing in Desktop you can rely on version history snapshots in help if you need to undo a change. And like with any semantic model, you can use Semantic Link Labs in Fabric Notebooks to make edits and TDML view in Power BI Desktop. Let’s look at an example. Goal: I want to get my deliveries data from a SQL database in Fabric using Direct Lake storage mode then add my deliveries target data from an Excel file using import storage mode. This example will work if you have a Fabric capacity or Fabric trial capacity. I’ll create a SQL database for deliveries data and create a CSV with the deliveries target data. I created a workspace, then from 'New item' created a 'SQL database'. I used this query to create and populate the 'Deliveries' table. -- Step 1: Create the Deliveries table CREATE TABLE Deliveries ( DeliveryID INT IDENTITY(1,1) PRIMARY KEY, ItemID INT, QuantityDelivered INT, DeliveryDate DATE, SupplierName VARCHAR(100), DeliveryStatus VARCHAR(20), DeliveryReference VARCHAR(50) ); -- Step 2: Generate 1,000 mock delivery records DECLARE @i INT = 0; WHILE @i < 1000 BEGIN INSERT INTO Deliveries ( ItemID, QuantityDelivered, DeliveryDate, SupplierName, DeliveryStatus, DeliveryReference ) VALUES ( ABS(CHECKSUM(NEWID())) % 1000 + 1, -- Assuming ItemID between 1 and 1000 ABS(CHECKSUM(NEWID())) % 100 + 1, -- Quantity between 1 and 100 DATEADD(DAY, -ABS(CHECKSUM(NEWID())) % 180, GETDATE()), -- Past 6 months CHOOSE(ABS(CHECKSUM(NEWID())) % 5 + 1, 'Acme Inc.', 'SupplyCo', 'Warehouse World', 'BulkBuyers', 'Global Goods'), CHOOSE(ABS(CHECKSUM(NEWID())) % 3 + 1, 'Delivered', 'Pending', 'Delayed'), CONCAT('DEL-', FORMAT(@i + 1, '000000')) ); SET @i = @i + 1; END; And this SQL query to make a Calendar table to group the delivery dates into month also. CREATE TABLE Calendar ( DateID INT PRIMARY KEY, Date DATE NOT NULL, Month DATE NOT NULL ); DECLARE @StartDate DATE = GETDATE()-180; DECLARE @EndDate DATE = GETDATE(); WHILE @StartDate <= @EndDate BEGIN INSERT INTO Calendar (DateID, Date, Month) VALUES ( CAST(FORMAT(@StartDate, 'yyyyMMdd') AS INT), -- e.g., 20250101 @StartDate, DATEFROMPARTS(YEAR(@StartDate), MONTH(@StartDate), 1) ); SET @StartDate = DATEADD(DAY, 1, @StartDate); END; I also created a CSV file and added it to my OneDrive. Depending on when you follow this tutorial, you may need to update the months to be in the last 6 months. MonthDeliveries target1/1/20251512/1/20251633/1/20251534/1/20251845/1/20251636/1/20251997/1/20251868/1/20251719/1/202512110/1/202510311/1/202513112/1/2025187 Now the data is ready! I now go to the New item again in the workspace and choose semantic model. A_screenshot_of_using_new_item_in_the_workspace_to_create_a_new_semantic_model_wA screenshot of using new item in the workspace to create a new semantic model with Direct Lake or import storage mode tables. From here I can select the 'OneLake catalog tile' and choose the 'Deliveries SQL database' I created. I named my semantic model Deliveries analysis and chose both tables. A_screenshot_of_the_semantic_model_creation_dialogA screenshot of the semantic model creation dialog. In a few moments my semantic model is created and I’m in the web modeling experience. From here I can add my import table by going to 'Get data'. Deep_dive_into_composite_semantic_models_with_Direct_Lake_and_import_tablesA screenshot of Power BI web modeling showing how to add an import storage mode table with the Get data button. I choose Text/CSV connection and navigate to where I saved my CSV file with the targets in my OneDrive. Alternatively, you can choose blank table to paste in the values or upload the file directly. A_screenshot_of_Power_Query_online_in_Power_BI_web_modeling_to_add_the_import_taA screenshot of Power Query online in Power BI web modeling to add the import table from different connector options. Then you can do additional transformations, if needed. I had an extra blank row that I was able to remove. A_screenshot_of_the_transform_data_experience_in_Power_Query_online_in_the_PowerA screenshot of the transform data experience in Power Query online in the Power BI web modeling experience. Note: If you hadn’t set up a credential for this data source before you may be prompted to do that in the Power Query online experience. After that, if the load data fails on saving the transformations, go to the schedule refresh page of the semantic model and set the credentials there as well before returning to web modeling and refreshing. Now I have my import and Direct Lake tables in my model! A_screenshot_of_Power_BI_web_modeling_with_tables_in_Direct_Lake_and_import_tablA screenshot of Power BI web modeling with tables in Direct Lake and import table storage modes. Tooltips here on the Direct Lake tables will give details about the source name, source schema, source type, and the name and workspace it’s from. When adding tables from different Fabric data sources, I can quickly see where all my tables are coming from. Now I need to create the relationships and measures. I can do that here but now I am going to edit this in Power BI Desktop and give you a TMDL script to run instead. Go to the 'Editing' drop down in the top right corner and pick 'Edit in Desktop'. A_screenshot_of_Power_BI_web_modeling_to_show_where_to_continue_editing_in_DesktA screenshot of Power BI web modeling to show where to continue editing in Desktop from the Editing drop down. After Power BI Desktop loads, I am now live editing this composite semantic model. A_screenshot_of_live_editing_a_composite_semantic_model_in_Power_BI_Desktop_withA screenshot of live editing a composite semantic model in Power BI Desktop with tables in both Direct Lake and import storage modes. I can add in my relationships and measure by running this TMDL script: createOrReplace relationship CalendarAndTarget toCardinality: many fromColumn: 'Deliveries target'.Month toColumn: Calendar.Month relationship CalendarAndDeliveries relyOnReferentialIntegrity fromColumn: Deliveries.DeliveryDate toColumn: Calendar.Date ref table Deliveries /// Calculates the average quantity delivered per delivery by dividing the total quantity delivered by the number of deliveries. measure 'Avg Qty per Delivery' = ``` DIVIDE([Total Quantity Delivered], [Deliveries]) ``` formatString: #,0.00 changedProperty = FormatString changedProperty = Description /// Counts the number of unique delivery records by calculating the distinct count of DeliveryID in the Deliveries table. measure Deliveries = ``` DISTINCTCOUNT('Deliveries'[DeliveryID]) ``` formatString: #,0 changedProperty = FormatString changedProperty = Description /// Calculates the percentage of deliveries achieved relative to the monthly target by dividing the number of deliveries by the monthly target. measure 'Target Attainment %' = ``` DIVIDE([Deliveries], [Monthly Target]) ``` formatString: 0%;-0%;0% changedProperty = FormatString changedProperty = Description /// Calculates the difference between the number of deliveries and the monthly target to show how actual performance compares to the goal. measure 'Target Variance' = ``` [Deliveries] - [Monthly Target] ``` formatString: #,0 changedProperty = FormatString changedProperty = Description /// Calculates the total quantity delivered by summing the 'QuantityDelivered' column in the Deliveries table. measure 'Total Quantity Delivered' = ``` SUM('Deliveries'[QuantityDelivered]) ``` formatString: #,0 changedProperty = FormatString changedProperty = Description ref table 'Deliveries target' /// Calculates the total deliveries target for the month by summing the 'Deliveries target' column. measure 'Monthly Target' = ``` SUM('Deliveries target'[Deliveries target]) ``` formatString: #,0 A_screenshot_of_TMDL_view_when_live_editing_a_composite_semantic_model_in_PowerA screenshot of TMDL view when live editing a composite semantic model in Power BI Desktop to add relationships and measures. And right-click the Calendar and 'Mark as date table'. I also hid the base columns I used in measures. Now I want to create a report. The report view is not included when live editing, so I can open a new instance of Desktop to create a report, or I can go back to web modeling and select 'File' then 'Create new report'. If you had closed the browser window from earlier, you could go to the name of the file in Desktop to open a drop down with link to the semantic model in web. A_screenshot_of_Power_BI_web_modeling_showing_how_to_create_a_new_report_from_thA screenshot of Power BI web modeling showing how to create a new report from the file menu. Now I can drag and drop my fields to create some visuals. I can use Copilot as well to quickly create a report. A_screenshot_of_the_Power_BI_report_editing_experience_in_the_webA screenshot of the Power BI report editing experience in the web. I can now use the new organizational themes to apply a theme used in my organization by going to 'View' then 'Theme'. A_screenshot_of_the_Power_BI_report_editing_experience_in_the_web_to_apply_a_theA screenshot of the Power BI report editing experience in the web to apply a theme. And I even have performance analyzer also available to me when editing a report in the web, previously only available in Power BI Desktop. A_screenshot_of_the_Power_BI_report_editing_experience_in_the_web_to_use_performA screenshot of the Power BI report editing experience in the web to use performance analyzer. There are other ways to create these composite models with Direct Lake and import tables. From an existing semantic model with Direct Lake on OneLake and/or import tables you can simply add in additional Direct Lake tables or import tables. You can learn more about Direct Lake in the Direct Lake overview documentation and try it out today!
Welcome to the April Power BI update! Power BI’s April 2026 update is here, bringing continued improvements across Copilot and AI, reporting, visuals, and modeling. This release includes more flexibility when working with layouts and visuals, expanded Copilot experiences—especially on mobile—and several preview features that continue to enhance performance and authoring workflows. You’ll also find important announcements and deprecation notices to keep in mind as you plan ahead. With FabCon fresh on our minds, now is a great time to dive into what’s new and see what’s coming next. Contents April Power BI Video https://youtu.be/Nn19PQF59MM April Power BI Desktop Power_BI_April_2026_Feature_Summary Version number: v: 2.153.910.0 Date published: 21/4/2026 Events and Announcements The FabCon SQLCon live recap series starts April 14th Whether you were there or not, this is your fast track to staying in the loop. In this series, we break down keynotes and corenotes into clear insights and standout demos you can use right away. If it mattered at FabCon, you’ll find it here. Join us, get inspired, and stay connected to what’s happening across the Fabric community. Register now. Could you be the next Power BI Dataviz World Champion? During the recent world championships, one of the finalists confessed that they almost didn’t enter the contest. When we say, “You’ll never know unless you enter!” we really mean it! Don’t miss your chance to enter the next one. Let us know you are interested and we’ll let you know when it starts! General Deprecation of Old File Picker Experience in Power BI Desktop Starting in April, as part of the SU04 release, users will no longer be able to access the old file picker experience in Power BI Desktop. Last January, we announced an updated file picker experience that provides users with a more intuitive, straightforward way of navigating between files and folders. As of SU04, we are moving the updated experience out of preview and making it the default experience in Power BI Desktop. With this change, users will no longer be able to toggle between the old and updated experience. Note: No action is required from users as part of this deprecation; this is simply an informational announcement. Deprecating the Built-In Netezza ODBC Driver The IBM Netezza ODBC driver has been Generally Available for several weeks, and we are beginning the deprecation from the previously built-in ODBC driver to the newer version. Customers do not need to install the new connector; you may reuse your existing connector but will need to install the new ODBC driver. We encourage customers to do this as soon as possible to ensure a smooth transition. IBM_ODBC_Netezza_driver_that_will_be_deprecated_from_Fabric Image: IBM ODBC Netezza driver that will be deprecated from Fabric. Refer to the IBM Netezza ODBC documentation for more information. Copilot and AI Copilot in Power BI mobile now offers expanded features In-report Copilot in Power BI Mobile apps just got a major upgrade: you can now have a full, back-and-forth chat with your report, right from your phone or tablet. Instead of stopping at summaries and prebuilt prompts, in-report Copilot in the mobile app now supports open-ended questions and follow-up conversations, all grounded in the specific report you’re viewing. Ask about a metric or KPI, dig into what’s driving the numbers, and even get AI-generated visualizations to take your analysis further. Every answer includes citations back to the exact visuals Copilot used, so it’s easy to validate insights and keep exploring with confidence on the go. On iPhone and iPad, voice dictation makes it even faster to get hands-free answers while you’re prepping for a conversation or reviewing results away from your desk. Two_mobile_phone_screenshots_display_a_sales_report_for_Contoso_showing_revenue Figure: Using in-report Copilot on the Power BI Mobile app to ask natural‑language questions and explore insights directly within a report. To learn more, refer to the documentation In-Report Copilot in Power BI Mobile Apps Reporting Modern visual defaults and customize theme improvements (Preview) This month's update adds a base theme switcher to the Customize current theme dialog (View ribbon > Themes). If your custom theme doesn't yet work with the new modern defaults, you can use the base theme switcher to revert to the previous base theme until you've had a chance to update your custom theme. You can also use the base theme switcher to update an existing report created with an older base theme to the latest base theme. Screenshot_of_the_Customize_current_theme_dialog_showing_the_base_theme_switcher Figure: Base theme switcher in Customize current theme lets you revert to the previous base theme or update an older report to the latest modern defaults. This update also includes common page sizes for each aspect ratio type in the Canvas settings > Size drop-down. Custom sizing remains available for any dimensions you need. Canvas_settings_panel_in_Power_BI_showing_Type_dropdown_set_to_16_9_and_Size_dro Figure: Canvas settings with preset size options for 16:9 aspect ratio reports in Power BI Desktop. Table and matrix built-in styles have been fixed, with banded rows now enabled by default, as well as default +/- buttons for matrix visuals. Axis colors now also use the correct structural color, fixing issues with the Innovate and Orchid custom themes. Additionally, the built-in theme tiles have an updated look and the Reset to default tile is distinguished from the other built-in theme tiles. The behavior of the tile is not new; it simply clears any custom theme applied, leaving any formatting changes to individual visuals untouched until the visual itself is reset to default in the formatting pane. Power_BI_theme_gallery_showing_eight_built-in_theme_options_as_thumbnail_preview Figure: Built-in theme gallery in Power BI Desktop with the "Reset to default" option to remove any custom theme and update report to latest base theme. To get the latest version in an existing report, go to the Customize current theme dialog and select Update theme. Or choose the Reset to default tile in the Theme drop-down. Thank you for your feedback during the preview. You can continue to provide feedback and learn more about Visual defaults in Power BI reports. Fixed size layout for card, button slicer, and list slicer visuals Card, button slicer, and list slicer visuals now support a Fixed size option in the Layout section of the format pane. Instead of specifying how many items to display, you can define the exact pixel dimensions for each card, button, or list item. When the visual container isn't large enough to display all items at the specified size, scroll bars appear automatically. This update also renames Autogrid to Fit to space for clarity. When Fit to space is on, items grow or shrink to fill the visual container based on the items present. When Fit to space is off, the visual reserves space for the specified number of items, even when fewer items exist. For list slicer, the Fixed number of buttons option, equivalent to Autogrid off, is also renamed to Fit to space for consistency, especially when changing between visual types. Fixed size gives you precise control over each item's dimensions. As you resize the visual container, items maintain their specified height and width rather than scaling proportionally. This behavior is useful for creating consistent layouts across the report page or ensuring uniform button, list, or card sizes across multiple visuals. For list slicers specifically, fixed size provides a more natural experience when working with hierarchies. Expanding and collapsing hierarchy levels causes the number of visible items to change dramatically, and fixed-size items ensure consistent spacing as you navigate through the data. To use fixed size: Select your visual. Expand the Layout section in the Format pane (found under Multi-card layout or Multi-button layout and toggle Fixed size to On. For Vertical arrangement, set the Height value in pixels. For Horizontal arrangement, set the Width value in pixels. For Grid arrangement, set both Height and Width value in pixels. When Fixed size is enabled, Fit to space is disabled since dimensions are now controlled explicitly. Screenshot_of_Power_BI_Desktop_showing_a_list_slicer_visual_with_the_Format_pane Figure: List slicer with Fixed size enabled and Height set to 30 pixels. To learn more, refer to the documentation for card visuals, button slicers, and list slicers. Card visual: Category interactivity and formatting updates The card visual now provides clear visual feedback when you select a category header—the selected card appears highlighted while others dim, making it easy to see your current selection. When you add multiple data columns to the category field, the values concatenate in the category header for a cleaner display. You can also use Edit interactions to control which visuals the card filters, giving you more flexibility in how your report responds to selections. Additionally, top-level images display correctly when the image data is base64 encoded, so you can use images from your data without extra conversion steps. Power_BI_card_visual_with_a_category_header_selected_the_selected_card_is_highli Figure: Card visual showing category header selection highlighting and dimming. For more information about the card visual, refer to Create a card visual in Power BI. Azure Maps visual: Map style picker sync When you change the map style using the in-visual style picker, the selected style now persists with the Format pane. Your styling choices stay consistent across both the style picker and the Format pane, giving you predictable behavior as you design your reports. Azure_Map_visual_in_Power_BI_showing_North_America_with_blue_bubble_markers._An Figure: Azure map updating the format pane style when the report creator adjusts the style on the visual itself. For more information about map styling options, refer to Get started with Azure Maps Power BI visual. Easily identify preview visuals in the Visualizations pane We appreciate your continued feedback on visuals and have finished addressing the concern that preview visuals weren’t always clearly notifying their preview status. Preview visuals now display (preview) after their names in the Visualizations pane, making it easier to identify which visuals are still in preview. Additionally, preview visuals now appear below the divider in the pane (alongside custom and unpinned visuals), clearly separating them from generally available visuals. These changes help you quickly understand when you’re working with a preview feature. Power_BI_Visualizations_pane_showing_the_Build_visual_tab_with_visual_type_icons Figure: Visualization pane when editing a report in Power BI Desktop showing preview visuals below the divider line and with (Preview) after their name. For more information about visuals, refer to Visualizations overview in Power BI. Narrative Visual Default Type Update The Narrative visual currently offers two modes: Copilot and Custom, giving report authors flexibility in how they generate and customize summaries. Previously, authors needed to explicitly choose which mode to use when creating the visual. Power_BI_April_2026_Feature_Summary We’ve recently improved this experience by introducing a smarter default. If a user has a Copilot license, the Narrative visual now opens in Copilot mode by default, making it quicker to get AI‑powered insights right away. We also increased the character limit to 10,000, enabling richer prompts and more detailed narratives. Authors can still easily toggle between Copilot and Custom modes at any time, ensuring full control over their storytelling workflow. Power_BI_April_2026_Feature_Summary This improved default is available now, and learn more with the Create Smart Narrative Summaries documentation. Modeling Direct Lake calculated columns and tables (Preview) Direct Lake storage mode accelerates time to insights by unlocking incredible performance directly against OneLake, without the need to manage costly, time-consuming refreshes for large volumes of data. Calculated columns (unmaterialized) on Direct Lake on OneLake tables is in the process of deployment and will be available in the service in the next few weeks. Calculated tables referring to Direct Lake on OneLake columns. These features are particularly helpful when adding columns and creating tables upstream isn’t feasible, such as when data preparation in OneLake is owned by another team. Refer to the Create calculated columns in Power BI Desktop documentation for more information on calculated columns. Screenshot_of_DAX_expression_to_define_a_calculated_column_to_define_customer_ag Figure: To use the feature in Power BI Desktop, you must enable the Direct Lake calculated columns (unmaterialized) preview feature switch. User context aware calculated columns (Preview) We are introducing the ability to make calculated columns user-context aware by dynamically responding to DAX functions including UserCulture(), UserPrincipalName(), CustomData(). This enables new scenarios like data translations, and we’re excited to see the creative ways the community will use this! User-context-awareness can be set for calculated columns on Direct Lake on OneLake, Import and DirectQuery tables using the Expression Context property. Direct Lake on OneLake is in the process of deployment and will be available in the service in the next few weeks. Refer to the Create calculated columns in Power BI Desktop documentation for more information on user-context-aware calculated columns. Screenshot_of_a_DAX_expression_in_the_formula_bar_to_dynamically_return_a_column Figure: A DAX expression in the formula bar to dynamically return a column with translated values based on the USERCULTURE() function. In the following example, a multi-lingual semantic model and report uses both data and metadata translations. By changing the language URL parameters to simulate a different browser locale, everything is displayed in Portuguese, including product names from the Product table. Animated_GIF_of_a_sales_report._The_browser_locale_is_overridden_to_Portuguese_a Figure: To use the feature in Power BI Desktop, you must enable the User-context-aware calculated columns preview feature switch. DAX user-defined functions (Preview) Alongside our ongoing preview of DAX user defined functions, we’ve enhanced the DAX NAMEOF function to give you much finer control over how object names are returned. NAMEOF now supports optional parameters that let you choose exactly which part of a table, column, measure, or calendar name to return, and control how that name is formatted for display. The new function signature is: NAMEOF ( <object> [, <component> [, <escaped>]] ) This makes it possible to programmatically reference just the table, just the column, or just the measure and improve usability for display scenarios, while keeping existing behavior unchanged for current models. Visualizations Date Picker by Powerviz The Powerviz Date Picker offers a modern calendar view, Presets, Pop-up mode, Smart Button Label, Custom Preset Title, and more, making it a must-have date slicer for Power BI reports. Its rich formatting options help with brand consistency and a seamless UI experience. Key Features Smart Button Label: Make the Pop-up button instantly understandable by showing the selected range, preset name, or both as per your choice. Custom Preset Title: Localize preset names to your users’ language. Fully customize what each preset displays across the report. First Day of the Week: Customize your calendar experience by choosing which day your week starts — perfect for global teams and regional preferences. Display Mode: Choose between Pop-up and Canvas modes. Presets: Many commonly used presets like Today, Last Week, YTD, MTD, or create your preset using field. Default Selection: Control the date period selected when the user refreshes or reopens the report. Filter Type: Choose between Range and Start/End types. Themes: 15+ pre-built themes with full customization. Holidays and Weekends: Customize holidays/weekends representation. Month Style: Select single- or double-month date slicer. Other features included are Multiple date ranges, Import/Export Themes and more. Try Date Picker visual for FREE from AppSource Check out all features of the visual Step-by-step instructions YouTube Video Learn more about visuals Follow Powerviz Power_BI_April_2026_Feature_Summary Power_BI_April_2026_Feature_Summary Drill Down Waterfall PRO by ZoomCharts With powerful customization options and intuitive interactions, Drill Down Waterfall PRO helps report creators present financial and operational data in a way that is easy to explore and understand. The latest update expands the functionality of Change thresholds with a new Automatic mode that detects subtotals and calculates the difference between consecutive subtotal segments, enabling clearer storytelling in multi-period reports. Key features include: Automatic change thresholds: Automatically detect subtotal columns and calculate the change between each consecutive subtotal and the first and last subtotal. Custom Sequence: Have full control over the column order with the Sequence field. Drill Down: Use multiple categories to enable drill down directly on the chart. Automatic Subtotal Calculation: Automatically calculate subtotals if their fields are empty. Annotations: Display markers and comments on the chart from Comment and Comment Marker fields. Customization: Customize X and Y axes, legends, tooltip content, and adjust the appearance settings for positive, negative and total columns separately. Thresholds: Display up to four constant or dynamic thresholds as lines or areas. Cross-chart filtering: Dynamically filter data across multiple visuals to create interactive, insightful and intuitive reports. Get Drill Down Waterfall PRO on AppSource Power_BI_April_2026_Feature_Summary Power_BI_April_2026_Feature_Summary Closing That’s a wrap for the April 2026 Power BI update. This month’s release builds on recent investments across reporting, modeling, visuals, and Copilot, while introducing new previews for you to explore. As always, we appreciate your feedback—especially on preview features—and encourage you to continue sharing your input as we work on future updates. Power_BI_April_2026_Feature_Summary
Translytical task flows take interactive Power BI reports to the next level where users can update records, add annotations, and trigger actions in external systems—all without leaving the report. With translytical task flows, report consumers become active participants. Instead of viewing data and then switching to another application to take action, users can complete their entire workflow within the Power BI interface. Translytical task flows connect Power BI reports to Fabric User Data Functions. When a user interacts with a report—selecting a record, entering a value, and clicking a button—the report passes the context of what they have selected to a function to execute the requested action. Translytical_Task_Flows_Generally_Available Figure 1: Example of a translytical report experience, where users can review project status and add notes directly in Power BI. Capabilities Adding data: Insert new records into your database directly from the report. For example, a customer service representative can add a new customer record while reviewing existing accounts and see it immediately in the report. Updating data: Update existing records without leaving the report. A logistics coordinator can change an order status or add notes to a shipment record as they work through their queue. Deleting data: Remove records that are no longer needed. An inventory manager can delete discontinued products from active lists while reviewing stock levels. Calling external APIs: Trigger actions in other systems through API requests. A sales representative can request a discount approval that posts directly to Microsoft Teams, where a manager can review and respond. Not only can they trigger these but capture and pass on a message too.An_adaptive_card_requestion_an_update_in_a_Teams_channel Figure 2: An adaptive card in a Teams channel requesting an update to a status with link to the Power BI report to update it using translytical task flows. These capabilities can work together too. For instance, a sales opportunity report can include a discount request form. When a user selects opportunities from a filtered table, enters a discount percentage, and adds a justification, clicking the submit button sends all that context to a function. The function then processes the discount and posts the request to Teams with the relevant details, creating a complete audit trail from insight to action. The updated data is also immediately visible in the same report! Other scenarios for translytical task flows include: Data annotation and quality management: Field teams often discover data issues while working in reports. With translytical task flows, they can correct a misspelled customer name, update an outdated address, or add contextual notes to records immediately. This approach improves data quality at the point of discovery rather than routing corrections through separate processes. Workflow automation: Business processes frequently require approvals, notifications, or ticket creation. Translytical task flows can trigger these actions based on report context. A procurement analyst reviewing vendor performance can flag a supplier for review, automatically creating a task in the appropriate system with all the supporting data attached. AI-assisted decision making: Use AI functions in your extract load and transform process with Fabric Notebooks to categorize or summarize your data. These can then be reviewed and updated in your Power BI report. For data write-back scenarios, User Data Functions currently have native connection management for the following Fabric data sources: Fabric SQL databases Fabric warehouses Fabric lakehouses (for files) Once you have your scenario in mind, building your first translytical task flow involves these main tasks: Storing your data in a Fabric data source. Developing a User Data Function to handle the action. Creating a Power BI semantic model to use this data. Building a Power BI report with interactive elements to capture the user's input and call the function. Copilot in Microsoft Fabric is built into many Fabric workflows and GitHub Copilot in Visual Studio Code with MCPs to access learn documentation and connect to Power BI semantic models can help you accelerate this process too. Translytical task flows represent a shift in how organizations can use Power BI. Reports are no longer only for analysis—they enable you to take action. By connecting insight directly to execution, teams can reduce the time between identifying an opportunity and acting on it. Next steps Learn more: Refer to the Translytical task flows documentation for detailed guidance on building your first task flow. Explore User Data Functions: Visit the User Data Functions in Fabric documentation to understand all the capabilities it supports. Submit ideas: Share your feedback and feature requests through the Power BI ideas forum.
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Updated on 20 April 2026: Thanks so much for all the engagement on this topic since the original publishing of this blog post on 2 April 2026. We’ve seen several recurring questions, and we’d like to clarify our position on a few key points. Dataflow Gen1 remains supported, but it is in a legacy state and won’t receive future innovation. We understand many customers have built important, business‑critical solutions on Gen1, and those existing workloads can continue to run while future investments are planned around Dataflow Gen2. Primary call to action for Premium customers: For Premium customers who already have access to Fabric, Dataflow Gen2 is the recommended path to take advantage of the latest improvements across functionality, productivity, performance, pricing, scale, reliability, and more. Many teams find it helpful to start by evaluating Gen2 for new or evolving workloads where these benefits can be realized quickly. Guidance for Pro and Premium Per User (PPU) customers: Many customers rely on Dataflow Gen1 in Pro/PPU today, and it can continue to be the right choice depending on the scenario. If Gen1 best fits your current use case, it remains supported and existing workloads can continue to run as-is. As we introduce new Dataflow Gen2 paths for Pro/PPU scenarios, we’ll share clear guidance and recommended steps to help with a smooth transition. GCC support for Premium customers: For customers using Dataflows Gen1 in GCC environments on Premium, Dataflow Gen2 support in GCC will be available before any future transition milestones, ensuring a supported upgrade path. More granular Fabric controls: Customers have asked for finer‑grained enablement than today’s all‑or‑nothing Fabric switch. Work is underway to provide more granular administrative controls, including the ability to enable only Dataflow Gen2, so capabilities can be rolled out incrementally with the right governance. Original post (2 April 2026): For more than eight years, customers have relied on Power BI Dataflows (Gen1) as a core part of their analytics solutions. We’re grateful for the trust you placed in the Power Query experience to build reusable, low-code data preparation pipelines that power reports, semantic models, and downstream analytics. Now, we’re sharing an update on the future of Dataflows. Dataflows Gen2 builds on everything you know from Gen1—preserving the familiar Power Query authoring experience—while delivering major improvements in scale, flexibility, cost efficiency, and manageability. Going forward, all new Dataflow innovation will land only in Dataflows Gen2. The future of Dataflows Gen1 Power BI Dataflows Gen1 has reached the end of active innovation and is moving into a Legacy state: Existing Gen1 dataflows will continue to work for the foreseeable future. However, specific retirement dates for Gen1 are being finalized, and we’ll share details as plans progress. For customers running Gen1 at Premium capacity, we will provide at least 12 months’ notice before Dataflow Gen1 is retired. (If you’re using Gen1 on Pro or Premium-Peruser, we still strongly recommend planning a move to Dataflows Gen2 to take advantage of the latest investments.) In the meantime, Gen1 artifacts will remain available and will be clearly marked as Legacy in product experiences, including the New Artifact menu. No new features are planned for Dataflows Gen1. Support will be limited to a narrow set of high impact issues, where changes can be delivered safely within the existing architecture. Many of the remaining Gen1 limitations would require significant architecture changes and are best addressed by moving to Dataflows Gen2, which was designed to solve these scenarios more comprehensively. The previous statements apply to all Dataflows Gen1—including usage on Pro, PPU, and Premium licenses. What’s new in Dataflows Gen2 Dataflows Gen2 retains the familiar Power Query experience while introducing substantial platform-level improvements to scale, performance, governance, and cost efficiency. The following updates provide additional details on each benefit area to help customers understand the full scope of Gen2 enhancements. More flexible destinations Dataflows Gen2 supports a significantly broader range of output destinations, enabling alignment with diverse data architectures across business, departmental, and enterprise scenarios. SharePoint and OneDrive are ideal for business users who need refreshed files (CSV, Excel) for downstream workflows, Office automation, or integrations with Power Automate and Teams. Azure Data Lake Storage is a great fit for customers building scalable ingestion pipelines for data science, machine learning, or lakehouse scenarios. Azure SQL Database / SQL MI enables operational reporting, standardized relational storage, and hybrid analytics scenarios. Microsoft Fabric Lakehouse / Warehouse / SQL analytics endpoints are the most seamless destination option for customers aligning with the Fabric vision. Gen2 integrates deeply with Fabric runtimes, unlocking better performance, more consistent semantics, and governance alignment. Snowflake and other cloud databases support multi‑cloud architectures and reduces friction for enterprise customers already standardized on multiple warehouse technologies. This broader range of destinations allows teams to use dataflows as a general-purpose low-code data ingestion and transformation layer, enabling many more scenarios never supported before with Dataflow Gen1. Improved performance and scale Dataflows Gen2 is built on the Fabric runtime and a modernized execution engine that delivers a step‑change in performance, reliability, and scalability. Modern query evaluation leverages Fabric's elastic compute layer to automatically manage scaling behavior, minimizing the need for manual optimization or workload tuning. Fast Copy technology supports high-throughput ingestion into Fabric destinations, enabling sustained data movement measured in gigabytes per minute. Parallelized execution enables multiple partitions and transformation steps to be processed concurrently, significantly reducing refresh durations compared to Gen1. Enhanced support for large datasets includes improvements in memory handling, high-cardinality data processing, and unbounded ingestion patterns. Predictable refresh behavior ensures more consistent performance under varying workload conditions. Together, these improvements establish a more durable and high-performance engine for data preparation, especially in enterprise environments that rely on large or frequently refreshed dataflows. Built-in AI assistance Dataflows Gen2 introduces integrated AI capabilities designed to accelerate development, improve quality, and reduce the learning curve for users working with complex transformations. Copilot-assisted authoring converts natural-language instructions into Power Query logic, improving productivity and lowering the barrier to entry for users with limited M expertise. Code explanation capabilities translate complex or legacy M scripts into easy-to-understand natural language descriptions, improving maintainability and simplifying onboarding. Automated performance and foldability recommendations help users align transformations to foldable patterns, resulting in faster load times and lower compute consumption. AI-powered data quality insights assist with identifying semantic types, outliers, and join keys and detecting common data issues early in the pipeline. These capabilities provide consistent guidance across the authoring lifecycle and support organizations with diverse skill levels. Richer diagnostics Gen2 introduces a more comprehensive set of diagnostics designed to improve traceability, troubleshooting, and operational reliability. Detailed refresh history now includes detailed timing information to clarify where time is spent during execution. Expanded logging and instrumentation provide visibility into foldability decisions, connector behaviors, authentication flows, and network operations. More consistent refresh semantics across all destinations ensures uniform behavior regardless of the target storage system. Greater operational transparency supports root-cause analysis and reduces the time required to identify and resolve failures. These diagnostic improvements help teams manage dataflows more efficiently and maintain higher levels of operational readiness. Tiered pricing and potential cost savings The introduction of a tiered pricing model is a major advantage for customers transitioning from Gen1. Gen2 decouples Dataflows execution from Premium capacity consumption, aligning compute usage with Fabric’s capacity unit (CU)–based architecture. This model allows customers to pay only for the compute resources required rather than maintaining always-on Premium capacity for workloads that may be intermittent or variable. Elastic scaling ensures that high-volume or burst workloads can consume proportionally more compute during peak times, while lighter workloads incur lower costs. Organizations with unpredictable or seasonal refresh patterns may see material cost reductions compared to Gen1 running on Premium capacity. This flexible cost model provides more predictable and efficient resource utilization, particularly for enterprises with diverse or rapidly evolving data refresh patterns. Our recommendation: Start planning your upgrade If you’re starting a new project, we strongly recommend using Dataflows Gen2. It offers better performance, richer diagnostics, built‑in AI, broader destination support, and a more flexible cost model. For existing Gen1 dataflows, now is the right time to begin planning your upgrade: Use Save as Dataflow Gen2 for quick, low effort upgrades of individual dataflows. For larger migration scenarios, the Save As API enables bulk migration and automation, supporting CI/CD workflows. For programmatic or large‑scale upgrades, refer to the Migrate to Dataflow Gen2 (CI/CD) guidance. Starting now allows you to modernize incrementally, beginning with a subset of your portfolio while keeping all your Power Query skills fully transferable. Thank you Thank you again for your long-standing investment in Dataflows and Power Query. We’re excited to support your transition to Dataflows Gen2 and to help you unlock new capabilities for the next generation of analytics solutions.
If you haven’t already, check out Arun Ulag’s hero blog “FabCon and SQLCon 2026: Unifying databases and Fabric on a single, complete platform” for a complete look at all of our FabCon and SQLCon announcements across both Fabric and our database offerings. The next evolution of in-report Copilot on the mobile app is now available in preview: a full conversational chat experience that lets you summarize, inquire, and analyze report data directly from your phone and tablet. Previously, in-report Copilot on the mobile app supported summaries and predefined prompts. This made it easy to get a quick overview but limited how far users could go when they wanted to explore their data more deeply. With this update, in-report Copilot on the mobile app moves to a full chat experience, bringing the same chat‑based capabilities you know from Power BI service, optimized for mobile and grounded in the report you’re viewing. In_a_Contoso_sales_report_in_Power_BI_Mobile_a_user_opens_inreport_Copilot_from Figure: Using the in-report Copilot in mobile app to answer on-the-go data questions. Getting started Every report that meets Copilot requirements in Power BI has the Copilot button in its header, ready to assist you with just a single tap. In the chat pane you can: Generate a summary of the current report Ask a question about the report’s data Ask a follow up question to explore further Use the prompt gallery to help get started with sample prompts Two_mobile_phone_screenshots_display_a_sales_report_for_Contoso_showing_revenue Figure: Using in-report Copilot on the Power BI Mobile app to ask natural‑language questions and explore insights directly within a report. Just like in Power BI service, in-report Copilot on the mobile app analyzes the report content for you, surfacing insights that would otherwise take time and effort to uncover grounded on the report data. Discover more When you ask about a metric or KPI in a report, in‑report Copilot often includes a visualization along with its text response. Tap the visual to open a pane where you can interact with it and explore the details. If you ask in-report Copilot to summarize a report, the response will include a summary with relevant citations. You can tap on a citation to open the visual in focus mode to view and interact with the visuals. Keep chatting with your data. Ask in-report Copilot follow-up questions, refine your queries, and dive deeper into report insights. To share data insights with your team, copy the response using the Copy action or tap Share from the visual pane. Voice support: On iPhone and iPad, in-report Copilot supports dictation, so you can ask hands-free questions. Use Read aloud to listen to the response. The new in-report Copilot chat experience on the Power BI mobile app makes it easier to analyze report data in context—whether you’re reviewing results on the go or preparing for a discussion around a specific report. Share your feedback Stay tuned as we enhance your experience with more capabilities in the coming releases. We invite you to chat with in-report Copilot on the mobile app and share your thoughts in the comment section Your feedback will help us shape the future of chat on the Power BI mobile app.
