For a long time, dashboards have been where the data journey stops. Data is collected, cleansed, visualized, and then those insights are sent off to another system or team to take action. The process works, but it’s not exactly seamless.
That gap between seeing and doing is where translytical task flows come in. Built into Microsoft Fabric and Power BI, translytical flows make it possible to update data, kick off workflows, and collaborate with other process stakeholders right from the report itself.
In this post, we'll explore what translytical task flows are and how they work within Microsoft Fabric and Power BI. We'll also look at a few real-world scenarios that show how you can use them to simplify operations, close the loop between insight and action, and make Power BI an active part of daily workflows.
Under the Hood: How Translytical Flows Work
Translytical task flows are built on a relatively new capability in Microsoft Fabric called user data functions. These functions quietly handle the behind-the-scenes work every time someone interacts with a dashboard.
As you can see in Figure 1 below, user data functions are built using Python. Within a function definition, we can gather inputs from the dashboard and perform a variety of activities including:
Applying updates to Fabric data sources including lakehouses, data warehouses, and managed databases.
Performing complex calculation logic or data validations.
Making calls to RESTful web services—including services that leverage/integrate with AI services.
Calling APIs to copy data back to upstream ERP and CRM systems.
Triggering notifications and/or workflows to remediate issues detected online.
Figure 1: Working with User Data Functions in Microsoft Fabric
In translytical task flows, user data functions serve as the bridge between what users see in Power BI and the data models behind their reports. When someone edits a number, selects a status, or submits a form, these functions capture the input and write it back to the dataset in real time. Paired with new inline editing visuals in Power BI (see Figure 2 below), users can now interact directly with their data by updating values, choosing options, or triggering actions without ever having to leave the dashboard.
Figure 2: Linking a Button Visual to a User Data Function in Power BI
So, in summary, translytical task flows come together by:
Developing user data functions in Python to process data and execute form processing logic.
Building integrated forms in Power BI reports/dashboards using some of the new/revised form-building visuals.
Developing some DAX expressions to bind form data to function parameters so that we can pass data back-and-forth between the Power BI report in the foreground and the user data function(s) in the background.
Putting Translytical Task Flows to Work
OK, enough technical analysis. Let's explore some common use cases for translytical task flows. For a more thorough analysis on the art of the possible, check out Microsoft's introductory blog post here.
Building Interactive Dashboards
The first and most common scenario we'll look at is an interactive dashboard. In Figure 3 below, you can see a fairly typical sales opportunities dashboard with an opportunities list and a few slicers. However, on the right-hand side of the screen, you can see a discount form which allows sales reps to selectively apply discounts to relevant opportunities.
Figure 3: Updating a Sales Opportunity Directly in a Power BI Report
In simple cases, we can just update the discount amount/percentage in the form and then see the results in real-time inside the dashboard. However, in the animation shown in Figure 3, you can see that this particular example goes a step further by staging the proposed discount and routing it to a relevant approver using Microsoft Teams.
While this is a relatively simple example, it showcases some of the more powerful task flows that we can unlock. To put this into perspective, let's consider how this kind of scenario normally plays out in practice:
A sales rep/supervisor reviews a static report/dashboard in Power BI.
As they review the opportunity data, they might open up their CRM in a separate window to update selected opportunities one-by-one or simply jot down some notes to keep track of potential updates.
If an approval process is involved, then it almost certainly is driven out of Outlook email or Teams in ad hoc fashion.
As you can see, this process is highly manual and usually requires that the user(s) pay careful attention to what they're doing because the report is not usually in sync with ad hoc updates happening outside of the dashboard.
With translytical task flows, the process is much more streamlined. The report acts like a worklist that makes it easy for analysts to scan through the opportunities, drill in, and suggest a discount all within one harmonized user experience. Meanwhile, the user data functions work behind the scenes to coordinate an approval process and route approval tasks to the appropriate approvers in Teams—all within the flow of work.
Extending Translytical Flows with AI Integration
The next scenario we'll look at showcases what AI integration looks like. In the dashboard shown in Figure 4 below, you can see a list of influencers in the middle of the report layout. As users make selections, they can utilize the form on the left-hand side of the report to call out to a large language model (LLM) to generate a suggestion on behalf of the selected influencer.
Figure 4: Dynamically Generating AI Suggestions Within a Power BI Dashboard
Although this scenario is rather contrived, it gives us a peek at how far we can push the envelope with user data functions. Here, we can take some data from highlighted records, feed it to some kind of AI model, and then integrate the results back into the dashboard experience.
The only real limit with something like this is our creativity. Building on the core features of Fabric, Power Platform, and Azure, we can transform static reports into fully dynamic user experiences that fundamentally change the way that users work.
Closing Thoughts
The introduction of translytical task flows marks a meaningful shift in how we think about business intelligence. By connecting analytics with operational systems, Power BI is no longer just a tool for observing performance. When used properly, translytical task flows can transform Power BI dashboards into workspaces where decisions are made and actions are taken. This blurring of transactional and analytical boundaries helps teams move faster, reduce process friction, and ensure that insights are acted on while they’re still relevant.
As new capabilities like AI functions and user data integrations continue to evolve, the line between analytics and execution will only grow thinner. The future of work isn’t about switching between systems or waiting for reports; it’s about having intelligence built directly into the flow of daily operations. Translytical task flows are an early glimpse of a future where every dashboard becomes a dynamic part of how business gets done.
If you’re ready to explore what translytical task flows could look like in your organization, our team at Bowdark can help you get started with Microsoft Fabric and Power BI. Together, we can design experiences that put insight and action on the same page—literally.
