Microsoft Fabric Copilot: First Look-Test Drive, Power BI

Author: Etienne Oosthuysen

This is the first of several Fabric ‘first look’ Copilot test drives. In this post, I will focus on Copilot for Power BI, with Data Science and Data Engineering, and Data Factory test drives to follow in subsequent articles.

But before we jump, a bit of background, then some pre-requisites, some housekeeping, and then on to the test drive.

Test drive Copilot for Power BI – a first look: 

Create reports automatically by selecting the topic.

Here I will use AI to craft a report for me, by first asking for suggestions as a starting point, and then by authoring the full prompt of what I want. No coding, no visual drag and drop and design. Copilot does it all.

• Navigate to your Fabric workspace.
• Make sure you use the correct capacity. If you are in doubt as to which capacity applies, select the ellipses, then workspace settings then premium, and you should see either Premium capacity or Fabric capacity enabled.

• Now select the Semantic Model that you want Copilot to create the report from. Microsoft claims that you can now “create reports automatically by selecting the topic”. ‘Topic’, in my view, implies the subject matter of your semantic model. As stated in the important points I made earlier, a great semantic model will have been designed around a very specific business process, domain, unit, etc. Examples of this could be General Ledger, Accounts Payable, Accounts Receivable, Staff Onboarding, Bank Account Application, Mortgage Origination, CO2 Emissions across Building Assets, Works Management, and so on.
• The semantic model I will use today is a sales data model I created a while back, including sales transactions, some extended attributes, product hierarchies and location (cities). Here is the model visually represented (as a matter of context) – a pretty simple example.

• I select this Sales semantic model, then “Explore this data”, then “Create a blank report”. Note that “Auto-create a report” is also a neat feature but it is not a Copilot and AI feature but rather a vanilla native Fabric Feature that simply aims to represent the data across the semantic model visually (this feature is not discussed in this post).

• In the blank report pane, you should now see ‘Copilot’.

• Selecting Copilot will first show two important Preview disclaimers.

• If these are acceptable, continue with “Get started”. Copilot will give you some starter question examples, for example: “Suggest content for this report”, “What is in my dataset?”, or others. And “Create a page that shows…” as the opening statement to building something.
• Let’s start with inspiration first by selecting “Suggest content for this report”. Copilot now crafts some compelling suggestions based on what it has learnt of the semantic model (both from its design and its content). The suggestions Copilot made are: – Product Performance by Category.- Promotion Effectiveness by Type.- Store Comparison by Size and Type.- Sales Trends and Seasonality.

• Let’s go with Sales Trends and Seasonality. I can edit this, or, as in this example, simply hit Create. This delivered me a fully interactive report.

• I can of course also instruct it with appropriate prompts to create something from scratch. In this example I asked, “Create a page to compare the sales, revenue, and target achievement of stores based on their store IDs.” Here is the result, with each page build adds a new tab to my report.

Generate a summary of the report page.

I will now use AI to craft a narrative from a report page for me.

• In an existing report in edit mode, I select the Narrative visual.
• I then select Copilot. This now presents some cool options. For example:- Give and Exec summary,- Answer likely questions from leadership, and- Create a bulleted list of insights.
• I select “Answer likely questions from leadership”.

• Here is the narrative:

Generate synonyms for better Q&A

In the important points section, I described why the quality of your model is now more important than ever, and part of this is the way in which you name your entities, attributes, hierarchies, etc. One way to ensure a proper naming convention is by using synonyms. This will help Q&A work much better and accurately.

It must be noted that the Q&A visual and its natural language processing capabilities aren’t reliant on generative AI. However, you can use Copilot for Power BI to quickly improve the Q&A visual’s ability to understand user questions.

Adding synonyms for every data entity in your model can be time-consuming, even if they’re common synonyms for those names. Let’s see what Copilot can do:

• To use this feature, you need to enable it within your dataset.

• Now back in your report, select the Q&A visual. You might see a banner at the top of the visual or menu prompting you to improve your Q&A visual by getting or adding synonyms.

• If you select Add synonyms, then you will be prompted to review those synonyms.

Conclusion:

I like what I see. If you trust your semantic model, then it seems as if Copilot will do a decent job at report creation, explanations and improving quality. However, I have not assessed the accuracy of the report or narratives. This is simply due to the fact that this is a preview feature, and a disclaimer clearly states that there may be accuracy issues.