Use Copilot in Power BI Desktop to Create Measures from Numeric Columns

I have been thinking about a mechanism to generate measures from numeric columns on Power BI data models. Of course, we can use Tabular Editor, but it requires some scripting, which is all right. However, the more advanced our requirements get, the more complex the C# script. In real-world development scenarios, it does not make sense to blindly create measures for all numeric columns, such as the key columns used to define relationships between tables, making C# scripting a bit more complex.

In this blog and accompanying YouTube video, I explain using Copilot within Power BI Desktop to create measures from numeric columns. This feature represents a significant advancement in Power BI’s capabilities as of April 2024, enabling data analysts and BI professionals to streamline parts of their data analysis tasks.

Prerequisites

As explained in a previous post here, we first need to enable Copilot on the Fabric Portal. Please note that Copilot in Power BI Desktop requires either Power BI Premium Capacity or AT LEAST an F64 Fabric Capacity. Unfortunately, Copilot is NOT available on PPUEmbedded capacities, Fabric capacities smaller than F64 and Fabric Trial (FT) capacities.

We also need to have the latest version of Power BI Desktop installed on our machine. With that, let’s begin.

YouTube Video

Here is the video on YouTube where I explain the same thing in less than 5 min. But if you are after more details, continue reading.

Introduction to Power BI and Copilot

As Power BI evolves, it incorporates more sophisticated AI-driven capabilities that simplify various aspects of data analytics. The integration of Copilot in Power BI Desktop enhances user interaction with data in many ways. Our focus on this blog is specifically using Copilot to create simple yet crucial measures based on numeric columns that previously required manual effort.

Use Copilot for Measure Creation

Using Copilot is straightforward and demonstrates impressive intelligence in its operational logic. The following steps explain how to do so:

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Exporting Power BI Desktop Data Using Power BI Exporter

Exporting Power BI Desktop Data Using Power BI Exporter
Exporting Power BI Desktop Data Using Power BI Exporter

I am excited to announce that our amazing team at Data Vizioner has developed a lightweight and easy to use external tool for Power BI Desktop named Power BI Exporter. The Power BI Exporter is a free community tool available for download here. With External Tools General Availability, you can expect to see more and more External Tools built by our amazing community members. I personally stunned to see many amazing ideas turn into useful tools. Learn more about Power BI announcements about External Tools GA here. I am humbled to be named in the announcement though. So thank you to Microsoft for that.

Let me start with a little background. In the past few years, I wrote a series of blog posts about exporting data from Power BI Desktop and Power BI Service. Those posts are quite popular. They are in the top 10 most visited posts on my website, contributing to about 15% of my website visitors. Here are the previous posts I wrote on this specific topic:

Apart from my website statistics showing many people want to export data from Power BI Desktop, some of my customers asked the same question. They wanted to export the curated data from their data model within Power BI Desktop to CSV format and make the curated data available for their other platforms. While all the methods mentioned in my previous blog posts are working, some users still find them complex. So I thought, we can make it better. We can make a straightforward tool that exports the data with only two clicks. So we started building the Power BI Exporter as a micro-project. We added some more ideas to the original idea of only exporting the data. We thought it is good to export the data along with the table names, column names and relationships. Having that information handy, we can quickly build the same data model as the one we exported its data but using the CSV files as the data sources. The other idea was to pack everything in a ZIP file on the fly, so we have a single ZIP file, including the tables, columns, and relationships. As a result, the first version of the Power BI Exporter is born. In this post I explain how it works.

Downloading and Installing Power BI Exported

You can download Power BI Exporter from its official webpage from Data Vizioner website. You require to enter your email address then click the Download button as shown in the following image:

Downloading Power BI Exporter from Data Vizioner Website
Downloading Power BI Exporter from Data Vizioner Website
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Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI

Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI

I was working on a project a wee bit ago that the customer had conditional formatting requirement on a Column Chart.
They wanted to format the columns in the chart conditionally based on the average value based on the level of hierarchy you are at.
Here is the scenario, I have a Calendar hierarchy as below:

  • Calendar Hierarchy:
    • Year
    • Semester
    • Quarter
    • Month
    • Day

I use “Adventure Works DW2017, Internet Sales” Excel as my source in Power BI Desktop. If I want to visualise “Total Sales” over the above “Calendar Hierarchy” I get something like this:

Line Chart in Power BI, Total Sales by Year

Now I activate “Average Line” from “Analytics” tab of the Line chart.

Adding Average Line to Line Chart in Power BI

When I drill down in the line chart the Average line shows the average of that particular hierarchy level that I am in. This is quite cool that I get the average base on the level that I’m in code free.

Power BI, Drilling Donw in Line Chart

Easy, right?

Now, the requirement is to show the above behaviour in a “Column Chart” (yes! visualising time series with column chart, that’s what the customer wants) and highlight the columns with values below average amount in Orange and leave the rest in default theme colour.

So, I need to create Measures to conditionally format the column chart. I also need to add a bit of intelligent in the measures to:

  • Detect which hierarchy level I am in
  • Calculate the average of sales for that particular hierarchy level
  • Change the colour of the columns that are below the average amount

Let’s get it done!

Detecting Hierarchy Level with ISINSCOPE() DAX Function

Microsoft introduced ISINSCOPE() DAX function in the November 2018 release of Power BI Desktop. Soon after the announcement “Kasper de Jonge” wrote a concise blogpost about it.

So I try to keep it as simple as possible. Here is how is works, the ISINSCOPE() function returns “True” when a specified column is in a level of a hierarchy. As stated earlier, we have a “Calendar Hierarchy” including the following 5 levels:

  • Year
  • Semester
  • Quarter
  • Month
  • Day

So, to determine if we are in each of the above hierarchy levels we just need to create DAX measures like below:

ISINSCOPE Year		=	ISINSCOPE('Date'[Year])
ISINSCOPE Semester	=	ISINSCOPE('Date'[Semester])
ISINSCOPE Quarter	=	ISINSCOPE('Date'[Quarter])
ISINSCOPE Month		=	ISINSCOPE('Date'[Month])
ISINSCOPE Day		=	ISINSCOPE('Date'[Day])

Now let’s do an easy experiment.

  • Put a Matrix on the canvas
  • Put the “Calendar Hierarchy” to “Rows”
  • Put the above measures in “Values”
Detecting Year, Semester, Quarter, Month and Day hierarchy levels with ISINSCOPE in Power BI Desktop

As you see the “ISINSCOPE Year” shows “True” for the “Year” level. Let’s expand to the to the next level and see how the other measures work:

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