Learning Power BI? Try GPT-based natural language Quick Measure Suggestions for DAX

One of the key advantages of Power BI is the ability to create powerful custom measures in your data models using the Data Analysis Expressions (DAX) language. DAX allows users to create complex calculations based on their data, but it can be time-consuming to write DAX from scratch–especially for new users.

To help with this, Power BI debuted and now offers a feature called Quick Measure Suggestions, which uses natural language and GPT to generate DAX code for common measure scenarios quickly.

There are several benefits to using Quick Measure Suggestions, including:

1. Time savings: Quick Measure Suggestions can save time by automating the creation of commonly used measures, eliminating the need to write DAX code from scratch.
2. Reduced errors: Quick Measure Suggestions can help reduce errors that may occur when writing DAX code manually. Since the DAX code is generated automatically, there is less chance of syntax errors or other mistakes.
3. Easy to use: Quick Measure Suggestions is easy to use, even for users who are not familiar with DAX. Natural language examples can help users quickly generate DAX code for their specific measure scenarios.
4. Consistent measures: Quick Measure Suggestions can help ensure that measures are consistent across the organization. Since Quick Measure Suggestions generates DAX code based on predefined measure scenarios, measures created by different users will have a similar structure.

With Quick Measure Suggestions, you describe the measure you want to create and simply click Generate to get DAX measure suggestions. Supported measure types include:

• aggregated columns
• count of rows
• aggregate per category
• mathematical operations
• selected value
• if condition
• text operations
• time intelligence
• relative time filtered value
• most/least common value
• top N filtered value
• top N values for a category
• information functions

While Quick Measure Suggestions are a way to accelerate your DAX, you’re still in charge. It is essential to validate the DAX suggestions to make sure they meet your needs, and test the results afterward to ensure that the resulting measure accurately reflects your intended analysis.