Power BI: All DAX Measures & Expressions

Key insights

• INFO.MEASURES
  Run EVALUATE INFO.MEASURES() in the DAX Query View to return every measure in the semantic model, including each measure's name, expression, and metadata.
• DAX Query View
  Open Power BI Desktop, go to the DAX Query View, paste the query, and run it to see a live table of measures you can export or copy for documentation.
• Measure Metadata
  The output shows measure expressions, data types, and table associations, giving precise, current information for auditing and troubleshooting.
• No Third-Party Tools
  This method is native to Power BI Desktop, so you can inventory measures without external utilities or manual extraction.
• Practical Uses
  Use the list for documentation, governance, knowledge sharing, and debugging complex calculations across large models.
• Recent Enhancements
  Updated INFO functions and a stronger DAX Query View (2025) improve model visibility and make extracting measure definitions faster and more reliable.

Video Overview

In a recent YouTube video, Reza Rad (RADACAD) [MVP] demonstrates a simple, native method to extract every measure and its expression from a Power BI semantic model. He shows that you can run a DAX query inside the built-in DAX Query View to retrieve metadata without installing any external tools. Consequently, the approach highlights improved model visibility and makes documentation faster for report authors and auditors.

Throughout the video, Reza emphasizes the use of the INFO family of functions, especially INFO.MEASURES, to return a table that includes measure names, data types, and the full DAX expression for each measure. He illustrates the practical steps in Power BI Desktop and explains why this capability matters for governance and troubleshooting. As a result, viewers gain a clear, actionable technique to inventory calculations in their datasets.

How the Demonstration Works

Reza walks through opening the DAX Query View in Power BI Desktop and running a simple query such as EVALUATE INFO.MEASURES() to list measures and their metadata. He explains the output columns and shows how the returned expressions can be read, copied, or exported for documentation. This hands-on demonstration helps users see the feature in context and builds confidence in using the query view for model inspection.

For clarity, the video also showcases how the returned metadata updates dynamically as the model changes, underscoring that the method queries the live semantic model. Reza further compares this native approach with the familiar workflows that use third-party editors, indicating when the native query is sufficient and when external tooling might still be preferable. Thus, viewers can judge which approach fits their needs based on scale and complexity.

Benefits and Practical Use Cases

One clear benefit is that the process requires no third-party tools, reducing setup time and potential security concerns related to external software. It is particularly useful for small teams or quick audits because it produces an accurate snapshot of every calculation in the dataset. Consequently, tasks such as handovers, compliance checks, and peer reviews become faster and less error-prone.

Moreover, because the query reads the live model, the output always reflects the current state of the report, which helps avoid stale documentation. This is valuable when models change frequently or when multiple authors collaborate. Reza demonstrates these scenarios to show how the technique supports continuous documentation and immediate troubleshooting.

Tradeoffs and Challenges

Despite its advantages, the approach has tradeoffs that analysts should consider. For example, while INFO.MEASURES returns measure expressions and metadata, it provides limited context about how measures interact across visuals, how they are used in report pages, or their performance characteristics. Therefore, when performance profiling or dependency mapping is required, more advanced tools may still be necessary.

Another challenge arises with very large models: extracting and reviewing hundreds or thousands of expressions can be overwhelming and may demand downstream processing or tooling to categorize and analyze results. In addition, some organizations require richer governance features such as automated lineage, change tracking, or CI/CD integration, which typically rely on dedicated model-management solutions. Consequently, teams must balance immediacy and simplicity against the need for more comprehensive lifecycle management.

Practical Recommendations

For everyday auditing and quick documentation tasks, follow Reza’s workflow: open the DAX Query View, run EVALUATE INFO.MEASURES(), and export the results for review. If you need to perform deeper analysis, consider combining this native approach with light-weight external tools to classify measures or to map dependencies. This hybrid strategy keeps the initial step simple while allowing expansion into richer governance workflows when necessary.

Finally, Reza advises keeping a routine: regularly extract the measure list after major changes and store a revision history to support troubleshooting and audits. By doing so, teams can maintain transparency and reduce time spent tracking down the origin of complex calculations. In short, the video offers a practical, low-friction technique that improves model visibility, and it helps analysts choose the right balance between quick native inspection and more robust management solutions.

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