Automated Semantic Model Auditor & Data Dictionary Framework

The Challenge: Managing Technical Debt at Scale As semantic models grow, they inevitably accumulate technical debt. Over the years, I found myself spending hours manually debugging large models—hunting down unused columns that were draining RAM, untangling bi-directional cross-filtering, and manually reviewing poorly formatted DAX code.

Trying to document all of this manually in Word or Excel data dictionaries is a massive time sink, and the files are usually outdated the moment they are saved. I wanted to treat my Power BI models like actual code and automate the QA process.

The Solution: An Automated Auditing Framework Instead of clicking through DAX Studio or Tabular Editor manually, I built this automated Semantic Model Auditor (which I call the ModelLens Framework).

This report is entirely self-reflective—it is a Power BI dashboard designed to audit other Power BI models. It parses the metadata (via DMV queries and .pbip structure) and evaluates the model against over 100 architectural best practices to generate an overall "Health Score."

How to interact with this dashboard: I have connected this published version to a dummy "Contoso" dataset that I purposely filled with architectural errors so you can see the framework in action. Feel free to click around:

• Model Audit Overview: Check the main Health Score and see the prioritized list of critical errors.

• Unused Columns Tab: See how the tool flags columns that are taking up VertiPaq memory but aren't referenced in any visual, measure, or relationship.

• DAX Score: Look at the syntax evaluation—it instantly flags missing DIVIDE functions, hidden calculated columns, and poor use of iterators.

• Data Dictionary: See how a fully structured, exportable dictionary can be generated directly from the model's metadata without manual typing.

Under the Hood & Technical Deep Dive: Building this required combining metadata extraction with strict data engineering rules. If you are interested in the technical details of how I extract this data programmatically to build these blacklists and sanitizers, I have documented the entire process.

You can read the full technical deep-dive on my blog here: https://thebitoolbox.com/2026/05/31/stop-managing-power-bi-technical-debt-manually-build-an-automated-auditor-instead/

I hope this provides some inspiration on how you can automate your own documentation and governance processes! Let me know in the comments what your biggest pain point is when taking over a messy semantic model.

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