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MohamedFowzan1's avatar
MohamedFowzan1
Super User
1 month ago

Feedback for Semantic Model Health Dashboard

Looking for feedback from the Power BI community before I officially share this more broadly.

I've been working on an Automated Semantic Model Health Platform built using Microsoft Fabric, Semantic Link (sempy) and semantic-link-labs.

The solution automatically:

  • Discovers semantic models
  • Executes Best Practice Analyzer (BPA) rules
  • Calculates Health Scores
  • Tracks historical trends
  • Surfaces optimisation opportunities through a Power BI dashboard

I've documented the approach in the article below and would genuinely appreciate your feedback.

Specifically, I'd love to hear:

  • Is there anything you would approach differently?
  • Are there additional governance metrics you think should be included?
  • Have you built something similar using Semantic Link or other Fabric capabilities?

Article/Post link: https://www.linkedin.com/feed/update/urn:li:activity:7485267007152738305/

 

Thank you in advance for any suggestions!

Need Help

2 Replies

  • Prince0011's avatar
    Prince0011
    Solution Sage
    1 month ago

    This is a great initiative and a very practical use case for combining Microsoft Fabric, Semantic Link (sempy), and semantic-link-labs for enterprise Power BI governance.

    A few additional areas that could make the Semantic Model Health Platform even more valuable:

    1. Expand health scoring beyond performance

    In addition to BPA rules and optimization checks, consider adding governance metrics such as:

    • Unused or rarely used semantic models.

    • Dataset refresh failure frequency.

    • Refresh duration trends.

    • Model size growth over time.

    • Number of calculated columns vs measures.

    • Number of unused columns/tables.

    • Duplicate measures and inconsistent naming patterns.

    • DirectQuery vs Import model recommendations.

    2. Add dependency and impact analysis

    Since many organizations have hundreds of semantic models, it would be useful to include:

    • Report-to-semantic-model dependencies.

    • Downstream usage count.

    • Last accessed date.

    • Business owner/team ownership.

    • Impact analysis before making model changes.

    3. Historical trend analysis

    The historical health score tracking is a great feature. Some additional trends that could help administrators:

    • Health score improvement/regression over time.

    • Top recurring optimization issues.

    • Teams/workspaces with the highest technical debt.

    • Capacity impact before and after optimization.

    4. Include adoption and governance signals

    For enterprise environments, technical health alone may not be enough. Consider adding:

    • Workspace activity levels.

    • Report view counts.

    • Certified/promoted status.

    • Sensitivity labels.

    • Ownership and documentation completeness.

    One more interesting direction could be using AI capabilities to generate optimization recommendations, for example:

    "This semantic model has 15 unused columns, 8 high-cardinality fields, and 12 unused measures. Removing them could reduce model size by approximately X%."

    Overall, this is a strong example of how Fabric can move Power BI administration from reactive troubleshooting to proactive governance.

    Looking forward to seeing how this evolves. Great work!

    💡 Helpful? Give a Kudos 👍 — keep the community growing.

    ✅ Solved your issue? Mark this as the Accepted Solution ✔️

    Best regards, Prince Singh | Data Science & Microsoft Fabric Enthusiast