Hey Analytical Minds,

I just completed something I thought would take an entire week in just one hour.

Here’s what happened: I connected a Power BI semantic model to Claude AI and ran a comprehensive audit. What I discovered was shocking. The model had critical data quality issues, performance bottlenecks, and organizational problems that would have taken me days to find manually.

The Starting Point

The “Customer Profitability Sample” model looked fine on the surface:

  • 9 tables

  • 47,646 fact rows

  • 44 measures organized in display folders

  • 8 relationships configured

But underneath, there were serious problems.

The Critical Issues I Found

Issue #1: 21 Orphaned Customer Keys

I ran a simple DAX query through Claude: “Find customer keys in the Fact table that don’t exist in the Customer dimension.”

Result: 21 keys were referencing non-existent customers. This meant 1,953 fact rows were broken—they weren’t properly linked to any customer. Any report filtering by customer would give incomplete results.

Traditional approach: Manually compare the two tables, identify missing keys, decide how to handle them. Maybe 3-4 hours of work.

Claude approach: Identify the problem, suggest the fix, execute it. 10 minutes.

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Issue #2: Data Type Mismatches

I discovered that:

  • Fact[Product Key] was String, but Product[Product Key] was Int64

  • Customer[Industry ID] was String, but Industry[ID] was Int64

These type mismatches force SQL to convert data types on every join. That’s a 15-20% performance penalty on every Product filter. Hidden but costly.

Issue #3: Sparse Columns

Three columns had 99%+ zeros:

  • Material Costs: 99.97% zeros (47,630 empty rows out of 47,646)

  • Taxes: 99.96% zeros

  • Subscription Revenue: 99.78% zeros

These were wasting storage space and cluttering the field picker with essentially unusable data.

How Claude AI Solved This

Instead of manually fixing each issue, I used Claude to:

  1. Audit automatically - Identify all problems in one pass

  2. Generate code - Create the fixes in TMDL (the semantic model language)

  3. Execute changes - Apply fixes to the live model

  4. Validate results - Verify 100% data integrity

The Fixes

Fix #1: Add Unknown Customers

I asked Claude to add 21 “Unknown Customer” records for the orphaned keys. This took 5 minutes. The 1,953 broken rows now properly link to customers.

Fix #2: Align Data Types

Changed Product Key from String to Int64. Changed Industry ID from String to Int64. Both now align with their dimension tables.

Impact: 15-20% faster queries on Product and Industry filters.

Fix #3: Delete Sparse Columns

Removed Material Costs, Taxes, and Subscription Revenue columns. Also deleted 3 measures that only summed zeros.

Impact: 300 KB storage saved, cleaner field picker, zero functional loss.

The Results

Before optimization:

  • Data integrity: ~96% (21 orphaned keys)

  • Type mismatches: 2 (performance penalty)

  • Sparse columns: 3 (wasted space)

  • Documentation: None

After optimization:

  • Data integrity: 100% ✓

  • Type consistency: 100% ✓

  • Sparse data: Removed ✓

  • Performance: +15-20% ✓

  • Documentation: Complete ✓

The Time Comparison

Typical optimization work:

  • Audit: 4-6 hours

  • Data quality fixes: 3-4 hours

  • Type corrections: 2-3 hours

  • Documentation: 2-3 hours

  • Total: 11-16 hours

With Claude AI:

  • Audit: 15 minutes

  • Fixes: 20 minutes

  • Validation: 20 minutes

  • Documentation: 5 minutes

  • Total: 1 hour

That’s 14-15x faster.

What I Learned

AI isn’t replacing BI professionals. It’s eliminating the tedious parts so you can focus on strategy.

What Claude handled:

  • Pattern recognition (finding orphaned keys)

  • Code generation (TMDL edits)

  • Validation (checking all relationships)

  • Documentation (creating reference guides)

What I handled:

  • Strategic decisions (which columns to delete)

  • Quality judgment (validating findings)

  • Final reviews (ensuring correctness)

The Best Part

Every change is version-controlled. The entire model is defined in TMDL (text-based), so I can see exactly what changed, when, and why. This is reproducible, auditable, and maintainable.

Resources Used:

Drive Link

If You Work With Power BI

Ask yourself:

  • How many hours are you spending on manual audits?

  • How many data quality issues are you missing?

  • How long does it take to document your models?

  • How often do you fix performance issues?

Claude AI can handle all of this. Not perfectly—it still needs human judgment—but fast enough to change how you work.

What’s Next

I’m building out this workflow into a full optimization framework. Next: implementing this for larger models (500K+ rows) and exploring how to automate ongoing monitoring.

If you want to try this yourself, here’s what you need:

  1. Power BI Desktop (running locally)

  2. Claude AI access (API or web interface)

  3. Basic understanding of Power BI concepts

  4. 2 hours of time

The ROI is immediate.

Questions? Drop them in the comments. I’ll be working on advanced DAX optimization next—what topics would you like to see?

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Atikant Jain
Your Analytical Guy
admin@analyticalguy.tech