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5 Brutal Truths About GenAI + Unstructured Data in Power BI (Why Your Reports Feel Broken)

If you’re feeding raw text into Power BI, you’re not doing analytics—you’re creating confusion at scale.

Author

Glenda Carnate
April 02, 2026

Read time: 2.5 minutes

An awkward reality about Power BI: The problem isn’t its visuals... it’s what’s input into Power BI.

A company inputs customer reviews into Power BI and uses Copilot. Copilot generates summaries, and the dashboards look intelligent.

Then a question is asked, “How do we know if this information is true?”

Because nothing was structured, there was no definitive answer. The reports did not fail... the reports were never supported.

Where Power BI and GenAI Fail (and Fixing Those Failures)

1. Power BI is Not Intended to be an AI Engine.
It is a visualization tool... rather than an interpretation tool.
• Use GenAI prior to ingestion.
• Perform entity extraction, sentiment analysis and categorization externally.

2. Raw Texts Are Not Valuable to the User Experience.
Raw text cannot be aggregated.
• Turn it into Business Metrics (Score, Tag, Count).
• Anything that cannot be visualized should not be kept.

3. Copilot will not clean your Bad Data.
Summary does not equal clean.
• Set up pre-processing pipelines (Dataflows, Fabric, ADF, Python).
• Clean, load, then visualize.

4. DAX Does Not Support NLP.
Using workarounds for NLP will create future issues.
• Move logic upstream (GenAI + ETL).
• Leave DAX solution for calculations only.

5. No Structure = No Scale.
Having structured data will improve both performance and trust.
• Create a standardized schema (topic, sentiment, entity).
• Create consistent and reusable models.

💡Key Takeaway: 

It’s not the words that provide meaning in Power BI... it’s the way you structure them.

👉 LIKE if you have had difficulty in the past using unstructured information in Power BI.

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👉 COMMENT below with the most difficult obstacle you’ve experienced with unstructured data.

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