5 Brutal Truths About AI vs DAX vs SQL in Microsoft Power BI

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The 5 Harsh Realities Regarding AI Compared to DAX and SQL in Power BI

1. "Use Artificial Intelligence (AI) for analysis." → You've selected the incorrect instrument
AI operates on probabilities, BI operates on certainties.

Solution:

• QL: Joins, Filtering, Shaping

• DAX: Calculations, Aggregates

• AI: Enrichment, Classification

• If it absolutely must be precise, do not use AI.

2. "DAX can manage it" → You used DAX as a collection area
You have moved all your business logic and transformations into measures.

Solution:

• SQL/ETL: Data preparation

• DAX: Use for Lightweight (simple) measures.

• If you are using a complex DAX formula, then you have a flawed architecture.

3. "Let's include AI in Power BI" → You have mixed computation and visualization
You fused computation and visualization together.

Solution:

• AI = Pipelines (They exist outside of BI.)

• Power BI = Rapid Visualization

• Merging the computation and visualization layers destroys trust and performance.

4. "We'll determine how to do it as we proceed" → This is why your results will never scale.
A lack of structure will lead to duplicated logic and conflicting output.

Solution:

• Establish a tooling agreement.

• SQL determines the shape of the data.

• DAX is responsible for the metric.

• AI is responsible for the income.

• Create a record of this, and hold parties accountable for compliance.

5. "We've got the most current tools" → And you're still making decisions based on emotion.
More tools do not equal improved results.

Solution:
Define:

• Each use case for a decision.

• Which tools will enable each use case?

• What will provide metrics to validate decisions made based on the earlier discussion?

• If there is no decision to be made, no tool is needed.

💡Key Takeaway: 

Having better technology is not going to help you get better results. You need to make clearer decisions on how to use technology already in your current stack correctly.