Model Advisor
Use AI as a second set of eyes on a Power BI data model's relationships, star schema design, and performance.
Model Advisor
Reviewing a data model is harder to delegate to AI than a single DAX measure or Power Query step, since it depends on the whole model — but describing the model's structure and asking for a review still catches issues a developer might miss from being too close to their own work.
What AI Can Help Review
Relationship Design
Describing a model's tables and how they connect can surface issues like missing relationships, relationships going the wrong direction, or an unintended many-to-many.
Prompt:
"Here are my tables and relationships:
- DimDate (1) -> FactSales (many) on DateKey
- DimProduct (1) -> FactSales (many) on ProductKey
- DimCustomer (1) -> FactSales (many) on CustomerKey
- FactSales (many) -> FactInventory (many) on ProductKey
Does this relationship design look correct for a Power BI star schema?"A good response should flag the direct fact-to-fact relationship as a problem, and suggest connecting both fact tables through a shared DimProduct relationship instead. See Star Schema for why fact-to-fact relationships are avoided.
Star Schema Adherence
Describing the model's tables — which are facts, which are dimensions, and how wide each one is — can help confirm the model actually follows a star schema shape rather than a flatter, harder-to-maintain design.
Prompt:
"I have one table called SalesData with columns: Date, CustomerName, CustomerRegion,
ProductName, Category, Quantity, SalesAmount. Should I split this into a star schema,
and if so, how?"DAX Performance Patterns
Pasting a handful of measures alongside a description of the model can help identify patterns likely to cause slow visuals — heavy use of FILTER over large tables, iterators nested inside iterators, or measures that don't use variables and recompute the same subexpression repeatedly.
Model Organization
AI can suggest naming conventions, folder structures for measures (display folders), and whether a model has grown enough tables that some restructuring or documentation would help maintainability.
Cautions
- AI can only review what it's told about the model; it has no visibility into actual data volumes, cardinality, or real-world query patterns, all of which matter for genuine performance analysis.
- Treat model-level suggestions as a starting point for discussion, not a definitive verdict — validate real performance issues with Performance Analyzer or DAX Studio.
- A model that looks fine described in a paragraph can still have relationship or filter-direction issues that only show up in the model view itself. Use AI review to complement a visual inspection, not replace it.
A Reasonable Workflow
Describe the model's tables and relationships
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Ask for a review against star schema principles
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Cross-check flagged issues against the actual Model view
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Validate any performance concern with Performance Analyzer / DAX Studio