AI-Assisted Data Modeling: Best Practices

Using AI to assist in dimensional modeling (transitioning from 3NF operational tables to Star Schemas) accelerates semantic layer design when guided with appropriate architectural constraints.

1. Guiding AI Through the Kimball Modeling Process

When prompting AI to design an analytical star schema from an OLTP database, instruct it to execute the 4 Kimball steps:

1. Select the Business Process: Identify the operational process to measure (e.g., Sales Orders, Shipments, Customer Service Calls).
2. Declare the Grain: State the atomic measurement level (e.g., individual line item on a customer invoice).
3. Identify the Dimensions: Determine the descriptive context answering Who, What, Where, When, Why (e.g., Customer, Product, Geography, Calendar).
4. Identify the Facts: Extract the numerical, additive measurements (e.g., Quantity, Sales Amount, Cost, Freight).

2. Generating DAX Measures with AI

When asking AI for DAX measures:

• Specify that measures must be explicit (e.g., [Total Sales] := SUM(...)), never implicit columns.
• Require DIVIDE(Numerator, Denominator, 0) to guarantee safe division without #DIV/0!.
• Demand clear measure descriptions and formatting strings (e.g. Currency: $#,##0.00).