Automating Semantic Model Validation: Ensuring Accuracy When Optimizing DAX

Performance tuning Power BI Semantic Models is essential for fast, reliable analytics—but how do you ensure optimizations haven't altered business logic or changed results? Traditionally, validating DAX measure changes requires executing countless queries and manually comparing outputs between model versions: a time-consuming, error-prone process. This session introduces an automation-driven approach that simplifies and accelerates model validation, whether it be DAX changes in measures, relationship changes, or model properties. Attendees will learn how to import query sets from Power BI Performance Analyzer or DAX Studio Profiler, execute them against both original and optimized models, and instantly identify discrepancies with row-and-column-level precision. You'll walk away with practical techniques to automate validation workflows, ensure data integrity when refining business logic, and increase confidence in performance tuning across enterprise models. This presentation bridges the gap between optimization and trust in results—empowering teams to move faster without compromising analytical accuracy.

Automate validation workflows to compare query results between model versions instantly Detect discrepancies in DAX measures, relationships, and model properties before deployment Validate performance optimizations with confidence using repeatable, automated testing