Semantic Model Best Practices
Design reusable, enterprise-ready semantic models for consistent analytics.
Semantic models (formerly datasets) are the foundation of Power BI analytics. A well-designed semantic model enables consistent, performant, and user-friendly reporting.
What is a Semantic Model?
A semantic model is a curated data layer that defines: - Tables and relationships - Measures and calculations - Hierarchies and groupings - Business-friendly naming - Security rules
Design Principles
Star Schema Architecture Always use star schema design: - Central fact tables for transactions/events - Dimension tables for descriptive attributes - Clear relationships with single direction - Integer keys for performance
Business-Friendly Naming - Use plain English names, not technical codes - Remove prefixes like "dim_" or "fact_" - Include units in measure names (Revenue USD) - Create display folders for organization
Proper Data Types - Use Date type for dates (enables time intelligence) - Use Integer for keys and counts - Avoid unnecessary precision in decimals - Remove unused columns to reduce size
Enterprise Considerations
Reusability Design models to serve multiple reports: - Include comprehensive measures - Support various analysis angles - Document calculations and business rules
Performance - Remove unnecessary columns - Create proper indexes through sort columns - Use aggregations for large fact tables - Test with production data volumes
Governance - Define ownership and certification - Implement row-level security - Document data lineage - Schedule regular refresh validation
Frequently Asked Questions
What is the difference between semantic models and datasets?
They are the same thing. Microsoft renamed "datasets" to "semantic models" to better reflect their purpose of providing business meaning and context to data, not just storing it.
Should every report have its own semantic model?
No, best practice is to create shared semantic models that serve multiple reports. This ensures consistency and reduces maintenance. Only create separate models when requirements are fundamentally different.