Data modelling is the foundation of accurate and high-performance reporting in Power BI. Regardless of how good visuals look, poor modelling will result in slow reports, incorrect totals, and misleading insights. A well-structured model ensures data is accurate, reliable, and easy to analyze.
Fact and Dimension Tables
Fact Tables
Fact tables store measurable business data such as sales amounts, quantities, or transactions.
Key points:
Contain numeric values
Have many rows
Link to dimension tables using keys
Example: Sales, payments, water usage records
Dimension Tables
Dimension tables provide descriptive context for analysis.
Key points:
Contain categories and attributes
Used for filtering and slicing data
Example: Date, Customer, Product, Location
Diagram showing one Fact table connected to multiple Dimension tables
Star Schema
The star schema is the recommended modelling approach in Power BI.
Structure:
One central fact table
Multiple dimension tables connected directly to it
No relationships between dimensions
Why it matters:
Best performance
Clear filter behavior
Accurate aggregations
Simpler DAX calculations
Star schema diagram with Fact table at the center
Snowflake Schema
A snowflake schema splits dimension tables into multiple related tables.
Limitations in Power BI:
More complex relationships
Slower performance
Higher risk of calculation errors
Best practice:
Flatten snowflake structures into a star schema whenever possible.
Comparison diagram: Star schema vs Snowflake schema
Relationships in Power BI
Relationships control how data flows across tables.
Best practices:
Use one-to-many (1:*) relationships
Filter direction: Single direction (Dimension → Fact)
Avoid unnecessary many-to-many relationships
Proper relationships are critical to prevent double counting and incorrect totals.
Power BI model view showing correct relationships
Why Good Modelling Is Critical
Performance
Faster visuals and queries
Efficient use of Power BI’s VertiPaq engine
Accuracy
Correct totals and aggregations
Predictable filter behavior
Trustworthy reports
Simplicity
Cleaner DAX measures
Easier maintenance and scalability
Bad modelling directly leads to inaccurate reporting and poor decision-making.
Conclusion
In Power BI, data modelling determines whether reports are fast, accurate, and trustworthy. Using a star schema with clearly defined fact and dimension tables, supported by proper relationships, is essential for reliable analytics. Strong modelling is not just a technical best practice—it is a business necessity.
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