10 Steps to Improve Your Data Model
Are the visuals in your Power BI report not loading as quickly as you would like? Many people immediately start examining the DAX measures at fault. However, performance-related issues often trace back to the data model. A well-optimized data model is the foundation of any effective report. Different Power BI developers might create various models due to the artistic nature of data modelling, where multiple correct solutions might exist. Unfortunately, best practices are sometimes overlooked due to the need for quick fixes or a lack of knowledge. Here are 10 steps to optimize your data model:
1. Remove Unnecessary Columns and Tables
Ensure you only include columns and tables essential for your report. Unused columns and tables can bloat your data model and slow performance. Use external tools like Bravo to identify and remove unused data elements.
2. Aggregate Data to the Required Level of Detail
Aggregate data to the necessary level of detail for your report and remove unused rows. For example, if your visuals show sales broken down by state and month, you don’t need individual sales transactions.
3. Turn Off Auto Date/Time
The auto date/time feature in Power BI can significantly increase your model size by creating hidden date tables for each date column. Instead, create a custom date table to manage date data more efficiently.
4. Create a Star Schema, Not a Snowflake Schema or Flat Table
Avoid using a single flat table or a fully normalized snowflake schema. Instead, use a star schema with denormalized dimensions, which Power BI handles more efficiently.
5. Remove Unnecessary One-to-One Relationships
One-to-one relationships can often be avoided unless necessary for composite models, security reasons, or efficiency. They generally add unnecessary complexity to your data model.
6. Use Bi-Directional and Many-to-Many Relationships Only When Necessary
Bi-directional filters can be slow, especially with high cardinality columns, and may create ambiguities in your model. Instead, use measures to filter slicers.
Insert filter slicer instead
7. Reduce Cardinality
High cardinality (number of unique values) reduces compression and increases model size. Split date and time values into separate columns to reduce cardinality.
8. Assign Correct Data Types
Ensure that columns have the appropriate data types. Text data types take up more space than numeric types. Reduce precision for decimal numbers where high precision is unnecessary.
9. Use Measures Instead of Calculated Columns
Measures do not consume space like calculated columns, which are pre-processed. Use calculated columns only if necessary for grouping or filtering
10. Prefer Import Mode Over Direct Query
Direct Query is slower and should be used only when necessary, such as for very large datasets or the need for real-time data. For most cases, import mode provides better performance.
With these 10 steps applied, your Power BI reports should run smoothly and efficiently. If you want to become a better Power BI developer, consider joining our next Power BI Design Transformation Cohort, where we share frameworks and systems to consistently develop amazing reports.