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Five tips for performance optimization in Power BI

Performance optimization involves making changes to the current state of the data model so that it runs more efficiently. As a developer, you might find that your report runs well in both test and development environments. But when deployed to production for a broader user base, performance issues can arise. From a user's point of view, poor performance is characterized by dashboards, report pages, and visuals that take longer time to load and update. This poor performance affects the user experience negatively. 


As a BI Developer, you will spend most of your time working with your data, and usually, poor performance is a result of an inefficient data model, badly formulated DAX measures, or a mix of the two. The process of creating and designing a data model can be tedious, but it is crucial for the end result. It is recommended to address performance issues during development, as this will save time and give you a robust data model from the start. Another important to think about is that the data model should be scalable and easy to maintain. This becomes more relevant as the amount of data increases and the use of reports expands in an organization. 


In general, a smaller data model uses fewer resources in terms of memory and achieves faster data refresh, calculations, and rendering of visuals. Thereby, the process of performance optimization involves minimizing the size of the data model and ensuring  the most efficient use of the data in the model, this includes:

  • Ensure that the correct data types are used
  • Remove unnecessary rows and columns
  • Avoid repeated values
  • When possible replace numeric columns with DAX measures
  • Minimize cardinalities

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