Why is my Power BI dashboard so slow when calculating Year-over-Year growth measures?

All answers to this question.

Are you checking your performance in the Power BI Performance Analyzer tool to see if it’s actually the DAX or the visual rendering that’s slow?

   Answered 2025-02-18 by Brian Miller

• Brian, that’s a great point. Performance Analyzer is the first thing Kimberly should look at. Kimberly, if the "DAX Query" time is high, then Margaret’s advice on variables is your best bet. However, if "Other" is high, you might have too many visuals on a single page. I’ve found that reducing the number of cards and slicers can sometimes improve the perceived speed more than rewriting the DAX itself.

     Commented 2025-02-19 by Michael Fletcher

I always recommend using Star Schema. If your data is in a flat table, time intelligence functions have to scan the whole thing, which kills performance as the row count grows.

   Answered 2025-02-20 by Susan Mitchell

• Totally agree, Susan! Moving to a Star Schema changed my life. Kimberly, once I separated my "Sales" fact table from my "Calendar" dimension, my load times dropped by nearly 70%.

     Commented 2025-02-21 by Kimberly Lawson

Performance issues in DAX often stem from "callback dataids" or inefficient filter contexts. While SAMEPERIODLASTYEAR is convenient, it can be slow on large tables. A more performant approach is to create a dedicated, marked Date Table and use CALCULATE with a manual filter on the DateKey. Also, ensure you aren't performing arithmetic on columns directly within the measure; instead, use variables (VAR) to store results. This prevents the engine from re-calculating the same logic multiple times during the evaluation of a single visual, which drastically reduces the processing time for your dashboard.

   Answered 2025-05-16 by Margaret Sullivan