Question

How can I optimize Power BI visuals when working with extremely granular data?

When working with highly detailed, granular data in Power BI, visual performance can slow significantly due to the volume of data processed. What are the recommended approaches to optimize visuals and improve responsiveness in such scenarios, including techniques for managing data at various levels of aggregation?

Answer 1

• To make use of the beveled edge of Power BI visuals built on very granular data, one may begin by introducing some degree of data aggregation so that fewer data is processed in real-time. With granular data, it may make more sense not to load every single record and instead come up with summary tables or some pre-aggregates designed at various turning points, such as daily, weekly, and monthly aggregates. In this case, loading such schemes instead of focusing on all detailed data will allow you to speed up loading time and make interaction faster while still providing proper usage.
• In addition, filtering or slicing methods should be applied if a visual has to display a value for a specific period, category, or dimension, which is not the complete dataset. This mechanism helps out the Power BI engine since it does not have to process everything but rather what is important for the user at that instance. One can also arrange the visuals responsive to the slicer so that only an analysis-critical data subset is available, which assists in improving performance and user experience.
• Finally, consider improving efficiency by restructuring your data model in a positive way toward relationships and removing excess columns where possible. For example, assess with Power BI’s performance analyzer which visuals are the most ‘laggy’ in motion and, if possible, use Import Mode instead of Direct Query Mode because it is faster in rendering, especially for visuals. With the implementation of these approaches, you may improve visual performance while still preserving the rich and detailed nature of your data analysis.