Optimizing the performance of visualizations becomes critical for maintaining responsive reports when working with low-level data inside Power BI. Below are some techniques that would be useful in this case:
Data Aggregation and Grouping: Grouping or aggregating data is one of the most effective approaches when such details are not needed further in the report to lessen the amount of data used in visuals. When data is presented in a higher hierarchy that is no longer concerned with explaining the figures on a daily basis (for instance, monthly or quarterly), the bulk of the dataset will be greatly trimmed, enabling quicker load times. Tools like Group By in Power Query and Aggregation settings in the model are provided in Power BI, which enables users to pre-aggregate most of the data, thus lessening the burden on the visuals that do not require all the details.
Restrictive Filtering: Apply filters to limit the data presented in the visuals to the specifics necessary for that particular report. This may involve implementing a date filter for the last few weeks or filtering by a specific type or region in accordance with the necessities of the particular report. Restrictive filtration becomes handy when dashboards are used to show insights of a certain level, and there is no need to go to the lower-level data structure. You can also do row-level security so that relevant data is made accessible based on user decisions, which will optimize performance further.
Disable Unnecessary Visuals and Map Elements: In case the data set has any geographic data, attach the importance that map visuals take up a lot of resources if it is a highly detailed data points-based map turning on system-wide map visuals. If geographic-based analyses are not important, do not include map visuals. Besides, look for empty columns, visualizations that could be more efficiently used, or more types of charts and reduce them where possible. Simplification of visuals enhances not only the speed of report generation but also the efficiency of users by minimizing unnecessary intricacy.
