How to Optimize Power BI Reports? A Step by Step Guide

Steps To Optimise Power BI Reports

A. Data Model Optimizations

1. Analyzing Performance

The first step in optimizing the performance of your Power BI reports is to analyze their current state. Power BI provides a built-in feature called Performance Analyzer, which allows you to measure the load time of each visual in your report. To access the Performance Analyzer, go to the View ribbon in Power BI Desktop and select Performance Analyzer.

Once the Performance Analyzer pane is open, click on “Start recording” and then “Refresh visuals” to reload the visuals in your report. The Performance Analyzer will record the load time of each visual, helping you identify any bottlenecks in performance.

The load time of each visual is displayed in milliseconds, and you can see how long it takes for each element to load. Elements such as images, lines, cards, charts, tables, and slicers contribute to the overall load time. By analyzing the load time of each element, you can pinpoint areas that require optimization.

2. Reducing Cardinality Levels

One effective strategy for improving performance is to reduce the cardinality levels in your Power BI model. Cardinality refers to the number of distinct values in a column, and high cardinality can negatively impact performance. When there are too many distinct values in a column, it requires more processing power to aggregate and analyze the data.

To reduce cardinality, you can consider grouping or categorizing data. For example, if you have a column with a large number of distinct values, you can create a new column that groups similar values together. This can help simplify the data model and improve query performance.

Additionally, you can consider removing unnecessary columns from your data model. Columns that are not used in calculations or visuals can be safely removed to reduce the overall size of the model and improve performance.

Also Read: 20 Best Power BI Report Examples For Inspiration

3. Utilizing Variables for Performance Improvement

Variables can be a powerful tool for improving performance and troubleshooting in Power BI. By using variables, you can store intermediate results and reuse them within your calculations, reducing the need for repetitive calculations. This can significantly improve the performance of complex calculations and reduce the overall load time of your reports.

To utilize variables, you can create a DAX measure that calculates the desired result and assigns it to a variable. This variable can then be referenced within other measures, reducing the need for recalculations. By reducing the number of calculations performed, you can optimize the performance of your Power BI reports.

4. Optimizing DirectQuery Models

If your Power BI model uses DirectQuery, there are additional strategies you can employ to optimize its performance. DirectQuery allows you to query data directly from the underlying data source, without importing it into the Power BI model. This can be useful for large datasets or scenarios where real-time data is required.

To optimize DirectQuery models, you can leverage table-level storage. By default, DirectQuery models retrieve data from the underlying data source for each visual, which can result in slower performance. However, by enabling table-level storage, Power BI will store a copy of the data in a cache, reducing the need for repeated queries and improving performance.

You can enable table-level storage by going to the Model view in Power BI Desktop, selecting the desired table, and enabling the “Store data in the model” option. This will create a local copy of the data in the Power BI model, improving the performance of DirectQuery visuals.

5. Creating and Managing Aggregations

Aggregations can be a powerful technique for improving performance in Power BI reports that utilize large datasets. Aggregations allow you to precalculate and store summarized data at different levels of granularity, reducing the need for complex calculations during query execution.

To create an aggregation, you can define a new table in your Power BI model that contains the summarized data. This table can be based on existing tables in your model and can include calculated columns and measures that provide the desired level of granularity.

Once the aggregation table is created, you can configure Power BI to use it for query execution. This can be done by specifying the aggregation table as a “source table” for the visuals that require the summarized data. Power BI will then automatically route queries to the aggregation table, improving performance by avoiding unnecessary calculations.

Managing aggregations involves monitoring and refreshing the aggregated data to ensure its accuracy. You can schedule regular refreshes of the aggregation table to keep it up to date with the underlying data.

B. Report View Optimizations 

1. Analyzing Performance

Once you have optimized your data model, the next step is to optimize the report view. The Performance Analyzer can also be used to measure the load time of each visual in your report. By analyzing the load time of each visual, you can pinpoint areas that require optimization.

2. Reducing Visual Complexity

One effective strategy for improving performance in the report view is to reduce the visual complexity. This can be achieved by removing unnecessary visuals, simplifying visuals, and reducing the data displayed in each visual.

3. Using Drillthrough Filters

Drillthrough filters can be a powerful tool for improving performance in Power BI reports. They can reduce the amount of data displayed in a visual, making it load faster. Drillthrough filters allow users to click on a data point and drill down for more detailed information.

4. Using Paginated Reports

Paginated reports can improve performance in Power BI reports requiring high-fidelity printing or PDF generation. They are optimized for printing and can easily handle large datasets. By using paginated reports, you can improve the performance of your reports and provide a better user experience.

What Slows Down Power BI Reports?

Several factors can slow down Power BI reports, including large data, complex calculations, high cardinality levels, inefficient data models, and inadequate hardware resources. Additionally, using DirectQuery and real-time data can negatively impact report performance. Identifying and addressing these bottlenecks can optimize your reports’ performance and improve the user experience.