11 Best Practices of Power BI in 2024

Power BI

Power BI empowers analysts and scientists to transform disorganized data into visually insightful representations. To leverage Power BI effectively, aspiring professionals need to grasp key best practices. In this comprehensive document, I will elucidate 20 Power BI best practices in 2024, supplemented with real-world examples. Whether you’re a college student delving into analytics or a novice data analyst, mastering Power BI will propel your career and establish you as a valuable asset. Additionally, adhering to Power BI visualization best practices is crucial for presenting reports to senior leadership who demand high-quality information for swift decision-making. Power BI training is an indispensable resource that aspiring analysts should include in their skill set repertoire.

Power BI: An Overview

Microsoft’s Power BI stands as a pivotal resource for businesses navigating the intricacies of data visualization and decision-making. Power BI stands out as the top choice among professionals for transforming raw data into actionable insights due to its intuitive interface and extensive features. Its seamless capabilities facilitate effortless information sharing across teams, rendering it a vital asset for organizations committed to enhancing their data-driven decision-making processes. In essence, Power BI development best practices and Power BI data flow best practices constitute integral components of the Power BI best practices handbook, crucial considerations when preparing and publishing reports.

PowerBI Best Practices in 2024

Are you looking to make enterprise-level Power BI setups faster and more secure? You can achieve that by following Power BI’s best practices. Many business dashboards in Power BI are used by lots of people every day to run operations and marketing. This article aims to show you 11 great ways to use Power BI better.

1. Limit Visuals in Dashboards and Reports: 

To enhance report performance, limit each report page to a maximum of eight visuals and one grid. Ensure a cap of 30 points across different visual types (like cards, gauges, charts, maps, grids), and restrict dashboards to a maximum of 10 tiles.

2. Remove Unnecessary Visual Interactions: 

Improve report performance by eliminating unnecessary interactions between visuals. Control interactivity between visuals to minimize backend queries and enhance efficiency.

3. Enable Row-Level Security (RLS): 

Utilize RLS to restrict user access to specific data rows. Test roles thoroughly before deploying them and combine Power BI roles with backend roles for optimal performance.

4. Use Microsoft AppSource-Certified Custom Visuals: 

Opt for certified custom visuals from Microsoft AppSource to ensure reliability, performance, and compatibility with features like Export to PowerPoint and email subscriptions.

5. Avoid Hierarchical Filters: 

If encountering slow page load times due to hierarchical filters, consider using multiple filters instead. Removing hierarchical filters can significantly enhance Power BI performance.

6. Categorize Data for Power BI Reports: 

Categorize data (HBI for High Business Impact, MBI for Medium Business Impact, LBI for Low Business Impact) to define security levels and guide data sharing within and outside the organization.

7. Utilize On-Premises Data Gateway: 

Preferring using an on-premises data gateway over a Personal Gateway for importing data into Power BI. Enterprise Gateway is more efficient, especially with large databases.

8. Use Separate Power BI Gateways: 

Create distinct gateways for Live Connection and Scheduled Refresh to prevent performance issues caused by using the same gateway for both functions.

9. Test Custom Visuals for Performance: 

Ensure fast report loading times by testing each custom visual for performance, particularly with large datasets or complex aggregations.

10. Limit Complex Measures and Aggregations: 

Optimize performance by minimizing complex calculated columns and measures. Push calculated elements closer to the data source and prefer calculated measures over columns in star schema designs.

11. Import Only Necessary Data: 

Keep data models lean and efficient by importing essential fields only, rather than entire datasets. Prioritize narrower and leaner models for improved Power BI performance.


These top 11 best practices are crucial for mastering Power BI reporting and analysis. It’s highly advisable to adhere to these practices consistently in all your Power BI tasks. Strive to grasp their essence and integrate them into your workflow. I wish you the very best as you embark on successful Power BI endeavors armed with these invaluable best practices!