How to Optimise Your Power BI Reports for Performance and Speed

Power BI is one of the most popular tools for business intelligence, providing businesses with the ability to create visually appealing, interactive, and insightful reports. However, as reports grow in complexity and size, performance can suffer, leading to slow loading times and frustrating user experiences. Optimising Power BI reports is critical not only for improving performance but also for ensuring end-users have a seamless experience. In this guide, we’ll delve into actionable strategies to optimise your Power BI reports for both performance and speed.

Why Performance Optimisation Matters in Power BI

Performance issues in Power BI don’t just slow down reports—they can disrupt decision-making, strain resources, and reduce user adoption. Slow-loading dashboards might discourage stakeholders from using the platform, ultimately diminishing the value of your data investments. Optimisation ensures your reports are efficient, scalable, and user-friendly, empowering your team to make informed decisions without delays.

1. Simplify and Streamline Your Data Model

A clean, efficient data model forms the backbone of a high-performing Power BI report. Overcomplicated models with excessive data can severely impact performance. Here’s some ways to simplify your data model:

• Reduce columns and rows: Import only the data you need. Unused columns, excessive details, or irrelevant records inflate your dataset size and slow down queries.

• Use a star schema: A star schema—a central fact table surrounded by related dimension tables—is more efficient than flat or snowflake schemas. This structure enables faster querying and better performance in Power BI’s engine.

• Avoid calculated columns: Replace calculated columns with measures wherever possible. Measures are calculated dynamically at runtime and do not increase the size of your dataset.

Example: If your sales dataset includes a calculated column for "Profit" (Revenue – Cost), consider moving this calculation to a DAX measure instead.

2. Optimise Your DAX Formulas

DAX is a powerful language for creating calculations in Power BI. However, inefficient DAX formulas can be a major bottleneck. To ensure smooth performance, consider the following tips when constructing DAX formulas:

• Use variables: Declaring variables in your DAX expressions not only makes them more readable but also avoids redundant calculations.

• Minimise iterators: Functions like SUMX and FILTER are useful but resource-intensive. Use them only when necessary, opting for simpler aggregation functions like SUM or COUNT.

• Optimise nested measures: When measures depend on other measures, the calculation chain can become cumbersome. Simplify these dependencies to improve query efficiency.

Tip: Use the DAX Studio tool to profile your DAX expressions and identify areas for improvement.

3. Improve Data Refresh Processes

Frequent data refreshes can overload your system and reduce performance, especially for large datasets. Power BI provides several options to optimise refresh times:

• Enable incremental refresh: Incremental refresh updates only the data that has changed, rather than reloading the entire dataset. This is especially useful for large datasets with historical records.

• Filter data at the source: Apply data filters at the source system or in Power Query to reduce the volume of data imported into Power BI.

• Use query folding: Query folding pushes transformations back to the data source, where they can be executed more efficiently. Most native connectors in Power BI support query folding.

Example: If you're working with a 10-year sales dataset but only need the last two years, use a filter to import only the required range.

4. Streamline Visualisations

While the visualisation aspect of Power BI is its main appeal, too many visuals or overly complex charts can slow down report rendering. To optimise visuals:

• Limit visuals per page: Avoid overcrowding your report pages. Aim for less than 8 visuals per page for optimal performance.

• Use default visuals: Built-in visuals are typically more efficient than custom visuals. Custom visuals can be useful but are often slower to render.

• Reduce slicers: Instead of multiple slicers, consider using dropdowns or a single dynamic filter. Too many slicers increase processing demands.

Pro Tip: Use bookmarks and drill-through pages to organise your reports logically while keeping the individual pages lightweight.

5. Enhance Query Performance

Efficient queries are essential for fast-loading reports. Poorly optimised queries can cause delays and bottlenecks. To improve query performance:

• Disable auto date/time: By default, Power BI creates hidden date tables for each date column. Disabling this feature reduces unnecessary overhead in your data model.

• Optimise query folding: When using Power Query, ensure that steps such as filtering and grouping occur at the data source rather than in Power BI.

• Use fewer queries: Combine multiple data queries into a single query where possible. This reduces redundancy and improves refresh performance.

Example: If you’re using SQL Server as your data source, write a SQL query to pre-aggregate data before importing it into Power BI.

6. Leverage Power BI Service Features

When publishing reports to the Power BI Service, take advantage of its features to further optimise performance:

• DirectQuery or hybrid models: DirectQuery retrieves data on-demand, reducing memory usage. For large datasets, a composite model (DirectQuery combined with Import) can balance performance and flexibility.

• Dataset size limits: Premium capacity offers larger dataset size limits, making it suitable for enterprise-level reporting needs.

• Monitor usage: Use the Power BI Service’s Usage Metrics to identify which reports or datasets are most frequently used, allowing you to prioritise optimisations.

7. Monitor and Test Performance Regularly

Optimisation isn’t a one-time task—it requires regular monitoring and testing. Here’s how:

• Performance Analyzer: Use this built-in tool in Power BI Desktop to measure visual load times and identify bottlenecks.

• Test across devices: Test your reports on different devices, including desktops, tablets, and mobile phones, to ensure consistent performance.

• Incorporate user feedback: Engage end-users to identify specific pain points, such as slow-loading visuals or confusing layouts.

Example: A Sydney-based retail client improved their Power BI performance by using Performance Analyzer to identify slow visuals. By replacing a custom map visual with a standard table, they reduced page load time by 40%.

Power BI is a versatile tool, but without proper optimisation, even the most well-designed reports can falter. By focusing on data model simplification, DAX efficiency, data refresh strategies, and visualisation improvements, you can create reports that are not only beautiful but also lightning-fast.

Whether you’re managing a small business or an enterprise, optimised reports ensure better user adoption, faster insights, and a smoother decision-making process.

If your organisation is facing challenges with Power BI performance or wants to take your reporting to the next level, White Box Analytics is here to help. We specialise in crafting and optimising Power BI solutions tailored to your needs. Contact us today and let’s build a better data experience together.