Customer : POPULUS FINANCE GROUP

Problem Statement

• In the Store Operations report, 2-3 Power BI reports had significant load time issues, with some pages taking up to 1 minute to load.
• Each report was built with its own dataset, resulting in redundant models, large file sizes, and slow performance.
• Managing data relationships and value consistency across reports became increasingly difficult due to complex and large datasets.

Objectives

• Enhance the speed, responsiveness, and efficiency of Power BI reports and dashboards.
• Ensure scalability and reliable performance across large datasets and complex calculations.
• Implement best practices for data loading, transformations, and storage to minimize resource usage.
• Identify and eliminate performance bottlenecks using Performance Analyzer and DAX Studio.

Outcome

• Page load times dropped dramatically — from nearly 1 minute to under 10 seconds.
• Data consistency improved across all reports by using a single source of truth.
• Maintenance and updates became easier and faster with a unified data model.

The client was highly satisfied, and the solution was adopted as a template for future reports.

Solution

• Designed and deployed a common, centralized dataset to serve 7 different Power BI reports.
• Optimized the model by:
  • Reducing data size (removing unused columns and tables).
  • Streamlining relationships and minimizing data transformations.
• Used Power BI's shared dataset feature to connect all reports to the common model.
• Applied best practices for data modelling and performance tuning.

Implementation

• Simplified data model by removing unused columns and tables.
• Reduced data size using appropriate data types and filtering unnecessary rows at the source.
• Limited the use of complex visuals and applied visual-level filters only when needed.
• Analysed performance using Performance Analyzer and DAX Studio to identify slow queries.
• Enabled incremental refresh for large datasets to reduce refresh time.