2. Query Performance:
– Optimize your queries by writing efficient DAX (Data Analysis Expressions) measures.
– Use query folding wherever possible to push data transformation operations to the data source.
– Minimize the use of calculated columns and calculated tables, as they can impact performance.
– Leverage query reduction techniques such as query merging and predicate pushdown.
3. Data Refresh:
– Schedule data refresh during off-peak hours to reduce the load on data sources.
– Optimize data refresh by refreshing only the necessary tables or partitions.
– Consider using incremental refresh for large datasets to minimize data retrieval and processing time.
4. Visual Design:
– Simplify your dashboard layout to focus on key insights and reduce clutter.
– Use appropriate visualizations based on the type of data and the insights you want to convey.
– Limit the number of visuals on a single page to improve load times and user experience.
– Ensure consistency in colors, fonts, and formatting to enhance readability.
5. Interactivity:
– Minimize the use of slicers and filters, especially if they’re not essential for analysis.
– Implement drill-through and drill-down functionality to allow users to explore data at different levels of detail.
– Use bookmarks and buttons to create guided navigation and storytelling experiences.
6. Performance Monitoring:
– Monitor report performance using Power BI Performance Analyzer to identify bottlenecks and areas for improvement.
– Use the Performance Analyzer to analyze query duration, visual rendering time, and data model size.
– Regularly review performance metrics and take necessary actions to optimize dashboards and reports.
7. Data Security:
– Implement row-level security (RLS) to restrict data access based on user roles and permissions.
– Minimize data duplication and ensure sensitive data is appropriately masked or encrypted.
8. Testing and Validation:
– Test your dashboards and reports across different devices and screen sizes to ensure responsiveness.
– Validate data accuracy and consistency by comparing results with source systems and business requirements.