How to Optimize Data Model in Power BI

A sluggish Power BI dashboard is often a symptom of data model issues. If your visualizations take ages to load or your data refreshes are grinding to a halt, the problem likely lies in the design of your data model, not in the visualization layer. This tutorial will equip you with essential techniques to achieve optimal performance for your Power BI dashboards.

What is a Power BI Data Model (And why is optimization critical to reporting performance?)

In short, a Power BI report data model is a collection of two or more tables with predefined relationships. Think of report models as the architectural master plan for your raw transactional data. Data modeling connects disparate data tables, such as sales transactions and customer demographics, and provides the foundation for data discovery and exploration. Optimizing data models is crucial for three main reasons:

• Significantly Faster BI Reports: An effectively designed and optimized data model allows the Power BI engine to process data faster. Sluggish filtering or slow-loading visuals often indicate poor model design.
• Faster Data Refreshes and Updates: A non-optimized model will take longer for data refreshes and processing, leading to daily frustrations. Model optimizations can minimize workflow waiting times for Power BI users.
• Simplified DAX Reporting Tasks: Well-structured models make DAX reports more efficient and less prone to errors, allowing more effective data reporting.

1. Start at the Source: Aggregate and Summarize Your Reporting Data

Always aim for the desired BI outputs at the data source level. A common mistake is importing data with unnecessary granularity. Consider whether your business dashboards truly need access to high granularity data. Often, a summary level is sufficient for effective reporting.

Before starting any BI project, the first step in optimizing data processes is at the data source. Use these techniques to simplify your model:

• SQL Databases: Create daily summaries instead of importing all raw data. Use SQL statements like GROUP BY to reduce the data rows imported into Power BI.
• Excel Spreadsheets and CSV Files: Pre-process data by creating pivot summaries to aggregate information before Power BI imports. Import only the necessary data.
• Power Query Editor: Use Power Query for data aggregation tasks to improve efficiency even with unchanged raw data.

2. Trim the Fat: Filter Rows and Remove Columns

Your Power BI report is just one part of a broader business intelligence ecosystem. The Power BI engine performs best when it processes only the required data. Follow these steps for data reduction:

Remove Unused BI Report Columns

Only import columns that are necessary for your reports. Extra columns consume resources and slow down reporting.

• Avoid importing unnecessary administrative fields such as report_created_By or user_note from your systems.
• Transform complex tables with many unique keys into dedicated lookup tables.

Filter Unneeded Rows

Restrict your model to include only the necessary data range. If users need only two years of data, limit it accordingly.

3. Embrace the Reporting Analytics Friendly Star Schema Model

The Star Schema is a favored design methodology in BI reporting. It involves organizing report models with a core fact table surrounded by dimension tables, providing simplified and efficient data structures.

• Fact Tables: Contain necessary quantitative metrics of business operations and connect to dimension data.
• Dimension Tables: Contain details like product categories and regions, connecting to the fact table.

4. Select the Right Data Report-Model Column Data Types

Optimize data types for efficiency. Ensure columns have correct data types, using numbers instead of more complex formats when possible.

• Correct Data Types: Ensure tables use the WholeNumber format to optimize model performance.
• Splitting Datetime: Reduce cardinality by separating datetime columns into date and time.

5. Turn Off Auto Time/Date Setting for Your Reports

This Power BI default can create unnecessary hidden date tables. Turn it off in the data model loading panel to improve performance.

Access this setting via:

File » Options » Data Load

6. Making Use of DAX Columns vs. DAX Measures

Understand the difference between Calculated Columns and Measures:

• Calculated Columns: Suitable for static data calculations, require more computing power.
• Measures: Dynamic computations that occur only when needed, enhancing reporting speed.

Final Thoughts

Optimizing your Power BI models through pruning unnecessary data, adopting schema design principles like the Star Schema, and using appropriate data formats will significantly improve report performance. This ensures that reports offer key insights efficiently. At our organization, we've developed tools that enhance data modeling processes and eliminate lengthy manual preparation.

Graphed allows users to connect data sources and perform automatic analyses, saving time and providing insights without extended development cycles.