Matrix in Power BI

Power BI Matrix and the concept of custom measures are two important features that help users analyze and present the data. The matrix visual in Power BI helps the user to summarize data across multiple dimensions, while DAX in Power BI allows users to create custom formulas called measures. In this blog, let’s explore the matrix table and the creation of a custom measure in a matrix.

Table of Contents:

• What is a Matrix in Power BI?
• What is the use of the Matrix Table in Power BI?
• Difference Between Matrix and Other Kinds of Tables
• What is Custom Measure?
• Steps to Create a Custom Measure in Power BI
  • Step 1: Load the Data
  • Step 2: Create New Measure
  • Step 3: DAX Formula
  • Step 4: Apply the Measure to the Matrix Visual
  • Step 5: Resulting Visualization
• Common Pitfalls and How to Avoid Them
• Best Practices
• Conclusion

What is a Matrix in Power BI?

A Power BI Matrix is a data visualization tool that helps users display data in a grid format. The matrix table supports hierarchical data and can aggregate data at different levels. This makes it ideal for summarizing large datasets and comparing information across multiple dimensions.

What is the Use of the Matrix Table in Power BI?

The matrix Table is useful for various reasons:

• The Matrix Table displays multi-dimensional data such as sales by region and product.
• It helps in performing operations like sums, averages, and counts.
• It allows the user to look into specific information by expanding rows and columns.

Difference Between Matrix and Other Kinds of Tables in Power BI

What is Custom Measure in Power BI?

A custom measure is a DAX formula used to calculate a value based on the context of a report. It allows users to implement advanced logic, use conditional logic, and create custom calculations for visuals.

Steps to Create a Custom Measure in Power BI 

We will be using a dataset that contains sales data with Amount and weight, and calculate the Weighted Average Sales.

Step 1: Load the Data

Open the Power BI desktop and load your dataset.

This is how the data looks after loading it in Power BI.

Step 2: Create New Measure

Click on the New Measure in the Modelling tab. This will open a formula bar where you can write your custom DAX formula.

Step 3: DAX Formula

After clicking on New Measure, a formula bar will open in which you need to write your custom DAX formula. We need to calculate the Weighted Average Sales

WeightedAverageSales = 

SUMX(Sales, Sales[Amount] * Sales[Weight]) / SUM(Sales[Weight])

Write this DAX expression into the Formula Bar

Explanation:

• SUMX is the function that iterates over the Sales table.
• Sales[Amount]*Sales[Weight] multiplies the Amount by weight for each row in the table.
• SUM(Sales[Weight]) gives the sum of all weights.

Step 4: Apply the Measure to the Matrix Visual

Select Matrix visual in the visualizations pane and drag the Product field into the Rows section and WeightedAverageSales(new measure) into the Values section.

This is how the Visualization Panel looks, and as you can see, Product is dragged into the Rows section, and the WeightedAverageSales is dragged into the Values Section.

Step 5: Resulting Visualization

Explanation: Here, this visualization is called a matrix table, which shows the average sales of each product in Power BI.

Common Pitfalls and How to Avoid Them

1. Overcomplicating DAX formulas

When working with large datasets, writing complicated DAX formulas reduces performance and slows down report speed. Try to avoid nested functions, and keep your DAX formulas simple by using variables to store intermediate results.

2. Failing to Handle Blank and Missing Values

Handling null or missing values is very important when you are working with large data. If there are missing values, Power BI might return unexpected results. To handle these values, you can use a DAX formula like COALESCE, IFERROR, or ISBLANK.

3. Not Testing Custom Measure

After creating a custom measure, always test your calculations with different filters and slicers. Make sure that your custom formulas work as expected under various conditions.

4. Ignoring Data Relationship

In Power BI, it is very important to make sure that the data tables are related to each other. If you are working with multiple tables, then make sure that you define the correct relationship between them. If the relationship is not set, then it can result in a wrong matrix.

Best Practices

• Clear Naming: Use relevant names for your custom measures so others can easily understand the logic.
• Optimize DAX: Make sure custom DAX formulas are optimized to improve report performance.
• Use Variables in DAX: Improve readability and performance by using variables in DAX expressions.
• Limit Complex Logic: Avoid complex formulas as they can slow down your reports.
• Test Custom Measure: Ensure your DAX measure produces the expected results.

Conclusion

Power BI’s Matrix visual is combined with a custom measure, providing a powerful way to analyze and visualize complex and multi-dimensional data. By using custom DAX measures, you can create a calculation that suits your specific analysis needs. Understanding the differences between Matrix and other tables, while following best practices, will help you make interactive reports. 

To learn more about Power BI and its functions, check out this Power BI Course and also explore Power BI Interview Questions prepared by industry experts.

Matrix in Power BI – FAQs