Visualizing Patterns: 2 Methods to Create a Heatmap in Power BI

Heatmaps are an effective way to spot trends and highlight patterns in your data. With Power BI, creating them is straightforward—you just need to know the right steps. Whether you’re tracking sales across regions or monitoring monthly performance, heatmaps simplify complex numbers into color-coded visuals that are easy to understand.

In this guide, we’ll explore 2 methods to create a heatmap in Power BI. The first method uses the Matrix visual with conditional formatting, and the second employs a custom visual for a more intuitive, block-style layout. Each method serves a purpose, depending on how you want your insights to appear.

Method 1: Using a Matrix Table with Conditional Formatting

The Matrix visual in Power BI is a flexible and often underutilized tool. Think of it as a pivot table with visual customization at your fingertips. You can add row and column dimensions, insert values, and apply color formatting to highlight where your data is performing well—or where it’s falling short.

First, load your dataset into Power BI. This could come from Excel, a SQL database, or even a SharePoint list. For clarity, let’s use a simple dataset that tracks product sales across different regions and months.

Once your data is loaded, click on the “Matrix” visual from the Visualizations pane. Drag your “Region” field into the Rows section, “Month” into the Columns, and “Sales” into Values. What you now see resembles a pivot table—regions on the left, months across the top, and numbers in the grid.

The next step is where the heatmap part comes into play: conditional formatting. Click the dropdown on the “Values” field inside the Visualizations pane and choose “Conditional formatting.” Select “Background color” and choose “Color scale.” Power BI allows you to define a gradient; for example, low sales values could be pale yellow, and high values could be dark red. This gradient helps your eyes instantly scan where performance is weak or strong.

You can take this further. Let’s say you want to compare how different products performed month over month. You can add a slicer for “Product Name” to the report and make your matrix update interactively. This brings interactivity into your heatmap, letting users explore different slices of your dataset without needing multiple visuals.

This method is particularly effective for dashboards where space is limited and numerical precision still matters. You get familiarity with a table along with the visual flair of color gradients, creating a functional heatmap without requiring any external visuals.

Method 2: Using a Custom Heatmap Visual

While the Matrix + formatting trick does the job, sometimes you want something more visual—something that actually looks like a heatmap grid, where each cell is a colored block rather than a number. For this, Power BI offers custom visuals from the Microsoft AppSource marketplace.

Open Power BI and head to the Visualizations pane. Click on the three-dot menu ("…") and select “Get more visuals.” This opens the AppSource gallery. Search for “Heatmap” and you’ll find a few options, like “Heatmap by xViz” or “KPI Grid.” Install one that fits your needs.

Once added, the new visual will appear on your list. Click to add it to your report canvas. Now drag in your data just like you did with the Matrix. For instance, use “Region” for the Y-axis and “Month” for the X-axis. Place your “Sales” or “Performance Score” measure into the values field.

What you’ll get is a color-coded grid without numbers—just colors showing intensity. These visuals usually offer customization, like changing the color scale, setting min/max thresholds, and toggling gridlines. The heatmap itself becomes the message. You don’t need to read values; you see the trend in an instant.

This method really shines when precision isn’t critical and when you want to focus attention on general patterns. It’s perfect for management dashboards, marketing overviews, or large datasets where showing individual numbers might overwhelm the viewer.

Custom visuals also often support tooltips, so users can hover over a block to see the actual value. This gives you the best of both worlds—clean visuals and data detail on demand.

Just be aware that custom visuals may require more performance overhead, and if you’re sharing reports with others, they may need to enable those visuals in their viewing environment.

Choosing Between the 2 Approaches

Deciding how to create a heatmap in Power BI comes down to what kind of impact you want your visual to make. If your users are more analytical and prefer working with numbers, the Matrix approach with conditional formatting gives them the detail they crave. It’s also more native, which means it runs smoothly and integrates well with other visuals and slicers.

On the other hand, if your audience needs a quick, visual takeaway without diving into the numbers, a custom heatmap visual is the better choice. It’s cleaner, more visually striking, and ideal for storytelling. When paired with tooltips, it still offers access to detail but doesn’t overwhelm the viewer with too much data at once.

You also need to consider the type of dataset you’re working with. The Matrix visual works great with relatively small to medium-sized datasets and fewer dimensions. If you’re dealing with high-cardinality data—dozens of rows and columns—a custom visual may handle that layout better with scrollable or interactive grid structures.

Ultimately, both methods allow you to create a heatmap in Power BI, but they serve different purposes. It’s less about which is “better” and more about what fits your scenario and audience.

Conclusion

Creating a heatmap in Power BI doesn’t require advanced skills—just the right approach. Whether you use the built-in Matrix with conditional formatting or opt for a custom visual, both methods help uncover trends and patterns with clarity. The choice depends on your audience and how visually focused your report needs to be. A well-designed heatmap simplifies decision-making by making complex data instantly readable. With these 2 methods, you now have flexible tools to present your data in a more engaging, informative, and visual way—without overwhelming your viewers.