What is Cross Highlighting in Power BI?

Clicking an element on a Power BI report and watching other visuals instantly react is one of its most powerful features. This interactivity is what transforms a static slide into a dynamic dashboard for data exploration. This article breaks down one of the key interaction types, cross-highlighting, and contrasts it with its close cousin, cross-filtering, so you can build more intuitive and insightful reports.

What Exactly Is Interactivity in a Power BI Report?

Unlike a static chart you might find in a PowerPoint presentation, visuals in a Power BI report are designed to talk to each other. When you interact with one visual (by clicking on a bar, a pie slice, or a point on a map), it sends a signal to the other visuals on the page, telling them to update based on your selection.

By default, Power BI handles this interactivity in two primary ways:

• Cross-Highlighting: Shows the relationship between data points by highlighting a portion of a visual.
• Cross-Filtering: Narrows the focus by filtering visuals to show only the selected data.

Understanding the difference between these two is fundamental to designing a report that is both user-friendly and effective at communicating insights.

Diving into Cross-Highlighting

Cross-highlighting is often the default interaction between Power BI visuals. When you select a data point in one visual, it doesn't remove any data from the other visuals. Instead, it highlights the portion of data in other visuals that relates to your selection, dimming out the rest.

The main purpose of cross-highlighting is to show a data point's contribution to the whole. It answers questions like, "Of all my product sales, what portion came from the West region?"

A Practical Example of Cross-Highlighting

Imagine you have a simple report page with two visuals:

1. A bar chart showing Total Sales by Region (North, South, East, West).
2. A pie chart showing Total Sales by Product Category (Accessories, Bikes, Apparel).

If you click on the "West" bar in the bar chart, the pie chart will react through cross-highlighting. It will not change its shape or remove slices. Instead, each slice of the pie chart will be split into two shades:

• A fully saturated portion representing sales that came from the West region.
• A faded or grayed-out portion representing sales from all other regions combined.

You can still see the total sales for Bikes, but now you can instantly visualize how much of that total is attributed to the West. Cross-highlighting preserves the overall context, allowing you to see a part-to-whole relationship without losing sight of the bigger picture.

So, How Is Cross-Filtering Different?

Cross-filtering is a more direct interaction. Instead of showing the contribution, it isolates the selection. When you select a data point in one visual, it acts as a filter for the other visuals, causing them to redraw themselves to display only the data related to your selection.

Cross-filtering helps you focus on a specific segment of your data. It answers questions like, "What was the breakdown of product sales only in the West region?"

Revisiting Our Example with Cross-Filtering

Let's use the same report with the Sales by Region bar chart and the Sales by Product Category pie chart. This time, imagine the interaction is set to cross-filtering.

When you click the "West" bar in the bar chart, the pie chart will change completely. All data from the North, South, and East regions will be temporarily removed from view. The pie chart will re-render to display only the proportion of Accessories, Bikes, and Apparel sales that occurred within the West region.

This is extremely useful when your goal is to drill down and analyze a specific slice of your business. You momentarily remove all other distractions to get a clear view of one segment.

Cross-Highlighting vs. Cross-Filtering: A Side-by-Side Look

Choosing between highlighting and filtering depends entirely on the question you want your user to answer.

• Goal: Highlighting shows contribution, Filtering provides focus.
• Visual Result: Highlighting dims unrelated data, Filtering removes unrelated data.
• Contextual Clue: Highlighting preserves the original scope by showing the relationship to the total, Filtering narrows the scope to show only a subset.
• Ask Yourself: Do I want to see how much "X" contributed to "Y"? Use highlighting. Or, do I want to see the details of "Y" but only for "X"? Use filtering.

How to Control Interactions in Power BI

While Power BI has smart defaults, you have full control over how your visuals interact. You might want a map click to filter a list but only highlight a bar chart. You can manage this using the "Edit interactions" feature.

Here’s how to set it up:

1. First, select the visual that will act as the "source" – the one you want users to click on. For our example, this would be the Sales by Region bar chart.
2. Navigate to the Format tab on the Power BI Desktop ribbon.
3. Click on the Edit interactions button.
4. You'll notice new icons appear in the top-right corner of all other ("target") visuals on the page. These icons let you define the interaction.
5. For each target visual, you can now choose one of three options:
6. Select the desired interaction for each target visual. You can repeat this process for any visual on your page, fine-tuning your entire dashboard’s flow.
7. Once you're done, simply click the Edit interactions button again to exit the editing mode.

Best Practices for Using Interactions

Now that you know how to control the dashboard's behavior, here are a few tips to make your reports more effective.

• Be Purposeful: Don't just accept the defaults. Think about the journey your users will take. If someone clicks on a country, does it make more sense to filter the product list completely or to highlight which products contribute most? Tailor the experience to the insights you want to reveal.
• Prevent Confusion: On a crowded dashboard, having every visual react to every click can feel chaotic. Don't be afraid to set some interactions to "None" to simplify the user experience and keep the focus on the most important relationships.
• Use Slicers for Global Filters: Slicers are designed specifically for filtering an entire report page (or even multiple pages). They are the best choice for primary filters like date ranges, business units, or customer segments that users will set at the beginning of their analysis. Leave cross-filtering between charts for more ad-hoc, exploratory clicks.
• Test It Out: Once you've set up your interactions, click through the report as if you were a user. Are the responses intuitive? Does the dashboard guide you toward meaningful conclusions or leave you feeling confused? Test and refine until it feels right.

Final Thoughts

Mastering cross-highlighting and cross-filtering is about giving your users the power to ask and answer their own questions. The right interaction can immediately illuminate patterns in your data, turning your dashboard from a simple monitoring tool into a powerful engine for discovery.

While tools like Power BI offer incredible control, the manual setup of data connectors, report layouts, and visual interactions can be time-consuming. We built Graphed because we believe getting these insights should be much faster. You can connect your marketing and sales data sources with a few clicks, and then build dashboards in real-time just by describing what you want to see. Instead of manually editing interactions, you simply ask in plain English - "compare ad spend to revenue this quarter" - and our AI creates the interactive dashboard for you, keeping everything updated automatically.