How to Create a Scatter Chart in Power BI

A scatter chart is one of the best ways to see the relationship between two different numbers. If you’ve ever wondered whether your ad spending actually leads to more sales, or if daily temperature affects ice cream revenue, a scatter chart can give you a clear, visual answer. This guide will walk you through how to create a scatter chart in Power BI, from getting your data ready to uncovering insights with advanced features.

What is a Scatter Chart?

At its core, a scatter chart (also known as a scatter plot or scattergram) plots data points on a horizontal (X-axis) and a vertical (Y-axis) to show how one variable is affected by another. Each point on the chart represents a single data entry, showing its value for both variables simultaneously.

This visualization is exceptionally useful for answering questions related to relationships and distribution:

• Identifying Correlations: Do two variables move together? When one goes up, does the other go up (positive correlation), go down (negative correlation), or have no correlation?
• Spotting Outliers: Scatter charts make it easy to see data points that fall far outside the general pattern of the other points. These outliers can represent anomalies, errors in data entry, or unique events worth investigating.
• Seeing Clusters: You can often spot natural groupings or clusters within your data, which might suggest different segments or categories you haven't considered before.

When to Use a Scatter Chart

Use a scatter chart when you want to compare two numerical values. It's the right choice if you're trying to figure out if a cause-and-effect relationship exists. For example:

• Marketing: Plotting advertising spend (X-axis) against website traffic or sales revenue (Y-axis) to assess campaign effectiveness.
• Sales: Comparing the number of sales calls an agent makes (X-axis) to the deals they close (Y-axis) to understand performance.
• E-commerce: Visualizing product price (X-axis) versus units sold (Y-axis) to analyze price sensitivity.
• Operations: Charting machine operating temperature (X-axis) against production output (Y-axis) for efficiency analysis.

A scatter chart isn’t the right tool for comparing categories (use a bar chart) or showing trends over time (use a line chart), although you can incorporate a time element using Power BI's "Play Axis" feature, which we'll cover later.

Preparing Your Data for a Scatter Chart

Power BI makes creating visualizations straightforward, but your chart will only be as good as the data you give it. For a scatter chart, the primary requirement is simple: you need at least two columns of numerical data that you want to compare.

Imagine you have a spreadsheet with your monthly marketing performance. An ideal dataset would look something like this:

In this example, you have three clear numerical columns: Ad Spend, Revenue, and Clicks. You could easily create a scatter chart comparing Ad Spend to Revenue to see if your investment is paying off.

Before loading your data, check for a few common issues:

• Correct Data Types: Make sure Power BI recognizes your numerical columns as numbers (like "Whole Number" or "Decimal Number") and not as "Text." Power BI is usually smart about this, but you can always change the data type in the Power Query Editor or the Data view.
• Handling Blanks: Decide how you want to handle rows with blank values in one of the columns. Power BI typically excludes them from the plot, which is often the desired behavior.

Step-by-Step Guide to Creating a Scatter Chart in Power BI

Once your data is ready, creating the actual chart takes just a few clicks. We'll use our sample marketing data to visualize the relationship between "Ad Spend" and "Revenue."

1. Load Your Data into Power BI

First, open Power BI Desktop. From the Home tab on the ribbon, click Get Data and select the appropriate source (e.g., Excel workbook, CSV). Navigate to your file and load it. When the Navigator window appears, select your data table and click Load.

2. Select the Scatter Chart Visualization

With your data loaded, look at the Visualizations pane on the right side of the screen. Find the Scatter chart icon (it looks like a set of scattered dots) and click it. A blank chart template will appear on your report canvas.

3. Add Data to the Chart's Fields

With the blank chart selected, you’ll see several "wells" in the Visualizations pane where you can drag your data fields. For a basic scatter chart, you'll focus on the first three:

• X Axis: This is for your independent variable — the one you think might be causing a change. In our example, this is Ad Spend. Drag the Ad Spend field from the Data pane and drop it into this well.
• Y Axis: This is for your dependent variable — the one you suspect is affected. We'll use Revenue. Drag the Revenue field and drop it here.
• Values: This well is for your series. By default, Power BI may put a unique identifier here, like an ID column or whichever column you want to define each dot. Drag a field like Month here for a quick setup. Power BI will instantly populate your canvas, and you should see a single dot. Why? Because Power BI, by default, aggregates data. It has summed up all of your ad spend and revenue into one point.

To fix this, go to the Values (or Details) well where you placed Month. Right-click it and choose Show items with no data or decide not to summarize the X and Y axes in the formatting pane. A better way to get a dot for each month is to drag the Month field into the Details well. Now you see a scatter plot where each point represents a month’s data, showing its unique Ad Spend and Revenue values.

Customizing and Enhancing Your Scatter Chart

You have a basic chart, but the real power of Power BI is in customization. Let's make this visualization more informative.

1. Adding a Third Dimension with Size

What if you want to see if months with more clicks also had more revenue? This is where the Size well comes in, turning your scatter plot into a bubble chart.

Drag the Clicks field into the Size well. The dots on your chart will now change in size, with larger bubbles representing months with more clicks. This allows you to visualize three variables at once: ad spend (position on X-axis), revenue (position on Y-axis), and clicks (bubble size).

2. Breaking Down Data with a Legend

If your dataset includes a categorical dimension, like "Campaign Name" or "Region," you can use the Legend well to see how different categories perform.

Let's say you have a Channel column (e.g., 'Social Media', 'Search Ads', 'Email'). Drag Channel into the Legend well. Power BI will automatically color-code the dots based on the channel, making it easy to see if, for example, Search Ads consistently generate more revenue for a given spend compared to Social Media.

3. Formatting Your Chart for Clarity

Select your chart and click the paintbrush icon (Format your visual) in the Visualizations pane. This opens a new world of options:

• Markers (or Shapes): Change the shape of the data points from circles to squares, diamonds, or other options. You can also customize the size and color here.
• X and Y-Axis: Adjust the title, font size, color, and range for your axes. Adding clear titles like "Monthly Ad Spend ($)" is good practice.
• Data labels: Turn on data labels to show the exact values for each point directly on the chart, though this can get crowded on busy charts.
• General: Add a Title to your chart and adjust its font, size, and position for better context.

4. Animating Your Data with the Play Axis

The Play Axis is a unique feature that lets you animate your chart over time. If you have a date or time field (like a Date column), drag it into the Play Axis well. A timeline with a play button will appear at the bottom of the chart. Clicking play will animate the dots, showing how the relationship between ad spend and revenue evolved month by month. It's an excellent way to tell a story or spot emerging trends.

Advanced Analysis: From Visualization to Insight

A scatter chart is more than just a picture, it's a tool for analysis.

Adding a Trend Line

A trend line quickly illustrates the overall direction of the relationship in your data. In the Analytics pane (the magnifying glass icon next to the format painter), find the Trend line option and click Add. A dotted line will appear, showing the general correlation. In our example, you'd likely see an upward-sloping line, visually confirming that as ad spend increases, revenue tends to increase as well.

Finding Outliers

Scan your chart for any points that are far away from the trend line or the main cluster of data. A point with very high ad spend but very low revenue would be an immediate red flag. You can hover over this point to see which month it represents and then investigate what happened. Was it a failed campaign? A tracking error? This is where you move from seeing data to taking action.

Using the Clustering Feature

Power BI can automatically find natural groups in your data. Click the three dots (...) at the top-right of your visual, and if available, you'll find an option to "Automatically find clusters." Power BI's AI will analyze the data points and group them into logical segments, adding a new field to your Legend. This might automatically segment your data into "High Performance" and "Low Performance" clusters, helping you analyze them separately.

Final Thoughts

Learning to create a scatter chart in Power BI gives you a powerful tool for discovering relationships, finding outliers, and understanding the connections within your data. By moving beyond a simple plot and using features like size, legends, and trend lines, you can turn a basic chart into a source of clear, actionable business insight.

Power BI is an excellent tool, but it often involves a lot of manual clicking and setup. We’ve spent countless hours a week wrangling CSV files and building reports, only to have follow-up questions send us back to square one. We wanted a way to just ask for the visualization we needed — like "show me a scatter chart of ad spend vs. revenue from Shopify" — and have it appear instantly. That’s why we built Graphed, which uses AI to connect your data sources and create real-time, interactive dashboards from plain English prompts.