See Relationships in Your Data with Scatter Plots

See relationships in your data with scatter plots

When you need to evaluate the relationship between two variables, a scatter plot is one of the most effective data visualization tools. Scatter plots display each data point as a dot positioned along an x-axis and a y-axis. When you chart all of the points together, the overall pattern reveals trends, correlations, clusters, or outliers that may require deeper investigation.

A scatter plot is a simple way to see how two datasets relate to one another. It helps you understand whether the variables move together, move in opposite directions, or show no meaningful relationship at all. This makes scatter plots useful for identifying patterns and telling a quick visual story within large datasets.

What is a scatter plot?

A scatter plot charts the relationship between two numeric values. Each row of your data produces one point, with the x-value placed along the horizontal axis and the y-value placed along the vertical axis. With all points plotted, the collective shape can reveal positive correlations, negative correlations, curvilinear patterns, or no correlation.

Scatter plots can also highlight clusters, gaps, and individual outliers. If a trend is unclear, you can add a trend line to the chart. This line of best fit helps readers see the general direction and strength of the relationship between the two variables.

When should you use a scatter plot?

Scatter plots work best when visualizing large volumes of numerical data and when you need to understand how two variables relate to each other. They are helpful when you want to:

• Identify positive, negative, or no correlation between two variables
• Detect outliers or values that fall outside normal patterns
• Evaluate whether relationships are linear or non-linear
• Visualize how data varies when one variable changes
• Explore clusters or segment your data into groups

For example, a scatter plot can help answer business questions such as whether reducing the price of a product increases sales. By plotting the price of a product against the number of units sold, you can evaluate how closely the variables move together.

Scatter plots can also reveal whether two variables have little or no relationship. For instance, you may want to explore whether more PTO correlates with higher productivity among employees. A scatter plot makes these relationships easy to see.

Using a scatter plot in different business scenarios

Scatter plots can improve decision-making across teams and workflows. Common use cases include:

Develop user personas

Scatter plots can reveal clusters of similar data points. For example, plot purchase totals against demographic information to see whether customers naturally group into identifiable segments.

Track product performance

Plot units sold against revenue by product line. You will be able to see whether products are priced correctly, whether performance varies by category, and where intervention may be needed.

Identify high-spending departments

Visualize department spending across the year. Outliers become easy to spot and can be investigated further.

Understand sales trends

Plot sales of iced coffee against outdoor temperature or email sends against conversion rates. Scatter plots help you see whether two variables move together over time.

Detect anomalies in operations

Scatter plots can flag sudden increases in error rates, ticket volume, or supply chain delays by comparing two related operational metrics.

Using a scatter plot in a dashboard

Scatter plots are powerful on their own, but pairing them with other charts helps you explore root causes.

For example:

• Use a line chart to examine how values change over time after spotting an unusual cluster
• Add a bubble chart to include a third variable such as revenue, cost, or customer count
• Add color or shape variations to turn the scatter plot into a categorical comparison
• Pair with a bar chart to compare performance between groups after identifying an outlier

Scatter plots are also useful for interactive dashboards. Users can hover to see details, zoom into dense clusters, filter by product or region, or drill down into unusual patterns.

Best practices for using a scatter plot

Here are key guidelines to help you build clear and useful scatter plots.

Remember that correlation is not causation

Even when two variables move together, it does not mean one causes the other. Always analyze additional data before drawing conclusions.

Add a trend line

Trend lines help clarify the general direction of your data. They make it easier for users to see whether values move upward, downward, or remain flat.

Reduce overplotting

Large datasets can cause overlap, making it difficult to read the plot. You can reduce overplotting by:

• Lowering point size
• Adding transparency
• Breaking data into smaller segments
• Sampling data when appropriate

Use clear axis scales

Choose axis ranges that allow readers to see the overall pattern without distortion. Start at zero where appropriate and avoid overly compressed scales.

Label and format carefully

Use clear axis labels, consistent point markers, and optional tooltips for interactive charts. Color-coding or shaping categories helps highlight differences and patterns.

Scatter plots are a versatile and intuitive way to uncover relationships within large datasets. They help teams understand trends, spot outliers, and make more informed decisions. To explore additional chart types and best practices, visit the Domo Learn Center.

Scatter plots are a great way to visualize trends in large datasets. They can be a powerful tool for understanding correlational trends and predicting future paths. To learn more about other charts and data visualization techniques, check out our Learn Center.