DATA

Team survey correlation heatmap

Beacon’s people-analytics team uses a correlation heatmap to compare four survey measures at once. The diagonal cells are one because each measure is perfectly correlated with itself; the off-diagonal values indicate the direction and strength of the observed relationships. The chart helps reviewers locate relationships worth investigating, such as manager support and growth opportunities. It does not establish a causal explanation, and small or unrepresentative survey samples can make a colour pattern look stronger than it is. Before sharing it, compare the chart with the source table and make the scope explicit. A clear date range, population and unit help another reader interpret the pattern correctly and decide whether follow-up analysis is needed.

UPDATED 2026-09-25
EXAMPLETeam survey correlation heatmap
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CASE ANALYSIS

Scenario

Team survey correlation heatmap is prepared for a focused review.

Key decisions

  • Set the scope: Use one reporting period and one clear unit.
  • Read the pattern: Identify the largest concentration or directional change.
  • Check the source: Confirm the displayed values against the underlying data.

When to reuse this

Use this heatmap when the stated values and labels are the information the reader needs to compare.

FAQ

Frequently asked questions

What does this heatmap show?01
A heatmap uses colour to show the size of a value at the intersection of two categories. It helps readers spot concentrations, gaps and repeated patterns quickly.
How should I read this heatmap?02
Choose a colour scale whose minimum and maximum match the data, and include cell labels when precise values matter to the decision.
Can I adapt this heatmap example?03
Yes. Replace the title, labels and values with your own data, then check that the prompt still describes every important element in the chart.
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