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The Correlation Matrix maps the statistical relationship between every pair of KPIs in your dataset — including KPIs auto-connected across data sources by Nirvana. It surfaces which metrics move together, which are independent, and which are inversely related.

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How to read the matrix

Strong Positive

Values near +1.0 — the two KPIs tend to move in the same direction. A drop in one is likely accompanied by a drop in the other.

No Correlation

Values near 0 — the two KPIs move independently. A change in one carries no predictive signal about the other.

Inverse Correlation

Values near -1.0 — the two KPIs move in opposite directions. Useful for understanding trade-offs between metrics.

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How to use the Correlation Matrix

1

Open the Correlation Matrix tab

Navigate to Explorer and click the Correlation Matrix tab at the top of the dashboard. The matrix loads your dataset’s full KPI grid — each cell shows the statistical relationship between a pair of metrics.
Clicking the Correlation Matrix tab at the top of the Explorer dashboard to open the KPI relationship grid
2

Apply a dimension filter to scope the analysis

Use the Country filter (or any available dimension filter) to narrow the matrix to a specific segment. Filtering scopes the correlation calculation to just that population — useful when you suspect a relationship holds in one market but not across all data.
Selecting a country from the dimension filter dropdown to scope the Correlation Matrix to a specific segment
3

Select the KPIs you want to compare

Tick the checkboxes for the KPIs you want to include — revenue, orders, sales, or any metric in your dataset. The matrix updates to show only the selected KPIs, keeping the grid focused and easier to read.
Ticking checkboxes for multiple KPIs and the Correlation Matrix updating to include only the selected metrics
4

Read the color-coded correlation grid

Each cell shows the correlation coefficient between a KPI pair, color-coded by strength and direction. Dark positive colors mean the two metrics move together; dark negative colors mean they move in opposite directions; neutral shading means no meaningful relationship. Patterns stand out immediately without scanning individual numbers.
The color-coded Correlation Matrix displaying positive, negative, and neutral relationships between selected KPIs
5

Review the top correlation summary

Scroll to the summary panel below the matrix to see DataGenie’s highlights of the strongest correlations in your dataset. Use these to decide which KPI relationships to investigate further — or to validate a hypothesis before diving into Top Stories or Dimensional Analysis.
The top correlation summary panel highlighting the strongest KPI relationships detected in the dataset
Run the Correlation Matrix early when onboarding a new dataset. Understanding the KPI relationship structure helps you interpret Top Stories more accurately from day one — you’ll know whether a co-movement is structural or coincidental before you act on it.

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What’s next

Dimensional Analysis

Rank dimension values by Top N and Bottom N contribution for any KPI.

Data Playground

Build custom views and freeform comparisons.

KPI Attribution

Use correlation context to interpret contributors and side effects in a story.