Documentation Index
Fetch the complete documentation index at: https://docs.datagenie.ai/llms.txt
Use this file to discover all available pages before exploring further.
What the Correlation Matrix is
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 are strongly correlated, weakly correlated, or inversely related — giving you the structural context to interpret stories more accurately.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.
Practical uses
Identify co-movement pairs
Find KPIs that always move together. When one of these surfaces in a Top Story, check whether its correlated partner also shows movement.
Validate attribution
If KPI Attribution shows a side effect on a metric, use the Correlation Matrix to confirm whether this co-movement is structural or coincidental.
What to do 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.