Correlation measures the strength and direction of a relationship between two variables. In analytics, correlation helps identify whether changes in one metric are associated with changes in another.

Correlation values range from:

• +1: strong positive correlation

• 0: no correlation

• -1: strong negative correlation

For example:

• Marketing spend and leads may be positively correlated

• Price and demand may be negatively correlated

• Website latency and conversion rate may be negatively correlated

Correlation is often calculated using statistical measures like Pearson correlation (linear relationships) or Spearman correlation (rank-based relationships).

In BI, correlation is used to:

• Identify drivers of performance

• Detect relationships between metrics

• Prioritize optimization efforts

• Support hypothesis generation

However, correlation does not imply causation. Two variables may move together due to a third factor or coincidence. For example, ice cream sales and drowning incidents are correlated due to seasonal effects, not causality.

From a technical standpoint, correlation analysis requires clean data, proper normalization, and sufficient sample size. Outliers can significantly distort correlation values.

Correlation is often paired with:

• Regression analysis

• Feature importance analysis

• Root cause analysis

• A/B testing

Modern BI tools increasingly surface correlations automatically through AI-driven insights. However, human interpretation remains critical.

Correlation is a powerful starting point for analysis, but it should always be validated through experiments or deeper modeling before making decisions.