Catalog
concept#Data#Analytics#Observability#Software Engineering

Correlation

Correlation describes the statistical relationship between variables and quantifies the direction and strength of association. It is a basic analysis tool for exploratory data analysis and feature selection.

Correlation describes the statistical relationship between two or more variables, quantifying the direction and strength of association.
Established
Medium

Classification

  • Medium
  • Technical
  • Design
  • Intermediate

Technical context

Pandas / Python for computation and visualizationSQL databases for aggregated queriesObservability platforms (e.g., Grafana) for dashboard integration

Principles & goals

Correlation measures association, not causation.Check data quality and sample size before interpretation.Choose the appropriate correlation measure according to distribution and scale.
Discovery
Domain, Team

Use cases & scenarios

Compromises

  • Misinterpretation can lead to inadequate decisions.
  • Overreliance on correlation instead of further validation.
  • Automated alerts based on correlation can generate false positives.
  • Check distributions and use nonparametric measures when needed.
  • Segment data to detect heterogeneous subgroups.
  • Combine visual inspection with statistical tests.

I/O & resources

  • Tabular measurement or transaction data
  • Timestamps and contextual information
  • Meta information about scales and units
  • Correlation matrix (csv, json)
  • Visualizations (heatmap, scatterplots)
  • Interpretation and validation report

Description

Correlation describes the statistical relationship between two or more variables, quantifying the direction and strength of association. It is used for exploratory analysis, hypothesis generation and feature selection, but it does not establish causation and requires attention to sample size, outliers and non-linearity. Different measures (e.g., Pearson, Spearman) and visualizations help interpretation and communication.

✔

  • Quick identification of potential relationships in large datasets.
  • Supports feature selection and reduction of redundant variables.
  • Easy to visualize and communicate (matrices, heatmaps).

✖

  • Correlation cannot distinguish cause and effect.
  • Linear measures miss non-linear relationships.
  • Susceptible to outliers and biased samples.

  • Average absolute correlation

    Mean absolute value of pairwise correlations as a measure of overall dependence.

  • Share of significant correlations

    Percentage of correlations that are statistically significant.

  • Multicollinearity index (VIF)

    Measure to assess redundancy among predictors.

Pearson correlation in BI report

BI team uses Pearson correlation to show linear relationships between revenue and marketing spend.

Spearman for rank data

For non-linear ordinal metrics, Spearman correlation is used for more robust analysis.

Correlation matrix for feature selection

Data science project identifies redundant features via a correlation matrix prior to model training.

1

Define data cleaning, normalization and outlier handling.

2

Select appropriate correlation measures (Pearson, Spearman, Kendall).

3

Compute pairwise correlations, create matrix and visualize.

4

Validate results, check contextual influences and document.

⚠️ Technical debt & bottlenecks

  • Lack of metric standardization hinders comparability.
  • No automated validation routines for correlation results.
  • Insufficient documentation of data provenance and transformation steps.
Data heterogeneitySample sizeOutliers and noise
  • Platform team mutes alerts based on simple correlation and misses causal causes.
  • Feature engineering drops predictive variables because they correlate with others without model tests.
  • Reports claim 'correlation = cause' in management dashboards.
  • Overlooking spurious correlations due to seasonality or common drivers.
  • Ignoring differing time scales in time series.
  • Failing to control for confounding variables.
Basic statistics (correlation, significance)Data preparation and feature engineeringVisualization and result communication
Interpretability of analysis resultsData quality and representativenessScalability for large metric sets
  • • Reliability depends on data quality.
  • • Limited validity with small samples.
  • • Not all relationships are linear or stationary.