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3 C’s of Data Relationships

The 3Cs — Correlation, Causation, and Comparison — is the foundation of understanding the relationship between the variables in data ecosystem.

Correlation refers to the statistical relationship between two or more variables, this includes changes in one are associated with changes in another.

For example, Ice cream sales correlate with drowning incidents, but one does not cause the other; both are influenced by hot weather.

A company’s social media engagement and its sales figures show a positive correlation.

Causation establishes a direct cause-and-effect relationship, it means that changes in one variable directly result in changes in another.

Increased cigarette consumption causes a rise in lung cancer rates.

Implementing a strict cybersecurity policy reduces the number of cyber breaches.

And lastly, Comparison, is the process of evaluating two or more datasets, groups, or variables to derive insights based on similarities or differences.

The 3Cs allows us to interpret data and make decisions by distinguishing between mere associations, actual causative effects, and meaningful comparisons.