Data Insights – Changing Relationships
Finding insights in single metrics can be helpful, but often the most valuable insights are those that deal with the relationships between metrics. Specifically, when the relationship between two metrics change it can indicate a serious shift in your business.
For example, here is a chart of two metrics:
As you can see, they look highly related over time. In statistics, this means they are highly correlated, which you can verify by calculating the correlation coefficient of the two metrics. For this example, the two metrics are highly correlated with a coefficient of 0.989 (1.0 would be perfect correlation).
However, at a specific point in time (September), their high correlation was broken in an obvious deviation. Such a break indicates a change to the business processes and performance that drive those metrics, and in this example something clearly significant happened. Such an insight is as close to a smoking gun as you are likely to find in any data!
It can be hard to find these relationship shifts because it requires you to combine every pair of metrics in your business, which is usually quite large. However, often you can reduce the complexity after starting your data exploration by only investigating relationships between metrics you know are important.
Tomorrow, we’ll wrap up our review of data insights with the most advanced insight yet, identifying composition.
Quote of the Day: “The only true wisdom is in knowing you know nothing.” ― Socrates