Mutual Information

Mutual Information is a concept from information theory that measures the amount of information that one variable contains about another variable. In the context of business intelligence and data analytics, think of Mutual Information as a way to figure out if and how much two things have in common. For example, in a grocery store, Mutual Information could help understand the relationship between the time of day and the popularity of certain products like coffee or breakfast cereal.

By calculating the Mutual Information between two variables, analysts can determine whether knowing the value of one variable tells you something about the value of the other. If two variables are completely independent, their Mutual Information will be zero because knowing one does not give any information about the other. On the other hand, if two variables are closely linked, the Mutual Information will be higher.

For businesses, Mutual Information is valuable because it helps them uncover hidden patterns and relationships in their data. These insights can lead to more effective marketing strategies, improved customer segmentation, or even identifying new business opportunities. Unlike simple correlation, Mutual Information can capture both linear and non-linear relationships, making it a powerful tool for understanding complex systems.