Data Mining is a collection of techniques drawn from computer science that has a wide range of applications. Some of these techniques come from the field of automated pattern detection and recognition. They were developed when computers were being programmed to find specific patterns in large collections of data. One such application was programming a computer to count the number of bacterial colonies in the photograph of a culture dish. Other techniques come from the field of machine learning which tries to mimic how the human brain learns and discovers relationships.

A classic example of a result of using Data Mining in the grocery industry was the discovery of the beer and diapers relationship. Several months of data was captured for each person that checked out of a grocery store. When this was combined with demographic data such as age and gender of the shopper, it turned out that there was a significant correlation between the purchase of diapers and beer. It seems that men in their twenties and thirties, when they are picking up a pack or two of diapers in the evening, have a propensity for also buying beer. Putting a display of beer close to the diapers resulted in an increase in beer sales.

In the insurance industry Data Mining can be used to find these unexpected correlations. Such correlations can then go on to be the foundation of a very profitable cross-sell campaign. Data Mining can also be used to find combinations of factors which, when all are present on a policy, result in extremely high loss ratios or claim frequencies.

Although Data Mining incorporates many statistical concepts and techniques, it is in some sense at the opposite end of the spectrum from modeling. Modeling looks at the big picture and asks what happens "on average." Data Mining, on the other hand, looks at the little "nuggets" of information.