These advances have allowed the front office to make quantum leaps in its ability to feed information to the back office. The company's eyes and ears have become so acute that they have overloaded its brain. If a company was to use only traditional analytic methods, however, most of the customer feedback would be lost because the amount of information available is so great that it swamps traditional analytic tools and methods. This is the point at which new, highly advanced analytic methods, such as data mining, offer hope. Data mining provides the tools for penetrating a corporation's vertical barriers, allowing the insurance company to realize that if it cancels one automobile policy it could lose a lot of other business.
Some people say data mining is the process of finding patterns and regularities in data.
They point to statistical methods that have been developed to find correlation and dependencies in data. According to Dominic Clare of MapInfo Canada Compusearch Group, data mining can have many definitions. It can be the process of analyzing the data components and relationships within the database that one expects, or the process of running the data through sophisticated tools to discover relationships among data elements. This definition requires a flexible, process-oriented data mining solution that can be tailored to the specific business problem to be solved. Although data mining requires sophisticated software that many vendors offer, successful data mining also requires commitment and teamwork.