Using special mathematical formulas called data-mining algorithms, a computer can extract knowledge and reach conclusions that are beyond simple human analysis. Data mining can be used everywhere, from e- commerce to retail, telecommunications, banking, insurance and health care. In a Web scenario, for example, companies can sift through data to figure out what type of content, ads or cross-sales opportunities might interest visitors to its site, and then use that information to personalize the Web for customers. Insurance companies can browse claims and predict the likelihood of fraud, depending on specific attributes and historical experience. Banks might use data mining to determine whether certain loans should be approved based on applicant characteristics.
Data mining is especially compelling for e-commerce applications.
Mining this collected data will likely cause intriguing patterns and rules to emerge, which in turn can drive business decisions such as product promotions or bundling strategies. The next step would be to apply this set of rules to customers in real time. In this way, companies can personalize and improve the customer's Web experience and at the same time increase their revenue potential. Microsoft has embodied this approach to customer interaction on the Web in an initiative called Business Internet Analytics, which relies on both SQL Server 2000 and Commerce Server 2000 for these kinds of closed-loop interactions. Companies gain valuable understanding of the hidden patterns and relationships in their data.