Data Mining
In today’s business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer. Data Mining is generally used for four main tasks: (1) to improve the process of making new customers and retaining customers; (2) to reduce fraud; (3) to identify internal wastefulness and deal with that wastefulness in operations, and (4) to chart unexplored areas of the internet (Cavoukian). The fulfillment of these tasks can be enhanced if appropriate data has been collected and if that data is stored in a data warehouse. This makes it much easier and more efficient to run queries over data that originally came from different sources." When data about an organization’s practices is easier to access, it becomes more economical to mine. “Without the pool of validated and scrubbed data that a data warehouse provides, the data mining process requires considerable additional effort to pre-process the data” (SAS Institute). There are several different types of models and algorithms used to “mine” the data. These include, but are not limited to, neural networks, decision trees, rule induction, boosting, and genetic algorithms. Data Mining is largely, if not entirely used for business purposes. The highest users of data mining include banking, financial, and telecommunications industries (Two Crows). Data mining will have a different effect on different industries in the business world. The key to succeeding in this rapidly changing industry is to understand the customer, or the market that the customer represents. Through data mining, companies can know what their customers have done in the