today’s business world, information about the customer is a necessity for abusinesses trying to maximize its profits. A new, and important, tool in gainingthis knowledge is Data Mining. Data Mining is a set of automated procedures usedto find previously unknown patterns and relationships in data. These patternsand relationships, once extracted, can be used to make valid predictions aboutthe 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 thatwastefulness in operations, and (4) to chart unexplored areas of the internet (Cavoukian).The fulfillment of these tasks can be enhanced if appropriate data has beencollected and if that data is stored in a data warehouse. This makes it mucheasier and more efficient to run queries over data that originally came fromdifferent sources." When data about an organization’s practices is easierto access, it becomes more economical to mine.
“Without the pool of validatedand scrubbed data that a data warehouse provides, the data mining processrequires considerable additional effort to pre-process the data” (SASInstitute). 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 islargely, if not entirely used for business purposes.
The highest users of datamining include banking, financial, and telecommunications industries (TwoCrows). Data mining will have a different effect on different industries in thebusiness world. The key to succeeding in this rapidly changing industry is tounderstand the customer, or the market that the customer represents. Throughdata mining, companies can know what their customers have done in the past andwhat they will do in the future. With this information, the companies will be inideal positions to make business decisions based on the information they havegained from the data mining process.
