Data Mining

     In todayís 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 organizationís 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 past and
what they will do in the future. With this information, the companies will be in
ideal positions to make business decisions based on the information they have
gained from the data mining process.