Data Mining
In today's information age, many businesses rely on customer information for survival. Data mining is a relatively new and very important tool in aquiring knowledge on thier customers, spending habbits, geograpphical trends, and more. "Data mining is an information extraction activity which has as its goal the discovery of hidden facts contained within databases." What this means is its automated procedures used to find unknown patterns in order to make likely predictions such as consumer spending habbits. Eric Brethenoux, a research director for advanced technologies at the Gartner Group, is one of many who believes that datamining is a technology in which a company cannot afford to ignored. With present day data mining capabalities, it is now possible to spot trends in data in information companies stored from the past, creating a competative advantage for the company with the most information. The power of datamining lies in the way it does not rely on narrow human queries to produce results, but instead uses technol
The user would tell the software to search for a specific trend or question. Gaining a better perspective of the market profile can help in predicting what would be a successful future product. Identifying inefficient operations within the buiness is another use for data mining. As stated before, many businesses rely on this use of data mining for survival. When searching for trends or information, a common OLAP or DSS would understand and execute searches to a question such as "Did more customers purchase t-shirts from Levis then other stores last year?" A question that can be asked with data mining is "What were the shopping habits of customers in clothing stores last year?" An answer to such a question is likely to uncover more trends and relationships then the previous question. Without it, many businesses may not have enough customers to run a profitable and successful operation. Typically, less data reduces the chances of discovering trends and relationships. In specific, data mining is used for:claims processingcredit risk analysiscustomer profiling and retentione-commercefraud detectionnew product developementprocess controlmedical and pharmacutical researchtarget marketingstudent recruiting----------------------------------------------------------------------------------------------- Works CitedFirst Quote. Data mining has been successfully used to exploit fraud such as detecting credit fraud. The best results are achieved when sorting through massive amounts of data such as in a data warehouse, although with generally less success, it can be used with less data. This not only benefits a credit card company. Having a precise market profile can be the first step in determining how to satisfy the a market. A reduction of fraud leads to lowered premiums that once covered a higher insurance expense. Researchers use data mining techniques to obtain information of market profile.
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