Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both.
             Increasingly, organizations are generating vast amounts of data as a result of running a variety of information systems. This data is normally used to record transactions and for status reporting purposes. What data mining does is use elements of statistics, artificial intelligence, machine learning and advance modeling techniques to predict future business trends and customer behavior patterns from large data warehouses and other form of data resources. This is accomplished by running commercial-off-the-shelf applications to convert vast amount of data into actionable, proactive and knowledge-driven decisions.
             The two critical success factors for data mining are:
             • a large well-integrated data warehouse
             • clear understanding of the business process for the application of data mining
             Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.
             With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
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