Data Mining
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
Wal-Mart is pioneering massive data mining to transform its supplier relationships. * Store and manage the data in a multidimensional database system. The consequences of not doing data mining could be more disastrous with lost revenue, less customers or a market not being fully realized. 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. Group OpinionWe are still a long way from a viable automated approach to data analysis, whether for small or for large data sets. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Data mining makes it possible to analyze routine business transactions and glean a significant amount of information about individuals buying habits and preferences. Software must translate data from one system to another and select the address most recently entered. The myths of data mining are just that: myths. Another issue is that of data integrity. The more powerful the data mining queries, the greater the utility of the information being gleaned from the data, and the greater the pressure to increase the amount of data being collected and maintained, which increases the pressure for faster, more powerful data mining queries. * Analyze the data by application software. Data mining consists of five major elements: * Extract, transform, and load transaction data onto the data warehouse system. Finally, there is the issue of cost. This increases pressure for larger, faster systems, which are more expensive.
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