Data Mining: What is Data Mining?Overview Generally speaking, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different angles and synthesizing it into useful information - information that can be used. to increase revenue, reduce costs, or both. Data mining software is one of many analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns between dozens of fields in large relational databases. Continuous Innovation Although data mining is a relatively new term, the technology is not. Companies have been using powerful computers for years to sift through volumes of data from supermarket scanners and analyze market research reports. However, continued innovations in computer processing power, disk storage, and statistical software significantly increase the accuracy of analysis while reducing costs. For example, a Midwest grocery chain used the data mining capability of Oracle software to analyze local purchasing patterns. They found that when men bought diapers on Thursday and Saturday, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly shopping on Saturdays. On Thursday, however, they only purchased a few items. The retailer concluded that it had purchased the beer so that it would be available for the next weekend. The grocery chain could use this newly discovered information in a variety of ways to increase its revenue. For example, they could move the beer display closer to the diaper display. And they could make sure that beer and diapers were sold at full price on Thursdays. Data, information and knowledgeDataData are facts, figures or text that can be processed by a computer. Today, organizations accumulate vast and growing amounts of data in different formats and databases. This includes: operational or transactional data such as sales, costs, inventory, payroll and accounting; non-operational data, such as industry sales, forecast data and macroeconomic data; metadata: Data about the data itself, such as the design of a logical database or a data dictionary. definitionsInformationPatterns, associations, or relationships among all of these data can provide information. For example, analyzing retail point-of-sale transaction data can provide insights into what products are selling and when. KnowledgeInformation can be converted into knowledge about historical patterns and future trends. For example, summary information on supermarket retail sales can be analyzed in light of promotional efforts to provide insight into consumer purchasing behavior..