Data Mining, Data Warehousing and Data Dredging.

Over the last ten years as information technology and more precisely selective information storage has become slight expensive the amounts of entropy that businesses atomic number 18 holding onto has been increasing. Furthermore as the amounts growth there is a need to sort through and through this selective information to discover useful and relevant information. Some of these techniques are referred to as data mining, data wareho using and data dredging. thither is a caveat here because as more businesses gunstock data and information in hopes of making better decisions slightly market conditions, customer relations and predictions how do they know if the data is too old, duplicate or useless? With the massive amounts of resources including hardware, prison term and money being spent to maintain data warehouses possibly more effort should be put into figuring turn up if there is even a need for it. But do the benefits out weigh the problems? Using data mining, organizations can increase the profitability of their interactions with customers, detect fraud, and improve risk management. The patterns uncovered using data mining help organizations make better and timelier decisions. (SPSS Inc., 2008).

info mining is a technical term that describes the process of straighten through massive amounts of data to retrieve both tie in and unrelated information based on specific queries. info mining is the principle of sorting through large amounts of data and picking out relevant information.



It is usually employ by business intelligence organizations, and financial analysts, but it is increasingly used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. (Unknown, 2002). Data mining and data warehousing go hand in hand as mining uses software applications and complex queries to sift through information. It is also interesting to note that Sergey Brin, one of the two founders of Google also specialized...

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