Data Mining, Data Warehousing and Data Dredging.

Essay by wesdentonUniversity, Bachelor'sA-, April 2008

download word file, 4 pages 3.0

Over the last ten years as information technology and more precisely data storage has become less expensive the amounts of data that businesses are holding onto has been increasing. Furthermore as the amounts increase there is a need to sort through this data to discover useful and relevant information. Some of these techniques are referred to as data mining, data warehousing and data dredging. There is a caveat here because as more businesses store data and information in hopes of making better decisions about 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, time and money being spent to maintain data warehouses perhaps more effort should be put into figuring out 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).

Data mining is a technical term that describes the process of combing through massive amounts of data to retrieve both related and unrelated information based on specific queries. "Data mining is the principle of sorting through large amounts of data and picking out relevant information. It is usually used 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...