Requires Free Membership to View
When you register, you'll begin receiving targeted emails from my team of award-winning editorial writers on the latest customer relationship management (CRM)and call center technology issues today. Our goal is to keep you informed on the hottest issues facing this fast-changing industry.
Hannah Smalltree, Editorial Director
The data warehouse provides the data foundation for the data - the place where the data that goes into the process of knowledge discovery is stored. Data mining may be used to automatically perform knowledge discovery by giving the mining algorithm loose cues about potential relationships and letting the algorithm work on the data to discover the relationships and items to focus on further. OLAP is complimentary to data mining and is most likely the first, and most preferred, manner of discovering knowledge. OLAP works through a user performing specific, rather than general, interactive analysis with the data. If a data warehouse is present in the environment, either it or a data mart, would be the database used by OLAP.
For example, if you were analyzing sales, the data warehouse could contain 3 years of item level detail. Data mining could perform the advanced analytics required to determine what items are generally purchased together (market basket analysis) or what shopper demographics lead to the highest sales volume. These pieces of knowledge could lead to better product placement and marketing strategies. Likewise, an analyst could perform OLAP on the data warehouse to determine what products sell the most and what customers buy the most. While this information lacks the correlations that data mining would yield, it is nonetheless still valuable. This could also drive promotions strategy as well as vendor management strategies.
FOR MORE INFORMATION:
Browse through our business intelligence stories from around the Web.
This was first published in August 2004