Data Mining for the Enterprise

Authors:
Charly Kleissner
Affiliations:
-
Venue:
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Year:
1998

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Abstract

The emergence of comprehensive data warehouses which integrate operational data with customer, supplier, and market data have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of that data. However, there has been a growing gap between more powerful data warehousing systems and the users' ability to effectively analyze and act on the information they contain. Data mining tools and services are providing the leap necessary to close this gap. Data mining offers automated discovery of previously unknown patterns as well as automated prediction of trends and behaviors; its technologies are complimentary to existing decision support tools and provide the business analyst and marketing professional with a new way of analyzing the business. After a general introduction of the knowledge discovery lifecycle and the data mining lifecycle, this article examines the data mining issues and requirements within an enterprise. A comprehensive architectural overview proposes data mining integration solutions for data warehouses, application servers, thick clients, and thin clients. This article concludes with an analysis of current trends relevant to enterprise usage of data mining tools and methodologies.