ANALYSIS & COMMENTARY
DECISION TECHNOLOGIES IN DATABASE MARKETING: PART V
by Gene M. Ferruzza, Senior VP, Decision Technologies
For 14 years, Gene Ferruzza has provided integrated business solutions for clients in telecommunications, electric utilities, financial services, aerospace, manufacturing, and retail. He is an internationally recognized expert in strategic database marketing planning and implementation, as well as development and application of data marts, statistical and A.I. modeling, and decision systems for understanding and predicting human behavior. In addition, he directs research in statistical, neural, evolutionary, and hybrid modeling techniques, and the implementation of decision technologies for marketing programs and software productization. He has also developed and marketed his own database management and segmentation software and is currently advising in on-line market research services and products. Prior to CMS, he worked as a consultant and instructor for two leading neural network hardware and software providers (HNC and NeuralWare). Gene graduated from the University of Pittsburgh with a B.S. in Computer Science and Mathematics.
In this fifth installment of an extensive multi-part series Feruzza writes: "Understanding the human factor is critical for delivering data-mining results in ways that are actionable by the business user. Only when information is understood and actionable can it be considered beneficial in marketing programs. Sometimes it is useful for the user to visualize data-mining results in order to use them appropriately in marketing programs. Because data visualization often makes data understandable, it is considered an important basic data-mining technique and often is the final stage of the data-mining process." Parts I through IV of this series are available as D S * articles 100073, 100080, 100085, & 100091.
PAVING THE WAY TO DATA WAREHOUSE PROJECT SUCCESS: PART I
by Doug Laney
Foresight and planning are the cornerstones to a successful project. Learn Prism Solutions' proven methodology. Mr. Laney joined Prism Solutions following several years in the knowledge base systems field managing the development of complex decision support systems. Presently, he manages Prism's central region consulting group. He has headed the design of several data warehouse development projects and managed Prism's effort to produce Release 2.0 of its data warehouse development methodology, Iterations. Mr. Laney has also published several articles on data warehousing. For more information, see http://www.prismsolutions.com/
In this first article of a two-part series, Laney notes: "Data warehouse projects pose a truly unique set of analysis, design, technology and management challenges which are unlike traditional development projects. More than a set of technologies, successful data warehouse implementations are the result of an effective project approach. Confidently and effectively navigating your way through a data warehousing effort calls for a new kind of road map."
JUSTIFYING A DATA WAREHOUSE PROJECT: PART II
by D. J. Power
Editor of the World-Wide web site DSS Research Resources and the ISWorld pages on Decision Support Systems, Daniel Power is professor of Information Systems and Management in the College of Business Administration, University of Northern Iowa, Cedar Falls, IA. His research interests include the design and development of decision support systems and how DSS impact individual and organizational decision behavior. Power has published more than 20 articles and papers. He is also the senior author of a textbook titled Strategic Management Skills (Addison-Wesley, 1986). Power served as the Head of the Management Department at UNI from August 1989 to January 1996. He served as Acting Dean of the UNI College of Business Administration from January 1996 to July 31, 1996.
In the concluding segment of a two-part article on issues related to justifying a proposed data warehouse project, Power states: "Justifying a data warehouse project can be very difficult. We've looked briefly at quantitative and qualitative tools for evaluating a data warehouse project. Also, the upside benefits discussed in part 1 can provide a starting point for a quantitative or qualitative analysis. To explore all of the issues, we need to examine the major analysis of the Return on Investment for data warehouse projects that is being discussed by vendors and consultants. There are some downside issues we need to tackle, too." The first installment of this series is available as D S * article 100092.
ACTION ITEMS
QUOTE OF THE WEEK
100098
"Not only are the unique architectural considerations for data
warehousing absent among common methodologies, but even worse: anything that
looks remotely useful in one may in fact be the polar opposite of what an
experienced data warehouse architect would advocate."
-- Doug Laney, Prism Solutions
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