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D S *
The On-Line Executive Journal for Data-Intensive Decision Support
*** October 14, 1997: Vol. 1, No. 2 ***
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IN THIS ISSUE:
HERB EDELSTEIN DISCUSSES THE USEFULNESS OF DATA MINING
WHY DATAMINING? A BASIC CASE STUDY by Alan Parker
DATA MINING -- IT'S NOT JUST FOR STATISTICIANS ANYMORE by David Danziger
ANALYSIS & COMMENTARY
Herbert Edelstein is president of Two Crows Corporation and an internationally recognized expert in data mining, data warehousing and client-server computing, consulting to both computer vendors and users. His expertise has been recognized with numerous invitations to chair and keynote conferences on these topics. A founder of The Data Warehousing Institute and widely published author, he has also co-edited two books on data warehousing and co-authored the report Data Mining: Products, Applications & Technologies.
Prior to Two Crows, Edelstein was a founding partner of Euclid Associates, a consulting firm specializing in data warehousing and data management. Edelstein was also vice president of marketing and sales at Sybase, vice president of marketing and sales at International Database Systems, general manager of the Model 204 division of Computer Corporation of America, and a consultant for American Management Systems.
In his observations on fruitful and wasteful uses of data mining, Edelstein notes: "It's important to remember that datamining usually comes into an organization from the application side. Someone in marketing or finance has a problem (s)he wants to solve. Thus, a CIO who is IT oriented, who buys tools and then says, 'We'll now focus the company to use these tools -- pick whatever you want,' is asking for trouble."
Alan Parker, Ph.D. is the CEO and founder of APower Solutions, a consulting company specializing in training and advanced technology integration with emphasis on business value. He is the author of the text Algorithms and Data Structures in C++ (1993) which was adopted at Princeton University and primary author of the text Introduction to Microprogramming which was published by Texas Instruments (1989). He has published in excess of 50 journal articles and technical reports.
Parker actively serves as a member of the Georgia Institute of Technology College of Computing Continuing Education staff where he teaches courses to industry professionals. Serving as Principal at MRJ Technology Solutions, he led and executed multiple projects in data mining, allowing MRJ to extract business information from large repositories of internal and external corporate data. The process is a unique blend of object technology with parallel processing. Parker has also presented to high-level management, including CIO's of Fortune 500 companies, strategies for the successful integration of data warehousing and data mining. He is acknowledged in the text, Data Mining Techniques For Marketing, Sales and Customer Support, as someone who "truly understands data mining" and has led and executed many of the case studies presented in that text.
At IBM, Parker served in the Advanced System Architecture Department where he was responsible for the authorship of registered IBM documents on I/O architectures. The documents specified a common architecture for seven different IBM platforms. At Hayes microcomputer, serving in the capacity of principal engineer, Parker was the project leader and lead architect for Hayes Digital Simultaneous Voice and Data Technology. While serving as a Professor in Computer Engineering at Georgia Tech, Parker sought and received in excess of one million dollars in research funding from sources including Intel, NCR, NSF, and Texas Instruments. He brought each of the projects to a successful closure integrating a variety of resources from industry and academia.
Parker served as the chairman of the Atlanta section of the IEEE Computer Society from 1989 to 1991. He was elected to Senior Member of the IEEE in 1992 and served as the director of the National Science Foundation Young Scholars Program in 1993.
How should data-based decision making be distributed throughout an organization for maximum efficiency and profitability? David Danziger analyzes this subject; he writes: (U)ntil very recently, data mining's primary position within firms has been in one of two places: the IT department or the analytical department. Historically, this arrangement made perfect sense. IT staffers have been the only ones with clear access to the data being collected for analysis, and statistical analysts were the only ones with the know-how to use the complex statistical packages required to do any sophisticated manipulation and interpretation of the data. Yet that paradigm no longer holds.
David Danziger has roles in the sales and marketing departments at Trajecta, a data mining and optimization company headquartered in Austin, Texas. He joined Trajecta approximately six months after its inception.
ACTION ITEMS
Sequent, EMC, Intel and Oracle officials announced an alliance around "the next-generation data center," focusing on a nationwide symposium series. Stops include Washington, DC, New York City, Chicago, Dallas, and London. "Corporations are making major decisions based on 20 percent of (corporate) information," contended Don Lee, moderator of the upcoming symposium.
ComputerWorld has reported that Fleet Financial Group, Inc. is spending almost $38 million to build a data warehouse and a new customer marketing system as well as hire a 50-person team to run the analysis software it will use to develop more targeted advertising and promotions. The new marketing approach requires workers who know how to use database marketing software, plus statistical and decision-support analysts. None of those skills was much in evidence in the Boston-based bank's marketing department before the data warehouse project began.
Gartner Group Inc., Stamford, Conn., estimates that more than 80 percent of the world's largest companies either will have or will be planning a data warehouse by the end of the year. Some of those companies are constructing enterprisewide, multiterabyte data collections, while others are building smaller scale data marts to meet the needs of individual departments. Almost all will be using the services and expertise of resellers to help them. There are several reasons for the rapid growth and popularity of data warehouses: the need for comprehensive information, compensation for the lack of an enterprisewide data architecture and a high return on investment (ROI) are a few. But conventional data warehouse projects are not without pitfalls.
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