ANALYSIS & COMMENTARY
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 sixth installment of an extensive multi-part series Feruzza writes: "Segmentation techniques include basic and complex data-mining operations using attitudinal, behavioral, and demographic data. All customer segmentation schemes use a set of instructions that, when executed, will place a customer into a particular segment. This set of instructions is referred to as a 'model.' The remaining discussion of data-mining techniques (both basic and complex) focuses on segmentation models and the process of modeling."
Parts I through V of this series are available as D S * articles 100073, 100080, 100085, 100091, & 100097.
THE HOLY GRAIL: DETERMINING DATA WAREHOUSING RETURN ON INVESTMENT
by Jim Gallagher
Jim Gallagher manages the Data Warehousing Practice within Spectrum Technology Group, Inc. ( http://www.spectrumtech.com ), a consulting organization specializing in Data Warehousing, IT Management Consulting, Project Management, and Application Solutions. He has over 15 years of experience in building decision support and operational systems, organizational development and re-engineering, managing client relationships, and leading people. He is currently a member of Spectrum's internal R&D organization for Technology and Data Warehousing methodology development, and supports delivery for Spectrum Data Warehousing engagements across the nation.
Gallagher observes: "This is the challenge of the century. Talk to anyone who is trying to build a business case for a data warehouse; they will tell you how hard it is to spell out the direct payback in concrete terms. According to the Meta Group, the average data warehouse takes 3 years to build and costs 2-3 million dollars. With this kind of investment in time and money it is naturally important to review what the return on this investment is expected to be. And even if you are building a 'data mart', or a more modest warehousing effort, it is important to tie it back to business value."
PAVING THE WAY TO DATA WAREHOUSE PROJECT SUCCESS: PART II
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 concluding article of a two-part series, Laney notes: "Consider the range of individual talents needed to deliver a data warehouse. You may need at least two distinct sets of technical talent -- one for each of the source systems and one for the target (data warehouse) system. You will need at least five sets of tool vendors: computing platform, DBMS, extract-transformation, end user query/reporting tool, and meta data management. Each will be needed at different project phases. Include both designers and developers for the data access, data acquisition, and meta data management construction and you've only just begun."
ACTION ITEMS
QUOTE OF THE WEEK
100104
"Experts will tell you that most successfully implemented data
warehouses have a tremendous return on investment. One IDC study mentions
returns of over 1000%."
-- Jim Gallagher, Spectrum Technology Group
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