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Picture this. A team of Suits from New York grab their laptops and beat a path to Flagstaff, where they make their way to Northern Arizona University. With neither time for Zen nor vista, they’re heading toward the Center for Data Insight. There, they will find the oasis they seek: a hype-free, vendor-neutral environment where they can try out data mining technology, to see what kinds of packages work best for their types of organizations.
   Great idea? It’s reality. Last December, KPMG Consulting announced a unique campus-business type of partnership: Northern Arizona University has become the first academic seedbed to help companies test and develop data mining technologies.
    KPMG’s contribution: “The Center is an example of KPMG
    Consulting’s commitment to providing low-risk environments where companies, using their own data, can work through the knowledge-discovery process to solve real-world business problems,” says Steve Cranford, partner in charge of KPMG’s data warehousing. Numerous vendor partners have also entered the lineup: Angoss Software, Cognos, DataMind, SAS Institute, and Unica Technologies included.
   The win for KPMG Peat Marwick is that the consulting practice gets exposure as industry leaders in data warehousing solutions. KPMG consultants also stand to learn, by observing firsthand the concerns of corporations as they mine knowledge for gold:
   “What we see so far is a huge focus on building a customer-centric knowledge environment,” says Cranford. “Executives, working withwant to know as much about their customers as possible.”
   The CDI is a collaborative outcome of KPMG Consulting, Retrograde Data- Systems, and the university’s college of engineering and technology and college of business administration. Bern Carey, chair of the computer science and electrical engineering department at the university, is the CDI’s director.  “My background parallels what the center is all about,” says Carey. “I spent 12 years working for General Electric at its corporate research center. I was a R&D project manager and technical contributor.” Carey’s experience in managing groups and research synergized with business gave him a strong
   predisposition to IT/business linkage rather than pieces of technology in isolated silos.
   Rather than viewing OLAP, data mining, and other insight tools asseparate entities, the director sees them as parts of the analytic engine that a corporation places around its data warehousing environment. “What companies seek is to solve business problems,” Carey says, “and whether you speak of OLAP tools, query reporting, or data mining, it’s all part of solution set.”
   Referring to the center lab, which opened at year-end 1997, Carey says “All this is very new, but we already have numerous companies providing technologies. And the tools are running on some very high-end systems. Sun has put in a high-end server; Oracle is one of our main supporters. In all, some 14 companies have donated servers and software, amounting to a $1.1 million investment.”
   The corporate visitors have included professionals from a large financial company. “Two analysts spent four days here,” says Carey, “getting up to speed in data mining tools. For three of thosedays, they got a sense of what various mining tools can do. Then they ran test cases against their data set.”
   Results are impressive. “It took them about four weeks to get certain data from their older tools: Running that same data through one of the tools here got them results in two minutes and the results were more accurate.”
The scenario for corporate users is that they load their own data on to servers and can then take on a variety of data mining tools for whatever they seek:
knowledge discovery, knowledge management, or data analysis. They can take their own raw data, organize and analyze it, and then use different modeling techniques, such as customer-retention and lifetime value models. In looking at credit scoring at a large corporation, marketing executives saw new generation tools from SAS Institute and Unica. They are new generation in that they do not require a Ph.D in statistical analysis.
  Math and other axioms
Dr. Carey is mindful, on the other hand, about how deceptively simple business intelligence software can be.
   “There is no magic button in all this. You can oversell this technology, and it’s technology that must be used carefully. And this is what we are trying to do at the center—involve people who understand the technology from several sides. We have people who understand the math behind the algorithms, people coming in from the College of Business who see the business implications, and individuals from our math department who are good with OLAP tools.”
   Dr. Carey is fascinated about predictive models. “There are tools that tell you how your organization will behave in the future based on data generated in the past. You can refine your predictive model to make it more accurate. The fascinating part - there's a lot of math involved - is the notion of being able to modify the future."

           Reprinted from the June 1998 edition of BACKOFFICE MAGAZINE
                                            Copyright 1998 by PennWell


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