OLAP] market. "In setting up the center, KPMG sought to create a facility where
a client could see a blueprint for building a "knowledge management system."
What Cranford calls "knowledge management" refers to a practical set of
sophisticated techniques that a corporation can apply to a data warehouse to gain
Analysts disagree on what distinguishes data mining from more standard
OLAP. "You don't have to differentiate data mining from any form of analytics, "
says Alexis dePlanque, an analyst with the Meta group in Stamford, Conn. "But we use
more stringent criteria, defining data mining as analysis that incorporates artificial
intelligence and hypothesis generation."
Not all of what KPMG does at the center would meet this test.
Credit card scoring, which Cranford cites as an example , does not qualify under
dePlanque's definition. KPMG, however, is pushing the envelope of what enterprise
integrators normally do in this area precisely because it is so new and poorly defined.
In order to give clients an opportunity to test the waters, KPMG
has created a number of engines or analytic models. These models, running against a data
warehouse, will provide insight into profitability, customer risk and customer
segmentation. "We have used SAS Institute's software to build these analytic
engines, " says Cranford. "We source the data from a variety of systems to
create the data warehouse and possibly a number of smaller data marts."
One of the first clients to use the center after it opened in
December was Mobil Oil Corp. The first phase, loading the data, was easier in that
case because it came from Acxiom, a Conway, Ark., company that Mobil uses to maintain
good, clean customer data. Before any analytics can be run against a data warehouse
that data must be clean, free from most of the errors that often creep into
transactional data. " The data we get isn't always in such good shape,"
notes KPMG's Cranford. "we often have to do some of the cleansing
Cranford adds it's important for a client to come to the center
with a well-defined set of problems. Mobil's card business manager, Lenn Eason, says
he hopes to benefit from the engagement in three areas: Developing better customer
acquisition strategies, better retention strategies and more reliable credit risk
Says Cranford, "In the past, this information
usually cam from traditional market research which is self-reported, and hence not that
reliable." Data mining, according t Cranford, will
give Mobil actual patterns, and the analysis can be done at a very granular level.
Analysts, however, caution against putting too much faith in data
mining. "Initially, corporations though you could do retail basket analysis
with it[data mining] , "reports dePlanque. "And while you can identify
customer behavior, there isn't always an actionable solution. A year ago we thought
customer churn in telecommunications would be a big market. But even if you find a
customer who has switched carriers, what action do you take? Do you send another
Data mining software vendors, according to dePlanque, have had to
recognize that the market for generic data mining tools is negligible. "You
have to come up with packaged solutions which solve a specific problem. For certain
business problems it's valid."
The Flagstaff CDI is partly a response to these kinds of
questions about data mining. By partnering with NAU, KPMG has created a place where
clients can test the waters without making a huge investment. "KPMG approached
us to see if we would be interested," says Eason. "This project is a good,
low-cost way for us to get into data mining."
Cranford says the center is also a good place to test the various
data mining tools that are available. "We try to be objective in our evaluation
of tools. Ease of use is increasingly important. Corporations prefer that the
tools no require a Ph.D. to operate."
The top mining tools that are available at the center include
software from Thing Machines, Inc. of Bedford, Mass., and Enterprise Miner from SAS
Institute of Cary, N.C. "IBM's Intelligent Miner is not yet a preferred
partner, " Cranford adds. "There are issues of capability we are not yet
Given the interest in data warehousing, analysts were predicting
a boom in data mining. But dePlanque says the numbers haven't borne out that
belief. "The smaller data mining companies are seeing serious stagnation,
Even so, large corporations are searching for ways to get more
competitive advantage out of their data, and data mining is still the newest way to do
that. However, clients should be wary of large, loosely defined projects, warns
dePlanque. "I'm convinced the big enterprise integrators can sell anything.
A data mining project needs to be very well defined."