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********************* Official disclaimer ***********************************
* All opinions expressed herein are those of the writers (or the moderator) *
* and not necessarily of their respective employers (or GTE Laboratories) *
*****************************************************************************
January 20, 1997, Issue: 614
Section: InformationWeek Labs
Debunking Data-Mining Myths --
Don't let contradictory claims about
data mining keep you from improving
your business
By Robert D. Small
A great deal of what is said about data mining is
incomplete, exaggerated, or wrong. Data mining has
taken the business world by storm, but as with many
new technologies, there seems to be a direct
relationship between its potential benefits and the
quantity of often-contradictory claims, or myths,
about its capabilities and weaknesses. It's difficult to
fight these myths, which are based on
misunderstandings, hopes, and fears. The new
technology cycle typically goes like this: Enthusiasm
for an innovation leads to spectacular assertions.
Ignorant of the technology's true capabilities, users
jump in without adequate preparation or training.
Then, sobering reality sets in. Finally, frustrated and
unhappy, users complain about the new technology
and urge a return to 'business as usual.' When you
undertake a data-mining project, avoid a cycle of
unrealistic expectations followed by disappointment.
Understand the facts instead, and your data-mining
efforts will be successful. - Simply put, data mining
is used to discover patterns and relationships in your
data in order to help you make better business
decisions.
Myth: Data mining produces surprising results that
will utterly transform your business.
Fact: Most often, the results of data mining yield
steady improvement to an already successful
organization, often contributing important incremental
changes rather than revolutionary ones.
Nevertheless, data mining can lead to significant
change in several ways. First, it may give the talented
business manager a small advantage each year, on
each project, with each customer. Compounded over
a period of time, these small advantages turn into a
large competitive edge. For example, a catalog retailer
that can better target its mailing list can increase
profits by reducing the cost of mailings while
increasing the number