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something no pollster or election insider consider likely, or even possible. In fact, most 'experts'
expected Stevenson to win by a narrow margin, with some acknowledging that because they
expected it to be close, Eisenhower might also prevail in a tight vote. It was only late that night,
when human vote counts confirmed that Eisenhower was running away with the election, that
CBS went on the air to acknowledge first that Eisenhower had won, and second, that UNIVAC
had predicted this very outcome hours earlier, but network brass had refused to trust the
computer's prediction. UNIVAC was further vindicated later, when it's prediction was found to
be within 1% of what the eventually tally showed. New
technology
is often unsettling to people,
and it is hard sometimes to trust what computers show. Be patient and specific as you explain how
a new data mining model works, what the results mean, and how they can be used.
While the UNIVAC example illustrates the power and utility of predictive computer modeling
(despite inherent mistrust), it should not construed as a reason for blind trust either. In the days of
UNIVAC, the biggest problem was the newness of the technology. It was doing something no
one really expected or could explain, and because few people understood how the computer
worked, it was hard to trust it. Today we face a different but equally troubling problem: computers
have become ubiquitous, and too often, we don't question enough whether or not the results are
accurate and meaningful. In order for data mining models to be effectively deployed, balance must
be struck. By clearly communicating a model's function and utility to stake holders, thoroughly
testing and proving the model, then planning for and monitoring its implementation, data mining
models can be effectively introduced into the organizational flow. Failure to carefully and
effectively manage deployment however can sink even the best and most effective models.
DATA MINING AND YOU
Because data mining can be applied to such a wide array of professional fields, this topic has been
written with the intent of explaining data mining in plain English, using software tools that are
accessible and intuitive to everyone. You may not have studied algorithms, data tructures, or
programming, but you may have questions that can be answered through data mining. It is our
hope that by writing in an informal tone and by illustrating data mining concepts with accessible,
logical examples, data mining can become a useful tool for you regardless of your previous level of
data analysis or computing expertise. Let's start digging!
