PR ED IC TIVE A N A LY T I C S
certainly learn that if you keep reading.
This book is focused on predictive analytics, which is not the only type of analytics, but the most interesting and important
type. I don’t think we need more books
anyway on purely descriptive analytics,
which only describe the past, and don’t
provide any insight as to why it happened.
I also often refer in my own writing to a
third type of analytics—“prescriptive”—
that tells its users what to do through controlled experiments or optimization. Those
quantitative methods are much less popular, however, than predictive analytics.
This book and the ideas behind it are
a good counterpoint to the work of Nassim Nicholas Taleb. His books, including
“The Black Swan,” suggest that many efforts at prediction are doomed to fail because of randomness and the inherent
unpredictability of complex events. Taleb
is no doubt correct that some events are
black swans that are beyond prediction,
but the fact is that most human behavior is quite regular and predictable. The
many examples that Siegel provides of
successful prediction remind us that most
swans are white.
Siegel also resists the blandishments
of the “big data” movement. Certainly
some of the examples he mentions fall
into this category – data that is too large
or unstructured to be easily managed by
conventional relational databases. But the
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point of predictive analytics is not the relative size or unruliness of your data, but
what you do with it. I have found that “big
data often equals small math,” and many
big data practitioners are content just to
use their data to create some appealing
visual analytics. That’s not nearly as valuable as creating a predictive model.
Siegel has fashioned a book that
is both sophisticated and fully accessible to the non-quantitative reader. It’s
got great stories, great illustrations and
an entertaining tone. Such non-quants
should definitely read this book, because
there is little doubt that their behavior
will be analyzed and predicted throughout their lives. It’s also quite likely that
most non-quants will increasingly have
to consider, evaluate and act on predictive models at work.
In short, we live in a predictive society.
The best way to prosper in it is to understand the objectives, techniques and limits of predictive models. And the best way
to do that is simply to read Siegel’s book.
Thomas H. Davenport (www.tomdavenport.com)
is a visiting professor at Harvard Business School,
the President’s Distinguished Professor at Babson
College, co-founder of the International Institute
for Analytics and the co-author of “Competing on
Analytics” and several other books on analytics.
He is a member of INFORMS. This foreword by
Professor Davenport is excerpted with permission of
the publisher, Wiley, from “Predicti