Predictive
analytics
largely earns its keep by making it possible to efficiently
manage uncertainty. Sometimes that uncertainty manifests as risk; sometimes it
emerges as inefficiency and lost opportunity. It depends on the problem being
addressed.

While other analytics address other areas of concern –
establishing cause and effect in the marketplace, identifying obscure
influences on industry trends, and so on – predictive analytics have the unique
burden of informing tomorrow’s decisions.

Shrinking the landscape

A typical predictive analytics problem/solution, commonly
cited as an example, is the behavior of homeowners, per their mortgages. It’s
useful for a mortgage company to analyze payment histories and determine the
various markers in homeowner payment that indicate a homeowner in trouble, a
homeowner about to default, a homeowner likely to make an early payoff. Being
able to plan for those events months ahead of time is a huge financial
advantage for the mortgage company, on several fronts.

When those predictive numbers are delivered, they
reconfigure the near future as a concise new landscape, with small vistas of
constrained, well-articulated probabilities. Knowing how many mortgages are
likely to go away via early payoff is good for bottom-line planning; knowing
how many mortgages will need to be refinanced so that they don’t vanish is good
for resource allocation.

The overall point: each of these areas of the future
landscape is uncertain, and uncertainty is expensive, and the management of
that uncertainty makes good economic sense.


Make
the successful transition to actionable predictions


Through the window, into the mirror

It’s often possible to take this general concept a step
further. What about the uncertainty to be found in the results themselves,
beyond the uncertainties they address?

Even if you’ve spent a great deal of time working with
business intelligence, let alone analytics, it’s hard to keep in mind that the
results of any predictive process are tentative, and exist as
probabilities. Only hard-core scientists really have this in mind at all times,
and even they can slip. That means that the results we act on, when using
predictive analytics as decision support, are probabilities at best.

How does that affect the way we use them? Consider the
mortgage problems above: slow payment, impending default, early payoff. Each of
these problems has a substantial administrative cost, potential revenue losses,
and opportunity consequence. If I’m the one delivering the analytics, I want to
be able to not only report the most accurate projections of what will happen
next quarter, I want to be able to recommend where effort is most
effectively applied
.

In other words, I can do the same analysis on my
quarter-to-quarter results in each of these predictions and be able to add a
meta-recommendation: our default recommendations are more accurate than our
payoff guesses; more time and money should go into foreclosure relief next
quarter than acquiring new business!

Bottom line

The principle is clear: predictive analytics gives us a
handle on the near future, and helps us strategically deploy our time and
resources most effectively to do the best possible job in the months ahead. But
those are probabilistic results, and carry uncertainty of their own. By
measuring their reliability against each other, we can make our near-future
time and resource allocations that much more fine-tuned, and increase our
efficiency all the more.



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