Homeowner insurance claims in 2011 were a record-setting $105
billion, according to Marsh’s
2012 Insurance Market Report
(PDF). This made it challenging for insurance
companies to manage their policy risk, and the risk exposure was further amplified
by the fact that homeowners insurance (when compared to auto) was historically
more volatile in its claims performance.

The business challenge created an opportunity for big data
analytics that insurers could apply to their homeowner insurance portfolios in
order to better assess risk – and to predict where claims are most likely to occur.

Predictive analysis

As a result, insurance companies now use scoring methodologies
to predict future loss-ratio performance and also the likely behaviors of those
whom they insure. This governs how insurers determine individual homeowner
premiums, deductibles, and discounts on policies. Insurers also use predictive
data modeling to determine the likelihood of severe claims, fraud and even
litigation risks. The move to predictive analytics extends to the evaluation of weather patterns,
which are now changing with global warming

Meanwhile in the education sector, the University of Telecommunications
in Leipzig, Germany, wanted a predictive means of determining what future
hiring and talent needs in the telecommunications industry were likely to be.
The goals were to be proactive in curriculum revisions, to stay in step with the
job market, and to offer students the right types of training for the job market.

For its analytics, the university processed thousands of online job postings, analyzing unstructured
job market data in the process. It aggregated employment requirements across
the entire telecommunications industry so it could monitor emerging trends that
might not readily be obvious from simply perusing employment listings.

One of the first predictive discoveries
was that the demand was decreasing for degrees in electrical and communications
engineering, and instead shifting toward informatics, business administration,
and business informatics. This intelligence enabled the university to change its
curriculum at a record pace (the average new course was being launched in 2.5
months instead of in 12 months – a 76% improvement). The improved agility in
curriculum adjustments was noticed by students, also. The
university saw a 300 percent increase in demand for the new courses

roles clearly because big data success requires it

Business value but there is a flip side

Both the insurance and the
education use cases demonstrate the importance and the immediate business value
that can be derived from effectively harnessing big data into predictive analytics
that are actionable and that prepare organizations for better future outcomes.

On the flip side though, life is
not always predictable. Predictive analytics would not likely have selected
Beethoven, who was deaf, as a potential composer. Nor would it have predicted that
Bill Mazeroski, a scrappy second baseman who only hit 138 homers in his 16-year
major league baseball career, would be the one to win the 1960 World Series against New
York for the Pittsburgh Pirates with a home run

John Elder, CEO at Elder Research, a data mining firm, said
“The vast majority of analytic
projects are riddled with mistakes.” Elder estimates that
90 percent of the projects he sees are “technical
successes,” but that only
65 percent of that 90 percent are ever implemented by their organizations

Good news

Much of the problem rests in cultivating
clean and representative data that can be fed into analytical processes to
guarantee accurate results. In other cases, predictive analytics projects are scoped
so large that it becomes difficult to control the analytics process in order to
get to the outcomes that were initially expected of them.

The good news is that there are lessons
we are already learning about predictive analytics that can be used to improve
the odds of predictive analytics success. These include:

  1. Developing well defined business
    cases for predictive analytics that don’t try to do too much;
  2. Taking the time needed to qualify
    and sanitize the data that will flow into these analytics so that you know
    upfront that you have the best possible data that you can have; and
  3. Leaving some room for failure and even
    unexpected results – because the irony of predictive analytics is that it
    can be unpredictable.

Also read: