Ready i ng
for the
Dat a Delu ge
As copious new data sources and an improving array of storage
and analytic technologies herald the onset of the Big Data era,
insurers have some work to do just to keep afloat.
By Bill Kenealy
The ac T of aggrega Ting da Ta for the sake of mak-
ing better decisions is as old as the insurance industry itself. another
constant is that the tools used to gather and analyze this data have
continually evolved.
now, insurance and other industries that rely heavily on data to aug-
ment and in some cases supplant human decision making are entering
an era in which many feel evolutionary changes are about to give way to
revolutionary ones. dubbed Big data, the term refers loosely to series of
storage and algorithmic technologies that enable analysis of data sets too
large for traditional tools. in terms of data volume, this means the largest
insurers may soon be moving out of the realm of gigabytes and terabytes,
into the terra incognita of petabytes and beyond.
Yet, the size of the data sets is not Big data’s sole story. Many would
argue that for the insurance industry the novel nature of the data streams
comprising the data sets is of greater import. a good part of this growth
is in formerly unstructured data such as audio and video recordings. for
example, insurers can now readily avail themselves of new video and
audio search capabilities or face recognition technologies to aid in fraud
detection. another copious new source of new data for insurers is usage
information derived from networked sensors placed on everything from
automobiles to mobile devices to buildings.
Whether the information is culled from this nascent internet of Things,
or gleaned from more traditional sources such as social networks or sat-
ellites, its proper use will present a challenge for insurers, says Phillippe
Torres, ceo of technology consultancy in edge. “When you start factoring
in different data sources that insurers are not used to working with, you get
a whole new set of problems,” Torres says. “also, going to 1,000 times more
data has big implications for storage and infrastructure.”
tially struggle to squeeze all the business value out of these new informa-
tion sources. “This data may come in forms and formats the insurance
industry is not used to working with but it has great intrinsic value,”
he says.
anand rao, a principal with Pricewaterhousecoopers’ diamond ad-
visory Services, says this new glut of data will ultimately change how in-
surance companies operate. “in terms of the amount and types of data
available, [Big data] will be revolutionary,” he says. “The question for the
insurance industry will be how to convert all this information into some-
thing of value.”
few people likely realize the challenges and potential Big data presents
to insurers than Swati abbot, president of Blue health intelligence. abbot
came to her current position from health care analytics provider Medai,
eager to work with the gargantuan set of claims data Bhi has on hand from
the 54 million annual lives in the Blue cross network nationwide.
in addition to its vast population size, the data Bhi has to work with is
deep in a longitudinal sense, going back more than 5 years. “We have one
the best data assets in the country today,” abbot says. “it’s clean data, it
goes back several years and it’s representative of the whole country. i feel
very privileged to lead this company.” abbot says this depth is important
because it is safer making predictions when a pattern is observed over
multiple years. “one year of data is not enough to tell you if it is a repeat-
able answer you are getting,” she says.
as for the breadth of the data set, it is vital to identifying rare diseases that
are not very prevalent but can be very costly to treat if not diagnosed early.
“a smaller data set, with 500,000 people enrolled in a single health plan, is
not large enough to contain a significant number of people with low volume
disease,” she says, adding that as data sets get larger the algorithms used
in models tend to get smarter and better defined. “But when you have the
