Large companies process millions
of transactions every year and store huge amounts of data on these
transactions. Often, the data is spread across a variety of different computer
systems in different areas of the country or the world. This raw data, although
needed for record keeping, has little value to managers and decision makers
unless it can be filtered and processed into meaningful information. The
results can be a staggering increase in revenues and profits. But doing this is
the real challenge. Finding strategic information from a mountain of data can
be like finding a needle in a haystack, but the effort is usually worth it.
With today’s fast computers and a knowledgeable IS staff, the possibility of
turning raw data into useful and profitable information can become a reality.
This was the case with Farmers Insurance Group.
Like
other companies, Farmers Insurance Group was sitting on a huge amount of raw
data. The data, however, was spread across different computer systems in
different locations. As in all insurance companies, underwriting determines
what insurance policies a company can offer and at what premiums. Farmers’
underwriting business was responsible for assessing insurance risk, which can
make the difference between profits and losses. The people who are responsible
for determining insurance risk are called actuaries. According to Tom Boardman,
an assistant actuary at Farmers, “As competition has gotten more intense in the
insurance industry, the traditional ways of segmenting risk aren’t good enough
at providing you competitive advantage.” Boardman was referring to how most
insurance companies categorize risk. For example, high-powered sports cars are
more likely to be involved in expensive accidents than ordinary sedans. Thus,
insurance companies can put sports cars in a different risk category than
sedans and charge customers who own them a higher premium. In assessing risk,
an insurance actuary would traditionally have a hunch, such as sports cars are
more prone to accidents than sedans. Then the actuary would test his or her
hunch using the computer. According to Boardman, this was like using the
computer “to dig up data to prove or UN-prove those hunches.” One disadvantage
of this old approach is that small, but profitable, market niches may be
ignored or not priced correctly. As a result, Farmers decided to look into a
computer system to help it find profitable market niches.
The
company found the help it needed through IBM, which developed a customized
software product for Farmers called DecisionEdge. The computer system was an
advanced decision support system that combined raw data from seven different
databases on a staggering 35 million records. Consolidating the raw data into
useful information took about twice as long as expected, but the additional
wait was worth it. Farmers was able to locate market niches that it didn’t see
before the decision support system. For example, DecisionEdge helped Farmers
determine that not all sports car owners are alike—those who were older and had
at least one other car were less likely to be in an expensive accident. Once
this market niche was identified, Farmers could offer that segment of the
sports car market lower premiums. Using Decision Edge to find the market niche
resulted in millions of dollars of increased revenues for Farmers.
The
approach used by Farmers is sometimes called “data scrubbing.” It allows a
company to consolidate important information and squeeze additional revenues
and profits from it. After helping Farmers and seeing a market opportunity, IBM
also decided to offer its Decision Edge software to other insurance companies.
Discussion Questions:
1.
How was Farmers able to transform its raw data into
meaningful information and additional revenues?
2.
Describe how this approach could be used in other
industries.
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