However, little support from management seems to be forthcoming. A
survey of 193 executives from a variety
of industries by London-based consultancy The Information Difference Ltd.
found that one third of respondents rate
their data quality as poor at best and
only 4% considered their data to be of
excellent quality. What’s more, 63% of
respondents have not attempted to calculate the cost of data errors, and only
about 37% currently have some form of
data quality initiative in place. Information Difference also found that the top
two barriers to achieving quality data
were the fact that management does not
see data quality as an imperative, and
difficulties presenting business cases.
Forward-looking insurers recognize
the urgency of effective data manage-
ment and governance for delivering
strong enterprise analytic capabilities. In
the absence of quality data, insurers face
a number of challenges. “Incomplete
data could lead to higher costs for insur-
ance organizations,” says Pankaj Sinha,
practice lead for Tata Consultancy Ser-
vices’ Insurance Information Manage-
ment and Technology Excellence Group.
Bad data leads directly to “adjustment of
higher claims, setting lower premiums
for customers, and lack of proper risk
identification. Inaccurate data could lead
to an adverse impact on downstream
business processes and a ripple effect on
operations across the organization. For
example, capturing inaccurate customer
information leads to bad decisions on
policy levels, terms, premiums, loss of
additional selling opportunities and
wrong customer preferences.”
Insurers are taking a number of paths
to tackle this problem. One course is
through more effective governance,
along with initiatives that take data out
of the silos and makes it an enterprise
resource, such as the related approaches
of data warehousing and master data
management (MDM).
Enterprise-level data governance is
an important priority at Columbus, Ga.-
based Aflac, which has been building
up the capabilities of its data warehouse
environment. “Over time, our overall
data volume continues to go up as our
business continues to grow and suc-
ceed,” says John Keddy, VP of IT applica-
tion services for Aflac. “We’ve been mak-
ing some modest investments in our
enterprise data warehouse, and those in-
vestments have been very well received
by our organization.” For example, Ked-
dy says, his department has been pro-
moting greater self-service for business
users querying information from the
data warehouse. “So far in 2010, my de-
partment has completed 300 user que-
ries, but at the same point in time, our
For more about MDM, search “Master Data Management
Takes Hold” at www.insurancenetworking.com.
As with all data management efforts,
data governance should be the founda-
tion of MDM, says Daniel Teachey, se-
nior director of marketing for DataFlux,
a SAS company. “Naturally, the creation
of a master repository requires a deep
understanding of best practices in data
management, which is where data gov-
ernance comes in,” he says. “Forward-
thinking insurance companies have
started with data governance to learn
how to best manage data across the en-
terprise. Once that philosophy for data
is set, MDM becomes a much more
achievable goal.”
MDM is a key enabler for Chartis, ac-
cording Uphoff. Starting in 2009, the
firm initiated a multi-phase effort to
improve its business insight and enter-
prise data warehouse. “We’ve complet-
ed data governance and report consoli-
dation initiatives as the first step to
re-engineering it. We will soon evaluate
a redesign of our enterprise data ware-
house based on a single consolidated
master data management model. Our
enterprise data warehouse is policy-
and underwriting-centric, and it will be
enhanced using MDM to support all of
our business insight requirements in
the near future,” Uphoff says.
Likewise, at Plymouth Rock, MDM
practices have helped synchronize information across its family of companies. “It creates alignment of results,
where we can measure from one
company to another, or one geography to another, and take out any misunderstanding of what one was trying
to do,” Luongo explains. “Having the
data managed this way allows us to
make one decision across the company.” INN
Joe McKendrick is an author and consultant
specializing in information technology, based in
Doylestown, Pa., and a regular blogger for www.
insurancenetworking.com.
“So far in 2010, my department
has completed 300 user
queries, but at the same
point in time, our users have
successfully run 300 data
warehouse queries on their
own.” — John Keddy, Aflac
cal that the position reports to a senior
leader who has a strategic perspective of
the business.”
MASTER DATA MANAGEMENT
MDM, a method for taking disparate en-
terprise data and creating a central hub,
or “gold” copy of master data, is also an
important strategy taking hold across in-
dustries. A survey of 131 various compa-
nies by Information Difference found
users have successfully run 300,000 data
warehouse queries on their own.”
A strong business-driven data gover-
nance effort is behind this effort, Keddy
says. “The process that we’ve gone
through is a very collaborative one,
working with various people through-
out the organization.” He says that Aflac
even has its own internal “user group”
that works closely with his department
to better serve the business.
