Regardless of where within the insurance enterprise an analytics agenda originates (business or IT), the discipline is emerging as a central one for insurers.
As my fellow blogger, Joe McKendrick, so adroitly pointed out in
Lori Sherer, senior expert in McKinsey and Company’s insurance practice, confirms that insurers are dedicating more effort than ever to making analytics a priority, even to the degree of hiring or naming a C-level position to own the organization’s analytics strategy and execution. “The most important thing this new person needs to do is make friends with IT,” she says.
Why? Because from a business perspective, there are a growing number of reasons an insurer might consider ramping up the focus on analytics way beyond claims, especially in areas such as customer insight—and the data stores from social media, telematics, claims, etc. that support such initiatives. But from an IT perspective, there are any number of reasons to question long-term objectives, because implementation means data cleansing, creating a data mart, and implementing the tools that will enable business users to make sense of its vast number of stores. Let’s face it: IT doesn’t want to invest time, effort and budget to an unproven proposition.
So it’s no surprise that although many insurers have evolved their IT area to one that accommodates the basics of analytics (reporting), some organizations are still reluctant to bring sufficient capabilities and tools in house. Why? Because the benefits associated with the associated costs are not well-understood.
And although many would agree that IT needs to play a critical role when analytics is used pervasively throughout the business, maybe it’s easier to think of the results of an analytics project without that critical alignment—data that stays in siloes, creating limited, if not poor results.
Oftentimes what’s required is a basic, low-level demonstration of “this is what analytics can do for you.”
“You show them something that allows individuals and groups to view the business that they’ve taken for granted for so long and present it in a new light,” Jose Trasancos, SVP, Sr. Personal Lines Officer, Utica National Insurance Group, told me. He offers a good example: Data sliced and diced to reveal new levels of information, such as with the use of consumer credit in a continuous fashion for a tiered program as opposed to a threshold where you simple accept or reject the risk based on credit score alone.
Sherer believes alignment of core business objectives with the reality of what IT can do for the organization must begin with a frank discussion about the quality of the data. “It’s no fault of the systems or IT people who have optimized data for a different objective function,” she says. “But based on agreed-upon goals, both sides have an opportunity to improve the outcome.”
Maybe these newly appointed analytics czars should use early results to advocate that analytics be replicated across multiple business disciplines.
“Part of this and what is often unspoken is that the entire organization must commit to continuous improvement,” says Trasancos. “You establish a discipline where you segment, measure and adjust. You do that not once, but always.”
Pat Speer is an editorial consultant for Insurance Networking News.
Readers are encouraged to respond to Pat by using the “Add Your Comments” box below. She also can be reached at patricia.speer@sourcemedia.com.
This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.
