The insurance industry, as we all know, is a data-driven machine—being able to effectively analyze numbers is a make-or-break, live-or-die issue.
Trouble is—and it's one of the industry's most pressing challenges—a great deal of that data is tied up in legacy storage systems—some 30 years old, notes Ian Campos, vice president of insurance with Capgemini Financial Services, a consulting service.
To modernize their systems, insurers face essentially two paths. One is to make the massive investment in a wholly new, "best-in-breed" data platform, Campos says.
With this strategy, the costs are found not only with the hardware and software, but also with the gargantuan task of data migration.
"To translate all that information is quite a challenge, and it takes years to do it properly," Campos says.
Others are using "wrappers" or stop-gap measures, says Campos, where they build a newer system on top of the old main-frame applications.
With either approach, time is of the essence—not only because of the competitive pressure from carriers that have already made the transition, but also because the people who know how to operate these older systems are "walking out the door" as they retire.
As tough as both choices are, the modernization effort simply has to be undertaken in order for underwriters to realize the full predictive potential of their data.
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