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Insurers Seek More Data and Analytics Training

  • January 25 2017, 6:30am EST

Big data and analytics are drawing eyes and dollars across the insurance industry, but some power users are seeking more support from the technology.

In a survey of more than 2,000 insurance professionals, only 9 percent said they were able to access educational materials for the use of analytics tools. The survey was conducted by Celent and The Institutes and summarized in a report titled "Increasing the Adoption of Data and Analytics in Insurance: The Voice of Insurance Professionals," written by Celent analyst Michael Fitzgerald.

"Awareness appears high, but execution is hampered by implementation," Fitzgerald wrote. "When asked, users offer clear preferences for subject areas and topics that will allow them to apply data and analytics to their jobs in practical ways."

Thirty-seven percent of survey respondents said that their job frequently involves analyzing data in new ways. Actuaries were most likely to report frequent or very frequent use of data and analytics, followed by commercial lines underwriters.

But when it comes to finding educational materials to support new initiatives, only 6 percent of total respondents said it was easy to find. A quarter posited that such tools were "very difficult to nonexistent."

"The data signals that the population of insurance professionals as a whole, and business users in particular, encounter difficulty accessing knowledge about data and analytics," Fitzgerald writes. "This is likely driving lower than desired utilization rates in business positions."

Celent and The Institutes also asked what data and analytics subjects insurers wanted the information on the most. Six subjects received more than 2.5 points on a three-point scale:

Explanation of how to interpret the results of an analytical model
Description/case studies on analytics applied to insurance company functions
Overview of insurance industry data and analytics
Overview of advanced analytics techniques, such as predictive modeling
Explanation of how to build and validate a predictive model
Overview of the statistics used in analytical modeling


"Respondents voiced a preference for practical application of data and analytics," Fitzgerald writes. "Implementation efforts should reference their preferences regarding subject areas and topics and ensure that these tools and techniques are delivered."

(This article appears courtesy of our sister publication, Insurance Networking News)

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