Pushing for More Predictive Analytics, More Data Talent
Insurance Experts' Forum, June 27, 2013
In a recent note, Ovum's Barry Rabkin urges the insurance industry to move more aggressively into predictive analytics. Insurers need to mine data from social media, third parties and machine-to-machine communications to glean more predictive insights to better target business operations, as well as marketing and customer relations efforts. “Insurers will be able to determine which markets to enter or leave, shape target market initiatives and estimate potential losses for the book of business as each customer is added,” he points out.
Indeed, based on my own research — a new survey of 304 data managers conducted by Unisphere Research for SAP — 62 percent see predictive analytics as the number-one opportunity big data affords their organizations. This result is the same for the insurance firms responding to the survey.
However, the survey also shows that efforts to capitalize on big data are being stymied by two obstacles — lack of top-level support is first, followed by a lack of the right skills to make predictive analytics possible.
This is a challenge that will be acute within the insurance industry, since insurers will be competing for this scarce talent with a range of other types or organizations — from tech firms to government agencies to banks and financial brokerages. The question is: what are insurers doing to attract and maintain such talent? If not properly vetted and calculated, big data can be a dangerous thing. Yes, there are many vendors who say they are building algorithms into their products, so business users don’t have to worry as much about the underlying statistical logic. But betting future directions of the business on Big Data analysis shouldn’t be left to a vendor's black box.
Ovum's Rabkin talks about the emerging role of data scientists, and reports that he and his colleagues are “already seeing a growing number of insurance companies creating new departments of these types of quantitatively skilled professionals.”
But qualified data scientists are not out on the job market ready to jump into the organization. The best path for many companies is to train from within. Data scientists skills — a combination of computer skills and the ability to look at data in new ways and ask questions that have never been asked before — can be learned, and internal training programs should move in this direction.
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