As big data and its related processes grow in popularity, the insurance technology community is faced with finding just the right talent to access, manipulate, control and derive insights from its output.
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Chosen in 2008 by Patil (one of the authors), and Jeff Hammerbacher, who at the time lead respective data and analytics efforts at LinkedIn and Facebook, the data scientist title comprises a “high-ranking professional with the training and curiosity to make discoveries in the world of big data.”
The authors maintain that, although thousands of data scientists are already at work in both start-ups and well-established companies, their sudden appearance in the business arena proves that companies are indeed challenged with growing data stores—volumes and varieties of information not encountered before.
And like other industries wrestling with big data issues and a dearth of personnel to deal with them, the insurance industry is facing a supply and demand issue, making the data scientist sexier than ever.
Combining the terms “insurance, technology, data and scientist” may not conjure an evening of candlelight, wine and Marvin Gaye—that’s quite alright. What those terms should conjure up, however, is a new way of thinking about how this neoteric expert can efficiently brave new frontiers and drive value within your organization.
The authors recommend that companies learn how to identify and attract the talent they are looking for, and once it’s acquired, keep it as productive as possible. The challenge with this effort is the mere fact that the role is such a new one. “There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured,” say the authors.
Insurers can begin the identification process by understanding this person as one with intense curiosity, “a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested.”
At a basic level, the data scientist writes code, say the authors. Yet what will be required of them going forward is the ability to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both, say the authors. “Think of him or her as a hybrid of data hacker, analyst, communicator and trusted adviser. The combination is extremely powerful—and rare.”
In other words, these data scientists will move the discussion from a simple ad hoc analysis to one of ongoing deep insights based on heretofore untested theories that aid in new business direction and decision making.
Very sexy indeed.
Pat Speer is an editorial consultant for Insurance Networking News.
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