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Capturing Efficiency, Profitability, and Predictability Through Big Data

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Reprinted from the First Quarter 2018 issue of Inside Medical Liability magazine, PIAA. Copyright 2018.

In a modern world of rapidly evolving technology and constant innovation, we often hear that using “big data” helps companies become more effective, efficient, and profitable, while at the same time reducing volatility and increasing predictability.

The promise of a better tomorrow through predictive analytics related to underwriting or developing algorithms to help insurers improve their marketing prowess is now a focus of the executive suite, in much the same way that the decision to adopt a mainframe computing system was during the original technology revolution. Then, as is true today, the decision to invest in new technology required a flexible workforce that could lead the implementation of data storage, system conversions, and training around the new way of doing business.

As leaders face the challenge of an evolving workforce, it can be tough to understand what the next generation of change will bring to the insurance community. Historically, the landscape of insurance company personnel has been dominated by actuarial, accounting, and distribution talent that helps sell, price, predict, and measure all things related to the business. While these professions will remain cornerstones of insurance companies, there is an emerging need within the insurance profession to develop data scientists and “master orchestrators.” But it would be inappropriate to make a statement like this without providing a framework outlining what these jobs actually entail.



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