While insurance companies have long relied on data and analytics technologies to aid in decision-making, the emerging group of technologies known as “big data” present both an opportunity and a challenge for the industry. To get the most out of big data, insurance companies will need to rethink the processes and technologies they use to capture, organize and analyze data. Business Insurance Associate Editor Bill Kenealy asked Matt Josefowicz, partner and managing director of insurance research and advisory firm Novarica, a division of New York-based Novantas L.L.C., about what insurance companies will need to do to position themselves to leverage big data.

Q. From a technology infrastructure standpoint, what are the primary obstacles to implementing big data?

Big data means a lot of different things, but generally refers to super-high volumes of structured and unstructured data. Few companies are set up to handle unstructured data or super-high volumes. Traditional data management and query tools are not designed for these tasks.

Q. From an operational standpoint, what adjustments will insurers need to accommodate big data?

The biggest question is how will insurers adjust their business models to take advantage of a world of data super-abundance? For example, most insurers spend a lot of their operations dollars gathering information — information that can now be accessed from external providers at much lower expense.

Q. Is the technology ecosystem surrounding big data mature enough for widespread adoption by insurers?

Certainly. Industries like consumer credit and other sectors of financial services are much more advanced in leveraging big data. Insurers don't have to invent anything, they just need to plan for it effectively and tie it to a change in operating strategy that really leverages these insights.

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Q. How can risk managers capitalize on analytics to improve enterprise risk management?

Enterprise risk management has always been about analytics. Improved data quality and analytical capabilities can have a huge effect on enterprise risk management. For example, companies that have strong (geographic information systems) capabilities and granular information about their risk portfolio can see areas of concentrated risk exposure more readily and spread risk among multiple markets.

Q. Can commercial lines carriers potentially derive as much benefit from big data as personal lines carriers?

While the early action has been in personal lines by leveraging existing modeling expertise and best practices from other retail industries, commercial lines carriers also will be transformed by big data and analytics — starting first with small commercial and then eventually large commercial and specialty — as external data sources get richer and easier to access. Things like sensor data, telematics and geospatial data are providing new inputs to underwriting for some insurers. Eventually, more and more risk will be able to be modeled automatically.

Q. Given the divide in analytics use according to insurer size, are there steps smaller and midsize insurers should take now to avoid the analytics gap?

Smaller carriers should immediately start to think of their own internal data as a core asset. This means understanding what they have, where the “single version of the truth'' is stored, and how data drives, or currently doesn't drive, their decision-making. They should also consider engaging external consultants with specialized analytics knowledge and capabilities to help them create their strategy and build models that can help them drive profitable growth.