January 27, 2016
Eating the Big Data Elephant
Despite improvements, big data still has insurers stymied. The solution is gradual, starting with an approach to absorbing social data.
How do you eat an elephant? One bite at a time.
What an old joke with a great premise. No matter how big the task, taking things one bite at a time makes any daunting task seem easier to swallow.
Take the big data challenge. By and large, insurance companies and traditional businesses are used to relying on paper files, mailrooms, fax machines and call centers as incoming data streams. Designed to handle internal data collected from limited sources, the systems showed their first hint of trouble with an inability to incorporate emails and SMS text messages into policyholder and claim files. Inefficiently integrated best-of-breed IT environments further complicated the issue by putting data in silos and restricting access to users.
Today, integration of systems has improved, and the move toward suites has enabled additional collaboration and data sharing benefits. However, big data, marked by its volume, velocity and variety, still has insurers stymied. And the move toward omni-channel distribution, the Internet of Things (IoT) and the connected world has amplified the need for insurers to incorporate even more data streams (both internal and external) into the risk assessment process. Cue the analytics software and reporting solutions, neither of which alone will make a legacy system more able to digest information from new data sources for rating and underwriting purposes.
Meanwhile, the big data behemoth is growing into the proverbial elephant in the room. The problem is no longer just Incorporating this data; analyzing it and acting on it are equally incomprehensible.
Buying data from traditional data sources –including motor vehicle reports (MVRs), historical flood data and credit reports on the property and casualty (P&C) side or health and medical records or test results on the life and health side is expensive. Furthermore, traditional data sources don’t allow insurers to pick and choose what may be most useful based on line of business, let alone product or policy type, geographic area or purchasing preferences.
Alternative data sources such as social data exist, but the unstructured nature of the information makes it especially difficult for insurers to internalize. Consider that today’s consumers, who are both existing and potential new policyholders, are creating mountains of data that could contribute to better risk decision making, but right now that data doesn’t make it to the underwriter’s desk. Social data is a silver bullet that can provide a predictive enhancement layer for traditional data sources, leading to more accurate underwriting and making insurers better able to select the best risks.
By breaking the traditional data collection and utilization mold as it relates to risk assessment, insurers can integrate social data with core administration systems, making unstructured social data both accessible and actionable across all industry segments and lines of business. By capitalizing on the explosion of social data as a resource for better insurance risk assessment, insurers can improve underwriting, streamline the claims investigation process, decrease loss costs and potentially make insurance relevant to a whole new generation of insurance consumer.
The scope of the big data problem is just dawning on insurers. In an effort to not bite off more than can be chewed at one time, insurers can start to consume and absorb big data by incorporating social data into rating and underwriting. But keep in mind that social data is just the first bite of a very important meal.