Big data is big news, and rightly so. The ability to glean answers from huge datasets is enabling previously impossible innovation in insurance, just like these gents mentioned a few months ago. There are datasets that comprise decades of building history for most houses in the United States. There are geospatial models of the Earth’s surface, with elevations every 15’ or so for the lower 48 states and Hawaii. Then there are risk models and hazard maps that combine all sorts of scientific data. And then there are historical records of losses and loss-causing events. There are tons and tons of data — to no end.
However, there is an unspoken premise for solutions that excavate answers out of data: the right data is in there somewhere. Even though there are “predictive analytics”, “intelligent algorithms”, “virtual learning” and other software tricks to deliver answers, there is nothing quite like having the right data involved. Sometimes there is no “right” data – wouldn’t it be nice if there was a catalog of future floods or earthquakes? Without some predictive modeling, underwriters would be dealing with the Turkey Problem. But using the right information in predictive models or algorithms is, again, essential.
There is an even better type of “right” data: your own data. If the data used in an analytic is proprietary and inaccessible to others who are trying to do the same thing, there is a competitive advantage to be gained. Loss history is the obvious example here, being both over-utilized and undervalued by insurers. Beyond proprietary data, there is also an advantage to be gained with expertise. This translates into how big datasets are used, including emphasizing those datasets that more closely correlate with the answers they need.
So, how can insurers leverage the right data with their own expertise? And how can they do it quickly? There is a way: configurable risk scoring analytics. When an underwriter has easy access to an analytic that produces the exact information she needs, based on the best (not biggest) possible public and private datasets, and calculated in the way that conforms to her company’s expertise, she has the answer she needs. Now, that’s big news.
