In the insurance space specifically, the scope of a predictive analytics strategy will always be tethered to the amount of data an insurer can access.

A large carrier is less likely to believe they require additional data other than their own set, but many are missing data in key segments.

There are numerous instances where a predictive analytics strategy requires external data to deliver the optimal results.

Commercial Auto: If an insurer with expertise in writing tractor trailers in Montana builds a model with the goal of diversifying the vehicle types its writes within the same state, then it would be difficult to achieve with only its own data set and non-transactional third-party data.

The issue with using only an insurer’s in-house data is that the data is biased from years of honing in on risks that fit an insurer’s appetite; it won’t generalize to new areas.

Some insurers will make the mistake of believing their own data from years of expertise in writing construction in Wyoming will provide the necessary predictive insights for the same class code in Tennessee, but in fact, it will often do more harm than good.

The success of one analytics initiative versus another hinges on the execution of the predictive analytics strategy and access to the right foundational data.