No doubt to the vexation of carriers and vendors alike, business intelligence (BI) and predictive analytics are often lumped together. Given the rapid growth of the technologies in recent years, the confusion is understandable, especially when one considers that both technologies use the wealth of historical and third-party data insurers have on hand to build models to either augment human decision-making or eliminate it altogether through automation.

Despite this cosmetic resemblance, the differences are profound. Historically, BI has become synonymous with operational metrics and monitoring, querying and reporting functions. Modern BI solutions generate scorecards, a collection of metrics and reports on a unified interface used to measure against objectives and dashboards, which use metrics to give users the pulse of an organization. While BI is reactive, and looks backward to gauge performance, predictive analytics seeks to use data in real time for sub-second decisions to affect future performance. While BI tools enable slicing and dicing and give insurers a high-level view of what’s going on, predictive analytics promises insurers actionable knowledge and a granular view of their operations. Thus, if BI is a look in the rearview mirror, predictive analytics is the view out the windshield.

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