Garbage in, garbage out -- and nowhere is this more consequential than in insurance analytics.
Data quality issues -- incomplete records, inconsistent coding, siloed systems that cannot reconcile -- plague many insurers' analytics ambitions. The most sophisticated machine learning model cannot compensate for fundamentally flawed input data.
Investing in data governance before investing in analytics tools is not glamorous, but it is the difference between programs that generate insight and programs that generate noise.
The carriers building data quality disciplines today are positioning themselves for compounding analytical advantage as the industry becomes increasingly data-driven.
#InsuranceAnalytics #DataQuality #DataGovernance #InsuranceTech #PandCInsurance
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