Every insurance technology initiative eventually hits the same wall: data quality.
Whether a carrier is deploying a predictive loss model, automating policy issuance, or building a real-time dashboard, the value of the output is constrained by the quality of the underlying data. Garbage in, garbage out is not a cliche -- it is an operational reality.
Common data quality failures in insurance include inconsistent field-level coding across legacy systems, incomplete loss histories, and address data that has never been geocoded or validated. These issues are solvable, but they require intentional investment and ownership.
Treating data quality as a standalone initiative -- not as an afterthought of the next technology project -- is one of the most high-leverage decisions a P&C leadership team can make heading into 2026.
#DataQuality #InsuranceTech #PAndC #DataGovernance #DigitalTransformation
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