Data Governance Foundation

It is easy to get excited about predictive analytics, AI, and machine learning in insurance. It is harder to talk about the data governance work that has to happen first for those investments to deliver their potential.

The gap between carriers with strong analytics capabilities and those that are still struggling is not primarily a technology gap. It is a data quality and governance gap. Inconsistent data definitions, siloed systems, manual reconciliation processes, and undocumented data lineage are the actual limiting factors for most analytics programs.

The carriers that have done the less exciting work -- establishing enterprise data dictionaries, implementing data quality rules at point of entry, creating clear ownership for master data domains -- are the ones that can actually deploy the exciting analytics tools and trust the outputs.

Governance is not a project with a completion date. It is an ongoing organizational discipline, which is exactly why so many programs start well and erode over time when leadership attention moves to the next priority.