The race to apply AI in P/C insurance often hits a wall, not because of algorithms but because of confusion over data foundations. “Data Lake,” “Data Warehouse” and “Data Lakehouse” may sound interchangeable, but each plays a very different role in how carriers manage, govern and trust their data.

Related article: AI in Property/Casualty Insurance: Why Trusted Data is the Missing Link

Data Warehouse

A data warehouse delivers clean, structured and verified information for reporting and regulatory compliance. It’s schema-on-write, meaning data must fit a defined structure before loading. That discipline ensures accuracy and consistency for financials, KPIs and actuarial analysis. The trade-off? Warehouses move slowly when new or unstructured data sources appear. This has been a struggle for carriers needing to capture photos related to risks, claims notes and other types of unstructured data on the regular.

Data Lake

A data lake takes the opposite approach. It captures raw, high-volume data—everything from policy documents and claims notes to telematics and imagery—without forcing it into a rigid model. This flexibility fuels exploration and innovation, but without governance, a lake quickly turns into a “data swamp,” eroding confidence and slowing AI adoption. The introduction of data lakes to the landscape was a great evolution for carriers, but one that didn’t take things far enough.

Data Lakehouse

The lakehouse merges the best of both worlds. It manages structured and unstructured data together, maintaining the agility of a lake with the governance of a warehouse. By organizing data into Bronze (raw), Silver (standardized) and Gold (curated) layers, it creates a single, auditable source of truth that both humans and AI can trust. Data lakehouses are the data foundation for a modern IT stack and can empower carriers to take advantage of AI solutions in a way that is not possible with data warehouses or data lakes.

For carriers, the takeaway is clear: AI succeeds when data is unified, explainable and reliable. A modern data lakehouse, often built alongside an existing warehouse, provides the trusted foundation needed to move AI from pilot projects to enterprise performance.