Open Data Infrastructure for Insurance

Insurance platforms do not fail because they cannot store data. They fail because they cannot prove what happened when money, risk, and regulation collide.

Why it matters

Insurance data has a long half-life. Claims get reopened. Policies change. Fraud investigations need history. Actuarial models depend on reproducibility. At the same time, the data crosses boundaries: brokers, reinsurers, third-party data providers, and regulators.

That combination punishes architectures that hide contracts behind one closed platform boundary. It also punishes architectures that treat governance as a document instead of enforcement.

The ODI angle

ODI for insurance means an open storage and metadata foundation where policy, lineage, and auditability are first-class platform behaviors.

Insurance teams often underestimate the metadata problem. It is not enough to store claim events. You need to preserve meaning: which version of a policy model applied, which enrichment source was used, and which transformations produced the final decision artifacts.

For the general ODI framing, start with What Is Open Data Infrastructure? and ODI for Financial Services.

The architecture test

For insurance data leaders, the architecture test is whether you can support long retention and partner sharing while staying auditable and portable.

• Use open table formats for claims, policy, and event history data so the long-term contract stays portable.
• Standardize lineage capture so decisions can be reconstructed and defended.
• Enforce access controls in the data path, not only in BI tools.
• Design data sharing boundaries explicitly for partners and reinsurers.
• Build a disaster recovery posture that includes the catalog and metadata layers.

What breaks first

This breaks when an organization confuses "centralized" with "governed."

• Data is centralized, but lineage and audit are missing, so investigations become archaeology.
• Access rules are enforced in one tool, so alternate paths become shadow access.
• Partner data sharing bypasses controls, so risk increases as sharing increases.
• Long retention exists, but table maintenance and metadata hygiene do not, so performance and cost drift silently.

Questions to ask

Use these questions when you evaluate ODI for insurance platforms.

• Can you reconstruct a decision end to end, including model version, input data, and transformations?
• Which controls are enforced automatically, and which depend on a process document?
• Can you share data with partners without copying it into a new closed platform?
• Can you prove who accessed sensitive data and why?
• Can you exit a vendor boundary without rewriting your governance and audit posture?

If you cannot answer those questions, you do not have a foundation you can defend. You have a system you hope nobody audits deeply.

Sources to start with

Start with data security and risk management guidance, then anchor the technical contracts in open table and lineage standards.

• NAIC Insurance Data Security Model Law (Model #668)
• NIST AI Risk Management Framework
• OpenLineage documentation
• Apache Iceberg table specification