What is included in a managed data analytics service?

Pipeline operations to agreed service levels, automated data quality validation with exception reporting, governance of metric definitions, platform tuning and cost management, support for onboarding new sources, and access governance. The distinguishing feature is that someone is accountable every day, not just during a project.

Do we need to replace our data platform first?

Usually not. Most insurers have already bought capable technology. What is missing is the operational discipline around it, and a managed service supplies that on the platform you have. Replacement only makes sense where the platform genuinely cannot support the volume or workloads you now need.

How does this help with regulatory and actuarial reporting?

Mainly through lineage and consistency. When a figure can be traced from report back to originating transaction, and when a metric means the same thing in underwriting, claims and finance, reporting stops being a reconciliation exercise. That also makes month-end faster, because the manual bridging steps disappear.

We have just acquired a book of business. Does that change things?

It usually strengthens the case. Acquisitions arrive with their own systems, product codes and definitions, and migration is expensive enough that the older platform often stays running for the run-off. Integrating that data without adding permanent headcount is one of the more common reasons insurers move to a managed model.

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How managed data analytics services help insurers turn fragmented data into insight

How managed data analytics services help UK insurers turn fragmented policy, claims and broker data into reliable insight, with pipelines somebody owns.

23rd September 2026

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