Author: Chandra Matam - Principal Data Architect
Ask an insurer where its data lives and you will rarely get a single answer. Claims data sits in one system, shaped by the needs of adjusters and the claims-handling workflow. Underwriting data lives somewhere else, organized around submissions, risk appetite, and pricing. Risk and actuarial data occupy their own world, built for reserving, capital, and regulatory reporting. Each of these environments is internally coherent. The problem is that the most valuable questions an insurer can ask sit precisely in the gaps between them.
How does claims experience feed back into underwriting appetite? Which risk characteristics actually predict loss across the portfolio, not just within one line? Where is fraud leaking across products because no one can see the pattern that only becomes visible when policy, party, and claims data are joined? These are the questions that move loss ratios and protect margin, and they are exactly the questions a siloed data estate cannot answer without weeks of manual stitching.
This is the silo trap. And it is the trap Microsoft Fabric is built to break.
Most insurers know they have a fragmentation problem. The reason it persists is not ignorance; it is gravity. Each silo is anchored by a system of record that the business depends on every day, surrounded by years of pipelines, reports, and institutional knowledge. Pulling data out into a central warehouse traditionally meant copying it which created yet another version of the truth, another thing to reconcile, another place for governance to break down.
The result is a familiar paradox. The more an organization invests in moving data around to unify it, the more copies it creates, and the harder true unification becomes. Reconciliation becomes a full-time job. Executives receive conflicting numbers from different teams. And the analytical layer becomes so fragile that no one trusts it enough to build automated decisions on top of it.
Fabric approaches this differently, and the difference is architectural. At the center of Fabric is OneLake - a single, unified, organization-wide data lake. Instead of every team standing up its own storage and copying data into it, OneLake provides one logical location for the enterprise's data, stored in an open Delta-Parquet format that every Fabric engine can read.
The principle that matters most here is *one copy*. Through a capability called shortcuts, data that already exists in another domain, in an existing lake, in an external cloud store can be referenced and used in place, without being physically duplicated. Claims, underwriting, and risk data can be brought into a common analytical view without each team surrendering ownership of its source, and without manufacturing a fourth and fifth version of the data in the process.
On top of OneLake, a medallion architecture - raw, refined, and curated layers lets us bring each domain's data in as it is, progressively clean and conform it, and publish governed, business-ready data products that span domains. The claims team still owns claims. The actuarial team still owns reserving. But the organization gains a unified, trustworthy layer where the cross-domain questions can finally be answered.
Once claims, underwriting, and risk data share a governed foundation, capabilities that used to be aspirational become routine.
Underwriting can price with the benefit of actual claims experience, not a stale extract. Fraud detection can score across lines of business, because the data needed to spot cross-product patterns finally lives in one analyzable place. Actuarial teams can shorten reserving and exposure cycles because they are no longer reconciling inputs before they can begin the real work. And because all of this is built on the same platform that powers Power BI through Direct Lake, business users get fast, governed self-service on top of the unified data rather than waiting in a queue for IT.
Just as importantly, this unified foundation is what makes AI viable. A machine-learning model that scores fraud or predicts claims severity is only as trustworthy as the data feeding it. Train it on inconsistent, siloed inputs and it inherits and scales the inconsistency. Build it on a unified, governed foundation and the model becomes something the organization can actually put into production and defend to a regulator.
Unifying an insurance data estate is not a tool installation; it is a disciplined program, and it is the work our team does every day. KPI Partners brings close to two decades of enterprise data experience and a dedicated insurance practice to this problem, and we deliver it with accelerators rather than from scratch.
Our Enterprise Analytics Accelerators provide more than 32 pre-built accelerators across 11 enterprise source systems, organized into solution areas including ERP, customer and CRM, people, supply chain, and insurance-specific industry solutions for claims, actuarial, underwriting, and regulatory reporting. That means we do not begin a claims-and-underwriting unification engagement with a blank page; we begin with proven data models and pipelines that compress the timeline from months to weeks.
The results are tangible. For a global insurance advisory and risk-management firm with more than 58,000 employees, we migrated an aging Oracle-based reporting estate onto a modern cloud-native platform, rebuilt the data pipelines with automation, and delivered a governed model that replaced the reconciliation work that had been consuming the finance organization. The technical debt that had blocked their automation and AI roadmap was eliminated, and the engagement led directly to a second phase.
The silo trap is not broken by another integration project that creates a sixth copy of the data. It is broken by changing the architecture, by giving the organization a single, governed foundation where claims, underwriting, and risk data can finally be seen together, and by doing it without surrendering the ownership and discipline each domain depends on.
That is what Microsoft Fabric, built on OneLake, makes possible, and it is what KPI Partners is helping insurers achieve. The carriers that make this move stop spending their energy reconciling the past and start using their data to make sharper decisions about the future which, in the end, is the only reason to collect the data at all.
Unifying data is a means, not an end. The reason it matters is the outcomes it unlocks sharper underwriting and a healthier loss ratio, less claims and fraud leakage, faster reserving cycles, and trustworthy AI. Those are the goals insurance leaders are accountable for, and they are the goals KPI Partners is built to serve. With close to two decades of enterprise data experience, more than 300 clients, a dedicated insurance practice, and a Microsoft alliance, we deliver the full lifecycle from foundation to intelligence. Our Enterprise Analytics Accelerators - over 32 pre-built accelerators across 11 source systems, including insurance-specific industry solutions for claims, actuarial, underwriting, and regulatory reporting mean a unification program starts from proven models rather than a blank page.
Two engagements show what that delivers. A global insurance advisory and risk-management firm with more than 58,000 employees had a fragmented, Oracle-based estate that forced its finance organization into constant reconciliation; we migrated it onto a modern, governed foundation, automated the pipelines, retired the technical debt, and the work led to a commissioned second phase. A Fortune 500 insurance and professional-services group across more than 120 countries moved off a legacy analytics platform with our accelerators, with zero disruption at cutover, roughly 20% lower analytics operating costs, and stronger self-service adoption once teams could finally trust a single version of the data.
The silo trap is real, but it is escapable and we have the delivery record to prove it.