Most insurance migration programs already build 80% of a data platform — but then throw it away at go-live.

There is a sequence of events that plays out across insurance transformation programs with unfortunate regularity. An organization commits to replacing its legacy core system. Over the course of two, three, sometimes four years, a team of specialists builds out sophisticated data migration infrastructure — connectors into the legacy system, data cleansing logic, data transformations, reconciliation tests, validation dashboards, etc. A significant investment in time and budget to accumulate the relevant knowledge of how the company's processes and products are encoded into data.

Finally go-live is declared. The program winds down. External consultants are rolled off, and the complete migration platform is quietly decommissioned.

This article is not about what goes wrong during transformation. It is about what tends to get left behind once it succeeds, and what becomes possible when insurers make a deliberate choice to hold on to it.

The Prize Worth Keeping

Before examining what gets left behind, it is worth being clear about what is at stake.

The case for a robust data foundation in Belgian insurance is well-established. Regulatory obligations alone create a compelling need: NBB circulars, FSMA requirements, Solvency II reporting frameworks and EU Transparency regulations all require insurers to attest the correctness of statutory figures and trace them back to their source. As reporting obligations grow in scope and frequency, sustaining that level of traceability becomes increasingly difficult without a structured, integrated data environment to support it.

Beyond compliance, the strategic value of integrated data is significant. Accurate pricing benefits from combining historical claims data with current portfolio behavior. Customer-facing processes — from cross-selling to claims handling — improve materially when the right data is accessible at the right moment. Planning and performance reporting become more reliable when they draw from a single, consistent source of truth rather than reconciling figures across disconnected systems.

The challenge is that building such data foundations from scratch is a substantial undertaking. It involves complex architectural decisions, a lengthy analysis phase, and the development of a common data model that is costly to revise once implemented. For many insurers, it is a project that gets strategically prioritized in principle but deferred in practice.

You can avoid a seven-figure data strategy implementation by making the right decisions during the preparation phase of your transformation program.

This is precisely what makes the timing of a transformation program so relevant: A significant portion of these activities will already be in scope of the migration track.

More Than a Migration Tool

Consider what a migration program must construct to move data from a legacy system to a modern target platform.

It needs connectors into both systems — the legacy source and the modernized target — to extract data reliably and at scale. This is not a one-sided integration: validating and reconciling a migration requires loading data from the target system back into the migration platform and comparing it against the source, which means both systems are connected to a centralized store from the outset.

As it turns out, this is exactly the connectivity that post-migration reporting requires as well — and for two reasons that are easy to underestimate at the start of a program. First, most migrations are executed in phases over multiple years, meaning the active portfolio is split across two systems for an extended period and any meaningful report needs to span both. Second, inactive contracts are frequently left out of migration scope to reduce complexity, yet they remain subject to reporting requirements. The legacy system's data does not become irrelevant at go-live; it remains a necessary data source for as long as those contracts need to be reported on.

It needs transformation logic — field mappings, cleansing rules, model conversions — to reconcile the semantics of the legacy system with those of the target. This logic encodes something genuinely valuable: deep, specific knowledge of how the business works. How a policy structure maps to the new coverage model. How legacy premium calculations relate to both input and output of the new calculation engine. This knowledge is carefully assembled during the program, when the right people with the right expertise are available. The ability to preserve this as structured, reusable assets is an opportunity that does not last indefinitely.

It needs reconciliation and validation structures — summary tables and data marts that verify if all contracts, coverages, premiums, claims, reserves, and broker fees have been carried over correctly. These structures are, in substance, financial and operational reporting marts. The incremental effort to extend them into production reporting assets with built-in quality controls is considerably lower than building equivalent from scratch.

It needs orchestration tooling to schedule, sequence, and monitor pipelines — triggering reconciliation runs after each incremental load, managing dependencies, handling failures gracefully. This is structurally identical to what a reporting platform needs to refresh data marts and dashboards reliably, and to manage overnight and monthly reporting batches.

It needs historization and snapshotting to preserve point-in-time views of the insurance system and compare pre- and post-migration states. This same mechanism is what enables period-over-period reporting and historical trend analysis in an operational context.

And along the way, the program accumulates a foundational data catalog: Documentation of what fields mean, how they relate to different stakeholders, what business rules apply, etc. This institutional knowledge, captured when both systems are best understood, becomes the starting point of a mature data governance capability — one that would otherwise need to be built from scratch, after the fact, by people who were not in the room when all that business analysis was done.

By the time a migration program reaches go-live, both the source and target systems are connected to a centralized data store, with transformation and validation processes already running on top of it. When done thoughtfully, the architectural building blocks of a data foundation will largely be in place — built in service of migration, but reusable far beyond it.

This structural similarity between migration platforms and data foundations is not accidental. Both draw on the same underlying need: A coherent, cleansed, integrated view of the insurance portfolio, traceable to its sources and reliable enough to support decisions.

The practical implication is meaningful. Based on what we have seen across insurance transformation programs, the extraction and transformation layers alone — the connectors, field mappings, cleansing rules, and reconciliation logic — represent a substantial share of what it would cost to build a data foundation independently. When you add orchestration, historization, and the validated data marts built for migration sign-off, the overlap between what the migration program delivers and what a reporting foundation requires is considerable. The incremental investment to bridge that gap is a fraction of starting over.

An Architectural Choice, Not a Scope Discussion

This raises a natural question: if the logic is this clear, why does the migration platform get decommissioned so often? Part of the answer is perception. Seen from the outside, building with a data foundation in mind can look like inflated scope. In practice, it is an architectural stance — a set of design choices made early in the program that determine whether the platform can outlive its original purpose. The cost difference is modest. The difference in outcome is not.

The approach does not require a grand architecture. Centralizing all source data into a single store from the outset, including historical archives, is a foundational choice that costs relatively little if designed in from the start rather than retrofitted later. From there, the right model is to build small and iterate: Lightweight data models that serve specific use cases — a reconciliation report, a premium production dashboard — delivered directly on the presentation layer, in hours rather than months, and extended as needs evolve. Simplicity is a feature, not a limitation. Limiting transformation steps, avoiding redundant data copies, and resisting the temptation to model for every hypothetical future requirement keeps the system maintainable and the delivery timeline realistic. And with major cloud providers offering storage, processing, orchestration, and visualization on a pay-per-use basis, the infrastructure question has largely been answered — the focus can be on the data itself.

None of this is a heavy lift when it is built into the program from day one. The question worth posing early is not "can we afford to do this?" but rather "what would it cost to rebuild this capability from scratch in two or three years, once the program has closed and the context has dispersed?"

The Cost of Doing Nothing

Decommissioning the migration platform is not a neutral decision — it has a price, even if that price does not appear immediately on a balance sheet.

The infrastructure investment stops generating returns the moment the platform is shut down. The institutional knowledge embedded in the platform becomes harder to access and transfer once the program team disperses and documentation is no longer actively maintained. The organization enters the post-migration period with a modern target system but, in many cases, limited ability to report beyond what the system vendor provides natively.

The consequences tend to surface gradually. A CFO requests a report that spans both the legacy portfolio and the migrated book. The actuarial team needs a historical view the target system cannot produce. The regulator asks for an audit trail that requires tracing data across two systems. Each of these requirements triggers a new project — starting from a considerably less favorable position than if the migration platform had been designed and retained as data foundation.

These scenarios are not unusual, and they are worth factoring into decisions made at the start of a program rather than at the end.

The Conversation Most Programs Never Have

There is a practical dimension to this approach that deserves attention: In many transformation programs, the migration platform is not built by the insurer. The migration efforts are often placed in scope of the target system vendor, a system implementation partner, or a combination of both.

These partners are typically focused — rightly — on delivering a successful migration. Their scope, incentives, and contractual obligations are oriented around that outcome. Designing the migration platform as a durable, insurer-owned data asset may not be part of their default approach unless it is explicitly agreed upon.

This makes the pre-contract phase the right moment to raise the conversation. During the definition of the statement of work and commercial negotiations, it is worth asking directly: Will the migration platform be documented and handed over as a reusable asset? Will the transformation logic be written in a way that can be maintained independently going forward? Will the data architecture be designed with post-migration reporting in mind? If not, it may be worth broadening the selection process to include partners that bring both migration expertise and a genuine data platform capability — because the two are not always found in the same place.

These are reasonable questions, and the answers will reveal a great deal about how a potential partner thinks about long-term value creation versus project delivery. The choice of transformation partner is, in this sense, also a choice about the data capability the organization will have access to for years after the program closes.

One Program. Two Outcomes. One Decision.

The migration platform is not temporary scaffolding. It is the foundation of a future data capability — if the decision is made to treat it that way from the start.

That decision needs to be made early: In how the architecture is designed, in how contracts are written, in how partners are selected, and in how knowledge is managed and transferred throughout the program. It does not require a significant additional investment. It requires intent, and it requires asking the right questions at the right moment.

The insurers that get this right will emerge from their transformation program with more than a modern core system. They will have a connected, documented, and extensible data foundation — one that can support regulatory reporting, actuarial analysis, financial reconciliation, and management information from day one of the post-migration period, built largely on investment that was already committed to the migration itself.

In a market where data capability is increasingly a source of competitive differentiation — in pricing accuracy, customer experience, and operational efficiency — the gap between insurers that treat their migration as a foundation and those that treat it as a one-time project will become more visible over time. The good news is that the choice is available to anyone planning a transformation today. It just needs to be made.

If you are planning or currently running an insurance transformation program and would like to explore how to structure your migration investment so that it generates lasting data value, we would be glad to have that conversation. Helping insurers get more out of their transformation programs — and ensuring the right foundations are in place from the start — is exactly our expertise.