Software engineers have a useful concept called technical debt. It describes the accumulated cost of all the shortcuts, workarounds, and quick fixes that get baked into a codebase over time – none of them serious in isolation, all of them adding friction that compounds the longer they’re left alone. The metaphor works because it captures something real: debt has interest, and interest accrues whether you’re paying attention to it or not.
There’s an equivalent concept that gets less attention but probably matters more, and it’s worth naming. Call it data debt.
Data debt is what builds up when an organisation makes thousands of small, individually reasonable decisions about how to record, store, structure, and maintain its information over many years. A field that was supposed to be mandatory gets left optional because the validation broke once and nobody fixed it. A naming convention drifts between departments because nobody owns it. Two systems hold overlapping data because the integration was deferred. A scanned document gets filed instead of indexed because the team running the project didn’t have the budget. Each decision is small. Cumulatively, they produce a data estate that’s quietly riddled with inconsistencies, duplicates, gaps, and structures that don’t really make sense anymore.
Most of the time, none of this is visible. The systems still work. People still get their reports. The business runs. And then a cloud migration kicks off, and the debt comes due all at once.
Why migration is the moment of reckoning
Gartner’s widely cited research puts the data migration failure rate at around 83%[1] – the proportion of projects that either fail outright or significantly exceed their budgets and timelines. That number gets repeated a lot. What gets discussed less often is what’s actually going wrong inside the failures, and why the same patterns repeat across organisations that are otherwise quite different.
The recurring story is this. The migration starts with technology timelines and capability assessments. Everyone is focused on the destination platform – its features, its configuration, its integrations. The data is treated as an input to the project, something that simply needs to be moved, and the assumption is that it’s broadly fit for purpose because it’s been running the business for years. Then someone actually starts looking at what’s in the source systems, and the assumption stops holding.
By that point, the project plan has set expectations the data won’t support. Recent analysis suggests that 84% of migrations are affected by poor data quality and that around 61% of projects exceed their planned timelines[2], often by 40-100%. Legacy data formats and structures clashing with modern cloud platforms account for up to 45% of migration failures on their own.
These aren’t anomalies. They’re what data debt looks like when it can no longer be deferred.
You cannot refinance data debt. You can only pay it down or carry it forward
This is the part that catches organisations out most often, and it’s worth being blunt about.
When a migration project encounters data debt, there are essentially two options. The first is to deal with it before anything moves – cleansing, deduplicating, mapping, restructuring, validating, and accepting that this work takes longer than the steering committee initially assumed. The second is to push it into the new environment and plan to fix it later. The first option is uncomfortable because it delays go-live. The second option is much more uncomfortable later, because data debt that has been carried into a new system is significantly harder to address than data debt sitting in a system you’re about to retire.
The reason is practical. In the legacy environment, you can clean up data without disrupting people who depend on the system, because you’re already planning to leave it. In the new environment, every record is being actively used, every workaround is now embedded in someone’s workflow, and every cleanup operation risks breaking something. Worse, the workarounds people built up around the legacy data – the manual checks, the side spreadsheets, the institutional knowledge of “ignore this field, it’s always wrong” – don’t migrate with the data. The new system inherits the problems but not the coping mechanisms. Users encounter the issues fresh, decide the new platform is unreliable, and start losing trust in it. Once user trust in a new system erodes, it’s almost impossible to recover, and the migration starts to be remembered as a failure regardless of what was actually delivered technically.
This is why the “we’ll fix it after go-live” approach almost never works. The fixing rarely happens. What happens instead is that the organisation lives with the same problems in a more expensive environment, and the cloud investment never quite delivers on its business case.
The pre-migration audit nobody wants to do
The work that closes the gap between a successful migration and a troubled one happens before any data moves, and it doesn’t look much like a technology project.
It looks like an honest audit of the source data: what’s there, what’s duplicated, what’s missing, what’s inconsistent, and what’s structurally going to clash with the target platform. It looks like mapping decisions made by people who understand both the business meaning of the data and the technical structure of the destination system. It looks like agreement on which records are worth migrating at all – because legacy systems are often full of historical data that nobody’s used in years and that nobody will use in the new environment either, but that adds cost and complexity if it’s brought along by default. And it looks like a structured approach to validation, so that what arrives in the new environment can be confidently said to match what was supposed to arrive.
Organisations that do this kind of readiness work upfront have been shown to achieve 2.4 times higher migration success rates[3]. There’s nothing magical about the number. It reflects the fact that most migration failures are really data failures dressed up as technology failures, and that addressing data first prevents the failure mode at source.
The unstructured estate everyone forgets
There’s a particular blind spot worth flagging, because it’s responsible for a disproportionate share of post-migration regret.
Most migration plans focus on the structured side – the database content, the application records, the things that look like rows and columns. The unstructured estate – scanned contracts, archived correspondence, claim documents, supporting evidence, the various PDFs and images sitting in folders that have accumulated over the years – tends to be either deferred to a separate project or quietly assumed to be out of scope. Roughly 80% of enterprise data is unstructured[4], and in regulated sectors that figure is often higher. Leaving it out of a migration doesn’t actually leave it out – it just means it will need to be addressed later, at higher cost, and probably under operational pressure.
This is a particularly common cause of migrations that look successful on day one and then start unravelling six months later, when the business realises that the new cloud system has none of the historical context that used to live in the document estate of the old environment.
When multiple systems are involved
Most migrations aren’t a clean line between one source and one destination. They’re an exercise in reconciling several legacy systems into a single new environment, each with its own structures, its own quirks, and its own version of “the customer record” or “the claim file.”
This is where data debt really compounds, because the inconsistencies aren’t just internal to each system – they’re between systems. Customer A in the policy administration platform might be Customer A1 in the CRM, Customer Anderson in the document management system, and Customer A. in the claims platform. Bringing them together in the new environment means making decisions about which version is canonical, what to do with the conflicts, and how to handle the historical data that depends on each variant. None of this work can be automated end-to-end, and none of it is something the destination platform’s vendor will solve for you.
It’s also where the difference between specialist data work and generalist transformation work shows up most clearly. Multi-system reconciliation is a specific discipline. Done well, it’s invisible. Done badly, it’s the reason the new system shows different numbers depending on which screen you’re on, which is one of the fastest ways to lose user trust.
How Dajon helps
At Dajon Data Management, this is the kind of work that occupies most of our time with migration clients.
We help organisations confront their data debt before the migration plan starts setting expectations the data won’t support. That means a structured assessment of what’s actually in the source environment, a cleansing and restructuring programme designed around the requirements of the destination platform, and the multi-system reconciliation work that bridges the gap between several legacy environments and a single new one. It also means bringing the unstructured estate – the scans, the archives, the documents – into scope from the beginning, rather than discovering halfway through that it needs its own project.
The output isn’t just clean data. It’s data that can support the operational reality of the new environment from day one, hold up to scrutiny if a regulator asks about it, and earn the trust of the users who’ll be working with it. For regulated clients, that last point is the one that matters most in the long run.
The cloud doesn’t fix data debt. It inherits it
The strategic point worth ending on is this. A cloud platform is not a fresh start for data. It’s a new home for whatever you put into it, complete with whatever debt that data was carrying.
The organisations that get the most out of their cloud investments are the ones who treat migration as the moment to pay down data debt, not the moment to hope it goes away on its own. The work is unglamorous and it’s almost always longer than first scoped. But it’s the work that determines whether the cloud delivers the advantages it was supposed to – the scalability, the flexibility, the speed of insight, the readiness to support whatever AI initiatives come next – or whether it becomes an expensive recreation of the problems the organisation was trying to leave behind.
The cloud is only as good as what you put in it. Which means the question worth asking, before any timeline is locked in, isn’t “are we ready to migrate?” It’s “what are we actually planning to migrate, and what state is it in?”
Are you preparing your data for the cloud, or just hoping the cloud will fix it?
Dajon Data Management helps organisations prepare and migrate legacy data into modern cloud platforms with confidence. Get in touch to understand what your migration might involve before it begins.
References
- Top Data Migration Challenges & How to Overcome Them Gartner via Kanerika[↩]
- 10 Data Migration Challenges Every Business Must Solve in 2025 Cloudficient[↩]
- 50 Cloud Migration Statistics for 2026 MedhaCloud[↩]
- Unstructured Data: The Hidden Bottleneck in Enterprise AI Adoption Gartner via CDO Magazine[↩]