- Tension: Organizations know data migrations are inevitable, yet most still treat them as a technical project rather than a strategic business initiative.
- Noise: The conversation fixates on tools and technology, drowning out the truth that bad data governance and poor planning cause most migration failures.
- Direct Message: The top challenges of data migration are lack of standardization, inaccuracy, and failing to have a clear plan — and all three are preventable.
To learn more about our editorial approach, explore The Direct Message methodology.
A decade ago, Experian Data Quality published a study that should have changed how organizations think about moving their data. It found that 91% of businesses undertake data migration projects — and 85% of them hit a significant problem along the way.
That’s not a technology problem. That’s a planning problem. And in 2026, with cloud consolidations accelerating, AI pipelines demanding clean inputs, and regulatory pressure tightening, those same failure patterns are still playing out.
The tools have changed. The mistakes haven’t.
Data migrations remain among the most disruptive, expensive, and underestimated initiatives in any organization’s operational calendar. They happen constantly — triggered by mergers and acquisitions, system upgrades, platform consolidations, and compliance mandates. Yet despite their frequency, most companies still approach them reactively, treating a migration as a technical lift rather than a business-critical transformation. The result is predictable: delays, cost overruns, corrupted records, and frustrated stakeholders across every department.
The quiet crisis hiding in your data
Here’s the tension that rarely gets named: organizations want the benefits of better data infrastructure but resist the discipline required to get there. Migrating to a modern CRM or cloud data warehouse sounds like progress. But progress demands that someone audit what’s already broken before moving it somewhere new.
The Experian study identified five recurring failure points — lack of collaboration, poor data standardization, weak system design, inaccurate information, and misaligned business rules. What’s striking about this list is that none of these are fundamentally technical problems. They’re organizational ones.
A mismatch in date formats isn’t a database issue; it’s a symptom of teams that never agreed on a standard. Migrated records with wrong addresses don’t signal a flawed migration tool; they signal that no one checked the data before the move.
This matters more now than it did in 2015. IBM research estimates that poor data quality costs organizations an average of $12.9 million per year — and that figure climbs significantly when bad data is baked into AI training sets or automated decision systems. In an era when marketing personalization, predictive analytics, and machine learning all depend on clean, structured inputs, migrating inaccurate data doesn’t just create IT headaches. It corrupts the outputs that executives are making decisions from.
Why the standard advice keeps missing the point
Most migration guidance follows a familiar script: build a project plan, document your data, involve stakeholders, test before launch. Sound advice, technically. But it treats data migration as a checklist problem when it’s actually a culture problem.
Consider the collaboration issue. The Experian study found that siloed teams — IT working without clear input from business users — is one of the leading causes of migration failure. A decade later, this dynamic persists. Technology teams are handed vague requirements and left to interpret business logic they don’t fully understand. Business users assume IT will figure it out. No one owns the outcome clearly enough to catch problems before they become expensive.
The same dynamic shows up in how organizations handle poor system design, flagged by 33% of respondents in the original study as a significant challenge. The common fix — better scoping documents and centralized project management — is necessary but insufficient. What’s really needed is a shared accountability model where business owners define requirements in concrete terms, and technology teams have legitimate authority to push back when those requirements are unclear or incomplete.
A Harvard Business Review analysis found that only 3% of companies’ data meets basic quality standards — a figure that should reframe how organizations think about the starting point of any migration. You’re not moving clean data. You’re almost certainly moving a decade’s worth of accumulated inconsistencies, duplicates, and deprecated fields. The question isn’t whether problems exist. It’s whether you’ve planned for them.
The clarity that changes everything
Data migration failures are rarely about technology. They’re about the decision to move first and think later — and the organizational culture that makes that decision feel acceptable.
The original Experian study offered a practical observation that still holds: the organizations that navigate migrations successfully treat data quality as a prerequisite, not an afterthought. They audit before they migrate. They standardize before they transfer. They involve business stakeholders before the first record moves, not after the first problem surfaces.
This reframe shifts migration from a project that IT manages to one that the business owns.
Building migrations that actually hold
The lesson from a decade of repeated migration failures isn’t that the problem is unsolvable. It’s that organizations keep solving the wrong version of it. They invest in migration tools while underinvesting in data governance. They schedule go-live dates before they’ve assessed data readiness. They define success as completing the migration rather than confirming the migrated data performs as expected in the new environment.
Here’s what organizations that get this right tend to do differently. First, they treat the pre-migration audit as the most important phase of the project — not a preliminary step to rush through, but the work that determines whether the migration has any chance of succeeding. Second, they assign explicit business owners to data domains, so that when standardization decisions need to be made, there’s a person accountable for making them — not a committee that defers endlessly. Third, they build buffer time into the project plan, not as a sign of pessimism, but as an acknowledgment that data quality issues will surface and need resolution before cutover.
The investment in getting data right before a migration pays back in ways that are measurable: fewer post-migration defects, faster user adoption, and downstream analytics that reflect reality rather than inherited errors.
The Experian study identified the same five challenges that organizations are still tripping over today. Lack of standardization. Inaccurate data. Absence of a clear plan. Poor system design. Misaligned business rules. None of these require a new tool to fix. They require a decision — made early, upheld consistently, and owned by someone with enough authority to enforce it — that data quality comes before data movement. Every time.
