Data migration projects have a reputation for going sideways. Not a little sideways. Drastically over budget, months behind schedule, and sometimes abandoned entirely. And yet teams keep walking into the same traps.
According to Bloor Research, more than 60% of data migration projects experience significant time or budget overruns. Gartner puts the number even higher, estimating that 83% of migrations fail outright or miss their targets. McKinsey’s 2021 survey of 450 CIOs found that 75% of cloud migrations ran over budget, with only 15% of organizations finishing on time and within original estimates.
These numbers have barely moved in 20 years of research. New tools, better infrastructure, and more experienced teams have not fixed the core problem. So what is the core problem?
In most cases, it comes down to decisions made before a single line of migration code gets written.
Data migration failure rates at a glance:
| Finding | Number | Source |
| Migrations that fail or miss budget/schedule | 83% | Gartner |
| Cloud migrations that ran over budget | 75% | McKinsey, 2021 |
| Migrations completed on time and within budget | 15% | McKinsey, 2021 |
| SAP migrations over budget or behind schedule | 60%+ | ISG, 2026 |
| Organizations that don’t fully trust their data | 67% | Precisely / Drexel, 2025 |
| Delayed projects that overrun on timeline avg. | 41% | Bloor Research |
The Failure Starts Earlier Than You Think
Most teams treat data migration as a technical execution problem: map the fields, write the scripts, run the transfer, and validate the output. That mindset is part of why projects fail.
The ISG State of SAP Migrations report from early 2026, which surveyed over 200 senior decision-makers at large enterprises, found that the primary driver of delays was not technology. It was governance. Fragmented ownership, unclear accountability, and misaligned stakeholders caused more project failures than any technical challenge.
An Experian study of 270 data professionals found that only 43% of project teams had a solid understanding of data migration best practices before starting, and the same percentage had effective governance structures in place. That leaves the majority of projects starting without the organizational clarity they need to succeed.
The fix is not more documentation after kickoff. It is establishing ownership, defining success criteria, and auditing what you are actually working with before the project timeline begins.
Data Quality Problems Are a Pre-Migration Responsibility
One of the most consistent findings across migration research is that data quality issues do not surface during migration. They surface after go-live, when they are expensive and painful to fix.
The report from Precisely and Drexel University surveyed over 550 data professionals and found that 64% of organizations cite data quality as their top data integrity challenge. More striking: 67% said they do not fully trust their organization’s data for decision-making.
If your team does not trust the data before migration, the target system will not trust it either. Bad data moved to a new system is still bad data. It is just in a different location.
A thorough pre-migration data audit should include:
- Profiling source data for completeness, duplicates, and format inconsistencies
- Identifying fields with no clear owner or definition
- Flagging legacy data that has no place in the target schema
- Setting a clear threshold for acceptable data quality before migration starts
Scope Creep Kills Timelines Before the Work Begins
A study of 200 companies found that 78% of respondents said too many topics were folded into their migration programs. Projects that start with a clear scope tend to expand as stakeholders realize the migration is a chance to fix other things. That is understandable. It is also one of the fastest ways to blow a deadline.
McKinsey’s own data found that 38% of migrations were delayed by more than one quarter, with 13% stretching three or more quarters beyond the original plan. Bloor Research found that delayed projects run an average of 41% over their original timeline.
A practical way to manage scope is to explicitly separate the migration from any transformation work. Moving data to a new system while also improving the data model or business processes doubles the complexity. Teams that treat these as separate phases tend to have much cleaner outcomes.
Development teams working with custom software environments often have an advantage here because they can isolate the migration layer from the application logic more cleanly than off-the-shelf platforms allow. Companies like Paradigm Solutions take this approach, defining the boundaries of the migration scope before any development work starts in order to protect timelines and limit rework.
The Checklist Teams Skip
When Velostrata and Dimensional Research asked IT professionals what they would change in a migration do-over, the responses were telling. 56% said they would do more pre-migration testing. 50% would set a longer timeline. 45% would bring in a specialist earlier. 42% would budget more money. None of these is a complicated fix. They are the basics that get dropped under schedule pressure.
Before your next migration begins, work through these questions:
- Who owns each source data set, and are they actively involved in the project?
- Has someone profiled the source data independently of vendor or tool estimates?
- Is the scope documented and signed off by stakeholders who can actually hold the line on it?
- Does the timeline include a buffer, or is it based on a best-case scenario?
- What is the rollback plan if validation fails after the transfer?
Start Before You Start
The research on migration failures is consistent across two decades of studies. Most problems are predictable, and most can be addressed in the weeks before development begins. Data quality audits, governance setup, scope documentation, and realistic timeline planning are not the exciting parts of a project. They also happen to be the difference between the 15% of teams that finish on time and the 85% that do not. The code is the easy part. Everything that makes the code matter has to come first.
Mila Rowe
Mila Rowe is a technology writer passionate about digital transformation, AI, and enterprise innovation. She simplifies complex ideas into actionable insights for modern businesses.