Why 73% of Migration Projects Fail

Most enterprise migration projects are effectively over before the first record moves. 

According to Kanerika (2025), 73% of migration projects fail to meet their objectives, exceed budget, or disrupt business operations. That is not a technical problem. It is a data strategy problem. And until organisations start treating it as one, the failure rate will not change.

The projects that succeed are not better funded or better staffed. They simply start in a different place.

The Real Reason Migrations Fail 

The most common mistake is treating migration as an infrastructure exercise. Teams focus on the pipe rather than the payload, selecting tools and platforms before they have any real understanding of what the data actually looks like. 

Data is not static. Fields take on new meanings over time. Relationships between systems go undocumented. Invalid records accumulate quietly for years. None of this surfaces until the data is actually moving, at which point the project transforms into an unplanned, expensive data cleaning exercise. 

This late stage discovery is the primary driver of the cost overruns and missed deadlines that define the failing 73%. And it is almost entirely avoidable. 

What the Successful 27% Do Differently 

The organisations that get migration right mandate a data first approach. Before a single record moves, they use AI and machine learning scanning to build a fully documented picture of their data estate, across mainframes, databases, files, and API payloads. 


This is not a nice-to-have. According to Gartner, poor data quality costs the average organisation at least $12.9 million every year (Gartner, 2020). Carrying that problem into a migration does not make it cheaper to fix. It makes it exponentially more expensive. 


The proactive discovery phase allows teams to surface hidden data patterns, undocumented relationships, and quality issues before they become project-ending problems.

Four Things to Do Before You Move a Single Record 

1.  Build a documented data dictionary 
   Use AI and deep data scanning to create a comprehensive map of all data structures in both source and target systems. You cannot migrate what you do not understand.

2.  Locate PII and sensitive data 
   Scan the entire estate to identify Personally Identifiable Information before migration begins. If you want to outsource your migration you cannot use the PII data, it will have to be masked. Regulatory compliance cannot be an afterthought.
   
3.  Run early data quality audits
   Generate quality reports to identify invalid or morphed data early. This allows you to make a deliberate decision about what to clean, what to migrate, and what to leave behind.
   
4.  Map hidden business relationships
   Identify common data elements across disparate technologies. Hidden dependencies between systems are one of the most common causes of mid-migration failure.
   

These four steps are not optional extras. They are the foundation that separates the 27% from everyone else.

The Takeaway

If your migration plan starts with the technology, start again. The organisations that consistently deliver successful migrations start with the data, understand it completely, and only then build the pipeline to move it. 

References:

Kanerika (2025). Why Data Migration Projects Fail in 2026 (And How to Fix It). Available at: https://kanerika.com/blogs/data-migration-challenges/

Gartner (2020). Data Quality: Best Practices for Accurate Insights. Available at: https://www.gartner.com/en/data-analytics/topics/data-quality