{"id":16597,"date":"2026-03-12T09:40:35","date_gmt":"2026-03-12T09:40:35","guid":{"rendered":"https:\/\/www.kovair.com\/blogs\/?p=16597"},"modified":"2026-03-13T05:38:16","modified_gmt":"2026-03-13T05:38:16","slug":"why-data-migration-fails-mid-project","status":"publish","type":"post","link":"https:\/\/www.kovair.com\/blogs\/why-data-migration-fails-mid-project\/","title":{"rendered":"Why Data Migration Fails Mid-Project (And How to Prevent it Before you Write a Line of Code)"},"content":{"rendered":"\n
\"Data<\/figure>\n\n\n\n

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.<\/p>\n\n\n\n

According to Bloor Research<\/a>, 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.<\/p>\n\n\n\n

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?<\/p>\n\n\n\n

In most cases, it comes down to decisions made before a single line of migration code gets written.<\/p>\n\n\n\n

Data migration failure rates at a glance:<\/strong><\/p>\n\n\n\n

Finding<\/strong><\/td>Number<\/strong><\/td>Source<\/strong><\/td><\/tr><\/thead>
Migrations that fail or miss budget\/schedule<\/td>83%<\/td>Gartner<\/td><\/tr>
Cloud migrations that ran over budget<\/td>75%<\/td>McKinsey, 2021<\/td><\/tr>
Migrations completed on time and within budget<\/td>15%<\/td>McKinsey, 2021<\/td><\/tr>
SAP migrations over budget or behind schedule<\/td>60%+<\/td>ISG, 2026<\/td><\/tr>
Organizations that don’t fully trust their data<\/td>67%<\/td>Precisely \/ Drexel, 2025<\/td><\/tr>
Delayed projects that overrun on timeline avg.<\/td>41%<\/td>Bloor Research<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The Failure Starts Earlier Than You Think<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

The ISG State of SAP<\/a> 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.<\/p>\n\n\n\n

An Experian study<\/a> 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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Data Quality Problems Are a Pre-Migration Responsibility<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

A thorough pre-migration data audit should include:<\/p>\n\n\n\n