Most digital transformation programmes don’t fail because of technology. They fail because of data.
When organisations implement new ERP platforms, migrate to the cloud, or modernise core systems, the biggest risk is often not the software itself but the challenge of moving years of business-critical data into the new environment safely. According to Gartner, 83% of data migration projects either fail outright or exceed their budgets and timelines[1]. An Experian study reinforces this picture, finding that 64% of data migration projects go over budget and only 46% are delivered on time[2].
For IT consultancies delivering transformation projects, data migration is frequently the highest-risk component of the entire programme. And that is exactly why the smartest consultancies rarely attempt to handle it alone.
The hidden complexity of data migration
On the surface, data migration can appear straightforward: Move the data from one system to another and the job is done.
In reality, enterprise systems often contain years or decades of accumulated data. Over time, this data becomes inconsistent, duplicated, poorly structured, or stored across multiple disconnected platforms. Customer records may exist in several systems. Financial data may follow different formats across departments. Operational databases may contain incomplete or conflicting records.
When this data is moved into a modern platform without proper preparation, these issues quickly become visible. Incorrect mappings, incompatible formats, or missing fields can disrupt business operations immediately after go-live. Research into migration failures consistently highlights three critical factors: Inadequate planning, with 65% of failed projects spending less than 20% of their timeline on planning phases; underestimated data quality issues, with organisations typically discovering three to five times more problems during migration than anticipated; and scope creep, with 72% of projects expanding beyond original scope without corresponding timeline adjustments[3].
For consultancies responsible for delivering transformation programmes, this introduces significant delivery risk – not just to the technical programme, but to the client relationship itself.
Why partnership reduces project risk
Building an internal team capable of managing complex enterprise data migration is not always practical.
Migration expertise requires deep experience in data analysis, cleansing, mapping, and validation across multiple systems and technologies. Yet these skills may only be required during specific stages of a transformation programme. Maintaining a large permanent migration team carries overhead that cannot always be justified, particularly when the demand for those skills fluctuates from project to project.
Forward-thinking consultancies solve this challenge by partnering with specialist data migration experts. Instead of building large internal migration teams, they collaborate with partners who focus exclusively on preparing and migrating enterprise data. This allows consultancies to expand their capabilities on demand while maintaining a lean delivery model – accessing deep migration expertise precisely when it is needed, without the cost and complexity of building and retaining that capability in-house.
Research from BCG found that only 35% of digital transformation initiatives achieve their objectives[4], and the quality of integration is a significant differentiator: Organisations with strong integration achieve 10.3 times the return on investment compared to 3.7 times for those with poor integration. For consultancies, this makes the choice of data migration partner a strategic decision that directly affects client outcomes.
Delivering transformation with greater confidence
Partnership models also strengthen the overall success of transformation programmes.
Consultancies retain ownership of the client relationship and the strategic delivery of the project, while specialist partners ensure that the underlying data foundation is prepared correctly. This division of expertise reduces the risk of migration failures that could affect system performance, operational stability, or project timelines.
Testing is a critical part of this process. Failed migrations typically devote only 15% of project time to testing, compared with 30–40% in successful projects[3]. A specialist migration partner brings not just technical capability but the rigorous methodology – including trial migrations, reconciliation checks, and data validation processes – that significantly reduces the likelihood of problems appearing after go-live.
The result is a more reliable transformation programme and a stronger client outcome.
Where Dajon fits
This is where Dajon Data Management provides significant value.
Dajon works alongside IT consultancies, managed service providers, and implementation partners to support the data components of complex transformation projects. Through expertise in data migration, integration, and structured data preparation, Dajon helps ensure that business-critical data can move safely between systems.
By preparing and validating data before migration begins, Dajon reduces delivery risk and enables consultancies to confidently deliver complex digital transformation programmes. Dajon’s approach includes detailed data analysis, quality assessment, cleansing, mapping, and rigorous pre-migration testing – ensuring that when data enters the new environment, it is accurate, consistent, and ready to support business operations from day one.
For IT consultancies, this means the ability to pursue larger opportunities, expand their service offering, and deliver transformation projects without the need to build large internal migration teams – while offering clients the assurance that the most complex and highest-risk component of their programme is in specialist hands.
References
- Top Data Migration Challenges & How to Overcome Them Kanerika / Medium[↩]
- Data Migration Risks And The Checklist You Need To Avoid Them Monte Carlo Data[↩]
- The Hidden Complexity of Data Migration Kaopiz Software / Medium[↩][↩]
- Data Transformation Challenge Statistics Integrate.io[↩]