Data migration is among the most underestimated stages of digital transformation. Companies often see it as a purely technical task. In reality, it is a test of data quality, business processes, and cross-team collaboration. How can you ensure your migration is a success and delivers long-term value to your organization?
According to Gartner’s report: LumenData & Oracle – Data Migration: Put Your Data First or Your Migration Will Come Last, 83% of data migration projects either fail or exceed their budgets and timelines. Does this mean your migration is doomed to fail? Not necessarily. It simply means that instead of treating it as a standard IT implementation, you must see it as a complex business, organizational, and technological project.
Why Do Companies Decide to Migrate Data in the First Place?
Usually, circumstances force their hand: the legacy system is no longer supported, the infrastructure is outdated, or a merger requires consolidating data from multiple systems and sources. However, migration is increasingly driven by an organization’s growing digital maturity. Companies are starting to realize that while software comes and goes, data remains one of the enterprise’s most valuable assets.
Why Does Data Migration Fail So Often?
The main error is treating migration as a solely technical task. In truth, migration audits data, tests processes, and checks organizational maturity. It exposes long-hidden inconsistencies, exceptions, workarounds, and errors tolerated by legacy systems.
In practice, the highest risks are concentrated in four areas: data quality, the human factor, project planning, and the approach to technology.
Risk 1: The Illusion of High Data Quality
One of the most common issues is the belief that source data is in much better shape than it actually is. When a vendor requests a representative data sample, they usually receive a dataset from the best-maintained part of the network – clean, consistent, and complete. Unfortunately, this sample completely fails to reflect the disorder in the rest of the systems that will need to be addressed during the migration. Reality quickly shatters this illusion.
Technical and spatial data is particularly challenging. It may appear correct on maps or in CAD files, yet fails to form a coherent, logical structure. The documentation may seem accurate, but a deeper analysis reveals that the data was recorded using different standards and varying logic. It makes sense to a human, but not to the target system.
Risk 2: People, Silos, and Resistance to Change
Technology is only part of the challenge. Ultimately, the success or failure of a migration depends on people: the decision-making process, the level of cross-departmental collaboration, and the organization’s readiness for change.
A serious risk is the lack of a stable, decisive leader on the client’s side. If the project manager changes frequently, the company undergoes reorganizations, and tough decisions are delayed or watered down across multiple stakeholders, the project will quickly stall.
Another major issue is information silos. The sales department might work with completely different data (about the same network) than the technical department. Migration is the moment when these discrepancies surface and become highly problematic.
Resistance to change cannot be ignored, either. People naturally avoid stepping out of their comfort zone. Employees used to a legacy system are often reluctant to adopt new tools, especially if they do not understand the purpose of the change and were not involved early enough in the process.
Tribal knowledge (informal knowledge) holds a special place here – the wisdom of experts who remember exactly why data was entered a certain way. In many organizations, crucial information isn’t found in documentation, but in people’s heads and their daily routines. Without this knowledge, even the best tools won’t fully grasp the context of the data.
Risk 3: Overly Optimistic Planning
Many migration projects are underestimated right from the start due to incomplete knowledge of the source data. If a thorough data profiling isn’t performed before work begins, the cost estimate will likely be too optimistic, and the actual project scope will expand as work progresses.
This, in turn, leads to another issue: an unclear scope. The lack of precise, upfront agreements leads to project bloat (scope creep). New requirements emerge, along with repeated attempts to redefine which data should be transferred, cleansed, and mapped.
Another common problem is an unrealistic schedule. Migration deadlines are often driven not by an actual workload analysis, but by business pressure – expiring contracts, budget cycles, boardroom commitments, or grant funding deadlines.
As a result, you might end up in a situation where data analysis and the development of migration tools happen simultaneously. This is a severe methodological flaw – comparable to building a bridge without examining the ground it stands on.
Risk 4: Over-reliance on Technology and Automation
In migration projects, there is a frequent temptation to view the migration tool as a silver bullet. However, software alone isn’t enough. It will not fix poor data quality, remove process inconsistencies, or replace business decisions.
Another common mistake is handing the migration tool over to the client and leaving them without genuine support. Such an endeavor requires conscious guidance and the involvement of people who understand the data, the processes, and the project’s business objectives. They are the ones who help interpret exceptions, make decisions, and refine data mapping rules. Without them, even a good tool can become a source of additional complications rather than an asset.
You must also avoid blind faith in automation. Trying to automate the entire process at all costs often turns out to be simply unprofitable. It can consume more time, budget, and energy than the well-planned manual work of specialists. Effective migration relies on a smart combination of technology and your team’s knowledge and experience.
How to Prepare Your Company for Data Migration?
Knowing the most common pitfalls helps avoid disaster, but it is solid preparation that guarantees a successful migration. How can you plan this process to minimize risks?
- 1. Conduct a data audit. Effective migration begins long before the actual data transfer. First, you need to honestly assess the state of your data. A data audit should be your starting point – the foundation of the entire project.
- 2. Invest in a Proof of Concept (POC). A highly critical stage is the Proof of Concept – a migration test performed on a broad and diverse data sample. Such a pilot phase helps detect issues early, provides a better estimate of the workload, and shows how the data will behave in the new environment.
- 3. Sign an NDA and share your data for analysis. To accurately estimate the scope, risks, and time required for the migration, the vendor should be able to safely review the source data beforehand. Signing a Non-Disclosure Agreement (NDA) enables this analysis before a proposal is submitted. This mitigates the risk of underestimating the project, making false assumptions, and facing delays down the line.
- 4. Cleanse your data BEFORE migration. Tidy up your data while it is still in the legacy system. That is where users best understand the operational logic and dependencies. The old environment is more forgiving. The target system will likely enforce stricter validation rules and reject erroneous records, potentially leading to the irreversible loss of critical data.
- 5. Involve your internal SMEs. Equally important is the involvement of business and technical Subject Matter Experts (SMEs) – especially those with tribal knowledge not documented in any system or file. They are the ones who understand the data and processes best and can explain exceptions and historical dependencies.
- 6. Be a partner, not a prosecutor. Do not treat the vendor as an enemy. Data issues are usually the result of years of technical debt, not a one-off mistake by a single party. Therefore, migration requires collaborative problem-solving, not pointing fingers.
When Can You Call Migration a Success?
A migration cannot be considered successful when all the records have been formally transferred to the target system. You can only declare victory when the organization actively uses the new solution and trusts the data it provides.
The simplest test is highly practical: the very first report generated in the new environment must not “lie”. If the data is reliable, consistent, and actionable for business decisions, then you can confidently say the migration was done right.
Summary – 5 Rules for a Successful Migration
- Your data IS worse than you think. Accept it and brace yourself for surprises.
- Migration is a BUSINESS project, not just IT. It requires organization-wide commitment.
- PEOPLE are just as important as technology. Without decisiveness and expert involvement, the project will fail.
- Start with ANALYSIS, not cost estimations. A solid understanding of the source data is the foundation of success.
- Tools aren’t everything. Even the best system won’t fix bad data or replace collaboration between you and the vendor.
Want to learn more?
This article was written based on the presentation, “Data Migration – What Can Go Wrong (Repeatedly) …and What Can You Do About It?”, delivered by Maciej Kowalski during the conference “Network 4.0: Digital Twin and Data Quality in Telecommunications, Energy, and Other Network Industries”, organized by Globema.
If you’d like to discuss data migration in your company, get in touch with us.
