Why Do SAP Data Migrations Fail? (And How to Avoid It)

The short answer. SAP data migrations fail mostly because of the data, not the technology. The common causes are poor data quality carried into the new system, underestimating the data preparation effort, starting the data work too late, skipping reconciliation so problems go undetected, and migrating too much. Nearly all of these trace back to treating data as a late-stage task rather than the main workstream. The fix is to profile early, clean upstream, and prove the result.

Migration projects have a reputation for running late and over budget, and industry data backs up the concern: a large share of projects exceed their budgets regardless of approach. But the reasons are rarely mysterious. They repeat from project to project, which means they can be anticipated and avoided. Here are the real causes and how to prevent them.

The real causes of failure

• Poor data quality. Duplicates, gaps, and inconsistencies that were tolerated in ECC block or corrupt the load into S/4HANA. This is the most common root cause, and it is a data problem, not a technical one.
• Underestimating data preparation. Teams plan for the technical conversion and treat data cleansing as a minor task. It is usually the largest and least predictable part of the project.
• Starting data work too late. When data preparation begins near the end, there is no time to profile properly, rehearse, or fix what is found. The crunch is guaranteed.
• Skipping reconciliation. Without reconciling the target against the source, no one can prove the migration was accurate, so silent data loss goes undetected until it causes a problem in production.
• Migrating everything. Bringing across old, inactive, and duplicate records adds cost and risk for no benefit, and inflates the effort at every step.

The pattern behind the failures

Migrations do not fail because the technology could not do the job. They fail because the data was not ready for it.

Look at the causes together and a single pattern emerges: data is treated as a late-stage task rather than the main workstream. The technical conversion of S/4HANA is a known, well-tooled process. The data preparation is the open-ended part, and it is the part that expands to consume whatever time was not planned for it. Teams that internalise this and lead with the data tend to finish calmly; teams that lead with the technology and bolt data on at the end tend to be the ones fighting fires at cutover.

The data model makes it worse

S/4HANA raises the stakes because it uses a different data model from ECC. The clearest example is the Business Partner model, which unifies the separate customer and vendor records ECC kept apart. Because of changes like this, legacy data cannot simply be lifted across; it has to be transformed and, often, cleaned to meet stricter requirements. Data that was good enough for years in ECC suddenly fails validation against the new model. This is why a migration exposes data quality problems that had been invisible: the new system holds the data to a higher standard than the old one ever did.

How to prevent it

1. Profile before you plan. Understand the true state of your data before committing to a date or approach, so the plan is built on evidence.
2. Start data work early. Begin cleansing and de-duplication as early as possible, in parallel with everything else, because it is the long pole.
3. Decide what actually needs to move. Leave old, inactive, and duplicate data behind rather than carrying it forward.
4. Rehearse with mock loads. Run the migration repeatedly against a copy of the target until the last run is boring, so cutover holds no surprises.
5. Reconcile every load. Prove the result against the source. A completed load is not a successful one until reconciliation confirms it.

The organisational causes

Not every cause of migration failure is technical or even about the data directly; some are organisational, and they are just as damaging. Unclear ownership means data problems in a domain go unaddressed because no one is accountable for them. Poor coordination between the business and the technical team means the people who understand the data are not involved in decisions about it. Unrealistic timelines, often set before anyone has profiled the data, build failure in from the start. And a lack of executive sponsorship leaves the migration without the authority to make the hard calls about what to migrate and what to leave behind.

These organisational factors often underlie the technical symptoms, which is why the most successful migrations pair strong data discipline with clear ownership and realistic, evidence-based planning.

Catching problems early is the whole game

If there is a single principle that separates migrations that succeed from those that struggle, it is when problems are discovered. Every problem found early, during profiling or a mock load, is cheap to fix and causes no disruption. The same problem found late, during or after cutover, is expensive and public. This is why front-loading the data work matters so much: it is not just about having more time, it is about moving the discovery of problems to a point where fixing them is easy. A migration that surfaces its issues in profiling and rehearsals, and resolves them there, arrives at cutover with little left to go wrong. The failures happen when problems are allowed to hide until the moment they can do the most damage.

The mindset that succeeds

The teams that migrate successfully share a mindset more than a method: they treat the migration as a data programme with a technology event at the end, rather than a technology event with some data cleanup attached. This reframing changes everything downstream. It means profiling happens first and shapes the plan. It means data preparation gets the calendar time and the people it needs. It means reconciliation is treated as a requirement, not a nice-to-have. The technology of S/4HANA migration is well understood and rarely the thing that fails. Adopting the mindset that data is the main event, and resourcing it accordingly, is the most reliable predictor of a migration that finishes on time and stands up afterward.

How deKorvai helps

deKorvai is built to take the common causes of failure off the table. It profiles data early to expose quality problems, cleanses and de-duplicates master data, transforms and maps it to the S/4HANA model while preserving referential integrity, and reconciles the loaded data against the source so accuracy is proven rather than assumed. Because profiling, transformation, and validation run on one platform as one flow, data readiness is continuous rather than a last-minute scramble. In one documented business partner migration, this approach moved more than 50,000 vendor records with 100% data accuracy and a 95%+ first-pass rate.

Key takeaways

• Migrations fail on data, not technology.
• The root pattern: treating data as a late task instead of the main workstream.
• S/4HANA's data model exposes problems ECC tolerated for years.
• Prevention: profile early, clean upstream, rehearse, and reconcile.

Frequently asked questions

Why do SAP data migrations fail?

Mostly because of the data, not the technology. The common causes are poor data quality carried into the new system, underestimating data preparation, starting the data work too late, skipping reconciliation, and migrating too much. Nearly all trace back to treating data as a late task.

Is it the technology or the data that causes migration failure?

Almost always the data. The technical conversion to S/4HANA is well understood and well tooled. It is the open-ended data preparation, cleansing, de-duplication, and transformation, that expands to consume unplanned time and causes projects to slip.

Why does S/4HANA make data problems worse?

Because S/4HANA uses a different data model from ECC, including the Business Partner model that unifies customer and vendor records. Legacy data cannot be lifted across unchanged, and it is held to stricter requirements, so problems ECC tolerated for years suddenly fail validation.

How do you prevent a migration from failing?

Profile data before planning, start cleansing early, decide what genuinely needs to move, rehearse with mock loads until they run clean, and reconcile every load against the source. In short, treat data as the main workstream rather than a late-stage task.