Why SAP S/4HANA migrations really fail, and it is not the software

Ask why an S/4HANA migration failed and people blame the software. They are almost always wrong. The platform works. What breaks is the data underneath it.

Ask why an S/4HANA migration failed and people blame the software. They are almost always wrong. The SAP platform works and the migration tooling is mature. What breaks is the data underneath it and the reporting on top.

• 30% of SAP migration projects run late or over budget. And the cause is rarely the technical conversion. It is data that was never cleaned and custom code that was never inventoried, surfacing under cutover pressure.

The real failure points

A decade or more on ECC leaves master data nobody governed. The same vendor entered five times. Materials under three part numbers. Units of measure that do not match, fields that are missing, custom ABAP that snaps the moment it meets the simplified data model. None of it shows up in a demo. All of it shows up at cutover, because an in-memory system exposes the data problems the old one tolerated.

Why S/4HANA migrations fail

None of these are surprises. They are the line items teams defer until they cannot.

• Dirty data discovered at cutover. Duplicates, missing fields, and obsolete records that an in-memory system exposes immediately.
• Custom code never inventoried. The ABAP estate is bigger and more entangled than anyone tracked. Remediation balloons.
• Scope creep into greenfield. A conversion quietly becomes a reimplementation once teams see the legacy mess.
• Talent booked elsewhere. Senior specialists are scarce and expensive in the 2027 run-up. Late starts get junior benches.
• Testing compressed to hit the date. The first corner cut when the calendar tightens, and the source of most quality misses.

Source: Thinklytics SAP data readiness practice, 2026.

The outcome data backs this up. Across the 2026 survey set, 65 percent of programs miss quality targets, 60 percent run an average of 30 percent longer than planned, and 55 percent exceed budget. Roughly 30 percent are late or over budget outright. The common thread is data.

Why it gets missed

Data work is tedious and low-margin, so it is the first thing cut when a program runs hot, and it usually lands on the most junior people in the room. Meanwhile the date and budget get committed before anyone measured the real condition of the data.

• 40% of the migration timeline is discovery and data cleansing. Pre-move cleansing with validation rules cuts post-migration defects by about 60% and post-go-live performance issues by about 25%. The work you skip up front returns as production incidents.

Discovery and cleansing actually account for about 40 percent of a healthy timeline, so a plan that treats them as a footnote was never realistic. The work does not disappear when you ignore it. It moves to production, where it is most expensive.

How to avoid it

Measure before you commit. A SAP data readiness assessment profiles your data and custom code before the date is set, moving the expensive discovery to the front where it is cheap. Clean the data before you move it, not in production afterward, where pre-move cleansing cuts post-migration defects by about 60 percent. Keep the data workstream senior, and plan reporting continuity as part of the migration. Don't move the mess. Clean it first, and the 30-day Analytics Truth Audit tells you how big the mess is.

Frequently asked questions

Why do S/4HANA migrations fail?

They fail on data, not on SAP. The platform and the tooling are mature. Programs stall on ungoverned master data, duplicates, dark data, custom ABAP that breaks against the simplified data model, and reporting that goes dark when the data moves. About 30 percent of migration projects run late or over budget, and the cause traces back to data nearly every time.

Is the SAP software the problem?

Rarely. What varies between programs is the condition of the data going in and the reporting that depends on it. The technical conversion is the most predictable part of the move.

What does the data actually look like after years on ECC?

Ungoverned. The same vendor entered five times, materials under three part numbers, units of measure that do not match, missing fields, broken classifications, and custom code nobody fully maps anymore. An in-memory system exposes all of it at cutover.

How much of a migration is data work?

Plan 25 to 30 percent of total effort for data preparation, and expect discovery and cleansing to consume about 40 percent of the timeline. Pre-move cleansing cuts post-migration defects by about 60 percent.

Why does the data work get missed?

It is tedious and low-margin, so it is the first thing cut when a program runs hot, and it usually lands on the most junior people. Meanwhile the date and budget get set before anyone measured the data.

How do we avoid a failed migration?

Measure readiness before you commit to a date, clean the data before you move it rather than in production, keep the data workstream senior, and plan reporting continuity as part of the migration instead of after it.

When does ECC support end?

Mainstream maintenance ends December 2027, extendable to 2030 at a premium. Starting early lets you choose your path instead of the calendar choosing for you.