The SAP migration mistake most teams make first.

The short answer

The most common SAP migration mistake is moving code before data. Teams plan the migration around custom code, workflows, and integrations, and treat the underlying data as something that will "come along for the ride." It does not. Migrate the data last, under deadline pressure, and you get the big-bang cutover weekend that nobody survives cleanly.

The fix is to invert the order: migrate and validate the data first, then move the code against data you already trust. Everything below is the sequence we use to do that.

Why data is the real migration, not code

Your data model is the contract between the old system and the new one. Every report, every integration, and every downstream service reads and writes against the assumptions baked into that model. Move the data wrong once, even in a small way, and every one of those consumers breaks until someone notices and fixes it, usually in production, usually on a weekend.

Code is comparatively easy to test in isolation. Data is not. A custom ABAP program either compiles or it does not; a thirty-year-old customer master with duplicate records, dead fields, and silent workarounds will pass every technical check and still corrupt every downstream calculation. That is why data, not code, is where migrations actually fail.

What the failure numbers actually say

The data points back this up. Industry analyses attribute roughly 70% of ERP migration failures to poor data quality rather than the software itself (CODA Technology Solutions). Gartner research, summarised in reporting by The Register, found that more than half of large ERP implementations run over budget, and SAP migrations are no exception.

The pressure is also rising. As of Q4 2024, roughly 39% of SAP's ~35,000 ECC customers had still not migrated to S/4HANA, nearly a decade after its 2015 launch (Gartner, via Rimini Street). With mainstream ECC maintenance ending in 2027, a large share of those projects will be run under exactly the deadline pressure that makes the code-first shortcut so tempting, and so costly.

The data-first migration sequence we use

1. Profile the real data, not the documented schema. Years of workarounds, manual corrections, and bolted-on custom fields mean live data rarely matches the data dictionary. We profile actual production data before writing a single migration script.
2. Fix data quality issues in the source system first. Migrating dirty data into a new platform just relocates the problem to a less familiar environment. Cleanup happens before cutover, not after.
3. Migrate master data, then transactional data, then history. Customers, vendors, and materials must exist and be correct before anything that references them can move. History comes last: it is the least urgent and the most forgiving of a slower, staged approach.
4. Run the new system in parallel before the cutover. We reconcile outputs between old and new for a full business cycle wherever possible, so discrepancies surface while the old system is still there to cross-check against.
5. Move code last, against data that is already trustworthy. By the time the application layer moves, the data beneath it has been validated. That is what turns the cutover into a non-event instead of a weekend-long fire drill.

How this avoids the big-bang cutover weekend

The big-bang weekend exists because teams try to validate data and code at the same time, under deadline pressure, with no fallback. When the data is migrated and verified well ahead of the code, the cutover becomes a much smaller, much lower-risk event: switching traffic to a system that has already been proven correct, rather than hoping a year of changes all work on the first try.

This is not slower overall. It front-loads the hard, uncertain work into a phase where mistakes are cheap and reversible, and leaves the irreversible step, go-live, until almost nothing about it is still unknown.

Key takeaways

• The biggest SAP migration mistake is sequencing: moving code before data.
• Around 70% of ERP migration failures trace back to data quality, not software.
• Profile and clean source data first, then migrate master data, transactions, then history.
• Run old and new in parallel for a full business cycle before cutover.
• Done in this order, go-live is a non-event, not a big-bang weekend.