Why system integrators underinvest in SAP data cleansing

System integrators underinvest in data cleansing because it is tedious, risky, and low-margin, so it gets staffed junior and cut first. That is the layer that decides the migration.

System integrators underinvest in data cleansing because it is tedious, risky, and low-margin for them, so it gets staffed junior and cut first under pressure. That is exactly the layer that decides whether a migration succeeds.

The structural reason

Big integrators are built to sell and deliver the platform implementation. Data cleansing is labor-intensive, hard to fix-price, and low-margin, so it is treated as a sub-line-item, staffed with junior rotating resources, and cut first when the schedule tightens. None of that is malice, it is how the model works.

Why the data layer gets under-resourced

• The SI delivery model. Platform-first. Built to sell and deliver the implementation. Data cleansing is low-margin, hard to fix-price, staffed junior, and cut first when the schedule tightens.
• A specialist data workstream. Data-first. Senior people accountable for the layer that causes most stalls. Often what keeps the broader program on schedule.

It is structural, not malice. The fix is to own the data layer separately, with depth instead of headcount.

Source: Thinklytics SAP data readiness practice, 2026.

Why it hurts you

The data layer is the most common cause of stalls and overruns. Under-resourcing it is under-resourcing the actual risk. Many programs discover this only when the data will not load.

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.

What to do

Own the data layer separately, with senior specialists accountable for it, whether in-house or with a partner who does only this. On many programs that data workstream is what keeps the broader SI program on schedule. Depth in the data layer beats headcount everywhere else. That is precisely the lane our SAP data readiness and migration practice runs.

Frequently asked questions

Why do system integrators underinvest in data cleansing?

Because it is tedious, risky, and low-margin for them. It gets treated as a sub-line-item, staffed junior, and cut first when the schedule tightens.

Is this a criticism of the big firms?

No, it is structural. Their model is built to sell and deliver the platform. Data cleansing does not fit that model well, so it gets under-resourced.

Why does that hurt the client?

Because the data layer is the most common cause of stalls and overruns. Under-resourcing it under-resources the actual risk, which surfaces when the data will not load.

What should we do about it?

Own the data layer separately with senior specialists accountable for it, in-house or with a partner who does only this work.

Can a specialist work alongside our SI?

Yes. On many programs the specialist data workstream is what keeps the broader SI program on schedule.

Does headcount fix the data problem?

No. Depth in the data layer beats headcount. A small senior team outperforms a large junior one on the work that decides the migration.