The short answer. The main SAP migration risks are data loss, poor data quality carried forward, broken Business Partner or financial data relationships, extended downtime, sensitive data exposed in test systems, and being unable to prove the migration was accurate. Nearly all trace to treating data as a late task. The mitigations are consistent: profile early, cleanse and de-duplicate before loading, preserve integrity through transformation, scramble non-production data, and reconcile against the source. Front-load the data work and the risk profile changes.

SAP migrations have a reputation for going over budget and over schedule, and the reasons repeat from project to project. That repetition is good news: known risks can be planned for. Here are the ones that matter most in an SAP context, and the mitigation for each.

The main SAP migration risks

Risk	What goes wrong	How to mitigate
Poor data quality	Duplicates and errors carried into S/4HANA	Profile and cleanse before loading
Data loss	Records or fields dropped in transit	Completeness checks and reconciliation
Broken BP relationships	Customer/vendor to Business Partner conversion errors	Clean master data before CVI conversion
Financial data mismatch	Balances that do not tie back to ECC	Reconcile ACDOCA data against the source
Extended downtime	Cutover runs long, disrupting the business	Rehearse with mock loads; stage and validate
Security exposure	Real data in test and QA systems	Scramble sensitive data for non-production
Unproven result	No way to show the migration was accurate	Reconcile and keep an audit trail

Poor data quality is the quiet killer

A migration rarely fails because the technology could not do the job. It degrades because the data was not ready for it.

Of all the risks, poor data quality does the most damage while drawing the least attention. It rarely stops a migration outright; instead it degrades the result, so the new S/4HANA system launches carrying the same duplicates, gaps, and inconsistencies as the old ECC one. Worse, S/4HANA's stricter model means quality problems that ECC tolerated now surface as load errors and post-go-live headaches. The fix is to treat quality as an upstream task, resolved before data reaches the load, which is exactly why profiling and cleansing belong early in the plan.

The SAP-specific risks

Two risks are particular to SAP and deserve attention. First, Business Partner conversion: because S/4HANA forces customers and vendors into the Business Partner model, unclean or duplicate customer and vendor data causes conversion errors, so cleaning master data before the CVI conversion is essential. Second, financial data: the Universal Journal (ACDOCA) consolidates finance, and the business will not accept balances in S/4HANA that do not match ECC, so reconciling migrated financial data against the source is non-negotiable. These are not generic migration risks; they are specific consequences of S/4HANA's data model, and they need SAP-aware mitigation.

The pattern behind the mitigations

Read down the mitigation column and one pattern repeats: profile early, cleanse upstream, preserve integrity, and reconcile at the end. Most SAP migration risk comes from treating data as something to move at the last minute rather than something to understand and prepare from the start. Front-load the data work, and the risks that dominate migration post-mortems, quality problems, conversion errors, financial mismatches, unprovable results, are largely designed out before they can occur.

The cheapest risk reduction is starting early

If there is one risk that magnifies all the others, it is starting the data work late. Every mitigation in this article, profiling, cleansing, Business Partner conversion, reconciliation, needs time, and time is exactly what a late start removes. A programme that begins its data workstream alongside the technical work has room to absorb the surprises that profiling always uncovers. One that leaves data until the technical build is done has converted a manageable set of risks into a countdown. The cheapest, most effective risk reduction available is simply to start the data work early, because it moves the discovery of problems to a point where fixing them is easy rather than a crisis. Every other mitigation works better with time, and only an early start provides it.

Give every risk an owner

A practical technique for managing migration risk is to make each risk someone's explicit responsibility, with a countermeasure attached. For every risk, name the check that catches it, the person accountable for that check, and the evidence that will show it passed. Data quality has an owner who confirms profiling and cleansing are done; Business Partner conversion has an owner who confirms master data is clean before CVI; financial reconciliation has an owner who confirms balances tie out. This turns a vague sense of risk into a concrete set of gates that someone is accountable for. The migrations that go quietly are almost always the ones where nothing was left implicit: every risk had an owner, a control, and a way to prove the control worked, so problems surfaced and were resolved before cutover rather than after.

Reconciliation is your insurance

Of all the mitigations, reconciliation deserves special mention because it protects you when everything else has been done and you still need certainty. It answers the question every stakeholder asks on go-live day: can we trust the new system? Reconciliation compares the migrated data against the source, confirming counts and values match, so that "the load completed" becomes "the load is provably correct." For SAP specifically, this matters most with financial data, where the business will not accept a general ledger that does not tie back to ECC. Without reconciliation, a migration ends on hope; with it, a migration ends on evidence, and that evidence is also the audit trail you will want later when someone asks why a figure looks the way it does. Treating reconciliation as optional is the risk that turns a technically successful migration into one no one quite believes.

How deKorvai helps

deKorvai is built to take these SAP risks off the table. It profiles data early to expose quality problems, cleanses and de-duplicates master data before the Business Partner conversion, preserves referential integrity through transformation, scrambles sensitive data for non-production sandboxes and QA clones, and reconciles migrated data, including financial data, against the source so accuracy is proven. 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, because the risks were handled before the load rather than discovered after it.

Key takeaways

SAP migration risks are predictable, which means they are preventable.
Poor data quality is the quiet killer: fix it upstream, before the load.
Two risks are SAP-specific: Business Partner conversion and financial reconciliation.
One pattern mitigates most risk: profile early, cleanse upstream, reconcile at the end.

Frequently asked questions

What are the biggest risks in an SAP S/4HANA migration?

Poor data quality carried forward, data loss, Business Partner conversion errors, financial data that does not reconcile, extended downtime, sensitive data exposed in test systems, and being unable to prove the migration was accurate. Most trace back to treating data as a late-stage task.

Why is poor data quality the biggest risk?

Because it degrades the result rather than stopping the migration, so S/4HANA launches carrying ECC's duplicates and gaps. S/4HANA's stricter model also surfaces quality problems ECC tolerated as load errors. The fix is to profile and cleanse upstream before the load.

What migration risks are specific to SAP?

Two in particular: Business Partner conversion, where unclean customer and vendor data causes CVI errors, so master data must be cleaned first; and financial data, where the Universal Journal means balances must reconcile against ECC, making migration reconciliation essential.

How do you reduce SAP migration risk?

Front-load the data work: profile early, cleanse and de-duplicate before loading, preserve referential integrity through transformation, scramble sensitive data for non-production, and reconcile the result against the source. This designs out the risks that dominate migration post-mortems.

Data Migration Risks & Mitigation: The SAP Edition