On-Demand WebinarData Readiness Before Migration: Stop Problems at the Point of Entry
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Most SAP S/4HANA migration timelines slip not because of technical issues with SAP S/4HANA itself, but because data quality problems discovered during UAT testing force rework. Duplicate vendors that should…
Most SAP S/4HANA migration timelines slip not because of technical issues with SAP S/4HANA itself, but because data quality problems discovered during UAT testing force rework. Duplicate vendors that should have been cleaned in the legacy system become a full remediation project. GL mapping errors that were hidden in legacy accounting surface when you try to validate opening balances. Material master records with incomplete attributes break downstream processes. The testing phase extends. Parallel run cycles multiply. Go-live gets pushed back months.
Leading finance teams take a different approach: they build data quality into the migration strategy before testing starts. That means running data validation against legacy systems now – not during UAT – and fixing issues at the point of entry. Duplicate vendors get consolidated before extraction. GL mappings get validated against actual balances. Material master completeness gets verified before data loads to S/4HANA. The testing phase becomes validation of a known-clean dataset rather than discovery of problems.
In this opening session, Paul walks through how finance teams approach data readiness as a distinct migration phase – what to validate, when to fix, and how to measure data quality improvement.
In this 20-minute walkthrough, you’ll see:
Where data quality problems hide in legacy systems – and what finance teams are discovering when they run validation before migration starts.
Master data validation strategy: GL chart of accounts, vendor master, material master, customer master – what needs to be clean for SAP S/4HANA to function, and how to identify gaps.
Duplicate and orphaned record remediation – the mechanics of consolidating vendors, merging GL accounts, and cleaning up historical data without breaking open transactions.
Data completeness assessment and remediation – which fields are mandatory in SAP S/4HANA vs optional in legacy, and how to fill gaps at scale.
Timeline impact: how much does data readiness add to the migration schedule, and what do you save in testing and parallel run when you start clean.
