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SAP Transformation

Seven Data Quality Issues That Can Derail an S/4HANA Program

When organizations prepare for an S/4HANA transformation, data issues can appear across business objects and processes. The most important risks are often recurring patterns rather than isolated defects.

1. Incomplete critical fields

Required business attributes may be missing, inconsistent, or populated differently across legacy sources. This can affect downstream processing and validation.

2. Duplicates and inconsistent identities

Customers, suppliers, materials, and other entities may exist in multiple forms. Duplicate or conflicting identities can complicate migration and business adoption.

3. Invalid or obsolete values

Legacy values may no longer align with target-state structures, classifications, or business rules. These require explicit transformation decisions.

4. Weak reference-data alignment

Codes, units, categories, organizational structures, and other reference values can require mapping and harmonization before migration.

5. Inconsistent business definitions

Different teams may interpret the same field or object differently. Transformation exposes those differences and forces decisions.

6. Poor historical relevance

Large volumes of old data can increase migration effort without delivering equivalent business value.

7. Unclear ownership

Issues without accountable owners tend to remain unresolved and reappear during later cycles.

Axiums perspective

The value of early assessment is not simply finding defects. It is identifying which patterns can affect the transformation and creating a controlled path to resolution.

Key takeaways
  • Profile critical objects early.
  • Prioritize issues by process and business impact.
  • Resolve mappings and definitions before repeated test cycles.
  • Make ownership part of the remediation plan.