It is one of the most consequential arguments in enterprise technology programme management — and it is almost never framed explicitly as a debate. Instead, it is resolved by default, in scope management conversations that happen under timeline pressure, without adequate representation from the functions that will live with the consequences.

The argument is this: should ERP migration programmes be responsible for data quality, or should data quality be treated as a separate initiative that precedes, follows, or runs in parallel with the migration?

Both positions have serious arguments behind them, and this episode gives each its full hearing.

The case for keeping data quality out of the migration scope: ERP migrations are already among the most complex, risky, and expensive programmes organisations undertake. Adding a data quality transformation to an already demanding programme increases scope, extends timeline, and creates additional failure modes that can jeopardise the go-live delivery that the entire investment is predicated on. Data quality is a business ownership problem, not a technology problem — the programme team should migrate what exists and let the business functions own the quality of their own data.

The case for treating data quality as a migration workstream: the migration is the only moment when the organisation has a genuine mandate, a defined programme structure, and the full engagement of the data owners required to address data quality at the source. Post-migration remediation is dramatically more expensive and organisationally difficult than pre-migration cleansing, because it requires modifying data in a live production system that the business is already operating on. And the canonical data model — the standardised, cross-entity, AI-ready data foundation — can only be built effectively during the migration, when the data architecture is being redesigned anyway.

We then examine a third position that the binary framing obscures: the bounded data quality approach that uses the migration as the moment to establish the data governance principles and the canonical model, without attempting to remediate every historical data quality issue before go-live. This approach has specific design requirements and specific limitations — but it resolves the tension between the two positions in a way that neither pure position achieves.

What We Cover:

00:00 — Introduction: The debate that gets resolved by default in scope meetings02:00 — The full case for keeping data quality outside migration scope05:00 — Where this position fails: what the post-migration remediation reality looks like08:00 — The full case for data quality as a migration workstream11:00 — Where this position fails: scope, timeline, and the complexity argument14:00 — The bounded approach: canonical model now, full remediation over time17:00 — What the bounded approach requires to work: design principles and governance20:00 — The AI readiness argument: why data quality decisions made now determine AI capability later23:00 — Programme governance: who should own the data quality decision25:30 — The economic model: pre-migration vs. post-migration remediation cost comparison28:00 — Closing verdict: conditions under which each approach is genuinely preferable

Keywords: ERP migration data quality debate, SAP S/4HANA data quality programme, ERP data strategy decision, SAP migration scope data quality, ERP canonical data model migration, data quality ERP programme governance, SAP data remediation cost, ERP migration data ownership, S/4HANA data architecture debate, enterprise data migration strategy

About the Host

Rıdvan Yiğit is the Founder & CEO of RTC Suite — the world's first Autonomous Compliance and Payment Intelligence platform, built natively on SAP BTP and operating across 80+ countries.

Connect with Rıdvan:

🔗 linkedin.com/in/yigitridvan✉

ridvan.yigit@rtcsuite.com

📞 +90 545 319 93 44

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