Why a Data Quality Audit Should Be the First Step in Any SAP Data Migration

After more than two decades of working on SAP data migration programs across industries, one lesson stands tall above all others: you cannot migrate what you cannot trust.

A successful journey to SAP S/4HANA begins not with tools or code, but with a thorough Data Quality Audit. This critical step ensures that what you move into your new system is not just data—but accurate, reliable, business-ready information that drives continuity, efficiency, and agility.

📖 A Lesson from the Field

I recall a global manufacturing client preparing to move to S/4HANA. Their initial thought was straightforward: “Lift and shift all the data from ECC.” When we performed a Data Quality Audit, however, we uncovered that nearly 40% of their vendor accounts were duplicates, with more than 9,500 addresses repeating across systems. Imagine what would have happened if those inaccuracies had flowed into their new system—procurement delays, incorrect shipments, and broken reporting lines.

By cleaning this before migration, they not only eliminated duplication but also cut their migration cycles down significantly, reducing test iterations by nearly half.

🚀 Why a Data Quality Audit Matters – A few examples

1. Supply Chain Resilience

Clean, harmonized data is the backbone of effective supply chain planning. With a Data Quality Audit, companies have been able to:

• Optimize stock-keeping units (removing redundant or outdated materials).

• Improve inventory positions for more accurate stock control.

• Automate reorder points, enabling faster replenishment and reducing stock-outs.

The result? Shortened supply chains, better cost management, improved agility, and fulfilment accuracy.

2. Financial Accuracy and Control

In finance, poor-quality data can distort credits, collections, and reporting. Through Data Quality Audits, we’ve seen clients achieve:

• A single version of truth across customers, vendors, and GL accounts.

• Improved accuracy of late-payment estimates and credit risk.

• Increased flexibility in pricing, with profitability tracking down to distribution center, customer, and product level.

The outcome? Trusted reporting, better spend efficiency, and sharper decision-making.

🔎 How a Data Quality Audit Differs from Profiling and Cleaning During Data Migration

Many customers ask us, “Isn’t data profiling or cleaning enough during the migration process?” The answer is no—because a Data Quality Audit happens before migration begins and serves a different purpose. Profiling and cleaning are tactical steps taken during the migration cycle to fix specific issues (missing fields, duplicates, formatting errors). An Audit, on the other hand, is a strategic assessment that measures overall data health, identifies systemic risks, and sets clear business rules for what “good data” looks like. In other words, profiling and cleaning treat the symptoms, while a Data Quality Audit diagnoses the disease. When done first, the audit ensures that profiling and cleaning are targeted, efficient, and dramatically reduce rework during migration.

✅ The Business Case for Prioritizing a Data Quality Audit

A migration project without a Data Quality Audit is like building a new house on a shaky foundation. The effort to fix data issues after go-live is exponentially higher—both in time and cost—than addressing them upfront.

At DataLink Dynamics, we’ve consistently seen clients who invest in a Data Quality Audit enjoy smoother migrations, fewer testing cycles, and a faster realization of business benefits in S/4HANA.

When you start your S/4HANA journey, remember this: migrating poor-quality data only moves your problems into a shinier system. A Data Quality Audit transforms data from a liability into a competitive advantage—delivering accuracy, trust, and business continuity from Day One.