🔍 The Integrity Dimension of Data Quality: A Deep Dive into SAP Finance, Customer, Supplier & Material Masters

In the world of enterprise data management, Data Quality (DQ) is not just a technology concern—it's a critical enabler of business performance, risk management, and operational efficiency. Among the six dimensions of DQ—accuracy, completeness, consistency, timeliness, uniqueness, and integrity—integrity is perhaps the most misunderstood and under-invested.

But when it breaks, it’s not subtle. It leads to misstatements in financials, failed procure-to-pay cycles, customer order rejections, and mismatched inventory values. This article explores Integrity in Data Quality using the lens of SAP’s core master data domains—Finance Master, Customer Master, Supplier Master, and Material Master—and how tools like SAP MDG (Master Data Governance) and SAP DQM (Data Quality Management) can be leveraged to address these issues.

🧭 What Is the Integrity Dimension of Data Quality?

Integrity in data refers to the validity of relationships across data entities. This includes:

• Referential integrity (e.g., foreign keys must point to valid entities)

• Logical integrity (e.g., value sets must conform to business rules)

• Temporal integrity (e.g., time-sensitive relationships are maintained)

1. 💰 Finance Master Data (e.g., GL accounts, Cost Centers, Profit Centers)

❌ Common Integrity Issues

• GL accounts assigned to incorrect account groups or with missing reconciliation accounts.

• Cost centers not linked to valid controlling areas.

• Profit centers not assigned to valid company codes or business areas.

• Invalid currency codes or fiscal year variants.

🛠️ Solution Using SAP MDG & DQM

• MDG-Finance: Enables centralized governance of chart of accounts, cost centers, and profit centers with built-in validations. Use business rules and BRF+ to enforce interdependencies (e.g., “GL accounts in group X must have reconciliation account Y”).

• SAP DQM Rules: Validate relationships during data creation/migration. Use SAP Information Steward to profile existing data for orphan records or invalid assignments.

✅ Best Practices

• Maintain a harmonized global chart of accounts across company codes.

• Enforce cross-entity validations during creation (e.g., profit center + cost center mapping).

• Regularly audit finance master data with DQ scorecards and exception reports.

2. 👤 Customer Master

❌ Common Integrity Issues

• Customers linked to inactive sales areas.

• Payment terms or partner functions not aligned with company policy.

• Incorrect account groups leading to misrouting in OTC processes.

• Ship-to addresses not maintained correctly, causing delivery failures.

🛠️ Solution Using SAP MDG & DQM

• MDG-C: Define re-usable templates and staging processes that verify completeness and relational validity (e.g., customer role, sales area, partner functions).

• Use Data Quality Dashboards in MDG or SAP Data Intelligence for monitoring address validation, sales org mappings, and credit control areas.

• Integrate with third-party address validation tools for postal accuracy and geocoding.

✅ Best Practices

• Use central creation and change processes to eliminate fragmentation.

• Apply country-specific field controls and validations using MDG data models.

• Monitor customer master changes with change request KPIs to ensure governance effectiveness.

3. 🏢 Supplier Master

❌ Common Integrity Issues

• Suppliers linked to blocked purchasing organizations or with mismatched bank details.

• Duplicate vendors created for different plants or company codes.

• Inconsistent tax classifications or missing withholding tax data.

• Logical inconsistencies like “payment blocked” suppliers with open POs.

🛠️ Solution Using SAP MDG & DQM

• MDG-S: Enforce multi-step approval processes and validation rules across purchasing, financial, and legal attributes.

• Use duplicate detection framework in MDG to identify and resolve suspected duplicates.

• SAP DQM checks for bank key + account number combinations, tax ID duplication, and logical rule violations (e.g., block status with open PO).

✅ Best Practices

• Maintain a global supplier registry with mapped relationships to local purchasing orgs.

• Periodically review vendor status vs open transaction records.

• Enforce usage of vendor onboarding workflows integrated with compliance checks (e.g., sanction screening).

4. 🏷️ Material Master

❌ Common Integrity Issues

• Missing UOM conversions leading to incorrect stock valuations.

• Materials created without proper plant or storage location views.

• Incorrect valuation classes not aligning with the GL account structure.

• Inconsistent MRP type vs procurement type (e.g., external procurement with internal MRP settings).

🛠️ Solution Using SAP MDG & DQM

• MDG-MM: Data model extensions and validation logic can enforce cross-view dependencies (e.g., a finished good must have MRP and work scheduling views).

• Use SAP Data Quality Management with Business Rules Framework to flag misaligned settings.

• Schedule periodic data profiling and completeness scoring via Information Steward or SAP Data Intelligence.

✅ Best Practices

• Implement template-based material creation with logic per material type.

• Validate interdependencies before activation—e.g., stock valuation vs costing method.

• Set up automated rules for UOM consistency, batch management, and product hierarchy.

📈 Closing Thoughts

The integrity dimension of Data Quality is about more than just completeness—it’s about making sure data behaves correctly in context. With the increasing complexity of global operations and compliance landscapes, the cost of poor integrity in master data is simply too high.

Using SAP MDG and DQM platforms, organizations can move from reactive cleansing to proactive governance—embedding integrity as a first-class citizen in every master data domain.

🔗 Let’s Discuss How is your organization addressing the integrity challenges of its master data? Have you used MDG or DQM in your SAP landscape? What worked, and what didn’t?

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