MDG Data Quality Management and Governance Architecture (Part 2)

Key takeaway

Combine governance workflow with data quality rules, duplicate prevention, consolidation and external data providers so MDG improves master-data fitness instead of only adding approvals.

Explanation

Good master data requires measurable quality. Define rules for completeness, validity, consistency, uniqueness and business-specific standards. Validation should run as early as possible in create/change processes and produce actionable messages rather than generic rejection.

Duplicate checking prevents multiple records for the same real-world entity. Consolidation and mass processing handle existing data populations, while central governance controls new changes. Data-provider integration can enrich or validate information when the approved architecture supports it.

Data quality KPIs should identify root causes by source, field and organization. A dashboard showing 92% completeness is not enough if nobody owns the missing 8%.

Cloud-ready mode provides a consistent framework for several governance/data-quality processes in supported domains, but the architecture still needs ownership, stewardship and remediation workflow.

Do not put every quality rule in code. Use configured rule frameworks where maintainable and reserve custom logic for requirements the standard framework cannot express safely.

Real project scenario

Supplier onboarding repeatedly failed because bank and tax fields were incomplete. Rather than adding more approval steps, the team moved validations earlier and showed field-specific errors before submission.

Common mistakes

Adding workflow instead of fixing data quality.
Measuring quality with no owner.
Duplicate checks with weak match rules.
Coding every validation unnecessarily.
Running cleansing once and never monitoring recurrence.

Best practices

Validate early.
Assign data-quality owners.
Tune duplicate matching with false-positive review.
Use standard rule frameworks where possible.
Track recurring defect sources.

Interview angle

Describe how central governance, consolidation, duplicate checking and data-quality rules work together.

Practice this lesson

Quick check

1. During hypercare for MDG Cloud-Ready Mode and Data Quality Management, which principle from MDG Data Quality Management and Governance Architecture is most likely to prevent repeat incidents?

2. A review of MDG Data Quality Management and Governance Architecture finds this risky pattern: "Assuming cloud-ready mode covers every domain identically.". Which corrective action is strongest?

3. Scenario — Supplier onboarding repeatedly failed because bank and tax fields were incomplete. Which response best addresses the design or operating risk in MDG Data Quality Management and Governance Architecture?

Interview questions

1. A project team is working on MDG Cloud-Ready Mode and Data Quality Management. Using the MDG Data Quality Management and Governance Architecture scenario, how would you approach the requirement from design through validation?

Answer it in your own words first, then compare.

2. You inherit an existing MDG Cloud-Ready Mode and Data Quality Management implementation covering MDG Data Quality Management and Governance Architecture. What would you review before changing it, and what would make you confident the change is production-ready?