MDGbeginner
Consolidation
Consolidation in SAP Master Data Governance covers the processes and tooling used to identify, cleanse, and merge duplicate or fragmented master data records (business partners, customers, vendors, materials) that have accumulated across systems or over time. It includes match rules, scoring, best-record survivorship, and workflow-driven review before data is confirmed as the single governed version.
Overview
Consolidation in SAP Master Data Governance covers the processes and tooling used to identify, cleanse, and merge duplicate or fragmented master data records (business partners, customers, vendors, materials) that have accumulated across systems or over time. It includes match rules, scoring, best-record survivorship, and workflow-driven review before data is confirmed as the single governed version.
Lessons in this topic
- SAP Consolidation Tutorial (Part 1)Understand the business problem consolidation solves in MDG, the difference between consolidation and central governance, and the core building blocks: source systems, match rules, and best record.
- Understanding Consolidation in MDG: Why Duplicate and Fragmented Data Exists (Part 2)An introduction to why master data consolidation is needed in MDG, what business and technical problems it solves, and how it differs from ongoing central governance.
- Configuring the Consolidation Process: Matching, Best Record Rules, and Steward Review (Part 3)Learn how a consolidation cycle is structured end to end, from loading source data through match rule configuration, best record survivorship logic, steward review, and controlled release of the cleansed data.
- Executing Match, Merge, and Best-Record Survivorship in Consolidation (Part 4)Learn how consolidation results in candidate match groups are reviewed, merged, and resolved into a single best record, and how survivorship rules and manual steward decisions interact with governance workflow.
- Configuring Match Rules, Scoring, and Best-Record Survivorship for Consolidation (Part 5)A practical walkthrough of how match rules, similarity scoring, and best-record survivorship decisions are configured and executed in an MDG consolidation process.
- Match, Merge and Survivorship Rules in MDG Consolidation Processing (Part 6)Explains how MDG consolidation processing uses match rules, merge/group decisions, and survivorship (best record) logic to turn multiple candidate source records into a single trusted golden record, and how consultants configure and validate this flow.
- Advanced Matching, Best Record and Survivorship in MDG Consolidation (Part 7)Design MDG consolidation matching and survivorship so duplicate records are identified confidently, merged with explainable source precedence and reviewed when certainty is insufficient.
Interview questions covered
- What are the core concepts of Consolidation, and how would you explain them clearly in an SAP interview?
- How would you apply Consolidation in a real SAP project? Walk through the design or implementation approach.
- A production issue is reported in Consolidation. What would you check first, and how would you troubleshoot it systematically?
- What are the most important design decisions, trade-offs, and best practices in Consolidation?
- Suppose you inherit an existing Consolidation solution. How would you review it before making a change?
- A senior architect is reviewing Consolidation. The project situation is: Two suppliers with similar names were merged because the matching rule over-weighted fuzzy name similarity and ignored distinct tax IDs. What design changes, validation evidence and production controls would you require before approving the solution?
- A project encounters this situation while working on Configuring the Consolidation Process: Matching, Best Record Rules, and Steward Review: During a post-acquisition data harmonization project, the consolidation team set an initial match threshold that was too permissive for vendor records, causing hundreds of distinct small suppliers with generic names like 'City Logistics' in different cities to be proposed as duplicates of each other. Walk through how you would analyze the requirement or issue, what you would validate, and how you would test the solution before release.
- A project encounters this situation while working on Executing Match, Merge, and Best-Record Survivorship in Consolidation: A retail company migrating two regional ERP systems into one S/4HANA instance ran an initial customer consolidation and generated several thousand match groups. Walk through how you would analyze the requirement or issue, what you would validate, and how you would test the solution before release.
- Using this SAP project situation — A manufacturing group acquired a regional distributor and needed to merge the distributor's customer master, held in a separate legacy ERP, into the parent company's S/4HANA system — explain Why Consolidation Exists in SAP MDG: Business Case and Core Concepts. What should happen, which concepts or process steps matter, and what common mistake would you avoid?
- Using this SAP project situation — A retail company merged three regional ERP instances into a single S/4HANA system — explain Understanding Consolidation in MDG: Why Duplicate and Fragmented Data Exists. What should happen, which concepts or process steps matter, and what common mistake would you avoid?
- A project encounters this situation while working on Configuring Match Rules, Scoring, and Best-Record Survivorship for Consolidation: During a vendor master cleanup project, a manufacturing company configured match rules weighting tax identification number and bank account number heavily, with company name given only moderate weight because many subsidiaries of large group vendors shared very similar names but were legally distinct entities requiring separate vendor records. Walk through how you would analyze the requirement or issue, what you would validate, and how you would test the solution before release.
- A project encounters this situation while working on Match, Merge and Survivorship Rules in MDG Consolidation Processing: A consumer goods company merging two regional ERP instances into one S/4HANA system needed to consolidate roughly 40,000 customer master records before go-live. Walk through how you would analyze the requirement or issue, what you would validate, and how you would test the solution before release.