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MDM - Approaches to Maintaining a Master List

 Managing master data efficiently is crucial, especially in environments where multiple applications store and use their own versions of master data. In existing systems, maintaining a single, accurate master list can be challenging due to data duplication and inconsistencies. Here, I summarize different approaches to maintaining a master list, which I will revisit in future discussions.

1. Single Copy of Master Data

  • In this approach, there is only one authoritative copy of the master data.
  • All changes are made directly to this master dataset.
  • Applications that use the master data must retrieve and work with the latest version.

Benefits:
✅ Ensures data consistency across all applications.

Challenges:
❌ Not always practical—existing systems may need significant modifications to access the single master.

2. One Master, Multiple Copies with Controlled Updates

  • A single master copy exists, and changes are made only to this copy.
  • Updates are propagated to other systems, which store local copies.
  • Local applications can modify their data only for non-master attributes.

Benefits:
✅ Minimal changes needed in existing applications.
✅ Master data remains consistent while allowing flexibility for local modifications.

Challenges:
❌ Risk of delayed synchronization, which may cause temporary discrepancies.

3. Merging Master Data Across Systems

  • Applications can update their local copies of master data independently.
  • Changes are sent to the central master, where they are merged.
  • The updated master data is then distributed back to all connected systems.

Benefits:
✅ Minimal changes required in existing applications.
✅ Provides greater flexibility for systems that need autonomy over their data.

Challenges:
❌ Conflict resolution is required if multiple systems update the same record.
❌ Risk of duplicate entries, requiring data matching and deduplication.
❌ When a new item is added in one system but already exists in another, re-merging is necessary.

Conclusion

Each approach has its own trade-offs between consistency, flexibility, and system impact. Organizations must select an approach based on their data architecture, system dependencies, and business needs. A hybrid approach combining these models may also be required for complex environments.

I will revisit these approaches in future posts to discuss practical implementations and real-world challenges.

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