Agent StoreProcurementSupplier Master Data Governance
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Supplier Master Data Deduplication Agent

ProcurementSupplier Master Data Governance

Scans the supplier master file for duplicate, inconsistent, or stale vendor records and automatically merges or flags them to maintain a clean, single source of truth.

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Process steps
5
Integrations
3
Data inputs

Supplier master files accumulate duplicate records over time as different business units onboard the same vendor under slightly different names, addresses, or tax IDs, fragmenting spend visibility, causing payment errors, and undermining accurate supplier performance tracking since transactions get split across multiple records for what is really one supplier

Manual deduplication projects are typically run once every few years as a major cleanup initiative because identifying duplicates across tens of thousands of records by eye is impractical, and the problem simply re-accumulates afterward

This agent continuously scans the supplier master for likely duplicates using fuzzy matching on name, address, tax ID, and banking details, distinguishes true duplicates from legitimate related entities like separate subsidiaries, and either auto-merges high-confidence duplicates or routes ambiguous cases to a data steward for confirmation, keeping the supplier master clean on an ongoing basis rather than through periodic cleanup projects

The agent runs on a continuous or nightly schedule, pulling the full supplier master record set and applying fuzzy matching algorithms across name, address, tax ID, banking details, and contact information to identify candidate duplicate pairs. It uses LLM-based reasoning to evaluate ambiguous matches, distinguishing legitimate related entities such as separate legal subsidiaries from true duplicate records created in error, and assigns a confidence score to each match. High-confidence duplicates below a configured risk threshold are automatically merged with transaction history reassigned to the surviving record, while lower-confidence or high-transaction-volume matches are routed to a data steward for manual confirmation before any merge occurs.

1

Scan Supplier Master for Candidate Duplicates

  • Pull the full active and inactive supplier master record set
  • Apply fuzzy matching on name, address, and contact fields
  • Cross-check tax ID and banking detail matches
  • Generate a candidate duplicate pair list with match scores
Outcome: A scored list of candidate duplicate supplier records is generated for review.
2

Distinguish True Duplicates from Related Entities

  • Analyze legal entity structure to identify legitimate subsidiaries
  • Check for distinct tax IDs indicating separate legal entities
  • Evaluate transaction and contract history for each candidate
  • Assign a confidence classification to each match
Outcome: Candidate matches are classified as true duplicates, related entities, or uncertain.
3

Merge or Route for Review

  • Auto-merge high-confidence, low-transaction-volume duplicates
  • Reassign transaction and contract history to the surviving record
  • Route ambiguous or high-value matches to a data steward
  • Log every merge and routing decision for audit purposes
Outcome: The supplier master is cleaned of confirmed duplicates while preserving full transaction history.
4

Monitor Ongoing Data Quality

  • Track duplicate rate trends over time by business unit
  • Flag onboarding processes contributing to repeat duplication
  • Generate a data quality scorecard for procurement operations
  • Recommend process changes to reduce future duplicate creation
Outcome: Procurement operations gain ongoing visibility into supplier data quality and its root causes.
SAP MDG
hosts and updates the supplier master data record
D&B Direct+
verifies legal entity and tax ID information
Oracle Procurement Cloud
reassigns transaction history on merge
ServiceNow
routes ambiguous cases to data stewards
Power BI
publishes data quality scorecards