Master Data Management
Same supplier, five spellings. One identity.
Normalize, score and cluster records from every system, link the confident matches under policy and send only the genuinely ambiguous pairs to a steward.
The capability
Finding who is who across every system
Entity resolution determines which records across systems describe the same real-world entity. In industrial enterprises, the same supplier appears in several ERPs, a procurement system and a supplier portal, each with its own spelling, legal-form suffix and partial address. Products and customers suffer from the same problem.
Autonera first normalizes records by removing legal-form suffixes, harmonizing punctuation and standardizing codes. It then generates candidate pairs and scores them using several signals such as name similarity, postal code and identifiers like tax numbers, where a conflicting identifier counts as evidence against a match. Matching rules are configurable per domain.
Two thresholds divide the results. Pairs above the auto-match threshold are linked under policy, pairs below the review threshold are treated as distinct and the ambiguous band between them goes to a steward. Linked pairs form clusters, each representing one resolved entity that can feed a golden record.
Problems it solves
Where this work usually breaks down
Exact matching misses real duplicates
"Calder & Rowe Ltd" and "Calder and Rowe Limited" never match on exact name comparison.
Fuzzy matching merges different companies
Similar names without supporting evidence combine distinct suppliers, which is worse than leaving duplicates.
Every candidate goes to a person
Without thresholds, stewards review hundreds of obvious matches and never reach the hard cases.
Pairs without a cluster view
Reviewing pairs one at a time hides that three records across three systems are the same entity.
How it works
Resolve seven supplier records from five systems
Toggle normalization steps and identifier evidence, then move the two thresholds. Watch scores, routing and resolved entities change, and decide the ambiguous pairs yourself.
- 100
A1Bergmann Fluid Technik GmbH
A2BERGMANN FLUID-TECHNIK
Auto-match - 100
B1Calder & Rowe Ltd
B2Calder and Rowe Limited
Auto-match - 85
C1Rowe Fastening Co
C2Rowe Fastening Company
Auto-match - 70
A1Bergmann Fluid Technik GmbH
A3Bergmann Fluid Technik Inc.
- 70
A2BERGMANN FLUID-TECHNIK
A3Bergmann Fluid Technik Inc.
RESOLVED ENTITIES
7 source records resolve to 4 entities.
Entity 1, 2 records
- A1ERP Germany
- A2Supplier portal
Entity 2
- A3ERP North America
Entity 3, 2 records
- B1ERP UK
- B2P2P
Entity 4, 2 records
- C1ERP North America
- C2Supplier portal
Functional capabilities
What Entity Resolution & Duplicate Detection includes
- Normalization
- Standardize names, legal forms, punctuation, addresses and identifiers before comparison.
- Candidate generation
- Block and pair records efficiently so large datasets are compared without every-to-every scans.
- Multi-signal scoring
- Combine name similarity, location and identifiers, including negative evidence from conflicting identifiers.
- Configurable matching rules
- Tune signals and weights per domain: product, supplier, customer or location.
- Two-threshold routing
- Auto-link confident matches, discard clear non-matches and route the ambiguous band to stewards.
- Duplicate clusters
- Group linked records into resolved entities that feed match and merge and golden records.
Autonomous operations
How autonomous execution changes entity resolution & duplicate detection
Agents run resolution continuously as new records arrive and learn from steward decisions which signals deserve more weight, under human-approved rule changes.
Agents carry out
- Resolve new records against existing entities
- Propose threshold and weight adjustments from steward decisions
- Explain the signals behind each score
- Detect clusters that may contain two entities
People decide
- Decide ambiguous pairs
- Approve rule and threshold changes
- Split clusters that were linked incorrectly
Evidence recorded
- Signals and score for every pair
- Steward decisions with reasons
- Rule version used for every link
In industrial operations
A manufacturer resolves its global supplier base
After several acquisitions, a manufacturer's spend reports overstated its number of suppliers and understated its leverage with the largest ones.
- Supplier records from four ERPs, procurement and the portal are normalized.
- Tax identifiers and postal codes are used as evidence, with conflicting tax IDs penalizing a match.
- Pairs above the auto-match threshold are linked; the ambiguous band is reviewed by procurement stewards.
- Resolved clusters feed supplier golden records used by spend analytics.
Result: Procurement sees one entity per real supplier, and stewards spend their time on the cases that need judgment.
Related capabilities
Works alongside
Match & Merge
Find duplicate records across sources, decide what survives and keep a reversible golden record.
Feature guideGolden Records
A trusted master record with source cross-references, provenance and field ownership.
Feature guideAttribute Survivorship
Field-level rules that decide which source value wins, with impact preview.
Feature guideData Profiling & Rule Discovery
Profile a new dataset and turn observed patterns into reviewed validation rules.
Capability areaMaster Data Management
See where Entity Resolution & Duplicate Detection fits in the wider Master Data Management capability.
Buyer FAQs
Questions evaluators ask
How is entity resolution different from match and merge?
Entity resolution identifies which records belong together. Match and merge consolidates those records and decides which values survive.
Can identifiers outweigh name similarity?
Yes. Matching identifiers add evidence, and conflicting identifiers such as different tax numbers reduce the score even when names are similar.
How are thresholds chosen?
Thresholds are configured per domain. Steward decisions in the review band help evaluate whether thresholds should move, and changes are approved before use.
Does this work for products as well as suppliers?
Yes. Product resolution uses signals such as manufacturer part numbers, brands and normalized descriptions.
Can incorrect links be undone?
Yes. Clusters can be split, and decisions are recorded so the same pair is not relinked automatically.
Your next step
See Entity Resolution & Duplicate Detection on data like yours.
Bring a real file, catalog or record set. We will walk through how Autonera would handle it and where your team keeps the final say.
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