Entity Resolution Engine

Combines exact-match rules with statistical models to resolve identities. Deterministic matching requires strict field agreement (e.g., Tax ID), while probabilistic matching uses algorithms like Fellegi-Sunter to calculate match likelihood based on weighted field similarities.

• Handles typos, transpositions, and missing data
• Outputs a match confidence score between 0 and 100%
• Automates high-confidence merges and queues low-confidence pairs for human review

Defines rules for merging matched records into a single golden record—the best, most complete version of the truth. Survivorship logic selects the most reliable value from each conflicting field based on data source trustworthiness, recency, and completeness.

• Applies field-level precedence rules (e.g., ERP master data over spreadsheet imports)
• Maintains full provenance lineage back to source records
• Creates an auditable, non-destructive merge history

Leverages knowledge graph structures to resolve entities by analyzing their relationships, not just attributes. A supplier might be matched by shared addresses, contact numbers, or corporate hierarchies even when names differ.

• Traverses edges between entities to discover hidden connections
• Identifies beneficial ownership and corporate family trees
• Detects duplicate records through shared transactional or locational context

Operates in both synchronous and asynchronous modes to serve different operational cadences. Real-time resolution matches entities on ingestion for live control tower alerts, while batch processing handles large-scale master data cleansing.

• Sub-second latency for streaming event matching
• Bulk deduplication across millions of historical records
• Incremental refresh to update golden records as source data changes

Standardizes unstructured text fields before comparison using natural language processing. Transliterates non-Latin scripts, expands abbreviations, and normalizes industry-specific terminology to ensure "Acme Corp." and "Acme Corporation GmbH" are recognized as the same entity.

• Handles multi-byte character sets and right-to-left scripts
• Applies domain-specific synonym dictionaries (e.g., "Aluminum" vs. "Aluminium")
• Strips legal entity suffixes and punctuation for clean comparison

Resolves complex many-to-many matches by grouping records into entity clusters. If Record A matches B, and B matches C, transitive closure logic ensures A, B, and C all belong to the same resolved entity, preventing fragmented identities.

• Builds connected components across the match graph
• Prevents duplicate golden records from emerging over time
• Supports manual cluster splitting and merging for edge cases

A standardized data model that translates diverse external formats into a single, unified structure for consistent internal processing. Entity resolution engines rely on canonical schemas to normalize records from disparate ERP systems, EDI feeds, and supplier portals before executing matching algorithms.

• Maps SAP IDoc, ANSI X12, and JSON API payloads to a common representation
• Eliminates structural heterogeneity before semantic matching begins
• Enables deterministic field mapping for fuzzy comparison functions

A data structure representing entities like suppliers, sites, and parts as nodes and their relationships as edges. After entity resolution merges duplicate records, the supply chain graph provides a deduplicated, connected view of all interdependencies.

• Exposes hidden multi-tier supplier relationships
• Enables graph traversal queries for disruption propagation modeling
• Serves as the deterministic grounding layer for Graph Neural Networks

The computational core of entity resolution that identifies non-identical strings referring to the same real-world entity. Techniques include Levenshtein distance, Jaro-Winkler similarity, and phonetic hashing like Soundex and Metaphone.

• Handles typographical errors in supplier names and addresses
• Applies token-based scoring for multi-word company names
• Uses learned embeddings for cross-lingual entity matching in global supply chains

The organizational discipline of creating and maintaining a single source of truth for critical business entities. Entity resolution engines serve as the operational matching layer within broader MDM frameworks.

• Establishes golden records for suppliers, materials, and customers
• Governs survivorship rules when merging conflicting attribute values
• Integrates with data stewardship workflows for human-in-the-loop exception handling

A statistical approach that assigns match probabilities to record pairs based on the agreement and disagreement patterns across multiple fields. Unlike deterministic matching, it handles uncertainty gracefully.

• Uses Fellegi-Sunter model to compute match weights
• Accounts for data quality variance across source systems
• Produces confidence scores that feed into automated resolution thresholds

Vector representations of entities and relationships learned from graph structures. After entity resolution creates clean nodes, embeddings enable semantic similarity searches that go beyond string matching.

• Identifies functionally equivalent suppliers across different taxonomies
• Powers link prediction to discover undisclosed supply chain relationships
• Enables transductive reasoning across the resolved entity landscape