What Is Record Linkage?

When a supplier sends a price list update and your catalog team has to manually hunt for the matching master record, that is a record linkage failure. The same part arrives as 6204-2RS in one feed, 6204 2RS C3 in another, and Deep Groove Ball Bearing, 20mm bore in a third — and without a reliable join, every import spawns duplicates, misfires enrichment, or overwrites the wrong SKU. Claro solves this at the root: it resolves product identity across supplier feeds, PIM exports, and ERP tables, then writes clean, matched records back into your existing systems rather than leaving you with a staging spreadsheet no one trusts.

Definition

Record linkage is the process of identifying records across one or more datasets that refer to the same real-world entity and then joining them so they can be treated as a single object.

It answers a deceptively simple question: do these two records describe the same thing? In product data, the “thing” is usually a SKU, a part, or a manufacturer item, and records arrive from supplier feeds, ERP exports, marketplace listings, PIM tables, and spreadsheets that were never designed to agree with each other.

The discipline has two flavors that often work together:

• Deterministic linkage joins records on exact agreement of a trusted key — a GTIN, a manufacturer part number plus brand, a normalized identifier. When the key is clean and present, this is fast and precise.
• Probabilistic linkage scores partial agreement across many weaker signals — title similarity, dimensions, pack quantity, category — and links records when the combined evidence crosses a threshold. This carries the burden when identifiers are missing, malformed, or reused.

Most real catalogs need both, because clean identifiers are absent far more often than catalog teams expect.

Record linkage is closely related to, and frequently confused with, entity resolution and deduplication. The distinction is one of scope: linkage is the matching step that pairs records, entity resolution is the broader job of clustering all matched records into resolved entities, and deduplication is what you do once those clusters exist — collapse them into one canonical row.

Why record linkage matters for product data

Without reliable record linkage, every downstream task inherits the ambiguity. A distributor reconciling MRO catalogs from forty suppliers cannot tell whether they stock one safety glove or six. A CPG brand syndicating to retailers ships the same item under conflicting GTINs. A furniture marketplace shows three near-identical listings that split reviews and confuse buyers. Linkage is the join that makes a catalog countable.

Consider an industrial distributor importing a new vendor price list. Linkage matches each incoming line against the existing master catalog so that price and availability updates land on the right record instead of spawning duplicates. Get it wrong in one direction and you create phantom SKUs; get it wrong in the other and you overwrite a distinct product with the wrong price. The same join powers enrichment (attributes flow onto the matched record), classification (the resolved entity inherits one taxonomy code), and AI search, where a clean linked product graph is what lets a model cite a single authoritative answer instead of three contradictory listings.

Before and after: messy vs trusted catalog joins

How Claro applies record linkage

For API-first platforms, linkage is rarely a one-time batch. New records stream in continuously, so matching has to run incrementally, return a confidence score, and route low-confidence pairs to review rather than silently merging them.

Claro runs deterministic and probabilistic linkage in combination — matching on GTINs and MPNs when they are reliable, falling back to attribute-level similarity scoring when they are not. Matches return with confidence scores and source attribution so your team can tune thresholds and audit every join. Clean, linked records write back into your PIM or ERP rather than sitting in a silo, so the canonical identity layer is live inside your existing workflow.

The result: catalogs that stay clean as new suppliers onboard, price lists update, and product ranges expand — without a manual reconciliation cycle after every import. See how this applies to the supplier-to-inventory matching workflow or the deduplication playbook for step-by-step guidance.

Related

FAQ

What is the difference between record linkage and deduplication? +

Record linkage is the matching step: it identifies which records refer to the same entity, including across different datasets. Deduplication is what happens afterward — once linkage has grouped matching records, deduplication collapses each group into a single canonical row. Linkage can also join records you want to keep separate (for example, a product and its supplier listing), so it is broader than dedup alone.

Is record linkage the same as entity resolution? +

They overlap but are not identical. Record linkage usually refers to the pairwise matching of records, often across two sources. Entity resolution is the end-to-end process of taking those matches, clustering them transitively — if A matches B and B matches C, then A, B, and C are one entity — and producing resolved entities. In practice, linkage is a core component of entity resolution.

Do I need exact identifiers like GTINs for record linkage to work? +

No. Trusted identifiers make deterministic linkage easy when they exist, but real product feeds are full of missing, wrong, or reused GTINs and part numbers. That is why probabilistic linkage exists — it scores agreement across many weaker attributes (title, brand, dimensions, pack size) so records can be matched even without a shared key.

How do confidence scores fit into record linkage? +

Each candidate pair gets a confidence score that reflects how strongly the evidence supports a match. You then set thresholds: high-confidence pairs link automatically, low-confidence pairs are rejected, and the uncertain middle band is routed to human review. Tuning those thresholds is the main lever for trading precision against recall.

Why does record linkage get harder at scale? +

Naively comparing every record to every other record grows quadratically, so large catalogs require blocking or indexing to limit comparisons to plausible candidates. Scale also surfaces more edge cases — variant explosions, multilingual titles, reused identifiers — that break brittle scripts. This is why teams move from hand-rolled matching to a dedicated linkage layer as their data volume and source count grow.