Data Matching
What is Data Matching?
Matching is the algorithmic problem of deciding whether two records describe the same real-world thing. Exact match (same email, same domain) is easy. Fuzzy match — different spellings of the same name, different formats of the same phone, variant domains for the same company — is hard, and it's where matching engines earn their keep. Modern matching uses a blend of rules (normalize email casing, strip 'inc/llc' from company names) and learned models (which features predict same-entity). The trade-off is always precision vs recall: aggressive matching catches more dupes but creates false merges; conservative matching is safer but leaves dupes uncaught. Most B2B systems tune matching deliberately conservative because false merges are much harder to recover from than uncaught dupes.
Why it matters
- The bedrock primitive for dedup, identity resolution, and any cross-source aggregation.
- Precision vs recall is a deliberate business choice — there's no universally correct setting.
- Quality of matching is invisible to non-technical users but governs the integrity of everything downstream.
Use cases
- Inbound match. Incoming form submit matched against existing records before record creation.
- Cross-source match. Matching CRM accounts to records from a separate enrichment provider.
- Person-to-company match. Matching individual contacts to their employer account.
How turgo helps
turgo's matching engine combines deterministic rules with learned models — exposing confidence scores so ops can review borderline matches rather than accepting or rejecting blind.
See turgo in action →
