Entity matching is the work behind a deceptively simple question: are these two records the same thing?
That thing might be a company, customer, supplier, property, product, vehicle, contact, or legal entity. In clean examples, the answer is obvious. In real business systems, it rarely is.
Why exact matching fails
- Company names have abbreviations.
- Addresses are formatted differently.
- VAT numbers are missing.
- Parent and subsidiary names are mixed.
- Contacts change email domains.
- Typos and imports create small variations that matter.
How good matching works
A reliable matching process combines rules, fuzzy matching, confidence scores, and human review for uncertain cases. The system should be allowed to say: this might be the same entity, but do not merge it without review.
That pause matters. A bad merge can corrupt reporting, account ownership, compliance, and customer history.
Entity matching is the foundation underneath deduplication, single customer view, and multi-source data merging.
Lucendata uses entity matching to help companies connect records across messy operational systems.