Entity resolution (also called record linkage or identity resolution) is the process of determining whether two or more records in a dataset refer to the same real-world entity, a person, company, or location. It's more sophisticated than simple deduplication because it works across datasets with different schemas, formats, and levels of completeness. "Robert J. Smith, VP Sales, Acme" in your CRM and "Bob Smith, Sales, Acme Corporation" in a trade show list might be the same person. Entity resolution figures that out.
Why It Matters
When you're combining data from multiple sources, CRM exports, event lists, purchased data, website sign-ups, the same entities appear in different forms. Without resolution, you can't build a unified view of your customers or prospects. You end up with fragmented profiles where some data lives in one record and some in another, and nobody can see the complete picture.
How Entity Resolution Works
- Blocking: Group records by a shared attribute (same company domain, same last name) to reduce the number of comparisons needed
- Pairwise comparison: Compare blocked records across multiple fields: name similarity, email, phone, company, title
- Probabilistic scoring: Calculate the likelihood that two records are the same entity based on how many fields match and how closely
- Clustering: Group all records that resolve to the same entity, even if they were matched through intermediate records
- Merge or link: Either merge records into a single golden record, or create a persistent link between them for cross-referencing
Example
A company acquires a competitor and needs to merge their customer databases. The acquiring company's CRM has 40,000 contacts. The acquired company has 25,000. Entity resolution finds 8,000 contacts that exist in both databases, merges them, and produces a unified database of 57,000 unique contacts with the best data from each source.
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