Glossary · Method
Entity resolution
Entity resolution is working out which records, in one system or across several, refer to the same real customer, person, supplier or deal, even when they are spelt differently.
- CRMExample Foods
- CRMExample Foods Ltd
- BillingEXAMPLE FOODS LIMITED
One customer
Example Foods
Matched on name and domain
Suffixes stripped, domain compared, then checked by hand.
In plain terms
Entity resolution is the work of deciding that “Example Foods”, “Example Foods Ltd” and “EXAMPLE FOODS LIMITED” are one customer, or that they are not. It is the foundation under every cross-system comparison.
It happens at two levels. Inside one system, it finds duplicates: the same customer entered twice. Across systems, it links records: the CRM account to the billing account, the deal owner to the person in HR.
Matching starts with the strongest evidence available: a shared ID, then an email domain, then normalised names with legal suffixes stripped, then fuzzy matching on what remains. Each match carries a level of confidence, and the weaker ones are checked by hand before they are relied on.
Getting it wrong in either direction is costly. Too strict, and real matches are missed, so customers look as if they are missing from billing. Too loose, and different companies are merged, so a forecast quietly double-counts. That is why every red flag built on a match is verified on sample records before it reaches the memo.
- See it in a real memoThe sample Truth Score ReadReal engine output on generated test data, not a client
- How we measure itThe method, including the capPillars, weights, bands and what is never guessed
- What happens to your exportsSecurity and data handlingExports only, NDA and data processing agreement first
Questions
Entity resolution, answered.
The questions people ask about Entity resolution, answered in plain English.
Book the callDo you need a shared customer ID to match records?
No, though it helps. Without one, we match on domains, normalised names and other fields, and the read says which matches rest on weaker evidence.
How accurate is fuzzy matching?
It is a strong first pass, not a final answer, which is why matches that matter are checked by hand on sample records before any finding is reported.
Is entity resolution the same as deduplication?
Deduplication is entity resolution inside one system. Across systems, the same method links records rather than merging them.
Related terms
Keep reading.
- Method Cross-system reconciliation Cross-system reconciliation is matching records between systems, such as CRM and billing, or HR and the CRM, to find where they disagree. Every system can look clean on its own; the truth is in the gaps between them. Read the definition
- The six pillars Data connectivity Data connectivity is whether records link up across systems: whether a customer in the CRM can be matched to the same customer in billing, and a person in HR to the accounts they own. Read the definition
- The six pillars Data accuracy Data accuracy is whether the values in your records are correct: real emails, valid dates, plausible amounts, and one record per real customer rather than three. Read the definition
- The Truth Score Truth Score™ The Truth Score™ is DataTruth’s score out of 100 for how far a company’s data can be trusted, built from six weighted pillars and placed in one of four bands, A to D. Read the definition
Definitions are the easy part. Your own number is the useful one.
Exports from two or more systems, matched between them, every red flag checked by hand. A signed memo with your Truth Score and a Leverability read, within 5 working days of complete exports.
Starts with a 30-minute call. Commits you to nothing.