Treat a possible duplicate as an identity decision, not merely a similar name. Define matching evidence, review ownership, and the consequences of combining records.
Prepared with AI assistance. These are practical scoping recommendations; examples are illustrative, not client results.
Choose matching signals
Compare the stable identifiers the systems actually provide. Names, addresses, and emails can help but may be shared, misspelled, or changed. Distinguish a confident match from a possible match. Do not let an uncertain automatic match silently connect orders or private information to a different customer.
Create a review workflow
Show the candidate records and the fields that caused the possible match. Give an authorized reviewer options to link, keep separate, or request clarification. Preserve the decision so the same ambiguous pair does not return to the queue every time synchronization runs.
Consider linked work
Before combining records, identify attached requests, documents, contacts, and account permissions. The business may want a shared parent relationship rather than a destructive merge. Ask which history must remain separate and how an incorrect decision can be corrected through a controlled process.
Test ambiguous examples
Use two companies with similar names, one customer with a changed email, and two contacts sharing a generic inbox. Verify that the integration follows the intended confidence and review rules. The goal is reliable identity across systems, not achieving the lowest possible record count at any cost.