Duplicate detection
Duplicate detection finds records that might describe the same entity. A match is initially only a suspected duplicate, not an automatic decision to merge.
Duplicate detection searches for records that may describe the same real-world entity. Rules can compare names, addresses or other attributes. Which similarities count as suspicious depends on the entity type and dataset.
In a hypothetical CRM, two companies share a contact address because one purchasing contact handles both. A rule based only on this address can flag a possible duplicate even though the companies are separate business accounts. Conversely, the same customer may remain undetected under different spellings.
Detection and merging are therefore separate steps. A suspected duplicate may trigger a review, warning or write block. Only a business-confirmed decision should determine whether records belong together and which details to retain. A similarity match alone does not prove identity.
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Related terms
- Mapping tableA mapping table stores specific correspondences, such as identifier pairs from two systems. It makes a confirmed link reusable.
- Master dataMaster data describes core business entities such as customers or products. It is reused across processes and can still change over time.
- Transactional inboxA transactional inbox uses durable message records to protect local processing against duplicate deliveries.
- Message acknowledgementA message acknowledgement tells the broker that a delivery has been received or processed according to the agreed rule.
- Retry jitterRetry jitter varies waiting times randomly to spread retries and reduce simultaneous requests from many clients.
- Test tenantA logically bounded customer context of a multitenant application, configured for testing.
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