DQE Matching identifies, compares, and reconciles records based on configurable matching rules. It is used to detect duplicates and consolidate customer data across large datasets.
How it works
Matching applies phonetic, linguistic, and fuzzy comparison algorithms to compare records across key fields such as name, address, email, and phone. It assigns a similarity score and a matching decision based on configured thresholds.
What can be matched
- Contact records (individuals)
- Company records (B2B)
- Address-based records
Matching rules
Matching rules define which fields are compared, what algorithms are applied, and what similarity thresholds trigger a match result. Rules are configured according to your data structure and business requirements.
Supported use cases
- Deduplication of CRM or marketing databases
- Identifying duplicate contacts before a data migration
- Ongoing deduplication as part of a continuous data quality workflow
- Reconciliation between two data sources
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