I have duplicate records that aren’t exact matches, and I need a reliable way to detect them.

Detect and flag duplicate records that are similar but not identical (for example, spelling variations, formatting differences, or partial overlaps). This helps you reduce clutter, improve data quality, and prevent double-counting across reports and workflows.

This feature helps you identify duplicate records that do not match exactly, such as entries with typos, different abbreviations, inconsistent capitalization, or variations in formatting. It is designed for situations where exact matching fails to catch real duplicates, leading to messy data and unreliable analytics. The feature supports comparing records using similarity-based detection so that near-matches can be surfaced for review. It enables you to find potential duplicates across key fields where duplicates commonly occur, such as names, email addresses, phone numbers, addresses, or IDs with inconsistent formatting. Results can be used to spot clusters of records that likely represent the same entity, allowing you to decide what should be cleaned up. By detecting these near-duplicate records, you can reduce manual effort spent scanning spreadsheets or databases for inconsistencies. This improves the accuracy of reporting by minimizing double-counting and conflicting values. It also supports downstream processes like customer communications, compliance checks, and operational workflows by ensuring records are not fragmented across multiple variants. Overall, it provides a more dependable way to locate duplicates when real-world data entry is inconsistent.