Accelerate the Value of Data

Configure match rules overview

Learn about match rules that determine if records refer to the same entity for data consolidation.

Organizations maintain entity and profile details across various systems. Sometimes, multiple records of the same entity exist across source systems, often with inconsistencies between them. For example, one copy of the record may have a complete address but no phone number, while another has a partial address and the correct phone number. How do you know if the records belong to the same entity or if the information available in a record is correct?

Reltio enables you to merge profiles into a single, comprehensive record. Using match rules, Reltio intelligently determines which profiles to merge and which to exclude. When configuring match rules, you can specify, for example, whether the system should apply exact or fuzzy matching. For more information on the different match rule options, see topic Match Rule Editor.

What is a match rule?

A match rule is a set of criteria that determines whether different records actually refer to the same entity. By examining attributes like names, addresses, and emails through configurable conditions and thresholds, match rules identify potential duplicates and automatically consolidate them. This process ensures that your data remains accurate and consistent across systems, giving you cleaner, more reliable insights.

A match rule uses specific criteria to identify and consolidate duplicate records across systems, ensuring a unified and accurate profile for each entity. It evaluates attributes like names, addresses, and emails against configurable conditions and thresholds to detect and automatically merge potential duplicates. Creating effective match rules requires comparison formulas and a tokenization strategy tailored to your data structure, delivering cleaner, more reliable insights across systems. Key steps include:
  1. profiling the data,
  2. selecting relevant attributes,
  3. addressing data quality issues, and
  4. balancing exact and fuzzy matching.
Carefully crafted match rules deliver accurate, consistent, and complete data insights across systems. For more information, see topics Fundamental Concepts Related to Matching, Match Token Generation, and Design your Match Tokenization Scheme.