Reltio Matching

Reltio Matching

Matching is the process of identifying records that are identical or related to one another. Typically, when considering matching data in a master data management system, we think about finding data that is completely identical. When such data is found, it is unnecessary to keep both sets of data. One of the features of Reltio MDM is that you can find these identical records easily, and also find records that are related. 

There are two methods of matching that Reltio supports. These methods are machine learning-based matching and rule-based matching. 

Rule-based matching is instruction-based, where the configuration provides the instruction and the platform executes the matching based on those instructions. With machine-learning based matching, customers can build their match IQ model, and use this as a model for matching records. 

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The Rule-Based Matching Process

Rule-based matching is based on instruction, which means the configuration provides the instruction and Reltio executes the matching dictated by those instructions. Then, Reltio merges these records either automatically or create a suspect match for the data stewards to review and resolve manually.

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Rules Based Matching

The matching process moves data from primary storage to secondary storages for use in UI, analytics, history logging, etc. The matching processor creates a match document, which is a copy of the record required for matching purposes, and performs the matching process on it. Currently, the matching process uses the default survivorship values but there is an opportunity for improvement by allowing the user to pick a survivorship rule for the matching process. The survivorship rule is tied to the user role today and tying the survivorship rule to the matching engine will require further consideration. Multiple OV concepts are used by many customers for different use cases but using a different survivorship rule for matching purposes has not been seen. The default survivorship rule is used for both matching and UI.

In this video, we're talking all things Matching and Merging. Reltio community program manager, Chris Detzel, and Reltio technical consultant, Joel Snipes, discuss match rule features, design and tuning in order to get the most out of your data. This session explores what makes a good merge rule, what makes a good potential match, and shows some of the common pitfalls new MDM practitioners fall into.

In this session, Suchen Chodankar, Reltio Product Manager, covers how different components of Reltio's matching engine such as tokenizer, comparators, and relevance score calculation work together to generate the matching result. Suchen also talks about the anatomy of the match rule and how various properties of the match rule configuration can influence the matching results.

Join Suchen Chodankar, Senior Product Manager and Chris Detzel, Director of Customer Community and Engagement for another community show. Translating matching requirements to the match configuration can be time consuming, complex and error prone. In this webinar, we will walk you through the machine learning powered Match IQ feature for matching your data efficiently without having to code or configure. We will demonstrate how easily any user with the knowledge of only matching requirements can build, train and publish the Match IQ models for matching.

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