Managing matching rules effectively is essential for maintaining data integrity and preventing duplicates in systems like Salesforce and Reltio. This blog post will guide you through the fundamentals of matching rules, their configuration, and best practices to optimize their use.
Understanding Matching Rules
What are Matching Rules?
Matching rules are criteria used to identify duplicate records in a database. They compare attributes of records to determine if they represent the same entity. For instance, in Salesforce, a matching rule can classify two contacts as duplicates if they share the same email address, based on either exact or fuzzy matching criteria.
Types of Matching Rules
Exact Matches: These require that the compared fields have identical values.
Fuzzy Matches: These allow for slight variations in data, such as different spellings or formats (e.g., "Robert Jordan" vs. "Bob Jordon") and utilize algorithms like Soundex
Key Components of Matching Rules
Attributes: The specific fields being compared (e.g., first name, last name, email).
Comparators: Operators that define how the comparison is made (e.g., Exact, Fuzzy).
Conditions: Additional parameters that can refine which records are considered for matching
Configuring Matching Rules
Step-by-Step Guide
Accessing Settings:
Navigate to the setup area of your platform (e.g., Salesforce).
Locate the section for Duplicate Rules and Matching Rules.
Creating a New Rule:
Define a clear name and description for your rule.
Select the object type (e.g., Leads, Contacts) that the rule will apply to.
Defining Attributes and Comparators:
Choose which attributes will be compared.
Set the comparator for each attribute (Exact or Fuzzy).
Setting Conditions:
Use conditions to limit the scope of the rule based on user roles or specific data characteristics.
Testing and Activating the Rule:
Test your rule with sample data to ensure it behaves as expected.
Activate the rule once satisfied with its performance.
Best Practices
Limit Complexity: Keep your matching rules simple to avoid performance issues. Aim for a minimal set of rules that effectively cover your business needs
Regular Review: Periodically review and update your matching rules based on changes in data patterns or business requirements.
Utilize Reporting Tools: Use analytics tools available in your platform to monitor the effectiveness of your matching rules and make necessary adjustments
FAQs
What is a duplicate rule in Salesforce?
A duplicate rule defines actions taken when users attempt to create or edit records that may be duplicates. Salesforce provides standard duplicate rules and allows for custom configurations
How can I bypass duplicate rules?
How many duplicate rules can be active at once?
What should I do if my matching rules are causing too many false positives?
By following these guidelines and best practices, you can effectively manage matching rules in your organization, ensuring cleaner data and improved operational efficiency.