MS Dynamics CRM Training - Dynamics 365 CRM Course
                     Data Quality and Duplicate Detection 
in Microsoft Dynamics CRM
 Importance of Data Quality in CRM Systems
 Managing Duplicate Records Effectively
 Best Practices for Maintaining Clean Customer Data
 Presenter Name
 Date / Organization
Introduction to Data Quality in Dynamics 
CRM
 Data quality refers to the accuracy, completeness, consistency, and reliability of CRM 
data.
 High-quality data enables better decision-making and improved customer 
relationships.
 Microsoft Dynamics CRM relies heavily on clean data for automation, analytics, and 
reporting.
 Poor data quality leads to operational inefficiencies and incorrect insights.
• Data governance and validation processes help maintain data integrity.
Importance of Maintaining Data Quality
 Improves customer experience through accurate information.
 Enables better marketing segmentation and targeting.
 Enhances reporting accuracy and business intelligence.
 Prevents miscommunication across teams.
 Supports compliance and regulatory requirements.
• Reduces operational costs associated with data correction.
Common Data Quality Issues in CRM
 Duplicate records created by multiple users or integrations.
 Missing or incomplete customer information.
 Inconsistent formatting and naming conventions.
 Outdated or incorrect contact details.
 Data entry errors caused by manual processes.
• Data silos across different systems.
Overview of Duplicate Detection in Microsoft 
Dynamics CRM
 Duplicate detection identifies similar or matching records.
 Helps prevent redundant entries for accounts, contacts, or leads.
 Uses predefined or custom matching rules.
 Can run during record creation, updates, imports, or scheduled jobs.
• Supports data consistency and avoids confusion in customer management.
Duplicate Detection Rules and Configuration
 Define matching criteria such as:
o Email address
o Phone number
o Account name
o Combination of multiple fields
 Configure rules for specific entities (Accounts, Contacts, Leads).
 Publish rules to activate detection.
 Set sensitivity levels to reduce false positives.
• Regularly review and refine matching logic.
Methods for Managing Duplicate Records
 Real-time duplicate detection during data entry.
 Bulk detection jobs for existing records.
 Merge duplicate records while retaining important information.
 Use workflows or automation to validate data.
 Apply standard naming conventions.
• Restrict duplicate creation through business rules.
Best Practices for Data Quality Management
 Establish clear data governance policies.
 Train users on correct data entry procedures.
 Use mandatory fields and validation rules.
 Schedule periodic data audits.
 Implement automated cleansing tools.
 Monitor integrations and data imports carefully.
• Maintain standardized data formats.
Benefits and Conclusion
 Improved data accuracy and reliability.
 Increased user trust in CRM information.
 Better analytics and decision-making.
 Enhanced customer engagement.
 Reduced duplication and operational inefficiencies.
• Continuous monitoring ensures long-term data quality.
If you want, I can also give you:
 High-quality corporate-level version (MBA/project submission standard)
 Interview presentation version
 Dynamics 365-specific advanced technical version (including Power 
Platform and Data verse concepts)
• Ready-made speaker notes for each slide
For More Information About 
Microsoft Dynamics CRM
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   Ph. No: +91-998997107
     Visit: www.visualpath.in
      E-Mail: [email protected]
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