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Improving Master Data Quality with Machine Learning
Intelligent duplicate detection for clean master data and reliable analyses
Duplicates in master data are a major obstacle to reliable financial reports, analyses, and data-driven decisions. Traditional methods for detecting duplicates quickly reach their limits: they require time-consuming, manual rule definitions, are highly data-specific, and can only be adapted to new data structures with significant maintenance effort.
In this keynote presentation, we’ll show how machine learning is fundamentally transforming duplicate detection in master data. Instead of rigid rules, machine learning models automatically identify patterns and relationships in the data, even when dealing with complex, heterogeneous data sources. Using a practical example, you will learn how existing master data management software can be specifically enhanced with customizable and interchangeable ML methods to sustainably improve data quality and significantly reduce manual effort.
What to Expect at the Online Business Lunch
- Why rule-based duplicate detection has its limitations
- Fundamentals of Machine Learning-Based Duplicate Detection
- Automatic detection of patterns and similarities in master data
- Integration of ML methods into existing MDM systems
- Customizable, interchangeable ML models instead of rigid logic
- Case Study: Extending MDM Software with ML-Based Duplicate Detection
- Better master data quality as the foundation for reliable reports and analytics
Target group
- Data Management and MDM Managers
- Data and Analytics Teams
- IT and Digital Transformation Managers
Speaker

Dr. Melanie B. Sigl
Managing Consultant

Igor Shmelev
Managing Consultant
Free registration for the webinar
Live webinar
April 30, 2026
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