Guide to Entity Resolution with AI-Native MDM

Editor’s Note: This post was originally published in June 2024. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

In the ever-evolving landscape of data management, organizations face increasing challenges when it comes to making sense of their vast and disparate data sources. To address these challenges, Tamr has developed and recently updated a comprehensive guide to entity resolution, designed to help organizations achieve unified, accurate data views.

The Importance of Golden Records

At the heart of every successful organization are the key business entities that drive operations, strategies, and overall success. These entities include customers, suppliers, employees, products, and more—all of which require precise management and understanding to fuel innovation, growth, and performance. However, managing these entities across multiple data sources often results in fragmented, inconsistent, and incomplete data.

Golden records—single, unified views of these critical business entities—are essential for fostering collaboration and ensuring that all stakeholders speak the same language. By resolving entities into golden records, businesses can not only capitalize on the value of their internal structured data but also harness the power of external and unstructured data.

The Challenge of Entity Resolution

Entity resolution, also known as entity linkage, record matching, or identity resolution, is the process of associating multiple disparate datasets into a single, logical entity. This data management technique addresses the challenge of reconciling records across different datasets by detecting and matching records that refer to the same entity despite variations in spelling, formatting, and other discrepancies.

In Guide to Entity Resolution with AI-Native MDM, we explore why entity resolution is inherently difficult. Key challenges include:

1. Heterogeneity of data quality: Data from various sources often use different formats, contain misspellings, and have missing information, making it difficult to connect data points across sources.
2. The problem of scale: With data volumes growing exponentially, traditional methods of matching records become impractical. Manual reconciliation, while accurate, does not scale.
3. Changing business context: Different use cases require different data sources and levels of precision. A compliance analyst might need more fuzzy matches, while a patient matching system must avoid linking different patients within one record.
4. Missing external context: Many times, internal data alone doesn’t provide the complete picture. External information, such as company name changes or email address updates, is crucial for accurate entity resolution.

AI-Native Solutions: The Future of Entity Resolution

Tamr's innovative, AI-native approach to entity resolution offers a solution to these challenges. By combining the power of AI and AI agents with human refinement and seamlessly integrating reference data, Tamr delivers highly accurate and scalable entity resolution. This approach not only addresses the limitations of traditional master data management (MDM) solutions, but also adapts as data continues to grow and evolve.

In our ebook, we highlight 10 key principles that make Tamr's AI-native entity resolution stand out:

1. Time-to-value: Quickly and easily onboard new data sources, both internal and external, to realize value faster.
2. Scalability: A cloud-based architecture optimizes the handling of large volumes of data.
3. Machine learning (ML): A machine learning-first approach improves with more data, increasing automation and efficiency.
4. Fit-for-purpose AI models: Pre-trained, fit-for-purpose AI/ML models plus human-in-the-loop guided agentic data curation enable organizations to close the “last mile” of data mastering and gain the highest level of accuracy possible.
5. Multi-domain connections: A connected view of multiple types of data in the form of an enterprise knowledge graph reveals previously hidden, actionable insights. 
6. Persistent IDs: Maintaining unique, persistent IDs provides a longitudinal view of entities.
7. Data lineage: Track record-level data provenance for compliance, audit, and trust-building purposes.
8. Match verification with external data: Incorporate external reference data maximizes match rates, accuracy, and confidence.
9. Enrichment: Establish linkages with trusted third-party sources to enable additional attributes to be integrated with just one click.
10. Real-time: Use real-time APIs to search the database, identify if a matching record exists (or not), and take the action to update an existing record or create a new one if no match is found.

Realizing the Promise of Golden Records

Golden records can transform how an organization operates by providing accurate, up-to-date views of key business entities. This comprehensive understanding of data enables businesses to uncover hidden insights, make informed decisions, and support data-driven collaboration across departments. As a result, businesses can enhance customer experiences, optimize operations, and achieve strategic growth.

At Tamr, we are committed to helping organizations overcome the challenges of entity resolution and unlock the full potential of their data. Our AI-native approach delivers golden records at scale that are accurate, reliable, and ready for consumption.

To learn about the value of AI-driven entity resolution and how Tamr can help your business achieve data-driven success, download Guide to Entity Resolution with AI-Native MDM today.