How Tamr Solves Real-world Entity Resolution at Scale: A Five-part Video Series Overview

Editor’s Note: This post was originally published in March 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.

‍‍As data ecosystems become increasingly complex, resolving entities accurately and efficiently at scale can be the key to unlocking the full potential of data for analytics, machine learning, and decision-making. But entity resolution (ER) is one of the most critical challenges facing organizations as they deal with increasingly large and fragmented datasets. Tamr, a leading provider of AI-native master data management (MDM) solutions, addresses this challenge in a five-part video series that delves into the intricacies of entity resolution at scale, sharing insights on how to tackle this problem using innovative technologies. Let’s take a look.

Part 1: Why is Entity Resolution at Scale Hard?

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The first video of the series focuses on the underlying complexity of entity resolution. While it may seem straightforward to match records of the same entity (e.g., matching customer records across systems), the task becomes exponentially more difficult as datasets grow in size and diversity. Tamr highlights two primary challenges:

• Volume and variety of data: As organizations scale, they must deal with more records from multiple sources in different formats.
• Ambiguity: Human errors, discrepancies in data, and inconsistent naming conventions introduce ambiguity that traditional methods struggle to handle.

Tamr’s AI-native technology emphasizes that the key to success lies in shifting from rule-based, deterministic systems to machine-learning-driven approaches that can adapt to the complexity of large datasets.

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Part 2: Making the Easy Part Cheap

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In this video, the focus shifts to efficiency. Entity resolution processes often include components that are relatively simple but computationally expensive, such as pairwise comparisons between records. These comparisons can grow exponentially as the dataset expands.Tamr proposes that companies can save money by focusing on optimizing the easy parts of the process. Specifically, by leveraging distributed computing and filtering techniques, they reduce the number of comparisons necessary while maintaining high levels of accuracy. This reduces computational expense and ensures that resources can be devoted to solving more challenging problems in entity resolution.

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Part 3: Making the Hard Part of Entity Resolution Scale

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The third video delves into what Tamr describes as the "hard part" of entity resolution—handling cases that involve significant ambiguity, such as missing or conflicting information across datasets. At scale, traditional deterministic approaches fail to accurately resolve these records, leading to incomplete or inaccurate results.

Tamr’s solution to this problem involves leveraging machine learning models trained on expert feedback to identify which records likely refer to the same entity, even in cases where there are substantial differences. This part of the process is critical for scaling, as the system learns from real-world data and improves over time, allowing it to resolve ambiguous records more efficiently.

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Part 4: Ensuring Effective Learning for Entity Resolution

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Ensuring that machine learning models are effective in the context of entity resolution is a challenge in itself. In this video, Tamr explores the strategies used to continuously improve the learning process. The key takeaway here is the role of human-in-the-loop learning: by incorporating expert feedback into the machine learning models, Tamr ensures that the system is able to adapt to new patterns and anomalies in the data.

This iterative approach allows for more accurate and reliable entity resolution over time, reducing false positives and negatives. Tamr's AI-native solution is designed to continuously learn, ensuring that the quality of resolution improves as it processes more data.

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Part 5: Coping with Change

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The final part of the series discusses how Tamr handles change. In dynamic environments, data is rarely static. New records are constantly being added, while existing records may be updated or deleted. Managing entity resolution in such a constantly changing environment requires flexibility and adaptability.Tamr copes with change through incremental updates and continuous learning. Rather than retraining models from scratch every time the dataset changes, the platform leverages a process that allows the system to update quickly while maintaining accuracy. This approach minimizes downtime and ensures that entity resolution keeps pace with the evolving data ecosystem.

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Conclusion

As you’ve witnessed throughout this video series, entity resolution is complex. However, by combining distributed computing, machine learning, and expert feedback, Tamr's AI-native approach offers a scalable, efficient, and highly effective solution to entity resolution. If your organization is dealing with large and diverse datasets, Tamr provides a roadmap for mastering data to unlock its full potential.

To learn more about how Tamr can help your organization resolve entities at scale, please request a demo.