Entity Resolution and Deduplication: Challenges and How to Solve Them with AI

In this age of technology, businesses have access to a massive amount of data, including client details, product listings, financial information, and social media activity. While all of this data is beneficial to companies, the primary challenge associated with it is duplicating the same data and having different versions in various systems.

Two of the most important processes for clean, reliable, and actionable data are Entity Resolution (ER) and Deduplication. Both of these can be automated and enhanced using Artificial Intelligence (AI). In this blog, we will identify challenges that come with ER and deduplication and demonstrate how AI-driven solutions can help organizations manage them. 

What Is Entity Resolution and Deduplication?

Entity Resolution (ER) describes the process of recognizing and merging records that refer to the same entity across various data sources. Whether it is a customer, supplier, product, or a location, ER create a single and complete view.

Deduplication, on the other hand, deals with the detection and removal of duplicate records from a single dataset. This is a crucial process during the data cleaning phase that guarantees that organizations do not have redundant, outdated, or conflicting information.

Common Challenges in ER and Deduplication

Despite its importance, making entity resolution accurate and effective is complex. Typically, organizations face the following challenges:

1. Data Inconsistencies Across Systems

It is very difficult to match records using exact rules due to data entry mistakes, variations in spelling, abbreviations, and varying formats such as addresses and phone numbers. Differences in culture and localization (e.g., date formats, name orders)  also add considerable difficulty. To resolve these inconsistencies, such as standardization, linking and enrichment of multi-source records, a powerful entity management system (EMS) is required.

2. High Volume of Data

Traditional matching methods do not work with large datasets due to their slow and inefficient performance. Older systems often struggle with the massive quantity of record comparisons.

3. Limited Ground Truth for Model Training

Most AI models need labeled data like,  duplicates and non-duplicates. Gathering and verifying these datasets is manual and often expensive.

4. Risk of Incorrect Merges

A big problem with using ER systems is the risk of false positives, which is merging two unrelated records incorrectly, and false negatives,  which is missing true duplicates. Any of these situations could result in severe business problems, which include poor decision-making or customer dissatisfaction.

5. Data Privacy and Security

When dealing with customer or business information, entity resolution raises data privacy challenges. There is always the challenge of merging data from multiple sources while ensuring compliance with privacy regulations like GDPR or CCPA.

How AI Can Solve Entity Resolution and Deduplication Challenges

The application of AI technologies, including machine learning (ML), natural language processing (NLP), and deep learning, has transformed how organizations perform entity resolution and deduplication. Here’s how:

1. Intelligent Matching with Machine Learning

Rather than using exact matches of names and emails, AI algorithms evaluate similarities using probabilistic models. For instance, fuzzy matching can recognize that “Jon Smyth” and “John Smith” are the same person despite slight differences. These models become more efficient over time through learning from previous matching decisions. Recent breakthroughs in generative AI solutions are now advancing entity resolution further by allowing systems to understand context, semantics, and relationships between records in ways that traditional ML model systems could not.

2. Scalability through Deep Learning

More advanced AI models, which include Siamese Neural Networks and transformer-based models, can process millions of data points quickly. They analyze trends using embeddings and vector comparisons across names, addresses, and product descriptions. These solutions are extremely fast and can be expanded with the addition of GPU accelerators.

3. Active Learning and Feedback Loops

When there is little labeled data, active learning methods offer a way for models to “ask” for human assistance when they are unsure regarding the matches. This sets up a human feedback loop that improves performance over time without having to do extensive manual work.

4. Handling Multilingual and Unstructured Data

NLP models can work with various languages and can understand and interpret unstructured data (like text within emails, reviews, or responses to open-ended questions). This is particularly important for companies operating at a global scale, where data may be gathered in different forms and dialects.

5. Privacy-Preserving AI

With the advent of AI, new techniques like federated learning allow for the use of data in model training without the need to transfer the data or reveal any sensitive information. This allows organizations to comply with privacy laws while still reaping the benefits of AI.

Conclusion

Entity Resolution and Deduplication tasks are more than just technical requirements. They perform critical functions in the framework of a data management strategy. Analytic efforts, relationships with customers, and compliance obligations all stand at risk due to inaccurate or duplicated data. AI presents intelligent, scalable, and adaptive solutions that resolve entity resolution problems and increase data trust and quality.

An organization can achieve a definitive entity resolution, lowering operational costs, earning better business intelligence, and making better use of its data assets by employing AI entity resolution tools.