What is Entity Resolution?

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.

‍Summary:

• Entity resolution is a process that identifies and matches records across multiple data sources to create a golden record of key business entities.
• It helps organizations overcome challenges with duplicate data, data integration, data quality, and regulatory compliance.
• Resolving entities involves comparing attributes and features of records to determine the likelihood of them referring to the same entity.
• Entity resolution is critical for modern data management, improving data quality, accuracy, and consistency.
• Using AI-native MDM is the best way to resolve entities and achieve golden records.

Data is proliferating. It's stored in diverse systems and undergoes multiple transformations, making it increasingly difficult to identify and reconcile duplicate or disparate records across the business. And that's the problem. Without a unified view of key entities such as customers, suppliers, or patients, businesses struggle to deliver exceptional customer experiences, spot untapped revenue opportunities, or identify growth opportunities. As data continues to grow and become increasingly complex, this challenge will only intensify. That's why businesses need entity resolution, a critical process that reduces data redundancies by creating golden records that represent the best version of their critical business entities.

Entity Resolution, Explained

Entity resolution, also referred to as entity linkage or entity matching, is a data management process that identifies and links records across multiple data sources and datasets to create a unified view, or "golden record," that represents the best version of critical entities such as customers, suppliers, products, or patients. Done well, entity resolution improves the quality, accuracy, and consistency of an organization's data, which, in turn, increases its usage and value across the business.

How Entity Resolution Works

Resolving entities involves comparing the attributes and other data elements of records to determine the likelihood of them referring to the same entity. It involves analyzing a set of records and inspecting them to find variations in the representation of the same entity, such as different spellings, syntaxes, relationships with other records, discrepancies in addresses and other fields, or missing information. It then involves combining the information into a single, golden record that comprises the best, most complete, and most accurate version of the entity.

For some businesses, entity resolution is a manual process. Analysts sort and filter data based on key attributes such as name or address, and then manually review and decide which records to merge. This approach is not only time-consuming and tedious but also requires significant human effort.

Other businesses employ a rules-based approach using traditional master data management (MDM) solutions. Using predefined rules, analysts compare records and determine whether or not they are referencing the same entity. This approach, while more efficient than one that is fully manual, is not without its flaws. MDM relies on rules. And when data changes, rules must change, too. Over time, numerous changes to the rules cause them to become overly complicated and brittle, making them difficult to update and impossible to scale.

The best way to approach entity resolution is by using artificial intelligence (AI) and machine learning (ML) that is highly-tuned and trained for this purpose. When businesses embrace AI/ML for entity resolution, everything changes.  

Human refinement is critical to ensure the AI models and the golden records they produce are accurate, reliable, and trustworthy. When paired with AI, humans reduce the amount of time they spend on tedious, rote tasks and increase the amount of time they spend adding unique perspective and value through feedback.

It's the best of both worlds: efficiency and scalability that come with AI plus expertise, emotion, and empathy that only humans can provide.

The Significance of Entity Resolution in Data Management

Entity resolution is a critical part of modern data management. Dynamic data from multiple sources and systems often contains inaccurate and duplicate records, prompting many decision-makers to question its integrity. But when businesses embrace entity resolution, they commit to creating golden records, a single, authoritative, accurate version of a business entity’s data across multiple data sources and datasets.

Using entity resolution, organizations can:

• Reduce duplicate records: data entry errors, inconsistencies, or slightly different representations of the same entity are the main culprits of duplicate data. Entity resolution identifies and merges redundant entities into a golden record, improving data integrity and accuracy.
• Improve data integration: because systems capture data attributes differently, integrating records across systems and sources can prove challenging. Entity resolution reconciles these differences, making it easier to consolidate multiple entities into a single, golden record that spans datasets.
• Increase data quality: data that is incorrect, inconsistent, and incomplete hinders data analysis and decision-making. Entity resolution improves data quality by spotting inconsistencies, standardizing the data, and filling in missing values to make the golden record as complete and useful as possible.
• Boost efficiencies: manually matching and reconciling data records is tedious, time-consuming, and expensive. With entity resolution, organizations can quickly and easily spot matches and reconcile them, saving time and reducing costs.
• Support regulatory compliance: maintaining accurate and consistent records is critical for regulatory compliance. Entity resolution helps data remain compliant by ensuring it is accurate, consistent, and complete.

Integrating Entity Resolution into Your Data Management Strategies

Given the relentless growth in the size and complexity of data, it's no surprise that entity resolution is quickly becoming an increasingly important component of modern data management processes. After all, having AI-powered, human-refined golden records is key for organizations that want to gain a competitive edge and secure a leadership position in an increasingly complex, dynamic marketplace.

If your organization is looking to incorporate entity resolution into your data management practices, here are four things you should consider:

• Agree on which entities are the most important for your business. For many organizations, customers are key. But for others, it may be suppliers, products, patients, or students.
• Identify all the systems and sources where records for this entity could exist. Don't just think about line-of-business database solutions. Also consider central data warehouses, data lakes, third-party enrichment sources, flat data files, and even Excel spreadsheets.
• Determine the technology you will use to support your entity resolution processes. As mentioned above, the best solutions use AI/ML to do the heavy lifting. And if the solution can resolve entities in real time, that’s even better. 
• Appoint people to support the process and validate the results. Human refinement is critical to ensuring the accuracy of the resulting golden records.

Resolving key business entities is critical to delivering the high-quality data that powers analytics and decision-making. Done well, entity resolution will deliver the golden records that companies need to drive more sales, improve customer experiences, minimize compliance risks, and transform their business.

‍To dive deeper into the transformative power of entity resolution and how it can benefit your organization, download our ebook Guide to Entity Resolution with AI-Native MDM to discover the strategies and technologies that leading organizations are using to turn their data into a powerful asset.