Entity resolution is the process of determining which data records refer to the same real-world entity. An “entity” in this context can refer to a number of things, but when talking about entity resolution, data practitioners are usually working with customer, product, patient, healthcare provider or location data. Simply put: people, places and things. In an AI-driven world, entity resolution has become foundational because AI systems can only be as trustworthy as the entity data they reason over.

Entity resolution is a necessary part of master data management, especially when dealing with large datasets or data from multiple sources. These variations across systems — different formats, outdated fields and occasional errors — make it challenging to determine when two records refer to the same real-world entity. This is where entity resolution software can be a huge help.

Entity resolution (ER) software is a key benefit of master data management that helps data professionals identify records that point to the same real-world object or entity. Entity software  is usually sold as a standalone application or as a feature within a larger data platform, like a master data management (MDM) or unified data management solution.

Entity resolution software works by:

1. Integrating data from a number of different sources
2. Identifying potentially matching records among those datasets
3. Merging records that match
4. Removing, or deduplicating, duplicate records

From there, the cleaned and deduplicated data is usually loaded into a data warehouse or data lake, where it can then be used by other business systems like CRM, BI and analytics or ERP. This data is usually referred to as “consumable” or “business-ready” data.

Corporate entity data management software helps businesses by centralizing, validating and connecting entity records so teams can continuously work with accurate, consistent information. With this solution, companies can better target their customers through advertising and product recommendations, operate more efficiently, reduce unnecessary spending and lower the risk of fraud.

Entity resolution tools handle data by ingesting information from multiple systems, standardizing it and matching records that refer to the same real-world entity. To do this effectively, the software pulls in data from sources such as: 

• CRM systems that capture data like customer name, email address, shipping and billing addresses, payment information, products viewed and purchase history
• Product information management (PIM) systems that store product-related data like SKU numbers, product images, size and weight measurements, availability regions, price and inventory counts
• Internet of things (IoT) devices that track everything from personal health information like blood pressure, heart rate, step counts and blood oxygen levels to information about machinery on a factory floor like machine temperature, supply levels, vibration readings, humidity and more

The best entity resolution software is a solution that provides more than just data deduplication, becoming a critical component of a broader MDM strategy. That’s the case with Profisee’s platform, which stands out by offering machine-learning-powered matching, flexible survivorship rules to create the highest-quality golden records and native integration with cloud ecosystems such as Microsoft Fabric. 

These capabilities position Profisee’s comprehensive entity resolution solutions as the definitive choice for enterprises seeking trusted data at scale and enabling AI readiness.

Entity resolution solutions in an MDM platform transform raw, disconnected data into a single, trusted golden record: the foundation of effective entity data management. Steps include:

1. Data integration: The MDM platform ingests data from CRM, ERP and other source systems and immediately standardizes it to ensure consistency
2. Entity matching: The entity resolution engine uses deterministic, fuzzy and probabilistic matching, often enhanced by machine learning, to compare records and identify which ones represent the same real-world entity, even when values differ or contain errors
3. Data governance and review: Data stewards review flagged matches, apply governance rules and resolve exceptions to ensure accuracy and compliance with business policies
4. Survivorship logic: The platform applies survivorship rules to select the best, most trusted value for each attribute, such as the most recent address or the verified phone number
5. Golden record creation: The system creates and publishes a unified golden record, making it available to operational applications, analytics platforms and cloud data environments for consistent, high-quality entity data across the enterprise

An example of entity resolution is a healthcare organization unifying patient records across multiple systems. Large healthcare networks often store patient data in electronic health records (EHRs), lab systems, scheduling tools and billing platforms, each holding different pieces of information about the same person.

In a master data management (MDM) system, entity resolution matches and merges all records for a single patient, even when names, IDs or formats differ. The result is one complete, accurate profile that includes medical history, test results, appointments and financial details.

There are multiple benefits of entity resolution solutions:

• Businesses can market their products and services to customers more effectively
• Eliminating redundant processes lowers costs and helps businesses be more nimble
• Customers enjoy a more seamless shopping experience and get better recommendations for products they might need or enjoy
• Healthcare providers can provide better, more holistic treatment to their patients
• Retailers can reduce the number of reshipments due to address errors and reduce customer service interactions

There are many other examples you can find across different organizations and industries, but these are some general benefits of using entity resolution.

You can implement entity resolution by establishing a clear business objective, preparing your data sources and deploying them as part of a master data management (MDM) platform that matches and unifies records across systems.

Follow these steps to implement entity resolution within an MDM solution:

• Develop a business case: You’re much more likely to get executive buy-in for an entity resolution project if you can tie it back to a specific business case. For instance, you might show that the company could save money by consolidating supplier contracts and negotiating better rates. That’s very difficult without performing entity resolution on supplier data to identify where multiple contracts exist with the same suppliers.

• Perform data discovery: Audit the source systems your organization uses to identify which ones store the kind of data you’ll resolve. Make a list. Secure the appropriate level of access to those systems so you can integrate their data into your MDM platform later.

• Configure data governance policies: If you don’t already have a data governance program, now is a good time to put one in place. If you do, make sure the policies that pertain to your entity data are written and published in your data governance or data catalog tool. Strong governance is what turns matched records into trusted ones.

• Deploy an MDM platform with entity resolution: Rather than bolting on a standalone matching product, choose an MDM solution that delivers entity resolution alongside governance, survivorship and data stewardship in one place. Talk to peers in your field for recommendations and check with analyst firms like Gartner for advice on what fits your organization.
• Integrate, deduplicate and standardize data: Using an MDM solution like Profisee, integrate the source systems you identified in step 2 to match records and remove duplicates. Prioritize survivorship capabilities so you keep only the most accurate, up-to-date information about each entity, then standardize those records against your governance policies to produce trusted golden records.

Machine learning models improve entity resolution by automating data matching, increasing accuracy and reducing the manual review required for ambiguous records. When integrated into a broader entity resolution or MDM platform, they accelerate work that would otherwise take hours to complete manually..

Tools like Profisee MDM use machine learning to identify matching records with greater speed and accuracy, resulting in fewer instances of records requiring manual review by data stewards. Most datasets are bound to contain some ambiguous data, so it’s important to keep a human in the loop to make certain judgment calls that a software tool can’t.

Profisee’s entity software provides an enterprise-grade MDM solution that offers a more sophisticated and scalable approach than basic, standalone matching tools. Here’s a breakdown of the main differences: