Entity Resolution Software

Entity resolution is the process of determining which data records refer to the same real-world entity. For data practitioners, entities are usually customer, product, patient, healthcare provider or location data. Simply put: the people, places and things a business runs on.

Different enterprise systems usually store the same entity in their own way, with different formats, outdated fields and typos. Master data management (MDM) tools with entity resolution service compare those records, decide which ones match and merge them into one golden record your teams and AI agents can act on.

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 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.

Entity resolution (ER) software is a tool that finds records pointing to the same real-world entity across your systems and merges them into one accurate record. Vendors offer ER as a standalone tool, as a managed entity resolution service or as a feature inside a master data management (MDM) platform.

Entity resolution tools handle data by putting it into a common format, then comparing records on several fields at once to decide which ones describe the same entity. This process allows an entity resolution platform to match records even when there’s no shared ID, a name has a typo or an address uses an abbreviation. Most tools combine four matching methods:

• Exact matching: A deterministic rule that links two records only when a trusted identifier, such as a tax ID or email address, is identical in both
• Fuzzy matching: A text-similarity technique that scores how close two values are, so it catches spelling variations, nicknames and abbreviations, such as “Acme Inc” and “ACME Incorporated”
• Probabilistic matching: A weighted scoring model that compares name, address, phone and date of birth together and rates how likely two records describe the same entity
• Machine learning matching: A trained model that learns from your data stewards’ past match decisions and uses that history to score new records more accurately

Profisee adds AI Similarity Matching, which compares what values mean instead of how they look, so “30” and “Thirty” count as a match. See how we combine these methods in our record linkage software.

The best entity resolution software for business data is the one that matches your own records accurately, explains every match decision and keeps the data accurate after the first cleanup. That’s the case with Profisee’s entity resolution 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.

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.

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 find matching records faster and more accurately, which leaves data stewards with fewer records to check by hand. Most datasets still hold some ambiguous records, so Profisee flags them for your data stewards, who make the judgment calls a model should not make alone.

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: