Every organization depends on data, but data is rarely clean or consistent. The same person may appear under slightly different names in separate systems, a company may be listed with multiple addresses, or a product may be described in several ways across marketplaces. These inconsistencies create duplicates, fragment insights, and make reliable decision-making harder.

Entity resolution (ER) addresses this problem. It is the process of identifying and linking records that refer to the same real-world entity, even when the data is messy, incomplete, or inconsistent. Without effective ER, a “single view” of customers, suppliers, or assets remains out of reach, limiting the value of analytics and increasing the risk of errors.

The importance of entity resolution extends across industries. Banks rely on it to spot fraudsters opening accounts under slightly different identities. Marketing teams depend on it to build a consistent Customer 360 profile. And cybersecurity analysts use ER to connect events tied to the same attacker infrastructure.

In this article, we will explore what entity resolution is, how it works, the techniques that power it, leading tools and frameworks, practical use cases, and best practices for implementing it effectively. By the end, you will have a clear view of why ER is essential for modern data management and how it can be applied in your own context.

What Is Entity Resolution (ER)?

Entity resolution is the process of determining when different records in one or more datasets refer to the same real-world entity. An “entity” can be a user, organization, product, place, or any object you want to track. The challenge is that data about these entities is often duplicated, inconsistent, or incomplete.

For example, the same customer might appear as “Jane Smith,” “J. Smith,” and “Jane Smyth” across different databases. Without resolution, these would be treated as separate individuals, leading to fragmented analysis and poor decisions. ER brings these records together, recognizing that they all represent the same person.

At its core, ER answers two questions:

1. Do these two records describe the same entity?
   
2. If so, how should they be merged into a single, consistent view?
   

This capability is fundamental to creating “golden records” in master data management, building accurate Customer 360 profiles, consolidating medical histories, or detecting fraud and security threats. By resolving entities, organizations move from scattered data points to reliable insights that reflect the real world.

Core Concepts of Entity Resolution

Entity resolution rests on a few foundational ideas that remain the same no matter which algorithms or tools are used.

Entities, Identifiers, and Attributes

Each entity—such as a person, organization, or product—is described through identifiers (like email, phone, or ID number) and attributes (like name, address, or description). Because identifiers are often missing or inconsistent, ER must consider multiple attributes to recognize when records refer to the same entity.

Matching and Linking

At the heart of ER is the decision of whether two or more records describe the same entity. Once a match is confirmed, the records are linked together so they can be treated as one.

Clustering and the Golden Record

When many records point to the same entity, they are grouped into a cluster. From this cluster, a unified “golden record” is created that captures the most reliable and complete information available.

Identity Graph

Resolved entities can also be organized as a graph. In an identity graph, each entity is a node, and its connections to identifiers, accounts, and attributes form the edges. This structure provides a richer view of how records relate to one another and supports advanced analysis.

Iterative Refinement

Entity resolution is rarely finished after a single pass. As new data arrives, matches are re-evaluated, clusters updated, and the identity graph expanded. This iterative process ensures that the resolved entities remain accurate over time.

How Does Entity Resolution Work?

Entity resolution combines different techniques to decide when records refer to the same entity and then unify them. These techniques operate at different levels: some decide matches directly, others provide supporting signals, while graph and execution modes shape how the process scales.

Ways to Decide Matches

• Deterministic Matching: Uses strict rules, such as treating two records with the same passport number as the same person. Reliable when unique identifiers are present, but brittle when data is messy.
  
  
• Probabilistic Matching: Weighs evidence across multiple fields. For example, high similarity in names, dates of birth, and addresses might give a 90% chance of a match. More flexible than deterministic rules.
  
  
• Machine Learning Models: Extend probabilistic methods by learning how to combine signals from training data. Models can be supervised (using labeled pairs) or unsupervised (clustering). Modern systems often use embeddings to capture semantic similarity in text or product descriptions.

Signals that Support Matching

• Similarity Measures: Algorithms like edit distance, Jaro–Winkler, cosine similarity, or phonetic encodings help quantify how close two values are. These signals are not complete methods on their own, but they feed into deterministic rules, probabilistic models, or ML classifiers.

Consolidating Matches into Entities

• Graph-Based Resolution: Pairwise matches can be treated as edges in a graph. Clusters of connected records then represent entities. In simple cases, this is equivalent to finding connected components. In noisier or large-scale settings, community-detection algorithms help refine the grouping. This shows how local match decisions become global entity structures.

Execution Modes

• Batch Resolution: Runs periodically on large datasets, often in data warehouses or master data management platforms.
• Real-Time Resolution: Evaluates each new record as it arrives, which is essential for fraud detection, personalization, and cybersecurity.
  

In practice, these approaches are combined. Deterministic rules cover clear cases, probabilistic or machine learning models resolve uncertainty, similarity measures supply signals, and graph structures consolidate the results into coherent entities.

Workflow of Entity Resolution

Entity resolution is not a single action but a sequence of steps that take raw, messy records and transform them into unified entities. The workflow can be thought of as moving from raw data → pairwise matches → clusters → unified entities → identity graph.

1. Data Preparation

Records are standardized and enriched so that attributes like names, addresses, or dates are in comparable formats. Without this, downstream matching would be unreliable.

2. Candidate Generation

Since comparing every record with every other record is too costly, candidate pairs are generated using techniques such as blocking or indexing. This step narrows the search space to likely matches.

3. Similarity and Matching

Each candidate pair is evaluated using deterministic rules, probabilistic scoring, or machine learning models. Local similarity measures such as edit distance or phonetic encoding help capture near matches. The output is a set of pairwise decisions that can be represented as edges in a graph.

4. Clustering

Once edges are established, records that are directly or indirectly connected form clusters. In graph terms, this is often done by finding connected components. In more complex settings, community detection algorithms refine the clusters to avoid over- or under-linking.

5. Golden Record Creation

Within each cluster, the system produces a single consolidated version of the entity: the golden record. This record merges attributes from all sources, choosing the most reliable or recent values where conflicts exist.

6. Building the Identity Graph

The final step is representing resolved entities and their relationships as a graph. Each entity cluster becomes a node, and its links to identifiers, accounts, or attributes form the edges. The identity graph goes beyond deduplication, enabling rich analysis such as tracing relationships across shared devices, addresses, or transactions.

‍

Best Tools & Frameworks for Entity Resolution

A wide range of tools exist for entity resolution, from lightweight open-source frameworks to enterprise platforms and cloud services. Below are some of the best end-to-end options.

Entity Resolution Use Cases

Entity resolution is a foundational capability across many domains because nearly every organization deals with duplicate, fragmented, or inconsistent records. Some of the most common use cases include:

Customer 360 and Personalization

Identity resolution is vital for accurate Customer 360 graphs. Businesses often store customer data in multiple systems—CRM, e-commerce, marketing automation, and support platforms. ER connects these fragments into a single customer view, enabling personalized recommendations, targeted marketing, and improved service.

Fraud Detection and Financial Risk

Fraudsters frequently create accounts with variations of the same identity. ER helps financial institutions and fintech companies link related records—such as shared phone numbers, addresses, or devices—to detect fraudulent patterns and reduce losses.

Cybersecurity and Threat Intelligence

For cybersecurity, analysts use ER to unify events tied to the same attacker infrastructure. For example, IP addresses, domains, and accounts may appear different but belong to a single adversary. Linking them improves detection of attack campaigns and response coordination.

Healthcare and Patient Safety

Hospitals and healthcare providers must merge patient records from different departments or systems to avoid dangerous errors. ER ensures that medical histories, prescriptions, and lab results are accurately linked to the right individual.

Government and Compliance

In areas like anti-money laundering (AML), counter-terrorism, or public records management, ER helps agencies reconcile large volumes of identity data. The goal is to ensure accuracy, avoid duplication, and surface hidden connections.

Supply Chain and Product Data

Products, suppliers, and inventory in the supply chain often appear under different identifiers across systems. ER aligns this information into consistent records, improving procurement, logistics, and regulatory reporting.

Academic and Research Data

In scientific publishing, author names and institutions often vary across papers. ER supports citation analysis and bibliographic databases by linking researchers and their works.

Challenges in Entity Resolution

While entity resolution is powerful, implementing it effectively comes with difficulties. These challenges explain why ER often requires a mix of techniques, domain expertise, and careful governance.

• Data Quality Issues: Typos, inconsistent formats, missing fields, and outdated information make it difficult to compare records accurately. Even advanced methods can fail if the underlying data is unreliable.
• Scalability: Naively comparing every record with every other record is computationally expensive. Efficient candidate generation (blocking, indexing) is essential, but designing it correctly for large datasets remains a challenge.
• Ambiguity and Uncertainty: Some records may look similar but refer to different entities (e.g., two people with the same name and birthdate). Others may lack enough information to be clearly resolved. Deciding how to handle ambiguous cases is non-trivial and often requires thresholds, probabilistic reasoning, or human review.
• Evolving Data: Entities are not static. People change addresses, companies rebrand, products are updated. ER systems must continuously refine clusters and identity graphs to keep pace with changing data.
• Privacy and Compliance: Entity resolution often involves personal or sensitive data. Ensuring that resolution processes comply with regulations like GDPR or HIPAA, and that linked data doesn’t expose more than intended, is a major concern.
• Integration Complexity: ER rarely operates in isolation. Integrating resolution outputs into CRMs, data warehouses, analytics platforms, or graph systems requires careful design, otherwise the benefits are trapped in silos.

Best Practices in Entity Resolution

The challenges of data quality, scale, ambiguity, evolving records, compliance, and integration can be addressed through a set of proven practices:

• Strengthen data preparation: Standardize formats, normalize text, and enrich records with reliable reference data before attempting matches.
• Combine complementary methods: Use deterministic rules for certain matches, probabilistic or ML models for uncertain cases, and graph clustering to consolidate results.
• Balance automation with oversight: Automate clear cases, but route ambiguous ones to human review, with transparent explanations for decisions.
• Treat ER as ongoing: Update thresholds, retrain models, and refresh clusters regularly as new data arrives or entities change.
• Safeguard privacy: Build controls that respect regulations and apply privacy-preserving techniques when linking sensitive data.
• Embed results into workflows: Ensure golden records and identity graphs are accessible in downstream systems so they support real decisions.

Bonus: PuppyGraph

Once entities are resolved, the next step is often to explore those relationships. Representing resolved entities as an identity graph makes it possible to see how people, accounts, devices, or products are connected. With a graph query engine such as PuppyGraph, this graph can be built directly on relational or lakehouse data, and algorithms like connected components can be used to reveal clusters of related entities.

As data volumes grow, entity resolution will remain a cornerstone of data quality and integration. Combining sound practices with the ability to represent results as graphs allows organizations not only to resolve duplicates but also to uncover the deeper patterns hidden in their data.

Conclusion

Entity resolution turns fragmented, inconsistent records into unified views of real-world entities. It combines rules, probabilistic methods, machine learning, and graph analysis to handle messy, large-scale, and evolving datasets. The outcome is more than just deduplication as it produces golden records for consistency and identity graphs that capture the relationships across systems.

If you want to see how identity graphs can be built and queried after entity resolution, try the forever-free PuppyGraph Developer Edition or book a free demo with our team!