What is Entity Resolution?

Understanding Entity Resolution

Entity resolution is a critical component of master data management (MDM), serving as the foundation for consolidating, reconciling, and maintaining accurate, consistent, and unified views of core business entities. Master data acts as a superset of a company’s broader data ecosystem, aggregating information from various internal and external systems that may all reference the same real-world entities such as customers, products, suppliers, employees, or locations each with potentially different formats, identifiers, or naming conventions.

Entity resolution is the process of identifying, matching, and merging records that correspond to the same entity across these disparate systems. For example, a single customer might appear in a sales database under one name, in a marketing CRM with a nickname, and in a support system under a misspelled variation. Without entity resolution, these would be treated as separate records, leading to data duplication, fragmented insights, and inconsistencies that affect analytics, reporting, and customer experiences.

One of the key challenges entity resolution addresses is the lack of standardization in data structures, naming conventions, and formatting rules. Fields may be structured differently (full names vs. first/last names), contain inconsistent formatting (phone numbers with or without country codes), or reflect variations due to manual data entry errors. This structural and semantic diversity contributes significantly to poor data integrity, as the same real-world entity may be misrepresented in multiple ways.

Entity resolution leverages techniques such as rule-based algorithms, machine learning models, and probabilistic matching to resolve ambiguities and establish relationships between records. This process is essential not only for cleaning and enriching data, but also for creating a trusted “single source of truth” that powers enterprise-wide operations, analytics, compliance efforts, and strategic decision-making.

By resolving duplicate or conflicting records into a single, authoritative representation, entity resolution helps maintain data integrity, reduce redundancies, and improve data quality across the organization.

What is Dynamic Entity Resolution?

In entity resolution, the process of matching different data points that could represent a single entity is called similarity analysis, and it’s an ever improving field. There are three common approaches to a similarity analysis each with increasing complexity: traditional matching, which focuses on directly matching records but yields poor results; batch entity resolution, which constructs better results into a single view of entities, and real-time entity resolution, which constructs a single view which remains current.

The next evolution of similarity analysis, referred to as dynamic entity resolution, emphasizes the regeneration of entity views from underlying raw data in real-time with respect to specific use case requirements. Similar to real-time entity resolution in that it remains current, dynamic entity resolution also remains more relevant.

In some use cases broader or tighter targeting or specificity may be required. So, the premise of regenerating entities is to allow different combinations of data matching criteria for individual entities instead of assuming that one criteria of an entity can fit all use cases. In essence, dynamic entity resolution allows different fuzziness levels that fulfill data access and application requirements. This has become beneficial for enterprise-level data solutions supporting multiple use cases.

Why is Entity Resolution Important?

Entity resolution is a foundational component of effective master data management, as it enables organizations to accurately consolidate and identify records that refer to the same real-world entity across multiple, disparate data sources. In large enterprises, data about customers, suppliers, products, or employees is often stored in different systems such as CRM platforms, ERP systems, marketing databases, and third-party sources, each of which may represent the same entity differently due to inconsistencies in naming, formatting, or structure.

Without entity resolution, master data cannot be reliably constructed. There would be no dependable method to unify information across systems, and any effort to draw insights or run analytics would be flawed due to duplicated or conflicting data. For example, a customer might exist as multiple fragmented profiles across departments, resulting in missed sales opportunities, poor customer service, or inaccurate reporting.

Therefore, the output of the entity resolution process is not just a merged dataset, it is the creation of high-integrity, trusted master data that supports enterprise-wide operations, compliance, personalization, and decision-making. In this way, entity resolution is not only critical but indispensable for any organization aiming to build a reliable, centralized data foundation.

What is the Process of Entity Resolution?

Entity resolution is a step within the larger process of the master data management key processing model, of which each stage overlaps and impacts overall data quality. Entity resolution effectiveness should not be considered in isolation. A comprehensive MDM environment will include the following processing steps.

Data Model Management — Master data is purpose built to transcend complications of inconsistent data that lead to poor understanding. The solution is to establish clear and consistent logical data definitions within the context of the business. Then data systems should be made to speak this language between each other.

Another established method is to use globally unique identifiers (GUID) that represent an entity and reference data can be associated through this GUID. In this way the data model overcomes the dependency on system speak, a principle which should also extend down to attributes that describe data within systems.

Data Acquisition — New data sources, and data within those sources may be inconsistent. Because of these external and internal inconsistencies, establishing a reliable, repeatable data acquisition process will support the ability to effectively manage and improve entity resolution activities, like validating, standardizing and enriching data.

Data Validation, Standardization and Enrichment — At a minimum to ensure good data consistency, validation, standardization and data enrichment should be implemented. Validation aims to eliminate erroneous data entries, like fake emails. Standardization conforms data to known values (like country codes), formats (like telephone numbers), and fields (like addresses). While data enrichment improves the process by adding useful attributes that aid in more accurate entity resolution. This results in cleansed data ready for entity resolution.

Entity Resolution — Entity resolution consists of a general workflow that subjects the validated and standardized data to a set of match rules which determine how to proceed based on deterministic and probabilistic matching algorithms. Similar entries are treated according to their score. Entities with scores that signal tight similarity may be automatically resolved, others that are fuzzier may be sent to a data steward for resolution. And still, entity cross-referencing may simply be recorded while the master record remains unchanged. Further entity resolution management activities include Master Data ID management—management of the Global IDs and Cross-Reference (x-Ref) information—and Affiliation Management—understanding and establishment of the relationships between MD entity records that correspond to the relationships they share in the real-world.

At this point, Identification Management and Metadata Management systems will begin to manage the growing metadata and Globally Unique Identifiers that support access to the data now connected to newly discovered entities.

What are Examples of Entity Resolution?

To illustrate, we use the following source data received by an MDM system. Imagine two data sets pulled together with very similar structures, but inconsistent entry data.

Between the three entries, standardization appears to be missing, but many similarities are present. Firstly, the surnames create overlap and because the addresses are very close to the same there is cause to believe these entries are related. But the abbreviated first name in entry 183 leaves questions, and the entities need to be resolved. Potentially this entry could represent the same entity as one of the other two, or a third entity living at the same address, or simply be out of date. Similar discrepancies in the telephone fields also present questions. If it’s learned that Jacob Smith’s telephone is different from Joanna Smith’s, then there is a better chance that entry 183 is Joanna Smith. But if entry 549’s telephone is identical to J. Smith, then more information may be needed to resolve the correct entity.

This simplified demonstration shows entity resolutions at a very basic level, sometimes it is performed manually on small data sets using spreadsheets. But these techniques are absurdly inadequate for organizations today who are leveraging their big data as an operational asset. In these big data cases entity resolution needs to be automatic to be effective and efficient. Master Data Management platforms provide these automated entity resolution capabilities.

Benefits of Entity Resolution

Entity resolution offers significant benefits across the data management lifecycle, especially in environments where large volumes of data are generated, stored, and used for decision-making. By accurately identifying and linking records that refer to the same real-world entities such as customers, products, suppliers, or patients, entity resolution helps organizations create a unified and trustworthy view of their data. Below are the key benefits of implementing effective entity resolution:

• Improved data quality and integrity: Entity resolution enhances the accuracy, completeness, and consistency of data by eliminating duplicate and fragmented records. By resolving different versions of the same entity into a single, consolidated record, organizations can ensure that their datasets are free from redundancy and contradictions. This improved data quality is essential for driving reliable insights, minimizing errors, and maintaining data integrity across the enterprise.
• Stronger master data management: At the heart of any MDM program lies the need to construct accurate master records that represent the single source of truth for each critical business entity. Entity resolution is foundational to this process, as it enables the matching and merging of records from various systems. Without it, organizations risk working with incomplete or conflicting information, which compromises MDM effectiveness.
• Enhanced customer 360 views: In customer-centric industries such as retail, finance, and healthcare, entity resolution enables organizations to consolidate customer data from multiple touchpoints such as sales transactions, service calls, social media, mobile apps, etc. into a comprehensive customer 360 view. This unified profile allows for better personalization, improved customer service, and more targeted marketing efforts, resulting in stronger engagement and loyalty.
• Improved operational efficiency: Duplicate or mismatched records can lead to inefficiencies in business processes, such as billing errors, repeated outreach, and poor inventory tracking. Entity resolution reduces these inefficiencies by streamlining workflows and ensuring that every department accesses consistent and accurate data. For example, a logistics company can more efficiently route deliveries by consolidating customer addresses that were previously duplicated across systems.
• Accurate and actionable analytics: Business intelligence and analytics rely on clean, reliable data. When data is fragmented or inconsistent, the insights derived from it can be misleading or entirely incorrect. Entity resolution ensures that analysis is based on a single, accurate view of each entity, increasing confidence in reports, dashboards, and predictive models. This is particularly valuable for data science, machine learning, and AI applications that require high-quality inputs.
• Regulatory compliance and risk reduction: Many industries are subject to strict regulatory requirements concerning data quality, security, and transparency such as GDPR, HIPAA, and CCPA. Entity resolution helps organizations maintain accurate records, monitor data lineage, and meet audit requirements by ensuring that data is traceable and consolidated. It also plays a role in fraud detection and anti-money laundering by identifying patterns and relationships across disparate records that may otherwise go unnoticed.
• Scalability across systems and domains: Modern organizations operate in hybrid environments with cloud-based and on-premise systems, as well as structured and unstructured data. Entity resolution provides the foundation to integrate and standardize entity information across these diverse platforms and data formats. This interoperability enables more agile data operations and supports digital transformation initiatives.

What is Augmented Entity Resolution?

Augmented entity resolution (AER) is a sophisticated data management technique designed to elevate the accuracy and connectivity of information within large datasets. Organizations today amass vast amounts of data from diverse sources; the challenges they face are not in collecting data but in finding the meaning in it. Identifying and linking related entities from large datasets is a complex task.

Augmented entity resolution uses a range of advanced algorithms and techniques—such as machine learning algorithms, natural language processing, and statistical models to refine the accuracy of entity matching and linking processes. By incorporating these technologies, AER can adapt to data complexities and improve the overall resolution process.

An example of AER in action is in customer relationship management. Consider a scenario where a retail company wants to merge customer data from multiple touchpoints, such as online purchases, in-store transactions, and customer service interactions. AER matches and links customer profiles across those data sources, providing a unified view of each customer’s journey and preferences.

Benefits of Augmented Entity Resolution

• Improved data accuracy: AER enhances the accuracy of entity resolution, reducing the likelihood of false positives and negatives. This improves the reliability and trustworthiness of integrated datasets, instilling more confidence in data-driven insights.
• Enhanced connectivity: AER adeptly identifies and links related entities, bolstering the overall connectivity of data. Establishing more connections enriches the depth and breadth of insights that can be derived from a dataset and provides a more comprehensive understanding of relationships and patterns within the data. Uncovering deeper insights leads to better decisions.
• Adaptability to diverse data sources: AER is a versatile solution capable of harmonizing structured and unstructured data seamlessly. From customer profiles to financial records, AER can integrate disparate datasets to provide organizations with a unified and holistic view of their data.

What is a Flexible Entity Resolution Network (FERN)?

Flexible entity resolution network (FERN) is Reltio’s advanced and unique solution that addresses the challenges of linking and resolving entities within complex datasets. FERN’s adaptability to diverse and dynamic data sources ensures accurate augmented entity resolution, providing organizations with reliable and actionable insights.

FERN uses advanced neural network architectures and flexible algorithms, allowing it to learn intricate patterns and enabling it to make intelligent decisions when resolving entities. The network processes input data, extracts relevant features, and produces refined outputs. Its sophistication makes FERN indispensable for organizations seeking a deeper understanding of relationships within datasets.

To understand and identify similarities, FERN uses embedding techniques, which capture semantic relationships between entities. This enhances FERN’s flexibility, enabling it to adapt to varying data structures and complexities. It also incorporates attention mechanisms, which focus on relevant information during the entity resolution process. FERN can assign varying degrees of importance to different parts of the input data. This enables better decisions, especially in situations where certain features are more critical for accurate resolution.

Financial institutions managing customer data for fraud detection demonstrate FERN’s capabilities. FERN flags suspicious transactions by analyzing transactional data, identifying patterns indicative of fraudulent activity, and accurately resolving entities across disparate datasets.

Benefits of Flexible Entity Resolution Network

• Improved accuracy and adaptability: FERN harnesses the power of neural network architectures to learn intricate patterns within data, enabling precise entity resolution even in the face of evolving datasets. This enhanced accuracy ensures that organizations can trust the insights derived from their data analysis processes.
• Efficient real-time processing: FERN’s efficiency enables entity resolution in or near real time, crucial for applications where the timely identification and resolution of entities is imperative. From fraud detection to customer data management to cybersecurity, FERN gives organizations the agility to respond swiftly to emerging threats and opportunities.
• Scalability: FERN handles large-scale datasets without compromising on performance. Its neural network architecture allows it to process substantial volumes of data, and it can scale along with an organization’s evolving data needs.

FERN combines advanced algorithms and neural network architectures to deliver accurate, adaptable, and scalable solutions for today’s data-intensive challenges.