Entity Resolution

Ever tried finding someone on Facebook, only to see they have five different accounts? Now imagine this happening at scale. Tens of thousands of duplicate accounts in a company CRM or database. 

That’s when you need entity resolution. A powerful record-matching process that identifies & consolidates these duplicate accounts to provide a single source of truth. 

In this article, we’ll explore what entity resolution is, advanced algorithms and techniques, and showcase real-world use cases to highlight its importance. 

Whether you’re a data professional or simply interested in data management, this guide will equip you with everything you need to know about mastering entity resolution..

What is Entity Resolution?

What is Entity Resolution

Imagine your business has countless customer records scattered across various databases. You need to know if “John Smith” in one record is the same as “Jon Smythe” in another. 

Entity resolution is the process that makes this possible. It identifies & merges records that refer to the same real-world entity, despite variations in how they are recorded. It connects dots that would otherwise remain isolated, and transforms fragmented data into a cohesive whole, enabling better decision-making. 

Take, for instance, a healthcare example. A patient, Michael Brown, is recorded as “Michael B.,” “M. Brown,” and “Mike Brown,” in the hospital database, at different touchpoints. His identity is tied together by his date of birth and other personal identifiers. 

However, because he has also changed phone numbers and physical addresses a couple of times, his information is not consolidated. If his date of birth data is ever corrupted, or if someone makes a mistake in recording this information, it would result in the generation of a completely new identity! 

Organizations that have entity resolution processes in place prevent this from happening. In industries like finance, banking, and insurance, where fake and duplicate identities pose a significant threat to the security and reputation of a business. 

In simpler terms, it helps businesses unify scattered customer data across multiple systems into a single profile, creating a master record that represents the most updated, complete information the business holds on the entity. With a database of millions of records, entity resolution is performed at scale, where records are not just consolidated, but also treated for duplication and redundant information. 

Additionally, advanced ER systems also identify relationships between entities, for example, linking married customers under a relationship entity to mark household data. 

Relationship awareness enhances ER by identifying subtle links between records, ensuring accuracy and efficiency. For example, in a hospital system, relationship awareness can recognize two patient records with different names but sharing a phone number and address as belonging to the same family.

Why Is It Important to Get Entity Resolution Right?

Why Is It Important to Get Entity Resolution Right

Entity resolution is the cornerstone for strategic business operations. Here’s why getting entity resolution right is essential:

Unified Vision for Smart Decisions

Entity resolution underpins strategic decision-making by providing a unified view of data. What if the hospital system did not identify Michael Brown as a single entity &  instead treated him as multiple individuals? 

This would lead to inaccurate medical history records, conflicting information during treatment & compromise patient care. 

Accurate entity resolution eliminates data silos, resulting in a consolidated view of customer data. With a single source of truth for all entities, it becomes easier to gain insights & make smarter decisions.

As highlighted in Science Advances, proper entity resolution is critical for ensuring data reliability & supporting strategic decision-making across various industries.

Building Trust with Precision

Misidentifying customers or failing to recognize repeat interactions can lead to customer dissatisfaction and damage trust. 

How will you feel if your bank doesn’t recognize you as a long-term customer? 

Entity resolution can accurately link customers to their interactions & provide personalized services that build trust & loyalty.

Enhanced Data Governance

Effective data governance relies on accurate entity resolution. It ensures that data policies are correctly enforced across all datasets, maintaining data integrity and compliance. 

How will you protect sensitive information if you can’t identify records linked to the same customer?

Innovation and R&D

In research & development, especially in sectors like pharmaceuticals & technology, accurate data integration from multiple sources accelerates innovation. 

What will happen if the same drug is approved under two different names? That’s where entity resolution comes into play. It helps identify and consolidate duplicate information.

Competitive Intelligence

Businesses use entity resolution to gain competitive intelligence. By accurately linking data from various sources, companies can monitor competitor activities, market trends & customer preferences.

Imagine the advantage you’ll have if you know that your competitor’s new product is targeting the same customer base as yours but with a different marketing strategy.

Personalization at Scale

Modern consumers expect personalized experiences. Ever seen the “Recommended for you” section on Amazon? 

Entities can be linked across datasets, to understand customer preferences, buying behavior & personalize services. With entity resolution, companies can provide unique experiences at scale.

Facilitating Mergers and Acquisitions

During mergers and acquisitions, combining datasets from different organizations is a complex task. 

Let’s say there is a merger between two banks. Without entity resolution, there would be no way to identify if “John Smith” in one database is the same person as “Jonathan Smythe” in the other database. 

Advanced Analytics and AI

Unified data is necessary for training reliable machine learning models & generating accurate predictive insights. What will happen if data scientists build models on fragmented and inconsistent data? 

Entity resolution enables better analytics, AI & machine learning capabilities. By linking related entities, it enhances the quality of data used for analysis.

In the world of data, don’t be the one still using a typewriter when everyone else has a supercomputer.

How Businesses Typically Manage ER

How Businesses Typically Manage ER

Entity resolution is essential, but it comes with significant challenges that can complicate the process, highlighting the need for advanced entity resolution techniques. Traditional processes for managing entity resolution often exacerbate these challenges.

Consistency Issues

Traditional ER processes rely heavily on manual data entry & basic rule-based matching. Different data sources often have varying formats, incomplete entries, or outdated information. A customer’s name might be spelled differently across databases, or addresses may be partially recorded. 

These inconsistencies make it difficult to match records accurately. Manual processes struggle to enforce consistency, leading to fragmented & unreliable data.

More data, more problems. But at least they’re high-class problems.

Complexity of Manual Processes

Traditionally, businesses handle entity resolution by manually sifting through vast amounts of data to identify duplicates & inconsistencies. This labor-intensive process requires significant human resources & time. Manual processes lack the precision and speed of automated systems. The complexity increases with the volume of data, making it nearly impossible to manage efficiently without automation.

Addressing False Positives and Negatives

One of the most challenging aspects of entity resolution is dealing with false positives and negatives. Traditional methods often involve simple string matching, which can misidentify records. False positives occur when two distinct records are incorrectly identified as the same entity. False negatives happen when two records that should be matched remain separate. 

Both scenarios can lead to significant issues, such as data fragmentation and misinformation. Manual processes are particularly prone to these errors due to their reliance on basic matching rules & lack of sophisticated entity resolution algorithms algorithms.

How Entity Resolution Tools Help with these Challenges

Entity resolution tools offer significant advantages over manual processes, addressing critical needs and enhancing efficiency.

Traditional entity resolution processes often face numerous bottlenecks & inefficiencies. Manual methods are prone to errors, time-consuming & unable to scale effectively with growing data volumes. 

However, advanced entity resolution tools provide robust solutions to these challenges:

  • Built-In Proprietary Match Algorithms With Unrivaled Accuracy: Advanced entity resolution tools use sophisticated algorithms & AI to identify and merge records with high precision. These tools employ a combination of data matching methods like fuzzy, numeric, exact & proprietary algorithms to detect subtle variations & inconsistencies that manual methods often miss. 

For example, they can resolve non-exact matches like “Olie” and “Oliver” by understanding human errors & similar-sounding names. In the same way,, these tools offer the flexibility to create custom rules, set up dictionaries & define match conditions. This ensures a granular control over data matching processes.

  • Reduced TimetoFirst Result: Time-to-first result refers to the time it takes to deliver initial, accurate results from data processing. Fast-paced business environments require timely data insights. Manual entity resolution is time-consuming and labor-intensive. ER tools automate this process. 

By efficiently handling large volumes of data, these tools provide faster insights, enabling businesses to make timely decisions & avoid missed opportunities. Just think about a tool that can transform 1 million records in just 3 minutes. Yes, that’s right.

  • Cost Savings: Manual data resolution is costly. It requires significant human resources and time, leading to higher operational costs. Entity resolution tools automate these processes, reducing the need for extensive manual intervention. This not only lowers costs but also frees up resources to focus on more strategic tasks.

Why ER Shouldn’t Be Ignored

Ignoring or poorly implementing entity resolution exposes organizations to significant, often underestimated risks.

✅ Hidden Costs: Inaccurate data leads to hidden costs. Incorrect billing, duplicate shipments & inventory errors accumulate, draining financial resources without immediate detection.

✅ Undetected Fraud Schemes: Fraudsters exploit fragmented data. By creating slight variations in their information, they avoid detection. Poor entity resolution fails to connect these dots, allowing sophisticated fraud schemes to persist undetected.

✅ Patient Safety Risks: In healthcare, poor entity resolution can jeopardize patient safety. Fragmented medical records might lead to missed allergies, incorrect treatments, or overlooked medical histories, directly impacting patient care.

✅ Supply Chain Disruptions: In logistics, entity resolution errors can disrupt supply chains. Misidentified suppliers and redundant orders lead to stock shortages or overstocking, disrupting operations and increasing costs.

✅ Missed Regulatory Deadlines: Regulatory bodies demand precise data for compliance reporting. Poor entity resolution can cause inaccuracies in financial disclosures or audit trails, leading to missed deadlines and substantial fines.

✅ Data Breach Exposure: Incomplete entity resolution increases vulnerability to data breaches. Disconnected data silos can leave security gaps, making it easier for attackers to exploit and access sensitive information.

✅ Operational Blind Spots: Inconsistent data creates operational blind spots. Managers might miss critical insights into performance metrics or customer behavior, leading to poor strategic decisions.

Let’s quote an example here. A fraudster opens bank accounts under the names “Robert Lee,” “R. Lee,” and “Bob Lee,” each with slight variations in Social Security Numbers and addresses. AI spots the similarities, like shared phone numbers and unusual transaction patterns, and flags these accounts for review, helping the bank prevent fraud and protect their customers.

What are the Most Common Entity Resolution Use Cases?

Most Common Entity Resolution Use Cases

Here are some of the most impactful entity resolution use cases:

Banking and Financial Services:

Entity resolution helps banks & financial institutions detect and prevent fraud by identifying duplicate accounts and suspicious transaction patterns. For example, a bank can use entity resolution to link accounts opened under different names but with the same address and phone number, uncovering potential money laundering schemes. 

By consolidating customer information from different branches and services, institutions can monitor activities more effectively and ensure compliance with anti-money laundering (AML) regulations.

Insurance Fraud Detection:

In the insurance industry, entity resolution is used to identify fraudulent claims by linking related records across different systems. For instance, if an individual files multiple claims for the same incident using slight variations of their name, entity resolution can detect these inconsistencies. 

By cross-referencing claims with historical data & external databases, insurance companies can reduce fraud, improve the accuracy of their claims processing, and save millions in fraudulent payouts.

Customer Data Management:

Businesses use entity resolution to create a single, unified view of their customers. For example, a retail company might consolidate records from its online store, loyalty programs, and physical outlets to build a comprehensive profile for each customer. 

This allows the company to enhance customer service, personalize marketing efforts with targeted promotions, and improve overall customer experience by recognizing and rewarding loyal customers across all channels.

Monetary Fraud Prevention:

Financial institutions use entity resolution to prevent monetary fraud by identifying relationships between seemingly unrelated accounts. For example, entity resolution can reveal that multiple accounts with different names but shared email addresses and transaction patterns are connected, indicating a potential fraud ring. 

This helps in detecting money laundering activities and other financial crimes, ensuring the integrity of financial transactions and compliance with financial regulations.

Immigration and Identity Verification:

Government agencies use entity resolution to verify the identities of individuals in immigration processes. For instance, by consolidating records from visa applications, border entries, and national ID databases, agencies can accurately identify individuals, prevent identity fraud & ensure proper administration of immigration policies. 

This ensures that an individual applying for a visa is not using a different identity to enter the country illegally.

Healthcare and Patient Management:

Hospitals and healthcare providers use entity resolution to create accurate patient records by linking fragmented data across different systems. For example, a patient visiting multiple clinics might have different records under variations of their name. 

Entity resolution ensures these records are merged, providing a complete medical history. This accurate data helps in delivering appropriate care, avoiding duplicate tests, and ensuring patient safety by considering all relevant medical information.

E-commerce and Retail:

E-commerce platforms and retail companies use entity resolution to consolidate customer purchase histories, both online and in-store. For instance, a customer might use different email addresses for online and in-store purchases. 

Entity resolution can merge these records, providing a holistic view of the customer’s buying behavior. This helps in delivering personalized recommendations, improving inventory management, and enhancing customer satisfaction by offering a seamless shopping experience.

Law Enforcement and Public Safety:

Law enforcement agencies use entity resolution to link criminal records, identify suspects, and prevent crimes. For example, a suspect might use different aliases across different jurisdictions. ER can consolidate these records, providing a comprehensive profile that enhances investigative capabilities. 

This helps law enforcement agencies to quickly identify and track suspects, connect related cases, and improve public safety through more effective crime prevention strategies.

How to Choose the Right Entity Resolution Software

Entity resolution is an expensive & complex process that requires careful consideration to ensure you choose the right tool for your needs. The effectiveness of your ER efforts directly impacts your data quality, operational efficiency & decision-making capabilities. 

When selecting an entity resolution tool, it’s essential to evaluate several key factors to make an informed decision. 

Here are some things to help you make the right choice:

Understand Your Needs

Start by identifying your specific requirements. What type of data do you handle? How large are your datasets? Knowing your needs helps in selecting software that aligns with your business objectives.

Evaluate Accuracy and Performance

Look for software that offers high match accuracy & performance. Check if the tool uses advanced entity resolution algorithms and AI to ensure precise data matching. Request demos or trials to see how the software handles your data.

Scalability

Ensure the software can scale with your business. As your data grows, the tool should be able to manage increasing volumes without compromising on speed or accuracy. Scalability is key to long-term efficiency.

Integration Capabilities

The software should easily integrate with your existing systems. Check for compatibility with your databases, CRM systems, and other data sources. Seamless integration minimizes disruption and ensures a smooth transition.

User-Friendly Interface

A user-friendly interface makes it easier for your team to use the software effectively. Look for intuitive design and comprehensive support resources. Training and user manuals should be readily available.

Cost and ROI

Consider the cost of the software and the potential return on investment. While some tools might seem expensive, they can save costs in the long run by improving data accuracy and reducing manual labor. Calculate the long-term benefits to make an informed decision.

Vendor Support

Strong vendor support is essential. Choose a provider that offers reliable customer service and technical support. Check reviews and testimonials to gauge their reputation.

Security

Data security is paramount. Ensure the software complies with industry standards and regulations. Look for features like encryption and access controls to protect your data.

Selecting the right software involves careful consideration of these factors. By focusing on your specific needs and evaluating each aspect thoroughly, you can find a tool that enhances your data management processes and supports your business growth.

Choose the Right Entity Resolution Software

Final Thoughts

Entity resolution cleans up messy data by merging duplicate records, giving businesses one clear view of their information. This process is crucial for making smart decisions, improving customer satisfaction & staying compliant with regulations.

By using advanced techniques and tools, entity resolution handles inconsistencies, scales with growing data & reduces errors. It has powerful applications in fields like healthcare, conservation & smart cities.

In short, getting entity resolution right helps organizations manage their data better, make informed choices, and operate more efficiently, ensuring they stay ahead in a data-driven world.

Written by Faisal Khan

Faisal Khan is a human-centric Content Specialist who bridges the gap between technology companies and their audience by creating content that inspires and educates. He holds a degree in Software Engineering and has worked for companies in technology, healthcare, and E-commerce. At WinPure, he works with the tech, sales, and marketing team to create content that can help SMBs and enterprise organizations solve data quality challenges like data matching, entity resolution and master data management. Faisal is a night owl who enjoys writing tech content in the dead of the night 😉

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