Imagine a doctor trying to retrieve a patient’s medical history, but instead of one accurate record, they find three profiles – one with an outdated address, another with a missing critical allergy warning, and a third under a slightly different name. This can not only cause dangerous delays in assessment, increase the risk of misdiagnoses, and, ultimately, endanger patient safety, but also puts a financial burden on healthcare institutions. Inaccurate patient identification costs about $2.5 billion per year to an average hospital in the US and over $6.7 billion to the health system.
Terrible, right?
But here’s what’s worse: This isn’t just a healthcare problem.
Duplicate customer accounts often plague banks’ databases, e-commerce stores deal with fragmented user data, and government agencies regularly fight fraud caused by mismatched records.
The real culprit behind these issues is – disconnected and inconsistent data across systems!
That’s where entity resolution can make the difference.
By intelligently linking (matching, merging) and deduplicating records across databases, entity resolution ensures a single, accurate view of every individual, organization, or asset. However, achieving this isn’t easy. Businesses face challenges like data silos, inconsistent formatting, and false matches that prevent accurate linkage.
Overcoming these issues requires a strategic approach and advanced entity resolution tools that ensure clean, reliable data, and thus, enable businesses to improve decision-making, enhance customer experiences, and maintain compliance with confidence.
What is Entity Resolution?
Entity Resolution and Information Quality defines entity resolution (ER) as ‘determining when references to real-world entities are equivalent (refer to the same real world entity) or not equivalent (refer to different entities).’
In simple words, it is the process of identifying and linking multiple records that refer to the same entity when those records contain variations in names, addresses, or other attributes.
This process eliminates duplicate, inconsistent, or conflicting records while also distinguishing records that belong to different entities to ensure that organizations maintain a single, accurate view of their data.
For example, if an organization has customer records stored in different databases under slightly different names, such as Jon Snow in a CRM system and John Snowden in an e-commerce database, an entity resolution software analyzes various data attributes to determine whether they represent the same person or different individuals.
The entity resolution process involves looking at the similarity of multiple records by checking it against unique identifiers. These are data points that are least likely to change overtime, such as social security numbers, date of birth, or postal codes. Here’s how it’s done:
In the above example, John Oneil, Johnathan O, and Johny O’neal are all matched through a unique identifier which is the national ID number.
ER involves linking and matching data across multiple records to find possible duplicates and removing the matched duplicates which is why it is often used interchangeably with the following terms:
- Record Linking – Connecting relevant records across different datasets.
- Fuzzy Matching – Identifying similarities between records even when they are not exact matches or when unique identifiers are not available.
- Merge/Purge – Combining and cleaning up duplicate records.
- Entity Clustering – Grouping related records together to unify fragmented data.
- Deduplication – Removing duplicate records from a dataset.
How Entity Resolution Works in Practice
Entity resolution follows a structured process to connect disparate data sources and unify, cleanse, and link records. Here’s a breakdown of the key steps:
1. Data Ingestion
The first step in entity resolution is consolidating data from multiple sources into a centralized view.
Enterprises often have data in disparate systems like databases, CRMs, Excel and PDFs, and formats such as text strings, dates, and numbers.
For example, a large mortgage and financial services company may have:
- A central database in MySQL
- Claims forms data in PDFs
- Homeowners records in Excel
Importing all this data into a unified system will help set the stage for record linking and duplicate detection. In some cases, combining data from different sources also requires standardizing database schemas to ensure consistency and compatibility for further processing.
2. Data Profiling
Once data is ingested, the next step is to check its health. This involves identifying any kind of statistical anomalies, missing values, inaccurate data, and formatting issues that could impact matching accuracy.
Profiling helps organizations pinpoint problematic records or datasets before cleaning and matching. When doing this, a user (typically a data analyst) may want to check if the fields conform to regular expressions (RegEx) that determine string types for different data fields. Based on this, the user can identify records that are either unclean or don’t conform to a set encoding.
Common data issues that profiling can help reveal include (but not limited to):
- Nulls values – e.g., missing email addresses in lead generation forms
- Leading/trailing spaces – e.g., “ David Matthews ” instead of “David Matthews”
- Punctuation errors – e.g., “hotmail,com” instead of “hotmail.com”
- Casing inconsistencies – e.g., “New york,” “dAVID mAATHEWS,” “MICROSOFT”
- Mixed data types (presence of letters in numbers and vice versa) – e.g., “TEL- 516 570-9251” (for contact number) or “NJ43” (for state)
3. Deduplication and Record Linking
Through matching, multiple records that are potentially related to the same entity are joined to remove duplicates, or deduplicated using unique identifiers. The matching techniques can vary depending on the type of field and data quality such as exact matching, fuzzy matching, or phonetic matching.
For instance:
- For variations in names, exact matching is often used where unique identifiers such as SSN or address are accurate in the entire dataset.
- If the unique identifiers are inaccurate or invalid, fuzzy matching proves to be a much more reliable form of data matching to pair two similar records (e.g., John Snow and Jon Snowden).
Deduplication and record linking, in most cases, are understood to be one and the same thing. However, a key difference is that the former is about detecting duplicates and consolidating information within the same dataset (i.e. normalizing the schema) while the latter is about matching and merging the deduplicated data across multiple datasets or data sources.
4. Canonicalization
Canonicalization is another key step in entity resolution for enterprise data projects. It involves converting multiple data representations into a standard form. This is done by:
- Selecting the most complete and accurate version of an entity’s record
- Leaving out outliers or noise (inconsistent or redundant variations) that could distort the data
- Creating a golden record that represents the entity with high confidence
For example, if multiple entries for “Microsoft Corporation” exist as “Microsoft, Inc.”, “MS Corp.”, and “Microsoft Corp.”, canonicalization would combine them into a single, standardized, accurate, representation.
5. Blocking
Matching entities across large datasets – as required in enterprise data projects – can involve billions of comparisons. Blocking optimizes this process by grouping records into smaller subsets based on specific business rules (e.g., ZIP code or last name). This significantly reduces the number of comparisons while maintaining data accuracy.
Challenges of Entity Resolution
Despite the many approaches and techniques available for entity resolution, it falls short on several fronts. These include:
1. ER Works Well Only If the Data Is Rich and Consistent
Perhaps the biggest problem with entity resolution is that the accuracy of the matches is dependent on the data’s richness and consistency across datasets.
For example:
If you have ‘Mike Rogers’ in two databases, an exact match or simple record linking can easily identify that one of them is a duplicate record. This is called deterministic matching and it is quite straightforward.
However, when you’re dealing with similar records with inconsistencies, such as misspellings, abbreviations, or nicknames (e.g., ‘Mike Rogers in one database and ‘Michael Rogers’ in another), it becomes difficult to match them. This phenomenon is called probabilistic matching and it becomes even more challenging when unique identifiers (such as address, SSN, or birth date) are missing or inconsistent across datasets, making it nearly impossible to perform any kind of deterministic matching, especially when handling large volumes of data.
2. ER Algorithms Don’t Scale Well
Traditional entity resolution techniques often fail when applied to big data environments like financial, government, and healthcare industries, where records span terabytes of data.
The business rules required to make the algorithms work would have to account for far larger data to work consistently. For example, the blocking technique – used to limit mismatched pairs when finding duplicates – is dependent on the quality of the record fields. If you have fields containing errors, missing values, or variations, you can end up inserting data into the wrong blocks and face higher false negatives.
3. Manual Entity Resolution is Complex and Resource-Intensive
It is not uncommon for organizations dealing with large volumes of data to opt for handling entity resolution projects in-house. The rationale behind this decision is that they can make use of their existing technical resources (software engineers, consultants, database administrators) without having to purchase any of the entity resolution tools available in the market.
However, there are a few problems with this approach.
Firstly, entity resolution isn’t a subset of software development. Sure, there are publicly available algorithms and blocking techniques that might be useful. But in the grand scheme of things, the skills it requires are vastly different, as the user will have to:
- Combine disparate unstructured and structured data sources.
- Know about encoding variations, nicknames, and linguistic differences very well for accurate matching.
- Adapt ER rules for different business use cases (e.g., customer data deduplication vs. fraud detection).
- Ensure that different matching techniques complement each other for consistency and reliable results.
It’s quite unlikely for a person to tick all these boxes, and even if it is possible, you have the risk of them leaving the firm. If the person leading or managing an ER project leaves the company, it can bring the entire initiative to a halt.
4 Reasons Why Entity Resolution Tools Are Better
Entity resolution tools offer significant advantages over traditional manual or rule-based ER methods. These include:
1. Higher Match Accuracy
Dedicated entity resolution tools that have sophisticated fuzzy matching algorithms and entity resolving capabilities in place can give far better record linking and deduplication results than common ER algorithms.
Matching records across heterogeneous datasets (e.g., SQL and NoSQL databases, spreadsheets, PDFs) can be exceptionally difficult due to different types of entities, encoding, formatting, languages, and schemas. Converting all this different type of data into a pre-defined schema through schema-matching and data exchange is a risky process and can result in the loss of valuable information.
Moreover, accurate fuzzy matching may require data analysts to use multiple string-metrics, such as Levenshtein Distance, Jaro-Winkler, and Damerau-Levenshtein. Implementing these manually is resource-intensive.
Entity resolutions tools incorporate a wide range of matching algorithms. They can seamlessly link records and significantly improve match accuracy and consistency across datasets.
2. Reduced Time-To-First Result
In most cases, time is critical for ER projects, especially in the case of master data management (MDM) initiatives that require a single source of truth. However, manual entity resolution projects can take months. Considering that the information relating to an entity can quickly change within weeks, the slow pace of manual ER can pose serious data quality risks and lead to missed opportunities and poor decision-making.
Let’s say a B2B sales and marketing firm wants to run campaigns on its top-tier accounts, Ideally, it should confirm that its targeted prospects haven’t switched jobs, changed titles, or retired before wasting any marketing spend. A manual ER process taking 6+ months risk many records becoming outdated, obsolete, or inaccurate. Most entity resolution tools can reduce this time by 50%, while some even bring the time-to-first result down to mere 15 minutes.
3. Better Scalability
Entity resolution tools are designed to process large-scale datasets without compromising performance. They are far more adept at ingesting data from multiple sources and run record linkage, deduplication and cleansing tasks at much larger scales.
For example, a government institution performing ER to identify (and prevent) fraudulent tax filings would find manual ER approaches and algorithms severely restricting and overwhelming. The sheer volume of the data in government databases (they have millions, if not trillions, of records) and the complexity of blocking techniques would make the efforts futile.
Automated entity resolution tools, however, can streamline the process. They can not only import data from various sources, but also ensure the efficiency of the process remains intact across large data volumes.
4. Cost-Savings
Entity resolution tools, particularly for enterprise level applications, require a sizable investment. For this reason alone, many data professionals tasked with ER may be reluctant to get them. They may feel that manual ER is more cost-effective and a better way to showcase their expertise (and improve their chances of promotion). However, the costs of project delays, poor matching accuracy, and labor resources often end up exceeding the investment in an automated ER tool.
ER tools allow data professionals to focus on high-value tasks, rather than spending months on manual matching. By reducing data errors, enhancing operational efficiency, and improving decision-making, automated ER solutions provide better financial outcomes in the long run.
How to Choose the Right Entity Resolution Software
Many ER tools differ in features, scope and value. Selecting the right entity resolution software for your needs is critical to ensuring accurate record matching, deduplication, and data unification. Here are some key factors to consider when choosing an entity resolution tool for enterprise data project:
1. Support for Disparate Data Sources
Enterprise data is often stored in different formats and scattered across multiple platforms like Excel, databases, CRMs, web applications, and delimited text files. The right entity resolution software should seamlessly import data from all relevant sources for the specific use-case.
DataMatch Enterprise’s Import module allows you to source data in various formats as shown above, which ensures its compatibility with diverse enterprise environments.
2. Scalable Data Profiling and Cleansing
The right entity resolution software must also profile and clean the data before any deduplication and record linkage efforts. DataMatch Enterprise (DME), using pre-built patterns based on Regex expressions, can determine valid and invalid records, null values, distinct entries, leading and trailing spaces, duplicates, formatting issues, and other inconsistencies.
Once a profile is generated, the software should then provide data cleansing opportunities using various functionalities such as:
- Merging Fields – Consolidating multiple fields into one
- Removing Unwanted Characters – Cleaning up punctuation or extra spaces
- Replacing Characters – Standardizing inconsistencies in input data
- Removing Numbers – Stripping out non-essential numeric values
DataMatch Enterprise simplifies this process by using pre-built patterns based on Regex expressions to detect and cleanse invalid records at scale.
3. Advanced Matching Algorithms for Higher Accuracy
Many entity resolution tools claim to provide a high match score, but their effectiveness depends on how sophisticated the algorithms used to match records within and across multiple datasets are.
DataMatch Enterprise employs a range of advanced matching techniques (Exact, Fuzzy, Phonetic), and string-metrics algorithms to establish distance across entities and makes use of domain-specific libraries (nicknames, addresses, phone numbers) to provide a match accuracy rate that outperforms industry competitors.
An independent study done by Curtin University found that DME’s match accuracy surpassed those of other vendors, including IBM’s Quality Stage and SAS Dataflux.
Scale, Save, and Succeed with Entity Resolution Software
As crucial it is for enterprises to do ER, manually carrying out deduplication, record linkage and other entity resolution tasks have serious limits when handling large datasets. Manual entity resolution approaches simply cannot scale to handle millions – or trillions – of records effectively. Without the right tools, businesses face inaccurate matches, inefficiencies, and higher costs.
By using an advanced entity resolution software like DataMatch Enterprise, enterprises can streamline data unification, improve match accuracy, and scale effortlessly – all while reducing operational costs and enhancing decision-making.
Visit our Entity Resolution solution page or contact our sales team to see how DataMatch Enterprise (DME) can improve your data strategy.
Frequently Asked Questions (FAQs)
1. How can I assess the quality of my data before starting an entity resolution project?
Before initiating an entity resolution project, it’s crucial to understand the current state of your data. DataMatch Enterprise offers comprehensive data profiling tools that analyze your datasets for anomalies, missing values, and inconsistencies. This assessment helps in identifying potential issues that could affect matching accuracy and allows you to address them proactively.
2. How do I measure the success of an entity resolution project?
Success in entity resolution isn’t just about matching records – it’s about improving data usability. Key success metrics include match accuracy rates, reduction in duplicate records, improved data consistency, and the impact on downstream processes (e.g., better customer analytics or compliance reporting). DataMatch Enterprise provides match confidence scores and detailed reports to help you evaluate success.
3. What are the common mistakes enterprises make in entity resolution projects?
Some of the biggest mistakes include:
- Skipping data standardization before matching (leading to false positives or missed matches).
- Relying solely on exact matching, which often fails when names, addresses, or identifiers have variations.
- Ignoring the need for a feedback loop, where business users review and refine matching rules.
DataMatch Enterprise helps prevent these issues by offering data cleansing, fuzzy matching, and rule-tuning capabilities.
4. How much manual effort is required for an automated entity resolution process?
Even with advanced automation, some level of human review is necessary—especially for high-stakes decisions like merging customer or compliance records. The goal with entity resolution tools is to minimize manual work by:
- Using confidence scores to prioritize review-worthy matches.
- Allowing business users to fine-tune matching rules over time.
- Implementing workflow automation to integrate manual review seamlessly into your data pipeline.
DataMatch Enterprise automates most of entity resolution tasks while keeping humans in the loop for complex cases.
5. Can I integrate entity resolution results into my existing analytics and BI tools?
Yes! A common challenge in entity resolution is ensuring that resolved entities are usable across your tech stack. DataMatch Enterprise allows you to:
- Export clean, de-duplicated data in formats compatible with BI tools like Tableau and Power BI.
- Sync resolved entities back into CRM, ERP, and master data management (MDM) platforms.
- Schedule automated exports or use API integrations to keep data continuously updated.
6. How does DataMatch Enterprise handle data privacy and security during the entity resolution process?
Data privacy and security are critical in any data processing activity. DataMatch Enterprise adheres to industry-standard security protocols to ensure that your data is protected throughout the process. It offers features like data encryption to safeguard sensitive information, which also helps your organization comply with data protection regulations.
How best in class fuzzy matching solutions work: Combining established and proprietary algorithms
