Fuzzy matching, when applied to your business rules, will help standardize your customer view for improved data quality.
In this fuzzy matching guide, we’ll walk you through creating a fuzzy matching strategy and how you can use a codeless fuzzy match solution for record linkage and data deduplication of a million records within minutes.
We’ll explore and explain fuzzy matching in detail with this article, including:
Fuzzy data matching for businesses
How does fuzzy matching logic work
Popular approaches to fuzzy matching algorithm
What is fuzzy search
How is fuzzy matching implemented
How reliable is fuzzy matching?
How companies approach fuzzy data matching requirements
What is codeless or No-Code data matching & how does it help with data transformation?
How does No-Code fuzzy matching work?
Fuzzy Data Matching for Businesses
A 2020 Trends in Data Management report states that trust in an organization’s data quality remains low, only 13.77%. Simultaneously, the highly respected Gartner Annual CMO Spend Survey Research reported increased demand for customer understanding and insight.
In 2022 there are many different ways to gain the insight necessary for business growth, one of these is fuzzy matching: a powerful tool transforming messy data to a standard customer view in line with your business rules.
What Is Fuzzy Matching?
For beginners, fuzzy matching defines a type of data matching algorithm used to calculate probabilities and weights in order to determine similarities and differences between business entities like customers. This data matching technique differs from comparing unique reference data, like name and birthday, deterministic data matching.
A Typical Scenario
Let’s imagine a typical scenario where fuzzy matching adds value to a business.
Say you entered 2022 down in sales due to the economy. You want to increase sales and get ready to launch a new marketing initiative in response.
So, you start to get together all your sales information to make a big splash with all your customers. You start with your customer relationship management (CRM) system and then move on to other marketing or product systems.
But each system contains slightly different information, resulting in messy data: duplicated and fragmented contacts, accounts, transactions, products, and addresses. You need to apply fuzzy matching algorithms in line with your business rules, standardize customer information, remove duplicate data, and reduce errors.
You need to turn messy data like the one below into clean, accurate, refined records.
Other business use cases and examples where fuzzy matching is required include:
1). Entity resolution for sales, marketing, and insights teams. Customer data is inherently messy, especially if they come in from multiple data sources. Entity resolution refers to cleaning, standardizing, and merging all of these records to create a unified, 360-degree view of the customer.
2). Identity resolution for government agencies. It’s not uncommon to hear immigration authorities flagging the wrong individual because of something as small as a spelling difference. A robust data-matching solution is required to help governments and authorities curb false identity problems.
3). GDPR & sanctions compliance for businesses. Sanctions are only getting stricter as world powers collide and companies dealing in international trading and transactions are required to ensure they meet sanctions compliance. On the other hand, companies in the EU/UK are required to meet GDPR compliance and other data privacy and safety requirements. The matching engine that a company uses for identity resolution must be able to detect matches in near-real-time and be scalable to handle data from multiple domains.
Fuzzy matching techniques or probabilistic data matching apply parameters that you choose, scoring data patterns mathematically. Then, fuzzy matching techniques compare sets of characters, numbers, strings, or other data types for similarities. When presented with the likelihood, that customer entities match your fuzzy matching search; you decide whether to link records and combine data into a single customer view.
A Quick Brief for Beginners
How Does Fuzzy Logic Work?
Fuzzy logic operates on estimates or approximations. Unlike boolean logic, there are no binary results. It’s not a yes or a no, but a maybe, with the highest approximate being the most positive response.
Boolean logic data matching uses exact spellings or characters to determine a match is highly limited. It does not consider variations in text or numbers which means it will always leave out potential matches. Fuzzy data matching finds similar strings instead of exactly alike strings. It determines similarity on the basis of distance, score, or a likelihood of similarity.
For example, it will use the Edit Distance (also called as the Levenshtein Distance) to determine similarity. Rise and Rice for example has a distance of 1 since only S and C are the different alphabets here.
Fuzzy Matching Algorithms
Fuzzy matching is not a new concept. Most programming languages provide a means to compare strings and programmers have been using fuzzy matching algorithms to implement a string comparison function that considers different characters and match options to return a true value.
The complication with a traditional fuzzy matching approach lies in setting up a match strategy. You will have to identify answers to questions like:
What will be the match criteria, for example, how many characters need to match to deliver a result?
What if letters are the same but are not in the same order, for example, Johnathan Junior or Jr. Jonathan. How does fuzzy matching score the two variations as a match?
What expressions (such as abbreviations) need to be included or excluded in the match process?
What fuzzy matching algorithm, such as Edit Distance, or Soundex (for same words, different sounds) can be used.
Generally, most fuzzy matching techniques and algorithms can be categorized into three types.
1). The Character Overlap Measures
The character overlap approach looks at strings that share many of the same characters indicating a high level of similarity. The Jaccard measure and the Jaro-Winkle measure are two of the most common methods/approaches under the Character overlap measures.
1a). Jaccard Measure
The Jaccard distance is one of the most basic methods for fuzzy string matching. It is based on how many elements are on either string, divided by the total count of distinct elements.
For example, if Robert and Roberta are two strings, then the Jaccard measure will report them having an 85% match since they share 6 out 7 letters.
Because the Jaccard measure is entirely dependent on string similarity, it can also give false positives (that is showing a match when there isn’t).
For example, name strings sharing the same letters (anagrams) like, ‘Abel, Bela’ will be a match despite it being the record of two different people.
This leads to the two most feared consequences of poor fuzzy matching – false positives and false negatives.
1b). Jaro-Winkler Distance
In the Jaccard method, strings with the same characters in different orders are also considered a match based on the number of similar letters. The Jaro-Winkler distance solves this problem in three ways – it measures the similarity between two strings, and the length of the common prefix at the start of the string, and adds a score to the number of common prefixes. The algorithm then returns a match result between the range of 0 and 1 where 0 means no similarity.
String 1 = Crate
String 2 = Trace
Would result in an approximation of 0.73 match (using the Jaro-Winkler formula) even though the character orders are not sequential.
Character overlap approaches are not efficient, can be computationally expensive, and do not model character order accurately.
2. Edit Distance Approach also Known as Levenshtein Approach
The edit distance approach measures similarity between two strings by defining the minimum number of changes required to convert String A into String B. Edit distances come in a variety of forms, but insertion, deletion, and substitution of characters are the most common types of operations to transform one string into another.
For example, transforming Maria into Mariam would require one letter and would have an edit distance of 1 letter. It is based on this distance that the algorithm would detect a match. The simple form edit operations are each given an equal weight which is known as the Levenshtein distance.
3). N-Gram Edit Distance
The edit distance method only involves single characters. One way to extend the capabilities of Edit distance is to capture multiple characters at a time, known also as an N-gram edit distance. It takes the idea of Levenshtein distance and treats each n-gram as a character. The matching works by limiting potential matches to those that share one or more n-grams with a query string.
These are but just three of the most common types of fuzzy matching approaches. Within these approaches, you’ll find many different types of algorithms at work to match all types of data. Other fuzzy matching algorithms include:
Determines whether a business name matches its acronym. For example, Advanced Micro Devices and its abbreviation AMD are considered a match, returning a score of 100.
Determines the similarity between two strings based on the number of deletions, insertions, and character replacements needed to transform one string into the other. For example, VP Sales matches VP of Sales with score of 73.
Determines the similarity of two sets of initials in personal names. For example, the first name Jonathan and its initial J match and return a score of 100.
Determines the similarity between two strings based on the number of deletions, insertions, and character replacements needed to transform one string into the other, weighted by the position of the keys on the keyboard.
Kullback Liebler Distance
Determines the similarity between two strings based on their sounds. This algorithm attempts to account for the irregularities among languages and works well for first and last names. For example, Joseph matches Josef with a score of 100.
Determines whether two names are a variation of each other. For example, Bob is a variation of Robert and returns a match score of 100. Bob is not a variation of Bill and returns a score of 0.
Determines the similarity between two strings based on their sounds. First, the character strings are converted into syllables strings. Then the syllable strings are also compared and scored using the Edit Distance algorithm. This matching algorithm works well for company names.
Determines the similarity between two strings based on their sounds. This algorithm attempts to account for the irregularities among languages and works well for first and last names. For example, Joseph matches Josef with a score of 100.
What is Fuzzy Search?
A fuzzy search uses several fuzzy matching techniques to filter and group customer data according to the set of user characteristics, likeness thresholds, and patterns you specify. In return, you get the potential matching customers of interest and the weight describing how likely one customer’s record resembles another.
Additional software lets you interact with fuzzy search results in a friendly user interface. You can locate less obvious relationships among hundreds of thousands of records and decide what records link and what customer to combine. You can see fuzzy matching search results below.
You find a 95% similarity between the “BHP Copper Inc” and “BHP Copper Inc,” indicating two records you may wish to merge. You scan the other similar company records.
You drill down deeper to see each company and customer record. From there, you can profile your data, plan your data cleansing tasks, and meet your business rules designed to standardize each customer entity.
How is Fuzzy Matching Implemented?
Fuzzy matching has traditionally been implemented in one of the four following programs.
Python: It’s the most common language used in data science to build complex algorithms. Python has a FuzzyWuzzy library consisting of the most common expressions you can use to perform approximate string matching.
R – It is a popular language used by statisticians, data analysts, and researchers to retrieve, clean, analyze and present data. It’s often used in comparison with Python to clean + match data.
Java – Often used with Python, Java is beneficial when you need to host business-critical data science applications. Spotify, Uber are companies that use both Java and Python to work with their data.
Excel – The good old Excel! Great for deterministic matching, cleaning up and merging records. You do have to know a bunch of Excel formulas to treat and match the data but Excel is highly limited in terms of scale and flexibility.
Other than the knowledge of these languages, implementing a fuzzy matching process will require knowledge of:
Fuzzy matching algorithms and how to mix/match them according to the data structure
Standardize, normalize and transform data (often from one format to another)
Create a match strategy that ensures accurate results of up to 96% (there is never a 100% accuracy in data matching).
How Reliable is Fuzzy Matching?
Fuzzy matching’s reliability depends on suitable fuzzy search parameters and software to return a low number of false positives and negatives.
A false positive happens when the software retrieves two customer entities as a match when they are not. For example, “Joseph Mc Connell,” who works in Birmingham, does not match “Joseph Mc Donnell,” who works in San Francisco. They identify as separate customer entities.
A false negative occurs when software does not pick up two customers as a match when representing the same entity. For example, the algorithm does not pick up that “Ted Doe,” who works at “Oral Technology LTD,” is the same person as “Edward Doe,” who works at “Oral Technology.”
False positives lead to wasted time spent combing through irrelevant records. False negatives lead to duplicates and errors in customer information.
To avoid false positives and negatives, you want to use reliable software to profile your data ahead of time. Next, you want to come up with the business rules and plans to clean the data. Then you want to use trustworthy automation to clean the data, meeting your goals.
With a reduced chance of false positives and negatives, you can be more confident your fuzzy matching software will meet your data cleaning needs.
How Companies Approach Fuzzy Data Matching Requirements
In the modern world where data sources are complex, varied, and inherently messy, fuzzy matching is required to perform two critical tasks: remove duplicates and link multiple data sources to get a consolidated view of the entity – also known as record linkage.
Deduplication and record linkage tasks are highly time-consuming and demand for highly accurate data matching abilities to weed out similarities. It must be noted that a poorly created fuzzy matching script will result in more false positives & negatives, thereby making the entire process ineffective.
Traditionally, developers and data scientists use the following fuzzy matching approach:
Determining the entities to match – this could be names, addresses, or any other tangible identifier
Scoring the entities based on their match similarities – for example, adding a percentage value (86% match)
Evaluating results and creating a new master record
Data scientists are hired to manually script codes to do mundane tasks instead of working on strategy.
You would require someone proficient in multiple programming languages and a strong knowledge of Excel to create fuzzy match algorithms that can catch variations of data sets.
A Typical Scenario:
Here’s a real-world scenario of how a simple record linkage task can take months.
You are required to merge records from marketing, sales, and customer service to get a 360-degree view of your customer.
Now, if we were to dig deeper into the data itself, you’ll have to spend more time fixing problems like poor address data as given in the example below.
Using a traditional fuzzy matching approach, it would take you:
3 months if you spend each working hour on the project to simply transform the data
Another month to script the matching code
Multiple iterations of scripting, testing, and measuring results
Expertise in at least two languages with full command in Excel
Total = Around 4 to 5 months on a simple 1,000-row data set from three departments.
This is too long to wait especially if a business wants access to insights faster.
What is Codeless or No-Code data matching & How Does it Help with Data Transformation?
You’ve heard about no-code or codeless software development, but did you know you can perform complex data matching and master data management process without a single line of code?
Now if you’re a programmer or a data scientist reading this, you’re probably scoffing at this and may even be skeptical!
But hear me out.
Data prep, data cleansing, data standardization, and data matching are mundane tasks that don’t require 100% of your time. It’s like driving a car. You, the driver needs to focus on the road and the destination. You’re not required to focus on the engine or the inner workings of the car. Similarly, your job as a data specialist is not to get lost in the mundane job of data cleansing. You are needed for strategy, for ensuring optimal outcomes, and for getting the job done on time.
No code fuzzy data matching can help:
>> Improve organizational efficiency by up to 60%
>> Reduce development cost up to 80%
>> Reduce dependencies & enables faster outcomes
>> Focus on strategic efforts & keeping up with business demands
The time you spent in manually fixing errors on Excel or other programs will likely lead to more errors and delayed project timelines. Using a no-code fuzzy matching solution, you can quickly match data from multiple sources, deduplicate records, and create a master record fit-for-purpose
Instead, fuzzy matching techniques or probabilistic data matching applies parameters that you choose, scoring data patterns mathematically. Then, fuzzy matching techniques compare sets of characters, numbers, strings, or other data types for similarities. When presented with the likelihood, that customer entities match your fuzzy matching search; you decide whether to link records and combine data into a single customer view.
As long as you know your goals and your requirements are straightforward, you can speed up your record linkage, data treatment, master data management and deduplication efforts without human and legacy system errors being a hindrance.
How Does No-Code Fuzzy Matching Work?
A codeless fuzzy matching platform works by combining common fuzzy matching algorithms along with their proprietary algorithm (WinPure, for example, has its own algorithm that works in tandem with other fuzzy algorithms). It can detect matches with a higher level of accuracy than traditional methods. Moreover, these platforms use visual interfaces to enable non-technical and business users to treat, merge, and consolidate data through simple drag and drop or point and click actions. Users don’t need to know statistics, programming languages, or have any previous coding experience to use the platform.
In addition to the fuzzy matching, codeless platforms also enable you to perform data cleansing which would otherwise be a tedious process when done manually.
Codeless fuzzy matching vendors compete based on speed and accuracy. You should be able to get match results for a million records in just about minutes!
Some no-code fuzzy matching solutions like WinPure also let you create custom expressions for specific data matching requirements allowing for flexibility and scalability.
Pros and Cons of Codeless Data Matching
Like all things, there are some pros and cons to be aware of. Here’s a compiled list of pros and cons with codeless data management platforms.
Gives you more time to focus on strategy
Straightforward requirements & should know what you want
Empowers business users to clean, merge, and dedupe data within their domain
Needs user management and defined roles to make sure only designated users can make changes
Improves organizational efficiency by up to 60%
Security issues can occur if there is a lack of control
Cost-efficient, computationally inexpensive, and easy to deploy (on/off premises or cloud)
License costs may differ according to requirements
Scalable and flexible
Requires increased hardware performance if there are millions of data sources to match
Performs data preparation & transformation along with data matching
Limitations in complex transformation and matching requirements
It’s important that you know a no-code solution is not a replacement to a data scientist, analyst, or engineer. It is meant to assist, just like most AI applications assist human talents to perform, deliver, and achieve business goals faster and better. A tool is only as good as the person using it!
What Features to Expect from WinPure’s No-Code Fuzzy Matching Solution
WinPure has been in the data management business for nearly two decades, being the first to offer no-code data matching for businesses of all sizes.
Over the years, we’ve gathered much intelligence on the struggles and limitations professionals as well as businesses face with record linkage and data deduplication – from failed master data initiatives to delayed mergers and acquisitions, we’ve seen it all.
WinPure’s fuzzy matching solution attempts to reduce and eliminate these struggles so businesses can keep up with the pace of a data-driven world.
Some of WinPure’s key data matching features include:
Data Profiling Function: It’s an established fact that you cannot perform a fuzzy match operation with messy data. WinPure kickstarts the matching process by first profiling your data. It lets you see the ‘health’ of your data and what areas need urgent attention.
Data Cleaning Function: Simply select columns or rows you want to normalize/standardize/clean. You can add in specific abbreviations in the WordSmith panel (a dictionary) to keep your original entry. WinPure also uses a Global Parsing Engine (GPE) to parse, standardize, verify, cleanse and format address data.
Data Deduplication Function: Duplicates destroys the credibility of your data and can lead to disastrous real-world consequences. WinPure’s Clean and Match solution uses a combination of fuzzy matching algorithms to let you deduplicate data efficiently.
Merge Data Sources for Final Records: After you’ve cleaned and transformed records, you can finally merge the data to create master records.
Complete Matching Report: A comprehensive, visual report depicting the number of duplicate records found and how many were treated.
Additional features include:
Ability to write regular expressions to meet custom requirements
Ability to integrate easily with CRMs, databases, and more
Ability to automate cleaning and matching schedules
As a trusted innovator in data cleaning and data matching, WinPure’s no-code solution has helped thousands of businesses worldwide save millions of dollars in expensive talent recruitment and in manpower hours.
Case Study – Centura Health Used WinPure’s No-Code Data Match Solution to Create a Single View
The health industry relies on accurate data to offer the best care to its patients. Centura Health, a renowned healthcare facility in the US needed to create a single view by identifying all donors who engage with their company and to also identify all the people who value the organization. With over 6,000 physicians and more than 21,000 donors, the facility needed a strong data matching solution to merge records and create a 360-view.
WinPure’s Clean and Match was used to link disparate data sources, dedupe data, and create single view records through an efficient data matching process – all without a single line of code!
You and your employees need trustworthy information for business operations. Fragmented and duplicated customer information from multiple systems disguises similar customer entities and less obvious duplications, leading to messy data. Fuzzy matching algorithms and fuzzy searches retrieve like data elements typically missed manually.
Fuzzy searches retrieve similar records based on your parameters and thresholds. They give data sets scores to profile data and what to clean, based on your business rules. Use fuzzy matching software you trust to gather reliable information about potential matching customer entities.
Fuzzy matching helps you plan and enact your data cleansing projects, combining customer records into a single view. With better data quality, enabled by fuzzy matching, you will have successful marketing campaigns and a greater readiness to add machine learning for better insights.
Market Hardware is the market leader of industry-specific Websites and Web Marketing products for service-oriented businesses. Market Hardware was formed in 2003 by a seasoned management team with extensive Web marketing, technology and small business experience. Today, they have Web experts serving more than 5000 small business clients in all 50 states.
The Wadhwani Institute for Artificial Intelligence (Wadhwani AI) is an independent not-for-profit research institute. They aim to harness the power of AI to find the break points that cause the world’s deepest problems — and then find innovative solutions to fix them.
Centura Health connects individuals, families and neighborhoods across Colorado and western Kansas with more than 6,000 physicians and more than 21,000 of the best hearts and minds in health care. Through their hospitals, senior living communities, health neighborhoods, home care and hospice services, they are making the region’s best health care accessible and affordable in every community they serve.
Edward B - Company Owner
Excellent Product & Customer Service
We perform multiple matching projects for our clients and WinPure has filled the bill for these. The product is easy to use and we can complete large matches in a very short time.
Richard F - Company Owner
Excellent Software & Support
WinPure is a really great product, we've been using it with excellent results for many years now, for finding and removing duplicate records and to keep our lists and database more accurate.
G2 Crowd Review
Best Data Cleaning Software
Not only does it execute its job with ease, but also provides ease of use and extreme comfort in doing so. This is the kind of product that once you start using you will not be able to drop down! I would highly recommend any business or user who has any data cleansing or matching needs to use this program!
Cynthia T - Director of Information Technology
Great Data Quality Software
WinPure Clean & Match works great to analyze data and find duplicates. It saves us tons of money when mailing catalogs. This is a great product for the money and easy to use.
Naveed B - IT Consultant
Always Recommending WinPure
A very powerful but easy to use tool for cleansing and removing duplicates from databases. I have used Clean & Match for many of my clients, and I am regularly recommending this product to other companies.
Fantastic Software with Exceptional Support
I cannot emphasise enough how valuable this data cleansing and dedupe software has been for us and I would recommend this to any business that requires their database to be cleaned and corrected.
9 Year User - Still Happy!
I've used WinPure for 9 years now (since 2007) and have found it to be the perfect companion to the many data projects I do for marketing and sales campaigns. Having started my own firm since then, I now have every client facing team member get Winpure on their machine to benefit from friendly UI, efficient speed, and dependability.
WinPure, a trusted innovator in Data Quality and Master Data Management Tools. Join the thousands of customers who rely on WinPure to grow faster with better data.