Data Profiling R 01

A problematic challenge in data quality is treating duplicate data that have the same name but different spellings. It’s not uncommon to find variations of an individual’s name in a database. For example, Katherine or Catherine, Stuart or Stewart, John or Jon, are variations that cannot be identified with exact or deterministic data matching. That’s where you would need to use fuzzy data matching to catch non-exact duplicates.

What exactly is fuzzy data matching and how does it work? Here’s everything you need to know.

Let’s roll.

What Is Fuzzy Matching?

Fuzzy data matching is a technique used in data preparation and analysis. It works by reviewing the similarity between two strings of text and produces a similarity score that takes into consideration factors like character overlap, edit distance, and phonetic similarity. The higher the score, the more likely strings are considered a match.

How does it determine these similarities?

By using one or a combination of the most common fuzzy match algorithms, which are:

  1. Levenshtein Distance (Edit Distance): measures how similar two words are by comparing their characters. It determines the number of changes needed to transform one word into another. For instance, how many changes does it take to make “cat” into “cot”?
  2. Jaro-Winkler Distance: It compares names or words by giving extra points if they start with the same letters. It’s like saying “John” and “Jon” are more similar than “John” and “Jane.”
  3. Soundex: is for names or words that have different spellings, but sound the same, like “Smith” and “Smyth.”

Intrigued? Read more about modern fuzzy match algorithms in “Fuzzy String Match Resource,” by DataCamp and the book, ‘Fuzzy Algorithms: With Applications To Image Processing And Pattern Recognition.’

We won’t bore you with the formulas and logic behind these algorithms. Instead, we will focus on what’s the best way to use fuzzy matches to solve your data quality problems.

How to Implement Fuzzy Match?

There are two common ways to implement fuzzy match:

  • Using programming languages like Python and R.
  • Using an automated data match solution

Let’s explore each of these with some examples.

Fuzzy Match Using Python and R

Python is a popular and flexible programming language for data matching with libraries like FuzzyWuzzy that contain a variety of algorithm packages you can extract and use.

Similarly, R is another popular language that has  “stringdist,” “fuzzyjoin,” and “RecordLinkage” packages to calculate string similarities, join datasets using fuzzy match, and perform record linkage operations respectively.

Both these languages are powerful tools for fuzzy matching, data visualization, statistical analysis, and data transformation projects.

However, they are also time-consuming and resource-intensive. To solve a record linkage problem with coding, you need to develop scripts for each step of the process which can take months to complete! Developers would need to:

  • Extract data from databases
  • Decide the match scope
  • Decide on what attributes to match
  • Clean and transform the attributes
  • Test different algorithms based on match scope
  • Fine-tune algorithms to get refined matches
  • Deal with false positives and negatives
  • Create master records
  • Ship as final records to the business team

It takes months for companies to identify duplicates and link disparate data sets using traditional programming methods. Not only does this impact the bottom line, but also affects efficiency and productivity. So while programming languages seemingly offer more control, privacy, and flexibility – they are also hard to implement, are time-consuming, and require skilled talent.

Fortunately, with advanced data matching tools available in the market, your data management team no longer needs to spend months in building or testing algorithms. With the right software, they can perform complex fuzzy data-matching operations in literally minutes.

Fuzzy Match Using a Data Match Tool

A powerful fuzzy data match tool allows business users and technical teams to manage the following key tasks without the need to code or build different algorithms. Users can effortlessly deal with problems like:

✅ Data Deduplication: Identifying and merging duplicate records within large datasets.

✅ Record Linkage: Connecting related records of individuals across multiple data sources to create a consolidated identity.

✅ Spelling Variations: Catching and rectifying spelling errors, typos, or variations in customer data for more accurate search and analysis.

✅ Abbreviations and Acronyms: Identifying, standardizing, and linking records with abbreviations and acronyms. For example, matching Limited with Ltd to create a standardized format.

✅ Data Integration: Connecting data from various sources into one on-premises platform for easy data sanitation.

✅  Name Variations: Handling variations in names, titles, or prefixes to ensure accurate customer profiling and personalized communication.

and much more. Over time, fuzzy data match tools have become an integral part of data quality management, data governance, and data analytics, serving various industries such as finance, healthcare, retail, and more.

But it’s not just the tool’s dexterity that makes them popular. Most of these solutions help control the dreaded problems of false positives and negatives in fuzzy matching.


A fuzzy match solution like WinPure, allows the user to create custom word libraries to avoid a false positive. Additionally, users can perform matches as many times as they want, without corrupting the data.

See how easy that was?

Resolving a problem like this using manual methods or coding requires additional steps that do not guarantee accuracy. Moreover, it impacts efficiency. Your team is wasting time on redundant problems!

That said, it is imperative false matches are reviewed by a domain expert before it is classified as a match. Given below is a quick overview of false matches and how to control them.

Watching Out for False Positives and Negatives

Simply put,

A false positive is when two records are a match even though they do not represent the same person.


For example: 

Junior Smith and Junior D. Smith are not the same person but the system flags it as a match.

A false negative occurs when two records are not a match, even though they do represent the same person.

For example: 

The system does not flag Mary Jones and Marie Jones as the same person because of the difference in first name!

Mind-boggling isn’t it?

False positives and negatives are a default side-effect of fuzzy matching. The logic clusters characters based on similarity, so when it detects the same characters in a text, it flags a match. Therefore, you always need to be careful of context when reviewing fuzzy-match results.

How Do You Control False Positives and Negatives?

You can’t avoid false positives and negatives completely, but you can prevent them from affecting your interpretation of the data. Here are some recommended best practices:

✅ Always match using high-quality data. Make sure that your data is clean, complete, and up-to-date. Messy data will always lead to corrupted match results!

✅ Set a fuzzy match threshold. Generally, a threshold must not be less than 85% or higher than 90%. The higher (or lower you go), the more you risk a false match. For example, with a 95% threshold, the algorithm will not classify John and Johnny as a match because it’s looking for near-exact matches. With an 85% threshold, the algorithm will classify John, Jon, and Johnny as all matches!

✅ Fine-tune the match criteria. For example, instead of just matching on name, you could also match on date of birth, address, and social security number. You may need to reiterate the. match process, or use word managers to get accurate match results.

✅  Always have a domain expert review the match. A domain expert is someone who has deep knowledge of the data you’re using. They can be helpful for developing and tuning the data-matching algorithm, and for reviewing the results of the data-matching process. For example, if you’re matching a government database, it would be good to speak to someone who understands why certain information is missing or not recorded.

By following these tips, you can control false matches and improve the accuracy of your results.

To Conclude: Fuzzy Data Match is the Backbone of Data Quality!

Fuzzy data matching is not a novel concept, however, it has gained popularity over the past few years as organizations are struggling with the limitations of traditional de-duplication methods. With an automated solution like WinPure, you can save hundreds of hours in manual effort and resolve duplicates at a much higher accuracy level without having to worry about building in-house code algorithms or hiring expensive fuzzy match experts.

Ready to dedupe complicated data? Feel free to try WinPure’s fuzzy match software on your sample data. To get started simply download the trial version by filling out the form at the end of this post!


Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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