Katherine and Catherine. They sound like two different people right? But what if we told you both Katherine/Catherine refer to the same person? A human being and even a computer could not be able to tell them apart at first.
Now imagine you are a business that keeps customer records. Having two database entries for the same person just because of spelling issues can turn into a problem.
Thankfully, we have something called fuzzy data logic to help sort out these spelling variations in databases. It is a model that helps businesses overcome the challenges of variations in text strings.
With fuzzy logic, you can flag spelling errors and detect duplicates on the basis of text similarities to improve data quality. How? We answer all your pressing questions in this comprehensive guide.
Fuzzy Matching: Your ultimate solution for data cleansing!
Fuzzy logic also goes by another name – 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. This score measures how similar the strings are, considering 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:
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.
Identify duplicates within minutes with no-code fuzzy matching!
There are two common ways to implement fuzzy match:
Let’s explore each of these with some examples.
Python is a popular and flexible programming language for data matching. It has libraries like FuzzyWuzzy that contain a variety of algorithm packages that 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.
Let us elaborate. 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:
That sounds like a lot. And guess what? Most companies are unaware of the steps it takes to perform record linkage. They hire a data analyst with Python or R skills and hope to get results in weeks. 🤷🏽♀️
While programming languages offer complete control, privacy, and flexibility – they are also hard to implement, are time-consuming, and require skilled talent.
We recommend hiring Python and R experts for data matching purposes only if you have the time, budget and need for one.
There is also an alternative. Most businesses don’t need a trained data analyst. Instead, your database manager or IT manager can do everything we mentioned above with fuzzy match software.
Fuzzy data match tools have been available for a while. Even companies like IBM and SAS offer these tools as part of their data management suite.
There are some cost-effective and powerful alternatives to data management.
Recently, standalone fuzzy data match solutions have emerged; assisting small and medium-sized business owners with cleaning, removing duplicates, and creating accurate records, without the need for:
These standalone fuzzy data match tools incorporate multiple algorithms to perform primary functions 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.
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.
In this live demo with a customer, WinPure’s solution specialist explains what to do when a fuzzy match returns a false positive – or when it doesn’t work the way it should!
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.
Find your perfect match: Fuzzy matching made easy!
A false positive is when two records are a match even though they do not represent the same person.
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.
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.
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.
Unlock the potential of your data with Fuzzy Matching!
Fuzzy data matching is not a new concept. Nevertheless, it continues to gain much attention over the years as organizations strive to connect multiple data sources, clean their database, and improve their data quality. However, fuzzy matching isn’t as easy as it sounds.
You need to have a strategy on how you want to match your data, and you also need domain experts to review match results. Remember, fuzzy match is just a technology – how you interpret the results from a match determines the validity of your results.
If you’d like to know how your data quality problems can be solved using fuzzy matching, feel free to get in touch. We can help!
We’re here to help you get the most from your data.
Download and try out our Award-Winning WinPure™ Clean & Match Data Cleansing and Matching Software Suite.
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