Fuzzy matching, when applied from your business rules, will help standardize your customer view for improved data quality.
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 an increased demand for customer understanding and insight.
In 2021 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.
A Typical Scenario
Let’s imagine a typical scenario where fuzzy matching adds value to a business.
Say you entered 2021 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 onto 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 error.
We’ll explore and explain fuzzy matching in detail with this article, including:
What is fuzzy matching?
Why do businesses need fuzzy matching?
Is fuzzy matching machine learning?
Different fuzzy matching techniques
What is fuzzy search?
How reliable is fuzzy matching?
What is Fuzzy Matching?
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.
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, customer entities match your fuzzy matching search; you decide whether to link records and combine data into a single customer view.
Why Do Businesses Need Fuzzy Matching?
Businesses need fuzzy matching to profile and clean up their data efficiently. Fuzzy matching steps in revealing duplicated and linked customer data, the human eye misses.
Say you start this process by exporting your CRM, marketing, and product data for your marketing campaign.
After a half-hour doing manual corrections and 10,000 more records to go, you determine you need a different approach. You then think about hiring contractors to clean up your data.
But you wonder if you would spend more time answering people’s questions.
How would a temp know that “J. Payne” may be either “Jonathan Payne” or “Jeff Payne” who both work at Super Treats? What if someone did not know that Emma Wright’s company correctly reads “The Write Way Ltd?”
So, you try an alternate approach by determining the types of data cleansing issues to address. You include:
Punctuation and spaces
Nicknames and other name variations
You need to profile this data to see if you miss anything when planning to clean this customer data. You will need to automate this task and involve fuzzy matching algorithms to help.
See how a major healthcare provider was able to save precious time by using fuzzy matching against their donor data, patients and other individuals.
Is Fuzzy Matching Machine Learning?
While investigating fuzzy matching techniques, you read that the best marketers are using machine learning to optimize campaigns. You wonder if fuzzy matching equals machine learning.
While machine learning and fuzzy matching use patterns, fuzzy matching algorithms do not require training a machine to independently decide what data to clean and how. Instead, fuzzy matching relies on fuzzy logic matching, returning values between 0 (not true) and 1 (true). You analyze these fuzzy matching results for the degree of likeness between two data sets and make decisions about data cleansing.
That you take charge of data profiling and cleansing makes fuzzy matching techniques more attractive. Machine learning tends to make poor data choices and use incorrect models without clean data in the first place.
Fuzzy Matching Techniques
You can apply various fuzzy matching algorithms to account for different business needs and data system architectures. For example, sql fuzzy matches handle sales and marketing data in your SQL system, like the one Microsoft provides. Other fuzzy algorithms (like the Levenshtein distance or Damerau–Levenshtein distance algorithms) leverage open-source libraries towards resolving specific patterns, like keying errors and initials.
You can find many different techniques being used today. See our list below of common fuzzy matching techniques:
Levenshtein Distance (or Edit Distance)
Each algorithm has its strengths and weaknesses and works best in combination. For example, although sql fuzzy matches harness the power of set theory and relational algebra to filter potential matches. The disadvantage is that sql fuzzy match requires a high level of skill to manipulate.
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 deciding 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 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 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.
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.
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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.
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