In today’s competitive, ever-changing business landscape, providing significant value for an organization is becoming increasingly difficult.  

Today, over 300 million terabytes of data are created each day, presenting organizations with both powerful raw information and opportunity. But if companies are unable to make sense of that data — and, more importantly, know the insights derived from it can be trusted — bad data can become a brick wall standing in the way of their most strategic initiatives. 

According to a recent Gartner article, 84% of customer service and service support leaders cited customer data and analytics as “very or extremely important” for achieving their organizational goals in 2023. That is a staggering number of organizations that depend on their data quality to grow their business. You can’t trust your strategic initiatives if you can’t trust your foundational data. 

When it comes to improving data quality, it’s important to consolidate disparate or duplicate data from multiple sources and organize it to ensure consistency across your entire business. But because data combined from multiple sources is often incomplete, inconsistent and not suitable for analysis, simply migrating all of it into a single data lake or system cannot solve the underlying data quality problems preventing deeper analysis. 

What is Fuzzy Matching? 

Enter fuzzy matching. Also known as approximate string matching, fuzzy name matching or fuzzy string matching, fuzzy matching is an AI and machine learning technology that identifies and matches similar or partially matching — but not identical — elements in data table sets. 

It is particularly useful when correcting datasets containing typographical errors and spelling/formatting inconsistencies or when comparing data from multiple sources. Fuzzy matching algorithms calculate the similarity between two records and convert them into scores to determine their similarity levels before placing them into a specific dataset. 

This is a foundational first step in building a matching strategy and a core building block of the golden record management found in commercial master data management (MDM) platforms. 

These matching algorithms consider several factors when matching separate records including: 

• Character similarity: The degree of resemblance or likeness between individual characters within the text. 

• String distance metrics: Mathematical algorithms that are used to quantify the difference or similarity between two strings of text. Fuzzy Matching uses the Levenshtein distance which measures the minimum number of single-character edits required to transform one string into another. 

• Phonetic similarity – The degree of similarity between two words or strings based on pronunciation and how they sound to the ear when spoken aloud. 

• Token-level matching – The process of comparing or matching individual units (tokens) of data within a defined dataset. Tokens often represent words, phrases or sequences of characters. 

A common example of fuzzy matching would be matching and/or grouping customer records. With manual data entry and formatting standards, there are resulting errors or differences.  

Although there might be two separate customer records with one filed under “Jane Depp” and the other under “Jayne Dep,” fuzzy matching can look at the similarities between other pieces of the records to determine if they are for the same customer. Fuzzy algorithms can determine they are likely the same person even if they aren’t an exact match in the source system.