FUZZY MATCHING ALGORITHMS EXPLAINED

Introduction to Fuzzy Matching

Fuzzy matching, also known as approximate string matching or fuzzy string matching, is a powerful data cleaning technique used to find strings that are similar but not necessarily identical across different datasets. This method is indispensable for reconciling real world data, which frequently suffers from inconsistencies, typos or non standardized formats. Most fuzzy matching algorithms quantify similarity by returning a score, typically as a percentage, where 0% signifies no match and 100% indicates an exact match. Understanding these algorithms is key to effective data deduplication and record linkage, especially when working with tools like Google Sheets for data analysis.

Defining Similarity Thresholds

A similarity threshold defines the minimum acceptable similarity between two strings. For example, a threshold of 0.85 means compared entries must be at least 85% similar to be considered a match. Lower thresholds allow more variation, while higher thresholds demand closer matches. Choosing the right threshold is crucial for balancing false positives and false negatives.

Benefits of Fuzzy Matching Software

Real-world data is rarely uniform due to diverse data collection and entry methods. Fuzzy matching software helps identify and rectify text-based discrepancies, such as spelling variations and formatting differences, reducing manual cleaning effort. A robust fuzzy matching tool streamlines data processing, improves efficiency, and allows both business and technical users to focus on higher-value tasks.

What Can Fuzzy Matching Software Do?

Fuzzy matching software is a versatile solution for a wide range of data challenges. By identifying non exact matches, it empowers users to enhance data quality, streamline processes and unlock deeper insights. Here is how various industries and roles benefit from its capabilities, particularly when dealing with large datasets in platforms like Google Sheets or complex databases:

Record Linkage: Connect related records across disparate data sources, even with variations in names, addresses or other identifiers. This is crucial for creating a single customer view, tracking entities across systems and ensuring comprehensive data analysis.
For more, see the Fuzzy Match and Merge documentation.
Efficient Data Deduplication: Identify and merge or remove duplicate records within large datasets. This significantly reduces redundancy, improves data accuracy for reporting and analytics and optimizes storage. Essential for maintaining clean mailing lists, customer databases and product catalogs.
See Remove Duplicates documentation.
Sophisticated Spelling Variation Analysis and Typo Correction: Automatically detect and correct spelling errors, typos and common variations in text data. This ensures more precise search results, reliable data entry and accurate natural language processing. Useful in search engines, document management and data input forms.
See Standardize Data documentation.
Comprehensive Data Standardisation: Standardize inconsistent data entries, such as abbreviations (e.g. "Limited" vs. "Ltd.", "Street" vs. "St."), acronyms and formatting differences. This creates uniformity, making data easier to compare, aggregate and analyze. Key for regulatory reporting and data migration projects.
See Standardize Data documentation.
Seamless Data Integration and Migration: Consolidate data from diverse sources, such as legacy systems, third party APIs or spreadsheets, into a single, clean and unified platform. Fuzzy matching helps resolve inconsistencies during the integration process, ensuring a smoother transition and more reliable master data.
Name Variation Matching and Identity Resolution: Manage and match variations in personal or company names, including nicknames, maiden names, aliases, titles or prefixes. This is vital for accurate customer profiling, fraud detection, compliance screening (e.g. KYC/AML) and personalized communication.
Enhanced Product Information Management (PIM): Match and link product data from various suppliers or internal systems, even with differing descriptions or SKUs, to maintain an accurate and consistent product catalog.
Improved List Cleaning and Segmentation: Clean and segment marketing lists or contact databases by identifying similar entries, correcting errors and removing outdated information, leading to more effective campaigns and better customer engagement.

By leveraging these capabilities organizations can significantly improve their data driven decision making, operational efficiency and overall data governance.

Fuzzy Matching in Action: A Real-World Example

Record linkage techniques can be used to detect fraud, resource wastage or abuse. In this story, two databases were merged and compared for inconsistencies, leading to a discovery that helped the U.S. government put a stop to fraudulent behaviour by some government employees:

In a period of 18 months leading to the summer of 2005, a database comprising records of 40,000 pilots licensed by the U.S. Federal Aviation Administration and residing in Northern California, was matched to a database consisting of individuals receiving disability payments from the Social Security Administration and it was discovered that names of some pilots appeared in both databases.

In a report by the Associated Press, a prosecutor from the U.S. Attorney's Office in Fresno, CA stated the following:

There was probably criminal wrongdoing. The pilots were either lying to the FAA or wrongfully receiving benefits. The pilots claimed to be medically fit to fly airplanes. However, they may have been flying with debilitating illnesses that should have kept them grounded, ranging from schizophrenia and bipolar disorder to drug and alcohol addiction and heart conditions.

In the end, at least 40 pilots were charged with the crimes of "making false statements to a government agency" and "making and delivering a false official writing". The FAA also suspended licenses of 14 pilots in total, while others were put on notice pending further investigations.

Popular Fuzzy Matching Algorithms

Cosine Similarity: Measures similarity by representing strings as vectors and calculating the cosine of the angle between them (score from 0 to 1).
Levenshtein Distance: Calculates the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one word into another.
Flookup's proprietary algorithm, Peregrine: Flookup's proprietary algorithm, developed by Andrew Apell, calculates percentage similarity between unique substrings in two text entries.
Damerau-Levenshtein Distance: Like Levenshtein, but also allows transpositions of adjacent characters.
n-gram: Compares contiguous sequences of n items (syllables, letters, words, etc.) from text entries.
Soundex: Indexes words by sound as pronounced in English. Flookup uses a refined version for sound similarity matching.
The Human Brain: Aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it deosn't mtater in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat lteteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe.