What is Fuzzy Matching?
How Fuzzy Matching It Works & Why It Matters
In the realm of data processing and analysis, precision is paramount. Whether managing customer databases, conducting market research or curating content, ensuring data accuracy is fundamental to making informed decisions and driving meaningful outcomes.
However, the reality is that data is often imperfect and prone to errors, inconsistencies, and variations. This is where the concept of “fuzzy matching” serves as a tool in the arsenal of data quality.
Intro To Fuzzy Matching
Fuzzy matching is a technique that is used to identify and link strings of data that may not be an exact match, but are likely to represent the same entity. Unlike traditional exact matching, which requires identical values, fuzzy matching employs algorithms that assess the similarity between strings of text or numerical data, allowing for variations, discrepancies, and errors to be accounted for.
Fuzzy matching makes it easier to connect the dots when you have messy or structurally inconsistent data. With fuzzy matching, you can better determine when real-world entities are the same, despite differences in how they are described or inconsistencies in how data was entered.
Why is Fuzzy Matching Important?
The importance of fuzzy matching cannot be overstated in today’s data-driven landscape. From enhancing data quality and accuracy to streamlining processes and improving efficiency, fuzzy matching plays a pivotal role across various domains and industries.
If you have many duplicates in your data, whether in a single data source or across multiple data sources, matching duplicates can be harder than you might imagine. Matching data is even harder when you donโt have a key to easily join records together. Thatโs where fuzzy matching comes in.
Fuzzy matching makes it easier to identify matches and connections, even when you have messy or structurally inconsistent data.
Is Fuzzy Matching the Same as Entity Resolution?
The relationship between fuzzy matching and entity resolution is that of a tool and its application. Fuzzy matching is a technique used to quantify similarity between data elements, and this mechanism can be employed within the broader process of entity resolution to identify, link, or deduplicate records that refer to the same or different entities across diverse datasets. For more, on this distinction, read our primer on the differences between Entity resolution and fuzzy matching.
However, fuzzy matching is important for entity resolution accuracy. To get a better understanding of why, watch the video below where Jeff Jonas breaks down fuzzy matching with entity resolution software in a little more detail.
Try Senzing Fuzzy Matching Software
Try Senzingยฎ entity resolution for yourself for free. See how it performs fuzzy matching on your own data, or use our sample truth set. If you have questions, support at Senzing is always free.
Video Transcript
Timestamps
0:00 Intro
0:48 Senzing Fuzzy Matching Software
You’re finding a lot of duplicates in your data, maybe a duplicate as in a data source or maybe you’re trying to match horizontally and you’ve realized that maybe itโs harder than it appeared because you don’t have a key to join it all together and now you’re thinking we need fuzzy matching.
Yes, you do. In fact, you need it plus plus plus. Fuzzy matching? Let me decode that. What do I mean?
Back in the old days, fuzzy record matching would be like using an algorithm called Soundex, do they sound alike. Later it became more advanced, Metaphone, Double Metaphone… Now we’re using Levenshtein for some use cases where, how many letters are off or numbers are off.
Fuzzy record matching would also include things like dates of birth that have dashes in them or slashes in them. Some of them are year, month, day. Some are month, day, year, and so on.
0:48 Senzing Fuzzy Matching Software
These are all examples of fuzzy comparisons of fields which is, you know, about fuzzy record matching. Now in addition to that, there’s lots of other stuff you need. Check out our software, download it, run our synthetic data set.
It all runs on your own computers, no data flows to Senzing, Inc. and check out what happens when you take fuzzy record matching to the nth and you add a bunch of other essential elements to entity resolution. You can also just run your own data. You’ll find it super fast, super easy, and you should see it for yourself.
