What is Fuzzy Matching?

By Senzing, published October 5, 2022

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.

Why is Fuzzy Matching Important?

If you have many duplicates in your data, in a single data source or across data sources, matching duplicates can be harder than you think. Matching data is even harder when you don’t have a key to easily join records together. That’s where fuzzy matching comes in.

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.

Try Senzing Fuzzy Matching Software

Try Senzing® 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

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 it’s 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.

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