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In life, there are few things black and white. There are gray areas everywhere and the lines that separate can be a little fuzzy. The same holds true for data – especially when it’s human entered. That’s why we have the Fuzzy Match Tool – if your data isn’t clear as day, you can still get value out of your records by matching them to something a little more standardized.
How to prepare two inputs for Fuzzy Match Merge Mode
Here is a quick reference guide that will help you prepare two different data sources for use with Fuzzy Match Merge Mode. This mode only compares data from different sources, and it is often used to merge new data back with the primary data source. The Fuzzy Match Tool accepts only one input, so you will need to prepare the data first.
Join the data together, results that match exactly will go through the J output anchor and do not need fuzzy matching.
The remaining data from the L and R output anchors will need record ids for matching. Add record IDs for the first input.
Use the Formula Tool to create a new column for each input showing the source. A function with just a text string in quotes will add that text to each record. Using Merge Mode, the Fuzzy Match Tool will only compare records with a different source.
Ensure the correct alignment of the columns in the Union Tool by using the Manually Configure Fields option.
Sort by the Source column so that all the Input A records with a record ID are placed first in the list.
Record IDs for the 2nd input are added after the join using an expression so that the IDs automatically start sequentially after the number of records in the 1st input.
In the Fuzzy Matching configuration screen, use the new Record ID and Source ID fields, as well as the fuzzy match fields that were stacked together with the Union Tool.
Here you can see all fields needed for Fuzzy Match Merge Mode Configuration are available.
The example workflow is attached. Once the Fuzzy Match merge is complete, there are many options for completing the workflow, such as adding a Unique Tool to remove duplicates and joining the matching records back with the original data. Please see the articles and training videos in the Additional Resource section for examples and more information.
Tool Mastery Fuzzy Match
Fuzzy Match Tool Alteryx Help Page
Alteryx Academy video training session: Fuzzy Matching for Beginners
Alteryx Academy video training session: Fuzzy Matching Intermediate Users
The Fuzzy Match Tool has the ability to match first names against a set of Nicknames to help return better matches. The Nickname table (which can be found at C:\Program Files\Alteryx\bin\RuntimeData\FuzzyMatch\Nicknames) is used as a lookup within the Fuzzy Match tool when you select it as an option. Selecting “Name w/ Nickname” as your Match Style automatically selects the Common Nicknames table, but often users would like to add to this list or even create their own custom table. This article will walk you through how to edit this list, and provide you with some tips and tricks when matching with nicknames.
Creating a Nickname table
The nickname table is installed by default in C:\Program Files\Alteryx\bin\RuntimeData\FuzzyMatch\Nicknames and is saved as an Alteryx database file (.yxmd). We can easily pull this into Alteryx to add additional names, or we can even generate our own table. The .yxdb file contains 2 fields:
Full Name goes here
Nickname goes here
Adding Additional Names: Creating your own file: Once the file is created, place the .yxmd in the directory above. You should now be able to see multiple tables available from the dropdown within the tool.:
Tips and Tricks when working with Nicknames
Set Generate Keys to “None” when using the Names w/ Nicknames match style IF you have the First Name in a single field.
If your name is contained in a single field (John Smith or Smith, John), you will want to select a method to Generate Keys and check the box “Generate Keys for Each Word”.
The “Soundex” method of generating keys is generally preferred when working with names.
The Fuzzy Match Tool provides some pretty amazing flexibility for string joins with inexact values – usually in the case of names, addresses, phone numbers, or zip codes because many of the pre-configured match styles are designed around the formats of those types of string structures. However, taking advantage of the custom match style and carefully configuring the tool specific to human entered keyword strings in your data can also allow you to use the loose string matching feature of the tool to match those values to cleaner dictionary keyword strings. If done properly, it can help you take otherwise unusable strings and, matching by each individual word, recombine your human entered data to a standardized format that can be used in more advanced analyses.