Fuzzy Match in with Automation Anywhere A360

Fuzzy Match in Automation Anywhere A360

Imagine you're playing a game of 'almost, but not quite'. That's essentially what the Fuzzy Match action does in Automation Anywhere A360. It helps your bot find items that are similar but not identical.

Fuzzy match in Automation Anywhere A360 is a feature that allows you to compare two strings or files for similarity. It returns a decimal value between 0 and 1, indicating the degree of similarity between the two items. The closer the value is to 1, the more similar the strings or files are.  

How it works:

• Comparison: The fuzzy match action compares two input strings or files character by character.
• Similarity score: It calculates a similarity score based on the number of matching characters, the order of characters, and other factors.
• Output: The output is a decimal value representing the similarity score.

When to use Fuzzy Match?

• Dealing with typos: If data is entered with small mistakes, fuzzy match can help find the correct information.
• Comparing similar items: When you need to find items that are almost the same, like product names or customer addresses.
• Validating data: Check if data matches expected values, even if there are slight differences.

• String 1	String 2	Expected Similarity Score	Reason
  "automation anywhere"	"automation anywhere"	1	Exact match
  "automation anywhere"	"automation anyware"	High	One character difference
  "automation anywhere"	"auto anywhere"	Medium	Missing word
  "automation anywhere"	"anywhere automation"	Medium	Different order of words
  "automation anywhere"	"robotic process automation"	Low	Different phrases
  "1234567890"	"1234567890"	1	Exact match
  "1234567890"	"1234567891"	High	One digit difference
  "1234567890"	"123456"	Medium	Shorter string
  "automation anywhere"	"Automation Anywhere"	High	Case difference (might depend on fuzzy match settings)

Example: Finding Similar Product Names

Let's say you're working for an online store and need to find matching products from two different systems. The product names might have slight variations due to typos or different naming conventions.

• Product Name in System A: "Red Apple iPhone Case"
• Product Name in System B: "Red Apple Iphone Case"

A regular match would likely fail because of the extra space and lowercase 'i'. But fuzzy match can identify these as very similar products and help you match them correctly.

How to use Fuzzy Match in A360

1. Add the Fuzzy Match action to your bot.
2. Input the two strings you want to compare.
3. Set the similarity threshold: Decide how similar the items need to be for a match.
4. Use the output: The action will give you a score indicating the similarity.

Use Cases:

• Data validation: Verify if extracted data from documents (like invoices, forms) matches expected values, even with minor typos or variations.
• Record matching: Identify and match records from different data sources with potential discrepancies in data entry.
• Duplicate detection: Find duplicate records or entries in a dataset based on similarity.
• Natural language processing: Compare text strings for semantic similarity.

By understanding and using fuzzy match effectively, you can create more robust and accurate automation processes.

Remember: Fuzzy match is a powerful tool, but it's important to set the similarity threshold carefully to avoid false positives or negatives.

Would you like to see a simple example of a fuzzy match automation?