Fuzzy Duplicates Analysis with ACL Prepared by: Kevin Legere Date: April 3rd, 2013

Agenda Overview Example FUZZYDUP command OMIT() Function Script Editor and RECOFFSET Q&A

Overview What is a "Fuzzy Duplicate"? Typically used for: Match based on criteria where the values are not exact but very close EX: "ACL Services" and "ACL Service" Typically used for: Keyword matching Invoice Number matching Vendor Name matching* Employee Name matching Can be simple or complex Completely depends on your approach and desired accuracy * focus for this presentation

Overview Simple Match Examples: Exact or 100% match "ACL" = "ACL" Force Upper or Lower case "ACL" = UPPER("acl") "acl" = LOWER("ACL") Removal of special characters "ACL" = EXCLUDE("*ACL." "!@#$%^&*().") Only compare numbers or letters "ACL" = INCLUDE(UPPER("ACL123") "ABCDEFGHIJKLMNOPQRSTUVWXYZ") "123" = INCLUDE("ACL123" "1234567890")

Overview Complex Match Examples: Removal of company type indicators (LLC, INC, LTD, etc) "ACL Services Ltd." = "ACL Services" Percent of word match AKA letter by letter "ACL Services" "ACL Service" 11/12 character match or 91.6% match Word by Word* "ACL Services" "ACL Champions" "ACL" "ACL" "Services" "Champions" = 50% match Levenshtein distance Sounds like NYSIIS *Most used by ACL Consultants

Vendor Master Analysis

Vendor Master Analysis Fuzzy Duplicates on Vendor Name Possible Risk Payments are being sent to more than one vendor May not involve risk. The desire can be to normalize the vendor master list to ensure that duplicates do not exist. Ideally, one unique vendor should exist in your vendor master list with one or more address records in your vendor address table

Vendor Master Analysis Sample file contains 75 vendors Only Vendor Code and Vendor Name Where do you start for Vendor Name matching? Look for exact duplicates Focus on Simple matching Sort or Summarize!

Vendor Master Analysis Step 1: Summarize your Vendor Master File Choose Vendor Name as your key field Add Vendor Code as the Other Fields for Summarizing Be sure to check "Presort"

Vendor Master Analysis Step 2: Quickly comb over the data to identify a common trend. We will focus on this issue, in the sample data: Create a computed field that corrects the trend (or cleans the data).

Vendor Master Analysis Functions used in Default Value text box: INCLUDE(UPPER(ALLTRIM(Vendor_Name)) 'ABCDEFGHIJKLMNOPQRSTUVWXYZ') Within ACL, the computed field will return the following:

Vendor Master Analysis Step 3: Perform a Duplicates Command on the computed field

Vendor Master Analysis Results are as follows:

FUZZYDUP command ACL 9.3 has new features that make Fuzzy Duplicate analysis easier FUZZYDUP command OMIT() function ISFUZZYDUP() function LEVDIST() function Important parameters to understand Levenshtein Distance Difference Percentage

FUZZYDUP command Syntax Example Levenshtein Distance (LEVDISTANCE) FUZZYDUP ON {key_field} <OTHER fields> {LEVDISTANCE value} <DIFFPCT value><RESULTSIZE value> <EXACT> TO table_name Example FUZZYDUP ON Vendor_Name OTHER ALL LEVDISTANCE 2 DIFFPCT 50 TO My_Results Levenshtein Distance (LEVDISTANCE) The number of edits required to make the strings equal EX: "Smith" and "Smythe" have a Levenshtein Distance of 2 Difference Percentage (DIFFPCT) The threshold for percentage difference between two strings EX: "Smith" and "Smythe" have a Percentage Difference of 40% (2/5) * 100%

OMIT() Function When Do I use OMIT()? Syntax Example When you want to refine fuzzy duplicate analysis Look for repeating strings you want to remove from your Vendor Name field Syntax OMIT(string1, string2 <,case_sensitive>) Specify T to make substrings specified for removal case-sensitive, or F to ignore case Example OMIT(Vendor_Name " Ltd, Inc, Corp, Corporation" F)

Script Editor and RECOFFSET

Implementation Consultant Contact Information Kevin Legere Implementation Consultant ACL Services Ltd. 1550 Alberni Street, Vancouver, BC, Canada V6G 1A5 kevin_legere@acl.com | @aclkevin www.acl.com/linkedin | www.acl.com/twitter | www.acl.com/facebook