Match algorithms - Precisely Data Integrity Suite

Data Integrity Suite
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Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
Precisely_Data_Integrity_Suite
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Data_Observability
Data_Quality
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Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
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Data Integrity Suite
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en-US
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pt_product_guide
copyrightfirst
2000
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2026

When you configure Match and Group step settings, you can select from exact, fuzzy, phonetic, and numeric algorithms.

Different data matching algorithms are used depending on the nature of the data to be compared. Data matching algorithms are categorized as exact, fuzzy, phonetic, or numeric.

  • Exact: Rejects any differences and finds full matches of character to character.
  • Fuzzy: Tolerates difference and uses probabilistic matching to evaluate the likelihood that two strings are similar. These include industry leading techniques such as Levenshtein Distance (Edit Distance), Jaro-Winkler Distance, and Metaphone 3.
  • Phonetic: Matches words by their pronunciation. This is useful for matching similar strings. Phonetic strings can be exact or fuzzy.
  • Numeric: Used to run a probabilistic match on numeric fields.

Algorithms supported by the Match and Group step

Algorithm Category Description
Acronym String Determines whether a business name matches its acronym by looking for acronym data, else it creates an acronym using the first character of every word.
Character Frequency String Determines the frequency of occurrence of each character in a string and compares the overall frequencies between two strings.
Consonant Exact

Only consonants are compared. Vowels are removed from the comparison. It returns a match if consonants from two values match exactly. Results may not be accurate if the data contains multibyte characters.

  • Sort input: Choose to sort input by Character or Terms.
Daitch-Mokotoff Soundex Phonetic Phonetic algorithm that allows greater accuracy in matching of Slavic and Yiddish surnames with similar pronunciation but differences in spelling. Coded names are six digits long, and multiple possible encodings can be returned for a single name. This option was developed to respond to limitations of Soundex in the processing of Germanic or Slavic surnames.
Date Date

Compare date fields regardless of the date format in the input records. Click Edit in the Options column to specify the following:

  • Require Month: prevents a date that consists only of a year from matching
  • Require Day: prevents a date that consists only of a month and year from matching
  • Match Transposed MM/DD: where month and day are provided in numeric format, compares suspect month to candidate day and suspect day to candidate month as well as the standard comparison of suspect month to candidate month and suspect day to candidate day
  • Prefer DD/MM/YYYY format over MM/DD/YYYY: contributes to date parsing in cases where both month and day are provided in numeric format and their identification can not be determined by context. For example, given the numbers 5 and 13, the parser will automatically assign 5 to the month and 13 to the day because there are only 12 months in a year. However, given the numbers 5 and 12 (or any two numbers 12 and under), the parser will assume whichever number is first to be the month. Checking this option will ensure that the parser reads the first number as the day rather than the month.
  • Range Options - Overall: allows you to set the maximum number of days between matching dates. For example, if you enter an overall range of 35 days and your candidate date is December 31st, 2000, a suspect date of February 5, 2001 would be a match, but a suspect date of February 6 would not. If you enter an overall range of 1 day and your candidate date is January 2000, a suspect date of 1999 would be a match (comparing December 31, 1999) but a suspect date of January 2001 would not.
  • Range Options - Year: allows you to set the number of years between matching dates, independent of month and day. For example, if you enter a year range of 3 and your candidate date is January 31, 2000, a suspect date of January 31, 2003, would be a match but a suspect date of February 2003 would not. Similarly, if your candidate date is 2000, a suspect date of March 2003 would be a match because months are not in conflict and it's within the three-year range.
  • Range Options - Month: allows you to set the number of months between matching dates, independent of year and day. For example, if you enter a month range of 4 and your candidate date is January 1, 2000, a suspect date of May 2000 is a match because there is no day conflict and it's within the four-month range, but a suspect date of May 2, 2000, is not, because the days conflict.
  • Range Options - Day: allows you to set the number of days between matching dates, independent of year and month. For example, if you enter a day range of 5 and your candidate date is January 1, 2000, a suspect date of January 2000 is a match because there is no day conflict but a suspect date of December 27, 1999, is not, because the months conflict.
Double Metaphone Fuzzy

Determines the similarity between two strings based on a phonetic representation of their characters. Double Metaphone is an improved version of the Metaphone algorithm, and attempts to account for the many irregularities found in different languages. Metaphone3 improves upon this algorithm.
Edit Distance Similarity and Distance Determines the similarity between two strings based on the number of deletions, insertions, or substitutions required to transform one string into another.
Euclidean Distance Similarity and Distance Provides a similarity measure between two strings using the vector space of combined terms as the dimensions. It also determines the greatest common divisor of two integers. It takes a pair of positive integers and forms a new pair that consists of the smaller number and the difference between the larger and smaller numbers. The process repeats until the numbers are equal. That number then is the greatest common divisor of the original pair. For example, 21 is the greatest common divisor of 252 and 105: (252 = 12 × 21; 105 = 5 × 21); since 252 − 105 = (12 − 5) × 21 = 147, the GCD of 147 and 105 is also 21.
Exact match String Determines if two strings are the same.
Initials String Used to match initials for parsed personal names.
Jaro-Winkler Distance Similarity and Distance Determines the similarity between two strings based on the number of character replacements it takes to transform one string into another. This option was developed for short strings, such as personal names.
Keyboard Distance Similarity and Distance Determines the similarity between two strings based on the number of deletions, insertions, or substitutions required to transform one string to the other, weighted by the position of the keys on the keyboard. Click Edit in the Options column to specify the type of keyboard you are using: QWERTY (U.S.), QWERTZ (Austria and Germany), or AZERTY (France).
Koeln Phonetic Indexes names by sound as they are pronounced in German. Allows names with the same pronunciation to be encoded to the same representation so that they can be matched, despite minor differences in spelling. The result is always a sequence of numbers; special characters and white spaces are ignored. This option was developed to respond to limitations of Soundex.
Kullback-Leibler Distance Similarity and Distance Determines the similarity between two strings based on the differences between the — of words in the two strings.
Metaphone Fuzzy

Determines the similarity between two English-language strings based on a phonetic representation of their characters. This option was developed to respond to limitations of Soundex.
Metaphone (Spanish) Phonetic It determines the similarity between two strings based on a phonetic representation of their characters. This option was developed to respond to the limitations of Soundex.
Metaphone 3 Fuzzy Improves upon the Metaphone and Double Metaphone algorithms with more exact consonant and internal vowel settings that allow you to produce words or names more or less closely matched to search terms on a phonetic basis. Metaphone 3 increases the accuracy of phonetic encoding to 98% by allowing for differences in spelling due to dialects or pronunciation. This algorithm was developed to respond to limitations of Soundex.
NGram Distance Similarity and Distance Calculates in text or speech the probability of the next term based on the previous n terms, which can include phonemes, syllables, letters, words, or base pairs and can consist of any combination of letters. This algorithm includes an option to enter the size of the NGram; the default is 2.
NGram Similarity Similarity and Distance

Determines similarity between two strings based on the length of the longest common subsequence of phonemes, syllables, letters, words or base pairs.

The algorithm includes the following options:

  • Ngram size: Enter the size of the NGram. The default value is 2.
  • Drop Noise Characters: Select the check-box to replace punctuation with space.
  • Drop Spaces: Select the check-box to merge words.
Numeric String String

Compares address lines by separating the numerical attributes of an address line from the characters. For example, in the string address 1234 Main Street Apt 567, the numerical attributes of the string (1234567) are parsed and handled differently from the remaining string value (Main Street Apt). The algorithm first matches numeric data in the string with the numeric algorithm. If the numeric data match is 100, the alphabetic data is matched using Edit distance and Character Frequency. The final match score is calculated as follows:

(numericScore + (EditDistanceScore + CharacterFrequencyScore) / 2) / 2
Spanish Metaphone Fuzzy Determines the similarity between two Spanish-language strings based on a phonetic representation of their characters. This option was developed to respond to limitations of Soundex.
Nysiis Phonetic Phonetic code algorithm that matches an approximate pronunciation to an exact spelling and indexes words that are pronounced similarly. Part of the New York State Identification and Intelligence System.

Say, for example, that you are looking for someone's information in a database of people. You believe that the person's name sounds like "John Smith", but it is in fact spelled "Jon Smath". If you conducted a search looking for an exact match for "John Smith" no results would be returned. However, if you index the database using the NYSIIS algorithm and search using the NYSIIS algorithm again, the correct match will be returned because both "John Smith" and "Jon Smath" are indexed as "JANSNATH" by the algorithm. This option was developed to respond to limitations of Soundex; it handles some multicharacter n-grams and maintains relative vowel positioning, whereas Soundex does not.

Note: This algorithm does not process non-alpha characters; records containing them will fail during processing.
Phonix Phonetic

The Phonix algorithm is a Soundex variant. While the Soundex phonetic property is restricted to the collection of similar sounding consonants into different classes, the algorithm for computing the Phonix codes uses elaborate substitution rules

Preprocesses name strings by applying more than 100 rules to single characters or to sequences of several characters. 19 of those rules are applied only if the character or characters are at the beginning of the string, while 12 of the rules are applied only if they are at the middle of the string, and 28 of the rules are applied only if they are at the end of the string. The name string is encoded into a code that is comprised by a starting letter followed by three digits (removing zeros and duplicate numbers). This is more sophisticated than Soundex. It is also more complex and therefore slower than Soundex.
Sonnex Phonetic Determines similarity for two French-language strings based on a phonetic representation of their characters.

It returns a Sonnex coded key of the selected fields.
Soundex Phonetic

Determines the similarity of two strings based on a phonetic representation of their characters. This is less sophisticated than Phonix.
SubString Exact

Compares two values based on a substring within the values. This determining the similarity based on a substring, particularly where those values contain long strings of characters, or many words.

  • Start position: Specifies the start position for the substring. The character count starts at 0 for the first character.
  • Length: Specifies the number of characters in the substring.
  • Sort input: Choose to sort input by Character or Terms.
Syllable Alignment Phonetic

Combines phonetic information with edit distance-based calculations. Converts the strings to be compared into their corresponding sequences of syllables and calculates the number of edits required to convert one sequence of syllables to the other.