Transform allows you to transform data (Abbreviate, Elaborate, Exclude, Normalize, Transliterate) in 5 spoken languages and from 12 entity categories.

Addressing elements are used on address fields, usually Address Line 1 and Address Line 2.

This category understands most common elements of address data. For example 'Gnds' is an abbreviation for 'Gardens', 'Rd' is 'Road', 'Av' is 'Avenue' etc. E.g. using this function 15 Hound Terrace will match 15 Hound Street. This would not match if you use Normalise however 15 Hound St. will match 15 Hound Street.

Again this is potentially dangerous if selected as a match field on its own, but when this address field is accompanied by a match defined on Postcode as well it becomes a lot more accurate. See the example below to better understand how this record is definitely a match, however some addressing elements have been incorrectly captured during data input.

Business Name elements are used on company name fields.

This category understands the most common elements of business name data. For example 'Ltd' is an abbreviation for 'Limited', 'Plc' is 'Public Limited Company', 'Grp' is 'Group' etc.

E.g.

Job Title elements are used on Name fields and Job Title fields.

This category understands most common elements of Job Title data. For example 'Mgr' is an abbreviation for 'Manager', 'Mkt' is 'Marketing', 'Col' is 'Colonel' etc.

E.g. using this function 'Marketing Manager' will match 'Mkt Mgr'.

The Countries category is used on an address field that contains the information about which country that record relates to.

This category understands most common styles of Country data. For example 'UK' is an abbreviation for 'United Kingdom', 'USA' is 'United States of America', 'De' is 'Germany' etc.

E.g. using this function 'United Kingdom' will match 'UK'.

E.g.

The Dates Category would only be used when your data set contains date information that you wish to match on.

This category understands most common styles of that a date can be written in. For example 'Jan' is an abbreviation for 'January', 'Feb' is 'February', 'Mon' is 'Monday' etc.

First name elements are used to standardise name fields.

This category understands most common elements of forename data. For example ‘Bill’ can be an abbreviation for 'William', ‘Bob’ can be an abbreviation for 'Robert' etc.

E.g. using this function 'Robert Dickson' will match 'Bob Dixon'.

This can be advantageous when you have Robert Dickson in a single field and you only wish to match on surname.

E.g. exclude initials and exclude forename and R Dixon will match Bob Dixon.

This has a potential danger if the both elements of the name are in the same field and the name is made up of two words that can be interpreted as forenames, such as George Michael or Elton John would have both elements excluded.

The Miscellaneous Category is very rarely used.

This category understands some obscure transformations that may be required in the matching process. For example ‘pm’ is an abbreviation for 'Post Meridian', ‘am’ is 'Ante Meridian', ‘&’ is 'and' etc.

E.g. using this function ’Tate & Lyle’ will match ’Tate and Lyle’

The Numbering Category should be used when you have different number formats within your data.

This category understands most common styles of that a number can be written in. For example '10' could also be 'Ten', '1000' could also be 'One Thousand', '1st' could also be 'First', '2nd' could be 'Second' etc.

E.g. using this function ‘1 to 1’ will match ‘One to One’ in a company name field.

Qualification elements are usually used on Name fields.

This category understands most common elements of qualifications that can be added to a person’s name. For example qualifications include 'Bsc' as an abbreviation for 'Bachelor of Science', 'Phd' is 'Doctor of Philosophy', 'MSc' is 'Master of Science' etc.

E.g. using this function 'Mr Robert Dickson BSc' will match 'Bob Dixon' during a phonetic match.

This is a rarely used function that can be used to exclude inappropriate language.

Salutation elements are commonly used on name fields.

This category understands most common elements of name data.

E.g. using this function 'Mr Robert Dickson' will match 'Bob Dixon' during a Phonetic match as the 'Mr' is excluded.

The Weights/Measures Category would only be used when your data set contains this type of information and you wish to match on this field.

This category understands most common styles of that a weight or measure can be written in. For example 'Oz' is an abbreviation for 'Ounce', 'Kg' is 'Kilogram' etc.

E.g. using this function ’12 Oz’ will match ’12 Ounces’

Abbreviate, when selected, allows you to transform the structure of your data to ensure a consistent format.

For example you can choose to Abbreviate business elements in a company name field. This will reduce 'Limited' to 'Ltd', 'Group' to 'Grp', 'Incorporated' to 'Inc' etc. and write the results directly back to the field you select in your data set.

Elaborate, when selected, allows you to transform the structure of your data to ensure a consistent format.

For example you can choose to elaborate business elements. This will expand 'Ltd' to 'Limited', 'Grp' to 'Group', 'Inc.' to 'Incorporated' etc. and write the results directly back to the attribute selected in your data set.

As with all rules there are times when you would not wish to use this rule. For example in a Forename field it would be very dangerous to elaborate your data. Pete would go to Peter, Bob to Robert etc. but you cannot define what Sam would go to e.g. Samuel or Samantha so Sam would be left the same. However if there were a Rob in your database that is short for Robin this would also be transformed to Robert.

Exclude, when selected, allows you to transform the structure of your data to ensure a consistent format.

For example, you can choose to exclude business elements in a company name field. This will remove 'Ltd', 'Limited', 'Grp', 'Group', 'Inc', 'Incorporated' etc.

Normalize, when selected, allows you to transform your data for the purpose of matching. It will perform unsafe operations to allow you to match similar records together.

Note: The output of a Normalize should only be used for matching, it should never replace the original value.

Transliterate, when selected, allows you to transform the structure of your data to ensure a consistent format.

For example, you can choose to transliterate in a name field to remove characters from foreign alphabets e.g 'ß' will change to 'ss'.

The DQ.TRANSFORM function transforms an input based on type and operation.

Parameters:

input (string), text to be transformed

transformType (string), type of data to be transformed, from list:

• Addresses
• Numbers
• Given_Names
• Businesses
• Business_Job_Titles
• Dates
• Miscellaneous
• Weights_and_Measures
• Qualifications
• Salutations
• Countries
• Family_Names
• Salacious

operation (string), transformation method, from list:

• Elaborate
• Abbreviate
• Normalize
• Exclude
• Transliterate

language (string), language of input, from list:

• English
• Spanish
• French
• Italian
• German

returns (string), transformed value