The Labeler transformation writes a descriptive label for each value on a port.
The following examples describe some of the types of analysis you can perform with a Labeler transformation.
Find records with contact data
Configure the transformation with a reference table that contains a list of first names. Create a token labeling strategy to label any string that matches a value in the reference table. When you review the output data, any record that contains the label is likely to identify a person.
Find business records
Configure the transformation with a token set that contains a list of business suffixes, such as Inc, Corp, and Ltd. Create a token labeling strategy to label any string that matches a value in the reference table. When you review the output data, any record that contains the label is likely to identify a business.
Use a token set of business suffixes you want to identify any business name. You can use a reference table of business names if you are certain that the table contains all the businesses you want to identify. For example, you can use a reference table that lists the corporations on the New York Stock Exchange.
Find telephone number data
Configure the transformation with character set that defines the character structure of a telephone number. For example, you can use a character set that recognizes different patterns of punctuation symbols and digits as United States telephone numbers. You can review the data to find records that do not contain the correct digits for a telephone number.
The character labels may use the following characters to analyze the column data:
c=punctuation character n=digit s=space
The following table shows sample telephone number structures: