You need realistic data for testing in a nonproduction environment. You have production data that includes sensitive data columns. You cannot use the data without the risk of compromising sensitive data. Use the Data Masking transformation to mask sensitive data before you use the data in a test environment.

The production data includes a table Personnel_Information with the following data:

First Name	Surname	DOB	Address	State
Uma	Hilel	03/12/1985	24, Atkins Avenue	CA
John	Sen	07/15/1963	49, Wheeler Road	MN
Keiko	Burns	11/24/1989	13, Draker Drive	PA
Isadora	Buckley	08/16/1990	20, Fountain Center	CA

In the Mapping Designer, add

Personnel

as a source transformation for the table Personnel_Information. Add a target transformation Personnel_test.

Add the Data Masking transformation to the mapping canvas and connect it to the data flow.

You need to mask the Surname, DOB, and the State columns to ensure sensitive data is masked. You can use the Substitution Last Name masking technique to mask the Surname column. This masking technique replaces data in the column with data from the dictionary file on surnames. You can use the Random Date masking technique to mask the DOB column. Use the Substitution State masking technique to mask the State column. This masking technique replaces data in the column with data from the dictionary file on U.S. state names.

When the mapping run completes successfully, you can view the masked data in the Personnel_test output table :

First Name	Surname	DOB	Address	State
Uma	Acothley	05/12/1985	24, Atkins Avenue	Michigan
John	Mcgovern	04/15/1963	49, Wheeler Road	Oklahoma
Keiko	Garsia	03/24/1990	13, Draker Drive	Montana
Isadora	Sonnier	01/16/1991	20, Fountain Center	South Carolina

You can now use the masked data in the table Personnel_test in the test environment.