In the Developer tool, define transformations that generate score values for source data based on data quality issues that you define. Define transformations that return text to describe the quality of the source data. For example, you can configure a Labeler transformation to check source data against reference tables and then write a descriptive label for each comparison. You can define an

IF/THEN

rule in a Decision transformation to examine a data field. You can define multiple transformations and mapplets that perform different data quality operations.

Configure an Exception transformation to analyze the record scores that it receives from the data quality operations. Configure the transformation to write records to database tables based on score values in the records. You can create separate tables for good records, bad records, quality issues, and rejected records.

Assign a quality issue port to each input port that might contain bad data.

Optionally, configure target data objects for good and bad records. Connect the Exception transformation output ports to the target data objects in the mapping.

Create the target data object for bad records. Choose to generate a bad records table and add it to the mapping. When you generate a bad records table, the Developer tool also generates a quality issues table. Add the quality issues table to the mapping.

Add the mapping to a workflow.

Configure a Human task to assign manual review of bad records to users. Users can review and update the bad records in the Analyst tool.