The column that contains the correct or required values is called the valid column. When a transformation reads a reference table in a mapping, the transformation looks for values in the non-valid columns. When the transformation finds a non-valid value, it returns the corresponding value from the valid column. You can also configure a transformation to return a single common value instead of the valid values.

The valid column can contain data that is formally correct, such as ZIP codes. It can contain data that is relevant to a project, such as stock keeping unit (SKU) numbers that are unique to an organization. You can also create a valid column from bad data, such as values that contain known data errors that you want to search for.

For example, you create a reference table that contains a list of valid SKU numbers in a retail organization. You add the reference table to a Labeler transformation and create a mapping with the transformation. You run the mapping with a product database table. When the mapping runs, the Labeler creates a column that identifies the product records that do not contain valid SKU numbers.