Let's say that a bank wants to search for duplicate bank account holders. The bank's customer data set includes columns for customer names and addresses, and the bank chooses

Contact

as the objective in the deduplicate asset. The bank decides to sort the input records into groups and to perform duplicate analysis on each group. The bank must select a column in the Deduplicate transformation on which to create the groups.

The following table shows a fragment of the data set:

In this scenario, you might identify the State column as the most suitable column on which to sort the records. You select the State column as the

GroupKey

field in the transformation.

When you select the State column as the GroupKey field, the deduplication operation enables the creation of five groups, one for each state. The likelihood that the bank has customers with the same contact information in different states is very low. Additionally, the data includes a Customer ID column that can add to the confidence of the deduplication process.

The Customer ID column is a poor candidate for group creation, as it is a primary key field. If you select the column as the GroupKey field, the deduplication operation creates a group for every unique ID and thus for every record.

The Country column is also a poor candidate for group creation, as the column contains the same value in every row. If you select the Country column as the GroupKey field, the deduplication operation adds all of the records to the same group. Your bank might have two or more genuine customers with the same name living across the country, and you do not want to deduplicate their entries.