The High Cost of Double Counting

Duplicate records are a silent killer in databases. In marketing, sending the same promotional email twice to a customer increases unsubscribe rates and damages brand reputation. In finance, double-counting revenue leads to disastrously inaccurate forecasting. Identifying and removing duplicates is a mandatory step in any data pipeline.

Defining a "Duplicate"

The hardest part of deduplication is defining what a duplicate actually is. Is a row a duplicate only if every single column matches exactly? Or is it a duplicate if just the Email Address matches, even if the First Name is spelled differently? You must establish a "Unique Key"—a column or combination of columns that should theoretically never repeat.

Keep First vs. Keep Last

Once you identify a duplicate, which version do you keep? If your data is sorted chronologically, keeping the "Last Occurrence" usually retains the most up-to-date information (e.g., the customer's newest address). Keeping the "First Occurrence" is useful if you want to preserve the original creation date of a record.

Using the Deduplicator

DataScrub's Deduplicator tool allows you to select specific columns to check for duplicates, rather than blindly checking the whole row. It highlights exactly how many rows will be removed, giving you confidence before you export the clean file.