Hitting "Remove Duplicates" on monthly sales data can quietly shred your revenue. If your dataset lacks timestamps or unique transaction IDs, a standard duplicate cleanup tool looks at every column. If Branch A sells two identical items for the exact same price in the same month, Excel or Power Query sees matching rows, assumes it's an accidental copy-paste error, and deletes one. Just like that, real sales vanish from your dashboard. When you're dealing with flat monthly files, blind row deletion is a trap. Protect your data with two simple adjustments: Inject a unique row index: Have your Python script or Power Query add an auto-incrementing index or source file name column. Even if the business metrics match, the unique index keeps real, coincidental transactions safe from accidental deletion. Group instead of deleting: If your data arrives as monthly summaries, use a `Group By` operation to sum up quantities and revenues rather than scanning for duplicate rows. Automation is powerful, but blind data cleaning can cost you real money. Always check your data structure before you hit delete.
