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Definition
Data hygiene is the routine work that keeps the data behind reports accurate and consistent, such as removing duplicates, fixing labels, using one currency and time zone, and recording known gaps. Clean inputs make the numbers comparable from one week to the next.
Clean order count = rows in the export - rows that repeat an order ID already countedExample with made-up numbers: Made-up scenario: an order export has 1,240 rows, and 36 of them repeat an order ID that already appears, because an import was retried. After removing the repeats, the count is 1,204. The report notes the cleanup and its date, and the team checks whether the same retry happened in the two weeks before.
How to use it
Typical tasks include checking that each order is counted once, merging duplicate records, matching names and codes to the agreed definitions, and using the same time zone in every source. Check for gaps as well. A missing day, a broken tag or a source that stopped sending data should appear in the report as a known gap, not as a zero.
Run a short hygiene check before each report, and keep a list of known issues with the date each one started and the date it was fixed. When a definition changes, record the date of the change, so past and present numbers are not compared as if they measured the same thing. Give one person ownership of the list.
