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Every column passes the obvious checks and the numbers are still wrong, in a way nobody notices until someone charts the values.
What you'll have at the end
A histogram or value count for each key column, with a written note on any shape that doesn't make sense.
You need
An export or table whose standard checks (structure, nulls, duplicates, plausible min and max) have already passed, with at least one numeric or status column that feeds a report's total, average, or chart.
Not covered
Deciding a standing rule for every future missing value, or investigating a single row that's ten times bigger than the rest, are both separate, narrower checks.
Leans on
The missing-value rule nobody wrote down for this column
when the odd shape turns out to be genuine missing data rather than a stand-in value, and the column needs a standing fill rule instead of a one-off flag
One outlier, three possible causes, one documented call
when it's a single extreme row rather than a whole cluster repeating one value
Write a reproducible data cleaning log
for the standing log format the write-the-note step here feeds into
Checked 26 Aug 2026
Part of the Data Cleaning and Validation for Analysts cookbook