Checks by column type

Identifiers

• Distinct count equals row count. If not, the key is not the key.
• No nulls. A null id is a broken row.
• Consistent format. Mixed formats mean two sources merged.
• Check for leading zeros lost to a numeric read. Zip codes and account numbers are the usual casualties.

Numeric

• Read as numeric, or did one bad value force text?
• Min and max physically possible?
• Zeros: real measurements, or missing values wearing a disguise?
• Negatives: possible for this quantity?
• Suspiciously round numbers in quantity suggest manual entry or a default.
• Units consistent throughout? Currency, weight and time are the usual mixed columns.

Categorical

• Distinct values, listed. Read them.
• Same category, different spelling: case, whitespace, punctuation, abbreviation.
• A catch-all value like "Other" or "Unknown" holding a large share hides structure.
• Categories present in only part of the date range mean a taxonomy change.

Dates and times

• Parsed as dates, or still text?
• Time zone: recorded, assumed, or mixed? Mixed is the common silent disaster.
• Any future dates? Any at the epoch, or at 1900-01-01?
• Gaps: whole days or weeks missing means a pipeline outage.
• Day-first versus month-first ambiguity: check whether any day exceeds 12.

Text

• Length distribution. A cluster at exactly 255 means truncation upstream.
• Leading or trailing whitespace.
• Encoding damage: mojibake, replacement characters, doubled encoding.
• Embedded delimiters that may have shifted columns during a bad parse.

Booleans

• How is it encoded? True/False, 1/0, Y/N, yes/no, or all of them in one column.
• Is null a third state with meaning, or just missing?

Across columns

• Do totals equal the sum of their parts?
• Do dates order correctly: created before updated, start before end?
• Do foreign keys resolve? Count the orphans.
• Are mutually exclusive flags actually exclusive?