A practical checklist for cleaning a CSV before analysis or import

Messy CSVs routinely fail downstream imports because headers drift, duplicate records accumulate, dates use mixed formats, and blank strings mean different things in different columns. Here is a compact, repeatable workflow.

1. Preserve the original and inspect structure

Keep an untouched copy. Check encoding, delimiter, row count, duplicate headers, unexpected columns, and a sample of the longest values. Do not silently discard rows before recording why.

2. Normalize headers and missing values

Trim whitespace, use stable machine-friendly header names, and map common aliases (for example e-mail and email) deliberately. Convert agreed missing markers such as empty strings, N/A, and null into one representation while retaining fields where an empty value is meaningful.

3. Standardize values by column type

Parse dates to one documented format, lowercase/trim email addresses where appropriate, normalize phone numbers only with a stated locale assumption, and preserve leading zeros for identifiers. Flag conversion failures instead of guessing.

4. Deduplicate with a documented key

Exact-row deduplication is safe only for identical rows. For customer data, define a key such as normalized email plus account ID, decide which source wins conflicts, and output a review file for uncertain matches.

5. Validate and export

Run validation rules (required fields, allowed values, parseable dates), produce an exception report, then export clean CSV and JSON with UTF-8 encoding. Keep a short transformation log so the result is reproducible.

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