Database Data Validation & Cleanup

I have a live database that contains a mix of text, numeric, and coded fields that now shows inconsistencies, typos, and the occasional duplicated record. What I need is a careful set of eyes—and solid data-handling skill—to validate every row, flag or correct anything that falls outside the accepted formats, and leave me with a reliable, production-ready dataset.

The work is entirely validation-driven: you will compare stored values against the reference rules I supply, normalise units, fix capitalisation, catch misplaced decimals, and make sure linked fields stay in sync. A portion of it will be manual review, but you’re welcome to speed things up with SQL queries, Excel filters, or any data-wrangling tool you prefer, as long as the final result is accurate.

Deliverables
• A cleaned and validated database file (same structure, corrected content)
• A concise log or report of the changes made, so I can audit what was touched

Acceptance criteria
• No duplicate primary keys or records
• All required fields populated and correctly typed
• Text fields free of obvious spelling errors and uniform in casing
• Numeric fields follow the prescribed format and range checks
• Cross-referenced tables remain relationally intact

If you have proven experience sanitising mixed-type datasets and can turn this around quickly without automated “one-size-fits-all” scripts compromising quality, let’s get started right away.