1) Quick setup (first 30 minutes)
Start with a one-line README sheet and a provenance header at the top of your working sheet. Create a new tab called README and write a single sentence: what this sheet is, the owner (name or role) and today’s date. In the main sheet add frozen top rows with fields like source, last_updated (YYYY-MM-DD) and owner — View > Freeze > 1 row (Sheets) / View > Freeze Panes (Excel). This small context reduces accidental misuse by humans and helps trace where an AI’s answers came from.
Why it matters for AI: a clear README and provenance help with traceability (so you can point an AI answer back to a row or source) and reduce hallucination when a model guesses provenance. Quick UI actions: Insert new sheet, type the one-line README, and freeze rows.
2) Locking, labelling and basic anonymisation (next 30–45 minutes)
Create a 'golden' sheet (a single canonical tab) and protect it. In Google Sheets: Data > Protect sheets and ranges and restrict edits to specific people; use File > Version history > Name current version. In Excel: Review > Protect Sheet and use OneDrive/SharePoint Version History or File > Save a Copy before major edits. Locking a golden copy prevents accidental edits that break downstream AI outputs and gives you a rollback point if something goes wrong.
Tag sensitive columns by prefixing headers with [SENSITIVE] or adding a column called sensitive_flag using Data > Data validation (List of items: Yes,No). For PII remove or anonymise values in a working copy: use a helper column with a simple mask (e.g. show initial and stars: =LEFT(A2,1)&"***" in Sheets/Excel) or replace whole values with REDACTED via Find & Replace. Create a canonical_id column (e.g. cust-0001 or a stable concat of email/number) so models and vendors can reference records without relying on names. Why this matters: tagging and masking reduce privacy risk and limit bias from obvious personal identifiers, while a canonical ID maintains traceability without exposing PII.
3) Quality checks, sampling, export and a short smoke test
- Add three lightweight quality flags: completeness (Yes/No if required fields present), recent_update (date), and source (form, import, manual). A simple completeness formula: set complete_flag to Yes only when key columns are not blank (Sheets/Excel: use a short IF/AND formula in a helper column).
- Sample rows for spot-checks: add a RAND() column (insert column, type =RAND() in Sheets), sort by that column and review 15–25 rows. If you find systematic errors, fix the source not just the row. For export rules: set Share > Settings in Google Drive to restrict downloads for viewers (disable download/print/copy), or set OneDrive links to view-only; when exporting to CSV, export from the working copy with PII columns removed or masked (File > Download > Comma-separated values).
- Smoke test before handing to an AI or vendor: confirm README exists; golden sheet is protected and versioned; sensitive columns are tagged or anonymised; canonical_id column present; the three quality flags are filled for most rows; you’ve spot-checked a random sample; and sharing is restricted or export is of a masked copy. If all pass, hand over the masked export and include the README and provenance fields so the AI vendor or tool can explain where answers come from.
If you want someone to run this checklist with you and leave a locked golden sheet in place, Optira can help with a short, practical session.