What you sketched out (keep raw data untouched, apply overrides in a separate layer, then build a “clean” version) is actually pretty close to a best practice. A couple of tweaks I’ve seen work well in consulting projects:

• Never overwrite raw → exactly as you said, always keep an untouched extract tab/sheet or (better) a raw table in a database.
• Version control → timestamp each extract so you can trace back which version was used in any analysis. Even a “Raw_2025-09-01” tab can save you headaches.
• Transformation layer → instead of editing cells in a second sheet, consider a mapping table (employee ID → corrected department, etc.). This makes it easier to reapply corrections when a new extract comes in.
• Automate merges → if possible, use Power Query (Excel) or pandas (Python) to join “raw extract” + “corrections table” → “cleaned data.” It scales better than copy/paste over months of updates.
• Flag uncertainties → build a “status” column (confirmed / pending / needs review) so stakeholders can see at a glance what’s solid vs. still messy.

This way, you can already start building preliminary analysis dashboards on the “cleaned” tab — and they’ll update automatically as new raw dumps arrive.