cleaning

name	cleaning
description	Deduplicate, enrich, and fix data quality — especially contacts, products, leads. Use for dedupe, merging duplicates, fixing formatting, enrichment, or data-quality audits.
triggers	["clean","cleaning","cleanup","dedupe","duplicate","dubbel","merge","enrich","verrijken","data quality","kwaliteit"]

Cleaning

Find bad data, propose fixes, execute with confirmation. Destructive — always confirm before writing.

Common problems

• Duplicates — same entity in multiple records (res.partner, product.product, mailing.contact)
• Formatting drift — inconsistent caps/punctuation (ACME vs Acme vs acme inc)
• Missing fields — partners without country/VAT, products without category
• Stale data — inactive that should be archived; unused tags/categories
• Orphans — m2o references to deleted records (rare but possible in imports)

Typical workflows

1. Dedupe contacts — fuzzy-match on email, vat, name + country, phone; cluster; propose master + merge list
2. Merge partners — use base.partner.merge.automatic.wizard or manual: move refs, archive losers
3. Normalize — batch update via update_records, e.g. title-case all partner names
4. Enrich — pull external data (web scrape, VIES VAT lookup, Clearbit) and fill empty fields
5. Archive stale — find records with no activity in N months, set active = False

Gotchas

• Merging loses history — archived losers keep messages but aren't easily findable. Always confirm before merging.
• Duplicates aren't always duplicates — same name, different country = different company.
• VIES lookup is rate-limited; batch with backoff.
• Don't mass-update without a dry-run: search_count first, show sample, then write.
• Archive (active = False) is reversible; unlink is not — prefer archive.