| 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
- Dedupe contacts — fuzzy-match on
email, vat, name + country, phone; cluster; propose master + merge list
- Merge partners — use
base.partner.merge.automatic.wizard or manual: move refs, archive losers
- Normalize — batch update via
update_records, e.g. title-case all partner names
- Enrich — pull external data (web scrape, VIES VAT lookup, Clearbit) and fill empty fields
- 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.
