data-hygienelisted

# Data Hygiene Clean before you enrich, and keep it clean with a queue rather than a quarterly panic. ## When to use - Before any paid enrichment run, every time - Bounce rates climbing - Duplicate accounts breaking account-level reporting - Ongoing, as a scheduled loop ## Inputs - Reads: `workspace/data/object-model.md` and the data dictionary, both required for the freshness windows - Needs from user: the list or export to clean, and the suppression sources ## Workflow ### 1. The pre-paid pass, in this order Order matters. Dedup after normalization finds more, and suppression before enrichment saves money. ``` 1. Normalize domains strip protocol, www, params, lowercase, resolve redirects 2. Normalize titles map to a seniority band and a function 3. Normalize geography to a consistent country and region scheme 4. Flag invalid emails syntax, disposable, role-based, free-mail on B2B rows 5. Dedup accounts on the normalized key, keep the most complete record 6. Dedup contacts on verified email, then on name plus account 7. Apply suppression customers, open opportunities, do-not-contact, competitors 8. Report drop reasons every removed row carries why ``` **Step 8 is the one people omit and the one that makes the pass auditable.** A cleanup that removes 40% of rows with no reason column is indistinguishable from a bug. ### 2. Merge, do not delete When deduping, merge into the most complete record. Field-level mer