Data Hygiene
Why it matters
Dirty data damages every decision and every automation built on top of it, quietly rather than all at once. Two records for the same customer split a history and make a report wrong. A blank owner field means nobody works the deal. A stale email address bounces, and bounces drag down deliverability for every message sent after it.
The risk grows with automation. An agent or workflow acting on bad data acts confidently in the wrong direction, and the mistake may not show until it has already run.
How to apply it
- Validate on entry. Reject or flag a record before it saves, rather than cleaning it up afterwards.
- Check for a duplicate before inserting a new record, keyed on email address or company domain.
- Put each fact in its proper field, not in free text, so enrichment and matching tools can use it.
- Schedule a regular hygiene pass that flags drift, such as monthly, and name the person who owns it.
- Merge or archive dead entries on a fixed schedule instead of waiting until reporting breaks.
What it is
Data hygiene covers the habits that keep a CRM, an email list or a customer table accurate. It has four parts: stopping bad records at entry, finding duplicates, updating what has gone out of date and removing what is dead. It is a routine, not a one-off clean-up, because records decay on their own. People change jobs, companies rename and email addresses stop working.
Common mistakes
- Treating clean-up as a project with an end date.
- Deleting records with no record of what was removed. Archive or log first.
- Fixing symptoms in a report while the source stays dirty.
