What is Data Hygiene?

Data hygiene is the ongoing work of keeping CRM and marketing data accurate, complete, consistent and free of duplicates, so reps, managers and reports all work from records they can trust.

At a glance

• It covers five things: wrong values, missing values, inconsistent formats, duplicate records and records that have gone stale.
• Gartner research from 2020 estimates that poor data quality costs organizations at least $12.9 million a year on average.
• Most bad data enters at a few points: imports, form fills, integrations and reps typing records in by hand.
• Rules at the point of entry do more than any cleanup project.
• Someone has to own it. Data nobody is responsible for drifts.

What data hygiene covers

Hygiene problems come in a few shapes. A job title is wrong because the person moved roles. An industry field is blank because the form didn’t ask for it. A state is written as “CA”, “Calif.” and “California” across three records. The same buyer exists twice. A contact who left the company two years ago still sits in an active list.

Each one breaks something different. Blank fields break segmentation and lead scoring. Inconsistent formats break reports and territory rules. Stale contacts drag down email deliverability and waste rep time. Duplicates are a job of their own, covered under deduplication.

How teams keep data clean

The cheapest fix is at the point of entry. Required fields on the records that matter, picklists instead of free text for things like industry and country, and duplicate checks on create stop most problems before they exist.

The second layer is scheduled work: a monthly pass for blank required fields, contacts with bounced emails and accounts with no owner, plus a refresh of key fields through data enrichment. Integrations need their own check, because a bad field mapping can overwrite good data with bad on every sync.

Example (illustrative): a team pulls a list of 2,000 operations leaders for a new campaign and finds 300 with no industry value and 150 with bounced emails from last year. Making industry a required picklist on new records, and suppressing bounced contacts from every list by default, fixes both going forward. The old records get enriched or archived once.

Common data hygiene mistakes

• Running one big cleanup and stopping. Without entry rules, the same problems are back by the next planning cycle.
• Adding fields nobody fills in. Every required field should feed a report, a route or a list. If it doesn’t, cut it.
• Letting every integration write to every field. Decide which system owns each field, and don’t let a second tool overwrite it.
• Deleting instead of archiving. Old records carry history that attribution and win-loss reviews need later.
• No owner. If hygiene belongs to everyone, nobody checks the report.

How data hygiene shows up in a revenue system

Hygiene decides whether the forecast means anything. Pipeline hygiene (close dates, amounts and stages kept current) sits on top of it, and both depend on the field rules set inside the CRM. Our RevOps work inside partners’ systems starts with those rules, so the lists, routing and reports have something solid underneath. See how we run RevOps.