What is data hygiene?
Data hygiene (also called data cleanliness or data maintenance) is the ongoing process of keeping business data clean, accurate, and usable. In CRM systems, data hygiene means regularly verifying contact information, removing duplicates, standardizing formats, and enriching incomplete records — preventing the slow accumulation of rot that makes every downstream process less reliable.
Data hygiene is not a one-time project. B2B contact data decays at 30-40% per year. Without continuous hygiene practices, a CRM that was clean in January will be 15-20% stale by July.
Core data hygiene practices
- Verification — Are records still accurate? Checking that email addresses are deliverable, phone numbers are active, job titles are current, and company information reflects reality.
- Deduplication — Removing redundant records that inflate metrics and cause double-touches. Enterprise CRMs average 20–30% duplicate rates without active dedup.
- Standardization — Ensuring consistent formats across records: "United States" vs "US" vs "USA", phone number formatting, company name variants (Salesforce vs salesforce.com vs SFDC).
- Enrichment — Filling missing fields from external sources. A record with just a name and email needs title, company, phone, LinkedIn profile, and firmographic data to be actionable.
- Archiving — Removing truly dead records: bounced emails with no alternative, people who have retired, companies that no longer exist. Dead records add noise without value.
Why periodic cleanups fail
Most organizations approach data hygiene as a periodic project — a quarterly or annual cleanup that fixes what's broken, then waits until the next cycle. This creates a sawtooth pattern: data quality spikes immediately after the cleanup, then steadily decays until the next one.
At a 30–40% annual decay rate, a CRM that's clean in January is already 15–20% stale by July. By the time the next annual cleanup runs, you've been making decisions on bad data for months.
The cleanups themselves are expensive — consultant hours, tool licenses, team time for manual review. And because the data has degraded so far between cycles, each cleanup is a larger project than it needed to be.
Continuous data hygiene breaks the sawtooth. By verifying, enriching, and deduplicating in real time, data quality stays consistently high instead of oscillating between clean and stale.
How Salmon maintains data hygiene
Salmon replaces periodic data cleanups with continuous data hygiene. AI agents verify, enrich, deduplicate, and monitor every CRM record in real time. Your data quality improves steadily instead of following the cleanup-decay-cleanup sawtooth.
Measuring data hygiene
You can't improve what you don't measure. Key data hygiene metrics include:
- Field fill rates — What percentage of records have key fields populated (title, direct phone, company, email)? Low fill rates indicate enrichment gaps.
- Bounce rates — A lagging indicator of email accuracy. Above 5% signals material data staleness.
- Duplicate rates — What percentage of records are redundant? Run dedup reports monthly, not annually.
- Decay velocity — How quickly are records going stale? Track the rate of job changes, bounced emails, and company changes per month.
- Time since last verification — How old is the most recent verification for each record? Records verified last week are more reliable than records verified last year.
Leading indicators (fill rates, verification recency) predict future problems. Lagging indicators (bounce rates, routing failures) confirm problems that already exist. Focus on leading indicators to stay ahead of decay.
Related concepts
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