Data Hygiene
The ongoing practice of maintaining clean, accurate, and deduplicated data in your CRM and marketing systems. Includes removing invalid records, merging duplicates, standardizing fields, and enriching incomplete data.
Dirty Data Is the Silent Killer of Revenue Operations
Every marketing campaign, sales report, and board metric starts with data. When that data is wrong — duplicated contacts, invalid emails, outdated company information — everything downstream is wrong too. Your lead scoring is off. Your routing sends leads to the wrong reps. Your forecasts are inaccurate. Bad data compounds into bad decisions.
The Most Common Data Problems
Duplicate records (the same contact exists 3 times with slight variations). Invalid email addresses (hard bounces destroying sender reputation). Outdated titles and companies (people change jobs). Inconsistent formatting (New York vs NY vs new york). Missing fields (no company size, no industry). Each problem degrades the effectiveness of your GTM engine.
Building a Data Hygiene Program
Automate what you can: deduplication rules, email validation on entry, field standardization, and enrichment refresh cycles. Manually audit what you cannot automate: data quality scoring, segment health checks, and integration data flow verification. Assign ownership — if nobody owns data quality, nobody maintains it.
The ROI of Clean Data
Clean data improves email deliverability (fewer bounces), lead routing accuracy (right rep, right account), forecast reliability (clean pipeline data), and campaign performance (accurate targeting). Companies that invest in data hygiene see 15-25% improvement in marketing campaign performance from targeting accuracy alone.
Frequently Asked Questions
How often should you clean your CRM data?
Continuously through automation, with a full audit quarterly. Set up automated rules to catch duplicates on creation, flag invalid emails on bounce, and update records when contacts change jobs. A quarterly manual audit catches what automation misses — orphaned records, outdated segments, and data drift.
What is the cost of bad data?
Gartner estimates poor data quality costs organizations an average of $12.9M per year. In SaaS, bad data leads to misdirected outreach, incorrect lead routing, inaccurate forecasting, and failed integrations. A CRM with 30% bad data is a CRM that nobody trusts.
