CRM Data Hygiene

Dirty CRM data corrupts everything downstream: routing, scoring, attribution, forecasting, outbound, and reporting. Every workflow that touches CRM data inherits its quality problems. A 10% duplicate rate means 10% of your outbound is wasted. A 20% invalid-email rate means 20% of your sequences bounce. Data hygiene isn't a cleanup project. It's an ongoing operating discipline.

The principle: prevent bad data from entering the CRM, detect and fix bad data that exists, and maintain quality continuously. In that order. Prevention is 10x cheaper than remediation.

The 5 Data Quality Dimensions

Every CRM hygiene program addresses these five dimensions.

Prevention: Stop Bad Data at the Gate

Required fields on creation

Define minimum required fields for every object type. Block creation without them.

Prevention rules:

• Enforce required fields in CRM settings, not in training. If a rep can create a contact without an email, they will. Make the system enforce it
• Don't require too many fields. 5-7 required fields per object is the limit. Beyond that, reps start entering garbage to bypass the form ("asdf" in title, "1" in phone)
• Use dropdown menus instead of free text for categorical fields (industry, country, lead source). Free text creates inconsistency. Dropdowns enforce it

Standardization rules

Define how data should be formatted and enforce it with automation.

Import hygiene

Most data quality problems enter through bulk imports: list purchases, event attendee uploads, enrichment dumps.

Import rules:

• Every import must go through a staging process. Never import directly into production CRM
• Deduplicate against existing records before import. Match on email (primary), domain + name (secondary)
• Validate emails before import. Run through verification tool (NeverBounce, ZeroBounce). Reject unverifiable emails
• Map import fields to CRM fields explicitly. Never auto-map. Column names like "Name" could be first name, last name, or full name
• Log every import: date, source, record count, who imported. This is the audit trail for tracing data quality issues back to their source
• Set a "data source" or "import batch" field on every imported record. When an import turns out to be garbage, you can find and fix all affected records

Detection: Find Bad Data That Exists

Automated hygiene reports

Run these reports weekly or set them as automated dashboards.

Duplicate detection

Duplicates are the most damaging data quality issue. They cause:

• Multiple reps emailing the same prospect (embarrassing and unprofessional)
• Inflated pipeline (same deal counted twice)
• Broken attribution (touchpoints split across records)
• Wrong scoring (engagement signals diluted across duplicates)

Duplicate matching rules:

Merge rules:

• Keep the record with the most activity history. Losing activity data is worse than losing a field value
• Keep the most recent field values when merging. If Record A has title "Manager" from 2024 and Record B has "Director" from 2026, keep "Director"
• Never auto-merge fuzzy matches. Auto-merge on exact email only. Everything else needs human review
• Log every merge: which records merged, which survived, who approved. Undo capability if a merge was wrong

Remediation: Fix Bad Data

The hygiene sprint

A one-time cleanup to bring existing data to baseline quality. Run this before implementing ongoing processes.

Sprint rules:

• Don't try to fix everything at once. Prioritize by impact: duplicates first (they break routing and outbound), then missing emails (they break sequencing), then missing titles (they break scoring)
• Set a quality baseline before the sprint. "14% of contacts have no email, 8% are duplicates, 22% have no title." Measure again after the sprint to prove progress
• Don't delete records during the sprint unless they're clearly invalid (test records, spam entries, competitors). Archive, don't delete. Deleted data can't be recovered

Ongoing hygiene cadence

After the sprint, maintain quality continuously.

Field-Level Hygiene Rules

Email hygiene

Title / role hygiene

Company hygiene

Automation Recipes

Recipe 1: Contact standardization on creation

Trigger: New contact created Actions:

1. Lowercase email
2. Standardize country (mapping table)
3. Standardize phone to E.164
4. Set "needs enrichment" flag if title, industry, or company size is empty
5. Run dedup check against existing contacts on email

Recipe 2: Stale deal cleanup

Trigger: Weekly scheduled automation Conditions: Deal close date < today AND stage not closed Actions:

1. Notify deal owner via Slack/email: "Deal [name] has a close date in the past. Update the close date or close the deal"
2. If no action in 7 days, auto-move to "Stale" stage
3. If no action in 14 days, auto-close as "Closed Lost - Stale"

Recipe 3: Contact left company detection

Trigger: Monthly enrichment refresh Conditions: Enrichment returns different company for same email Actions:

1. Flag contact as "Job Change Detected"
2. Update company if new company is in ICP
3. Notify account owner: "[Contact] appears to have moved to [New Company]"
4. If old company has no other contacts, flag account for review

Recipe 4: Duplicate prevention on import

Trigger: Bulk import initiated Actions:

1. Match import records against existing contacts on email
2. For matches: update existing record with new data (don't create duplicate)
3. For non-matches: create new contact with import batch tag
4. Log: records matched, records created, records rejected (invalid email)

Measuring Data Quality

Hygiene scorecard

Track monthly. Report to sales and marketing leadership.

Quality trend tracking

Plot these monthly to see whether quality is improving or degrading:

• Total records vs clean records (completeness trend)
• New duplicates created per month (prevention effectiveness)
• Records enriched per month (enrichment coverage)
• Bounce rate trend (email accuracy over time)
• Import rejection rate (import quality over time)

Anti-Pattern Check

• No required fields on contact creation. If reps can create contacts with just a name and nothing else, they will. Enforce email, company, and lead source as minimums
• Free-text fields for categorical data. Country, industry, lead source, and seniority should be dropdown menus. Free text creates infinite variations of the same value
• Cleaning data once and calling it done. Data decays at 2-3% per month (people change jobs, companies rebrand, emails become invalid). Hygiene is a continuous process, not a project
• Auto-merging fuzzy matches. Fuzzy name matching produces false positives. "John Smith at Acme" and "John Smith at Beta Corp" are different people. Only auto-merge on exact email match
• Deleting records instead of archiving. Deleted records lose activity history, attribution data, and relationship context. Archive to a "disqualified" or "inactive" lifecycle stage instead
• No import process. Bulk importing a purchased list directly into production CRM without dedup, verification, or field mapping introduces thousands of quality issues in one action
• No hygiene owner. If nobody is responsible for data quality, nobody maintains it. Assign one person (usually RevOps) as the hygiene owner with a weekly review cadence
• Measuring hygiene by record count instead of quality. "We have 50,000 contacts" means nothing if 15,000 are duplicates and 10,000 have no email. Measure clean records, not total records