data-governance-framework

name: data-governance-framework description: Maintain clean, accurate CRM data for rev ops. Use when defining data quality, hygiene, or governance for revenue operations.

Data Governance — Framework

How to maintain clean, accurate, and actionable CRM data. The foundation for every revenue operation — without it, automation fails, reporting lies, and sales loses trust.

Why Data Governance Matters

Bad data compounds. Every workflow, report, and automation relies on the underlying data quality. When data is dirty:

• Lead routing breaks — Leads go to wrong reps or don't route at all
• Reporting becomes fiction — Pipeline numbers are unreliable, forecasts miss
• Automation backfires — Workflows trigger on wrong conditions, spam customers
• Sales loses trust — Reps stop updating CRM because "the data is wrong anyway"
• Revenue leaks — Duplicate records, missed follow-ups, wrong attribution

The cost: Companies lose 12-25% of revenue due to bad data (Gartner). The average CRM has 25-30% duplicate records.

Data Quality Dimensions

The Six Pillars of Data Quality

Quality Scoring Model

Assign a Data Quality Score (DQS) to each record:

DQS = (Completeness Score x 0.25) + (Accuracy Score x 0.30) +
      (Timeliness Score x 0.20) + (Validity Score x 0.15) +
      (Uniqueness Score x 0.10)

Score ranges:
- 90-100: Excellent — ready for automation
- 70-89: Good — minor gaps, usable
- 50-69: Fair — needs enrichment before use
- <50: Poor — high risk, requires review

Data Decay Rates by Field Type

Data decays faster than you think. Annual decay rates by field type:

Decay Mitigation Schedule

Field Governance Matrix

Who Owns What

Field Edit Permissions

Standardization Rules

Country Names

Implementation: Use HubSpot workflow or validation rule to auto-correct on save.

Job Title Normalization

Phone Number Format

Rule: Always store with country code, spaces between segments, no special characters.

Company Name Normalization

Duplicate Detection and Merge Strategies

Duplicate Sources

Duplicate Matching Logic

Contact Matching (in priority order):

Company Matching:

Merge Rules: Surviving Record Selection

When merging duplicates, which record "wins"?

Field-Level Merge Rules

Data Enrichment Best Practices

Enrichment Hierarchy

Not all enrichment sources are equal. Prioritize by accuracy:

Waterfall Enrichment Strategy

Don't rely on one source. Use a waterfall approach:

Step 1: Check existing CRM data (free)
  ↓ If missing fields
Step 2: Clay enrichment (uses multiple sources)
  ↓ If still missing
Step 3: ZoomInfo/Apollo lookup
  ↓ If still missing
Step 4: Manual research (LinkedIn, company website)
  ↓ If still missing
Step 5: Flag for human verification

What to Enrich and When

Enrichment Quality Control

After enrichment, validate:

Data Quality KPIs and Targets

Contact Data Quality Metrics

Company Data Quality Metrics

Deal Data Quality Metrics

Reporting Dashboard

Build a Data Quality Dashboard with these widgets:

Row 1: Overall Health
- Data Quality Score (gauge: 0-100)
- Trend: DQS over last 6 months (line chart)
- Records needing attention (count)

Row 2: Contacts
- Completeness by field (bar chart)
- Duplicate trend (line chart)
- Enrichment coverage (pie chart)

Row 3: Companies
- ICP score distribution (histogram)
- Missing fields breakdown (bar chart)
- Duplicate rate trend (line chart)

Row 4: Deals
- Required field compliance by stage (heatmap)
- Amount fill rate trend (line chart)
- Close date accuracy trend (line chart)

CRM Hygiene Audit Framework

Weekly Tasks (15 minutes)

Monthly Tasks (1 hour)

Quarterly Tasks (Half day)

Annual Tasks (Full day)

Data Quality Automation

Automated Validation Rules

Automated Standardization

Automated Enrichment Triggers

Data Governance Roles

RACI Matrix

Legend: R = Responsible, A = Accountable, C = Consulted, I = Informed

Data Steward Responsibilities

Each team should have a designated Data Steward:

Implementation Roadmap

Phase 1: Foundation (Month 1)

• Document current state (fields, owners, issues)
• Define field governance matrix
• Implement basic validation rules
• Set up data quality dashboard
• Run initial duplicate analysis

Phase 2: Standardization (Month 2)

• Implement standardization rules
• Clean historical data (duplicates, formatting)
• Set up automated enrichment
• Train team on data entry standards
• Establish weekly/monthly audit cadence

Phase 3: Automation (Month 3)

• Automate validation on all entry points
• Automate enrichment workflows
• Set up duplicate prevention
• Implement data quality scoring
• Create alerting for quality degradation

Phase 4: Optimization (Ongoing)

• Monitor KPIs monthly
• Quarterly governance reviews
• Annual full audits
• Continuous improvement based on findings

Built by ColdIQ & Ivan Falco. For questions on implementation or anything not covered here, reach out to Ivan directly on LinkedIn.