Data Governance for Revenue Teams

As GTM operations become more data-driven, governance becomes essential. Without governance, you get chaos—inconsistent definitions, unclear ownership, security risks, and unreliable analytics. With governance, data becomes a trusted asset that powers confident decisions.

This guide covers practical data governance for revenue teams—not enterprise bureaucracy, but the right level of structure to enable scale.

Why Revenue Data Governance #

Common Governance Problems

Governance Benefits

• Trust: Teams can rely on data
• Efficiency: Less time finding/fixing data
• Compliance: Meet regulatory requirements
• Scale: Processes that work as you grow
• Alignment: Same definitions across teams

Governance Framework #

Pillar 1: Data Ownership

Every data element needs an owner.

Ownership Model

Owner Responsibilities

• Define data standards
• Ensure quality
• Approve changes
• Resolve disputes
• Document processes

Pillar 2: Data Definitions

Standardize what terms mean.

Data Dictionary Example

Documentation Requirements

• Plain language definition
• Calculation formula if applicable
• Data source(s)
• Owner
• Update frequency
• Related metrics

Pillar 3: Access Control

Right data to right people.

Access Levels

Data Classification

Pillar 4: Data Quality

Maintain data integrity.

Quality Dimensions

Quality Processes

• Entry validation
• Regular audits
• Exception reporting
• Cleanup procedures
• Quality scoring

Pillar 5: Change Management

Control how data structures change.

Change Process

flowchart LR
    A[Request] --> B[Review]
    B --> C[Approve]
    C --> D[Implement]
    D --> E[Document]
    E --> F[Communicate]

Steps:

1. Submit change request
2. Impact assessment
3. Owner approval
4. Implementation (dev/test/prod)
5. Documentation update
6. Communication

Change Categories

Implementing Governance #

Step 1: Assess Current State

Audit Questions

• Who owns what data?
• Where is data documented?
• What quality issues exist?
• Who can access what?
• What processes exist?

Step 2: Establish Foundation

Core Documents

• Data ownership matrix
• Data dictionary (critical fields first)
• Access policy
• Quality standards

Core Processes

• Change request workflow
• Quality monitoring
• Issue escalation
• Documentation updates

Step 3: Implement Controls

Technical Controls

• Field-level permissions
• Validation rules
• Duplicate detection
• Audit logging

Process Controls

• Review cadences
• Approval workflows
• Training requirements
• Compliance checks

Step 4: Operationalize

Ongoing Activities

• Weekly quality reviews
• Monthly governance meetings
• Quarterly audits
• Annual policy review

Governance by Team #

Sales Governance

Key Data

• Opportunities
• Activities
• Forecasts

Key Rules

• Stage definitions and requirements
• Activity logging requirements
• Forecast submission deadlines

Marketing Governance

Key Data

• Leads
• Campaigns
• Engagement

Key Rules

• Lead scoring criteria
• Campaign attribution rules
• UTM standards

RevOps Governance

Key Data

• Unified accounts/contacts
• Scores
• Territories

Key Rules

• Routing logic documentation
• Score model transparency
• Integration standards

Governance with Cargo #

Cargo supports governance through:

Audit Logging

Every action tracked:

• Who did what
• When it happened
• What changed

Access Controls

Role-based permissions:

• View vs. edit
• Workflow access
• Data access

Data Quality

Built-in quality features:

• Validation rules
• Duplicate detection
• Enrichment tracking

Governance Metrics #

Health Indicators

Governance Maturity

Best Practices #

1. Start small - Core data first, expand over time
2. Make it practical - Governance that enables, not restricts
3. Assign clear ownership - No orphan data
4. Document incrementally - Don’t try to document everything at once
5. Measure and improve - Track governance effectiveness

Data governance isn’t bureaucracy—it’s the foundation for trustworthy data that powers effective revenue operations.

Ready to govern your revenue data? Cargo provides the controls and visibility to maintain data quality at scale.

Key Takeaways #

• Governance is a system, not bureaucracy: without it you get chaos—inconsistent definitions, unclear ownership, security risks, unreliable analytics
• Five governance pillars: data ownership (who’s accountable), definitions (what terms mean), access control (who sees what), quality standards (what good looks like), change management (how things evolve)
• Every data element needs an owner: RevOps typically owns account/contact/enrichment, Sales Ops owns opportunities, Marketing Ops owns engagement data
• Data dictionary is essential: document term definitions, calculations, sources, owners, and update frequency for all key metrics
• Start small, expand over time: don’t try to govern everything at once—start with critical data, build incrementally

Frequently Asked Questions #