Data governance programs have a terrible reputation. Here’s how to build one that helps instead of hinders.
Why Most Governance Fails
Common Scenario: Company hires data governance consultant. Six months later:
- 300-page policy document nobody reads
- Committee that meets monthly and approves nothing
- Data quality hasn’t improved
- Analysts complain more than ever
Root Cause: Governance designed for control, not enablement.
The Enablement-First Approach
Philosophy: Governance should make it easier to use data correctly than to use it incorrectly.
Practical Example:
- Bad: “Request data access via ticket, 2-week approval”
- Good: “Self-service access with auto-expiring permissions, audit logging”
Essential Governance Components
1. Data Ownership (Not Stewardship)
The Difference:
- Owner: Makes decisions, accountable for quality, has budget
- Steward: Caretaker, enforces rules set by others
What Works:
- Finance data → CFO owns
- Customer data → Chief Revenue Officer owns
- Product data → VP Product owns
Owner Responsibilities:
- Define access policies
- Set quality standards
- Approve schema changes
- Own cleanup budget
2. Quality Standards That Matter
Bad Standard: “Data must be 99% accurate”
Good Standard: “Customer email accuracy >95%, measured by bounce rate”
Practical Quality Metrics:
- Completeness: % of required fields populated
- Validity: % passing format rules
- Consistency: % matching across systems
- Timeliness: Data freshness SLA
Real Example: E-commerce client
- Email completeness: 78% → 94% in 6 months
- Result: Email revenue up 32%
3. Access Management
Principle: Default to open, restrict sensitive.
Three-Tier Model:
- Public: Anyone in company (sales numbers, product metrics)
- Restricted: Business need + approval (customer PII, financials)
- Confidential: Executive team only (M&A, comp data)
Auto-expiring Access:
- Contractor access expires with contract
- Project-based access expires 90 days post-project
- Annual recertification for all restricted access
4. Change Management
Problem: Schema changes break reports. Every. Single. Time.
Solution:
- Deprecation period: 90 days notice before removing fields
- Version control: All schema changes in Git
- Impact analysis: Auto-identify affected reports
- Communication: Email affected report owners automatically
5. Documentation Standards
Minimum Viable Documentation:
- Data dictionary (field names, definitions, source)
- Data lineage (where does this come from?)
- Refresh schedule (how fresh is this data?)
- Owner contact (who do I ask questions?)
Tool Recommendation: Modern catalogs like Atlan, Alation, or Collibra.
Implementation Roadmap
Phase 1: Foundation (Months 1-3)
Week 1-4:
- Identify data owners
- Map critical data assets
- Define access tiers
Week 5-8:
- Implement access controls
- Set up data catalog
- Document top 20 data assets
Week 9-12:
- Define quality metrics
- Establish owner accountability
- Launch communication channels
Phase 2: Automation (Months 4-6)
- Automate quality monitoring
- Implement self-service access requests
- Set up schema change notifications
- Build usage dashboards
Phase 3: Optimization (Months 7-12)
- Expand catalog coverage
- Implement lineage tracking
- Launch data literacy training
- Measure adoption and impact
Governance KPIs That Actually Matter
Don’t Measure:
- Policies written
- Committee meetings held
- Certifications obtained
Do Measure:
- Time to access data (should decrease)
- Data quality incidents (should decrease)
- Analyst satisfaction (should increase)
- Compliance violations (should decrease)
- Self-service adoption (should increase)
Common Pitfalls
Pitfall 1: Too Much Process
Symptom: Data access requests take 2+ weeks
Fix:
- Auto-approve for public data
- Approval workflow for restricted data only
- SLA: 48 hours max for approvals
Pitfall 2: Technology Without Culture
Mistake: Buy expensive catalog tool, nobody uses it
Better:
- Start with lightweight documentation in wiki
- Prove value before buying tools
- Incentivize documentation (KPIs, recognition)
Pitfall 3: Governance by Committee
Problem: 12-person committee approves every change
Better:
- Data owners make decisions
- Committee sets policy, doesn’t approve transactions
- Monthly review, not weekly approval meetings
ROI of Good Governance
Client Example: $350M manufacturing company
Before Governance:
- 23 hours/week spent reconciling conflicting reports
- 6 compliance incidents/year
- 3 major data quality incidents
After Governance (12 months):
- 4 hours/week reconciliation time
- 0 compliance incidents
- 1 minor data quality incident
Value:
- Time savings: $180K/year
- Compliance risk reduction: Incalculable
- Better decisions: $500K+ impact
Investment: $120K (consulting + tools)
Payback: 7 months
Quick Start Checklist
Can launch in 30 days:
Bottom Line
Governance doesn’t have to be painful. Good governance feels like having better tools, not more bureaucracy.
Start small, measure impact, expand what works. Most companies need 20% of what the consultants sell.
Need help designing your governance framework? Schedule a consultation - we’ll help you build governance that actually enables your business.
