What Is Data Governance?
Data governance is the practice of creating policies, procedures, and standards to manage and protect data across an organization. Effective strategies must be collaborative and inclusive across all organizational roles—not siloed within IT or data teams.
What Happens Without Data Governance?
Organizations lacking proper data governance face three primary symptoms:
Work takes ten times longer with flawed data versus accurate data
Average annual cost of poor data quality per organization
Time wasted correcting errors instead of advancing goals
The Real Impact
- ✗Teams waste time correcting data errors instead of advancing business goals
- ✗Decision-making is impaired by inaccurate information
- ✗Expenses escalate from corrections and compliance issues
Three Characteristics of Strong Data Governance
Accurate
Data collection follows prescribed processes with quality assurance at every step
Organized
Events are documented in accessible formats with rigid guidelines for consistency
Secure
Tool and data access is appropriately granted with clear ownership and accountability
Establishing a Data Governance Council
Rather than the "Wild West" approach (uncontrolled democratization) or the "Single Point of Contact" method (bottleneck gatekeeping), we recommend establishing a data governance council with three roles:
Council Members
Manage the entire governance process and enforce standards across the organization. They set policies, resolve conflicts, and ensure alignment.
Data Owners
Oversee implementation and monitor the tracking plan for their domains. They ensure data quality and advocate for their team's needs.
Data Users
Work with data in a self-serve capacity within the guidelines. They provide feedback on what's working and what's missing.
Five Essential Policies
The governance council should create and maintain these policy documents:
Three Core Activities
1. Define and Document
Create clear event definitions using object-action naming conventions. Document everything in a centralized tracking plan that's accessible to all stakeholders.
2. Audit and Validate
Implement testing and monitoring to catch data quality issues early. Regular audits ensure the tracking plan matches reality.
3. Adapt and Iterate
Your data taxonomy and tracking plans will evolve. Build processes for proposing, reviewing, and implementing changes.
Common Scenarios to Plan For
New Employee Onboarding
Training should include:
- •Data policies and expectations
- •Taxonomy and naming conventions
- •How to read and use the tracking plan
- •Governance best practices
Product Releases
The council must address:
- •How releases affect event deployment
- •Accommodating new data sources
- •Preventing test data pollution
- •Communicating schema changes
GrowthBench's Four Core Pillars
Data governance is complex and often deprioritized. But forward-thinking organizations recognize its value for digital transformation success.