Data Governance Essentials
Foundational practices for organizations building trustworthy, compliant, and valuable data assets.
What is Data Governance?
Data governance is the framework of policies, processes, and standards that ensure data is managed as a strategic asset. It answers fundamental questions:
- Who is responsible for this data?
- What does this data mean?
- Where does this data come from?
- How should this data be protected?
- Who can access this data?
Core Components
1. Data Ownership
Every data asset needs clear ownership.
| Role | Responsibilities |
|---|---|
| Data Owner | Business accountability for data quality and usage policies |
| Data Steward | Day-to-day management and quality monitoring |
| Data Custodian | Technical implementation and security controls |
Key Questions:
- Who decides what data to collect?
- Who defines quality standards?
- Who approves access requests?
- Who is accountable for compliance?
2. Data Catalog
A searchable inventory of data assets.
What to Catalog:
- Databases and tables
- APIs and data feeds
- Reports and dashboards
- Files and documents
- Machine learning models
Metadata to Capture:
- Technical metadata (schema, format, location)
- Business metadata (description, owner, domain)
- Operational metadata (freshness, quality scores)
- Usage metadata (who accesses, how often)
3. Data Quality
Measurable dimensions of data trustworthiness.
| Dimension | Definition | Example Metric |
|---|---|---|
| Completeness | Required data is present | % of records with null values |
| Accuracy | Data reflects reality | % matching source of truth |
| Consistency | Same facts across systems | % of conflicting records |
| Timeliness | Data is current | Latency from source to target |
| Validity | Data meets format rules | % passing validation rules |
| Uniqueness | No unwanted duplicates | % duplicate records |
4. Data Lineage
Understanding data flow and transformation.
Lineage Captures:
- Where data originates
- How data transforms
- Where data is consumed
- Who changed what, when
Business Value:
- Impact analysis for changes
- Root cause analysis for issues
- Compliance evidence
- Trust building
5. Data Security & Privacy
Protecting sensitive data throughout its lifecycle.
Classification Levels:
| Level | Examples | Controls |
|---|---|---|
| Public | Marketing materials | None |
| Internal | Company directories | Authentication |
| Confidential | Financial data | Access control, encryption |
| Restricted | PII, PHI | Need-to-know, audit logging |
Privacy Considerations:
- What personal data do we collect?
- Why do we need it (purpose limitation)?
- How long do we keep it (retention)?
- How do we respond to subject requests?
Getting Started
Step 1: Assess Current State
Discovery Questions:
- What are our most critical data assets?
- Who currently manages them?
- What quality issues exist?
- What compliance requirements apply?
- What tools do we have?
Quick Inventory: Create a simple spreadsheet:
| Data Asset | Owner | Domain | Classification | Quality Score |
|---|---|---|---|---|
| Customer DB | J. Smith | Sales | Confidential | Unknown |
| HR System | M. Jones | HR | Restricted | Unknown |
Step 2: Define Governance Scope
Don’t boil the ocean. Start with:
- Highest-value data assets
- Highest-risk data (compliance, security)
- Most problematic data (quality issues)
Prioritization Matrix:
| High Risk | Low Risk | |
|---|---|---|
| High Value | Start here | Phase 2 |
| Low Value | Phase 2 | Later |
Step 3: Establish Ownership
For each prioritized asset:
- Identify business data owner
- Assign data steward
- Clarify custodian responsibilities
- Document in accessible location
Step 4: Implement Basic Quality Monitoring
Start Simple:
- Define 3-5 critical quality rules per dataset
- Automate rule checking (SQL, Python, dbt tests)
- Create dashboard showing quality scores
- Alert when quality drops below threshold
Example Rules:
-- Completeness: Email required for customers
SELECT COUNT(*) / (SELECT COUNT(*) FROM customers)
FROM customers WHERE email IS NULL;
-- Validity: Valid email format
SELECT COUNT(*) / (SELECT COUNT(*) FROM customers)
FROM customers WHERE email NOT LIKE '%@%.%';
-- Timeliness: Orders updated within 24 hours
SELECT COUNT(*) / (SELECT COUNT(*) FROM orders)
FROM orders WHERE updated_at < NOW() - INTERVAL '24 hours';Step 5: Document Critical Data
For high-priority assets, create data documentation:
Data Dictionary Entry:
## customers
**Owner:** Sales Operations
**Steward:** A. Johnson
**Classification:** Confidential
### Description
Master customer records including contact information,
account status, and relationship history.
### Fields
| Field | Type | Description | PII |
|-------|------|-------------|-----|
| customer_id | UUID | Unique identifier | No |
| email | VARCHAR | Primary contact email | Yes |
| created_at | TIMESTAMP | Account creation date | No |
### Quality Rules
- email: Required, valid format
- customer_id: Unique
### Lineage
- Source: CRM system (Salesforce)
- Consumers: Analytics warehouse, Marketing automationGovernance Operating Model
Roles and Responsibilities
Data Governance Council
- Executive sponsors
- Domain data owners
- Data management lead
- Compliance/legal representative
Responsibilities:
- Set governance strategy and priorities
- Resolve cross-domain issues
- Approve policies and standards
- Monitor program effectiveness
Data Stewardship Team
- Domain data stewards
- Data quality analysts
- Metadata administrators
Responsibilities:
- Maintain data catalog
- Monitor data quality
- Support data consumers
- Escalate issues to council
Meeting Cadence
| Forum | Frequency | Focus |
|---|---|---|
| Governance Council | Monthly | Strategy, issues, priorities |
| Stewardship Team | Weekly | Operations, quality, support |
| Domain Working Groups | As needed | Domain-specific topics |
Decision Rights
| Decision | Who Decides |
|---|---|
| Data collection/retention | Data Owner |
| Access requests | Data Owner (with Security review) |
| Quality standards | Data Owner + Steward |
| Technical implementation | Data Custodian |
| Policy exceptions | Governance Council |
Policies and Standards
Essential Policies
Data Classification Policy
- Classification levels and criteria
- Handling requirements per level
- Labeling requirements
Data Access Policy
- Request and approval process
- Access review cadence
- Privileged access requirements
Data Retention Policy
- Retention periods by data type
- Legal hold procedures
- Destruction requirements
Data Quality Policy
- Quality dimensions and standards
- Monitoring requirements
- Issue escalation process
Standard Templates
Data Sharing Agreement For sharing data with external parties:
- Permitted uses
- Security requirements
- Retention/destruction obligations
- Audit rights
Data Processing Agreement For vendors processing your data:
- Processing purposes
- Security measures
- Sub-processor requirements
- Breach notification obligations
Measuring Success
Key Metrics
Program Metrics:
- % of critical data assets cataloged
- % of data assets with assigned owners
- Data steward coverage ratio
Quality Metrics:
- Average quality score across domains
- Trend of quality over time
- Time to resolve quality issues
Compliance Metrics:
- % of access reviews completed on time
- Data subject request response time
- Policy exception volume
Value Metrics:
- Data consumer satisfaction
- Time to find and access data
- Data-related incident reduction
Common Pitfalls
Starting Too Big Trying to govern everything at once. Start narrow, prove value, expand.
Technology Before Process Buying tools before defining processes. Define what you need, then tool.
Governance as Bureaucracy Creating friction without value. Governance should enable, not impede.
Ignoring Culture Data governance is as much about behavior as policy. Invest in change management.
For help establishing data governance in your organization, contact our team.
