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:

1. Identify business data owner
2. Assign data steward
3. Clarify custodian responsibilities
4. 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 automation

---

Governance 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.