Data Governance Framework¶

Overview¶

The Data Governance Framework defines the operating model, policies, processes, and controls required to manage data as a strategic asset at Nissan North America (NNA).
It provides a structured approach for ensuring data quality, security, compliance, and accessibility across all domains, while enabling operational efficiency and informed decision-making.

The framework is designed to be scalable, supporting regional expansion across North and South America.

Guiding Principles¶

The framework is anchored in the following principles:

Principle	Description
Data as a Strategic Asset	Treat enterprise data as a valuable corporate resource with defined ownership and accountability.
Accountability & Stewardship	Clearly assign roles and responsibilities for data across all layers of governance.
Standardization	Establish consistent data definitions, classifications, and quality standards.
Transparency	Ensure visibility into data quality, lineage, and usage across systems and stakeholders.
Security & Privacy	Protect sensitive data and comply with internal policies and external regulations.
Scalability	Ensure governance practices can expand across domains, business units, and geographies.
Continuous Improvement	Regularly assess and refine governance processes based on metrics, audits, and lessons learned.

Core Components of the Framework¶

The framework is composed of six core components, each essential for effective governance:

1. Data Policy & Standards¶

Define enterprise-wide data policies including data ownership, classification, security, and usage.
Establish standards for data definitions, metadata, and data quality rules.
Ensure alignment with regulatory requirements.

2. Roles & Responsibilities¶

Assign clear ownership, stewardship, and custodianship for data assets.
Define decision-making authorities for strategic, operational, and tactical data-related decisions.
Maintain accountability through RACI charts and role charters.

3. Data Quality & Lifecycle Management¶

Define data quality metrics and thresholds (accuracy, completeness, consistency, timeliness).
Implement data lifecycle management from creation, processing, usage, archival, to deletion.
Track and resolve data quality issues across domains.

4. Metadata & Master Data Management¶

Maintain a centralized metadata repository for all enterprise data assets.
Implement Master Data Management (MDM) practices to ensure consistent reference and master data across systems.
Track data lineage and provenance for traceability and auditing.

5. Security, Privacy & Compliance¶

Enforce data access controls and security policies for sensitive data.
Ensure compliance with regulatory standards such as GDPR, CCPA, and industry-specific requirements.
Conduct audits and risk assessments regularly.

6. Communication & Change Management¶

Establish processes for training, adoption, and engagement across stakeholders.
Document governance procedures, playbooks, and templates.
Communicate changes, updates, and governance outcomes consistently.

Operating Model¶

The operating model defines how governance is executed day-to-day:

flowchart TD
    A[Executive Steering Committee] --> B[Data Governance Council]
    B --> C[Domain Data Stewardship Committees]
    C --> D[Data Owners & Stewards]
    D --> E[Data Consumers & IT Teams]