🧭 NNA Enterprise Data Governance Framework¶
This document defines the foundation of Enterprise Data Governance (EDG) for Nissan North America (NNA).
It provides a scalable structure to expand governance practices across North and South America, aligning data strategy, stewardship, and compliance with global Nissan objectives.
📘 Purpose¶
Establish a unified governance framework to ensure that data across NNA: - Is accurate, consistent, and reliable. - Supports operational efficiency, analytics, and AI initiatives. - Complies with regulatory, security, and privacy requirements. - Enables effective collaboration across business units and regional entities.
🏗️ Structure Overview¶
The document is divided into the following parts:
- Executive Summary
- Strategic Context and Objectives
- Governance Organization and Roles
- Data Governance Framework
- Data Management Capabilities
- Policies, Standards, and Compliance
- Data Quality Management
- Metadata and Master Data Management
- Technology and Architecture Alignment
- Change Management and Adoption
- Metrics and Maturity Assessment
- Roadmap and Implementation Plan
- Regional Scaling (Americas)
- Appendices and References
1. Executive Summary¶
Purpose:
Summarize the intent and scope of the governance initiative — why it’s being launched now, expected benefits, and the vision for NNA’s data-driven future.
Elements to include: - Background and strategic drivers. - Key challenges (data silos, quality gaps, compliance risks). - Objectives (trust, access, reuse, analytics enablement). - Expected benefits and ROI. - High-level timeline and milestones.
2. Strategic Context and Objectives¶
Purpose:
Define how Data Governance aligns with NNA’s business and digital strategy.
Elements to document: - Linkage to NNA’s business objectives (customer experience, operational excellence, electrification, AI readiness). - Current data landscape assessment. - Guiding principles (ownership, accountability, transparency, reuse). - SMART governance objectives. - Stakeholder map (executives, business units, IT).
3. Governance Organization and Roles¶
Purpose:
Establish the organizational model for governance, including committees, data owners, and stewards.
Elements: - Data Governance Council (executive decision body). - Data Stewardship Committee (operational body). - Roles and responsibilities: - Chief Data Officer / Data Governance Lead. - Data Owners, Data Stewards, Custodians. - IT and Analytics leads. - Decision-making and escalation paths. - RACI chart for governance activities.
4. Data Governance Framework¶
Purpose:
Define the components, processes, and interconnections of NNA’s Data Governance model.
Elements: - Core governance pillars: - Data Ownership & Accountability - Policy & Standards - Quality & Stewardship - Metadata & Lineage - Security & Privacy - Governance lifecycle: - Define → Implement → Monitor → Improve. - Visual framework diagram (to be created in Mermaid or draw.io). - Governance process map.
5. Data Management Capabilities¶
Purpose:
Detail the core capabilities needed to operationalize governance.
Capabilities to document: - Data Cataloging and Discovery - Data Lineage and Traceability - Data Integration and Interoperability - Data Access Control and Sharing - Data Issue Management - Data Lifecycle Management - Data Literacy Enablement
6. Policies, Standards, and Compliance¶
Purpose:
Set rules and compliance expectations for handling and managing data.
Elements: - Data classification and retention policies. - Data access, privacy, and protection rules. - Regulatory compliance (GDPR, CCPA, SOX, etc.). - Naming and metadata standards. - Audit, control, and review procedures.
7. Data Quality Management¶
Purpose:
Define how data quality will be measured, monitored, and improved.
Elements: - Data quality dimensions (accuracy, completeness, timeliness, consistency). - Data quality rules and scorecards. - Issue escalation workflows. - Data cleansing, validation, and enrichment processes. - Data quality dashboards and metrics (link to reporting).
8. Metadata and Master Data Management¶
Purpose:
Ensure data meaning and consistency across the enterprise.
Elements: - Metadata strategy and tools. - Business glossary and definitions. - Master data domains (Customer, Vehicle, Dealer, Product, etc.). - Reference data standards. - Data synchronization and ownership rules.
9. Technology and Architecture Alignment¶
Purpose:
Integrate governance into NNA’s data architecture and platforms.
Elements: - Reference architecture diagram. - Key platforms (Data Lake, Data Warehouse, MDM, Data Catalog, BI tools). - Integration with security, access, and identity management. - Cloud and on-prem data strategy alignment. - Tooling roadmap and interoperability standards.
10. Change Management and Adoption¶
Purpose:
Ensure governance is embraced across teams and sustained over time.
Elements: - Change management plan (training, communications, engagement). - Governance onboarding and certification. - Incentives and accountability mechanisms. - Communication templates and stakeholder engagement cadence.
11. Metrics and Maturity Assessment¶
Purpose:
Track progress and demonstrate value.
Elements: - Maturity model (e.g., DAMA, CMMI-based). - Governance KPIs (data quality scores, adoption rate, SLA compliance). - Maturity assessment schedule. - Continuous improvement loops and retrospectives.
12. Roadmap and Implementation Plan¶
Purpose:
Define the execution plan and governance rollout approach.
Elements: - Phased implementation (foundation → expansion → optimization). - Key milestones and deliverables. - Dependencies and resource requirements. - Roles for initial rollout. - Communication and success tracking.
13. Regional Scaling (Americas)¶
Purpose:
Outline how governance will expand from NNA to North and South America.
Elements: - Regional governance model and federated approach. - Localization vs. global consistency. - Regional data sharing and interoperability. - Cross-region data governance council. - Regional maturity and readiness assessment.
14. Appendices and References¶
Include: - Glossary of key terms. - Acronyms list. - Policy templates and data classification matrices. - Example RACI charts and workflows. - References (DAMA, DCAM, ISO 8000, NIST).
🧩 Next Steps¶
- Establish the initial Data Governance Council charter and members.
- Develop the data inventory baseline and maturity self-assessment.
- Identify priority data domains (Customer, Vehicle, Dealer).
- Initiate policy and glossary development.
- Stand up foundational tooling (catalog, lineage, quality monitoring).
🏁 Document Governance¶
| Role | Responsibility | Owner |
|---|---|---|
| Executive Sponsor | Oversight and funding | VP, Digital Strategy |
| Data Governance Lead | Coordination and rollout | Chief Data Officer |
| Contributors | SMEs and data stewards | Cross-functional |
| Review Cycle | Quarterly | DG Council |
Version: 0.1 – Draft
Author: Matthieu Ortala
Date: 22-10-2024 Status: Draft – For review and input by NNA stakeholders.