Data Architecture & Standards¶

Overview¶

Data Architecture & Standards define the structure, design, and technical guidelines for managing enterprise data across Nissan North America (NNA).
A standardized architecture ensures data interoperability, quality, security, and scalability across systems, analytics platforms, and business processes.

Purpose¶

Establish a consistent framework for designing, storing, and managing data.
Define standards for data modeling, integration, and technology selection.
Enable seamless data flow and interoperability across enterprise systems.
Support data governance, security, quality, and compliance initiatives.

Architecture Components¶

Component	Description
Data Domains	Logical grouping of enterprise entities (Customer, Vehicle, Product, Dealer, Supplier, Finance, etc.)
Data Models	Standardized representation of entities, attributes, relationships, and hierarchies
Data Storage & Repositories	Databases, data warehouses, data lakes, MDM systems, and analytics platforms
Data Integration & ETL	Processes and pipelines for moving, transforming, and validating data across systems
APIs & Data Services	Standardized interfaces for data access, sharing, and interoperability
Metadata Management	Capture of data definitions, lineage, quality metrics, and governance attributes
Security & Access Controls	Integration with enterprise IAM, encryption, masking, and monitoring policies
Standards & Guidelines	Naming conventions, data types, coding standards, versioning, and documentation

Data Architecture Principles¶

Standardization: Consistent data definitions, formats, and integration practices.
Scalability: Architecture should handle growing data volumes and complexity.
Interoperability: Enable seamless data sharing across domains, systems, and analytics platforms.
Security & Privacy by Design: Incorporate controls and privacy practices into architecture.
Reusability: Promote reusable components, APIs, and services.
Monitoring & Auditability: Ensure traceability, data quality checks, and lineage tracking.

Integration & Governance Alignment¶

MDM & Business Glossary: Architecture supports a single source of truth and semantic consistency.
Data Quality & Lineage: Integration ensures validation, monitoring, and traceability.
Compliance & Security: Architecture incorporates privacy, access control, and regulatory adherence.
Data Products & Analytics: Enables standardized pipelines for reporting, dashboards, and AI/ML consumption.

Roles & Responsibilities¶

Role	Responsibility
Data Architect	Defines enterprise data models, standards, and integration patterns
Data Owner	Approves domain-specific models and architecture guidelines
Data Steward	Ensures adherence to standards during implementation and maintenance
IT / Engineering Teams	Implement architecture, integrations, and enforce standards
Governance Council	Reviews architecture compliance, approves exceptions, and monitors evolution

Tools & Technologies¶

Data Modeling Tools: ER/Studio, Sparx EA, or equivalent
Data Integration Platforms: Informatica, Talend, Apache NiFi, or cloud ETL services
API Management: MuleSoft, Apigee, or internal enterprise API gateways
Metadata & Catalog Tools: Collibra, Alation, Informatica EDC
Repositories: Data warehouses (Snowflake, Redshift), data lakes, MDM systems
Monitoring & Quality Tools: Integration with Ataccama, data quality dashboards, and lineage trackers

Visual Representation¶

flowchart TD
    A[Source Systems] --> B[ETL / Integration Pipelines]
    B --> C[Data Repositories (Warehouse / Lake)]
    C --> D[Data Products & Analytics]
    B --> E[MDM & Business Glossary]
    C --> F[Data Quality & Lineage]
    C --> G[Security & Compliance]