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An enterprise-wide blueprint that organizes data domains, platforms, integrations, and governance to meet strategic business goals.
DESCRIPTION
Enterprise Data Architecture aligns data capabilities, platforms, and governance with business strategy. It defines target-state platforms, integration patterns, domain boundaries, and non-functional requirements. It guides technology choices, operating models, and data lifecycle management. Standards, reference architectures, and roadmaps ensure interoperability, compliance, and scalability. It coordinates with enterprise, application, and security architectures to deliver end-to-end outcomes.
ALTERNATIVE DEFINITIONS
TOGAF Standard
The Data Architecture describes the structure of an organization’s logical and physical data assets and data management resources.
Classical definition of data architecture scope from an enterprise framework.
Wikipedia
Data architecture consists of models, policies, rules and standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.
Broad definition emphasizing governance of data across its lifecycle.
Microsoft
This architecture demonstrates an enterprise-scale analytics approach with standardized data ingestion, storage, modeling, and reporting layers.
Illustrates layered enterprise-scale analytics architecture components.
RELATED TERMS
Data Architecture Reference Architecture TOGAF Data Platform Governance Integration Architecture Master Data Management
COMPETING TERMS
Decentralized Data Architecture Solution Architecture
RELATIONS
- Broader: Data Architecture
KNOWLEDGE AREAS
Data Architecture Data Management Data Engineering Cloud Platform & Tooling
TARGET AUDIENCE
Data Architect Enterprise Architect Chief Data Officer Chief Technology Officer Cloud Architect Data Strategy Consultant
EXTERNAL SOURCES
- Wikipedia - Data architecture
- The Open Group (TOGAF) - TOGAF definition of Data Architecture
- Microsoft - Enterprise-scale analytics reference
- AWS - Data platform architectural patterns
- Databricks - Lakehouse reference approach
TOP QUESTIONS
What problems does Enterprise Data Architecture solve?
It reduces fragmentation, aligns platforms to strategy, and standardizes integration, governance, and security across the enterprise.
Category: Relevancy
How does Enterprise Data Architecture relate to TOGAF?
It aligns with TOGAF’s Data Architecture domain, using ADM phases for planning and governance.
Category: Relationship
What are key components of Enterprise Data Architecture?
Domain models, reference architectures, platform blueprints, integration patterns, governance standards, and roadmaps.
Category: Requirements
How does Enterprise Data Architecture interact with Data Mesh?
It can incorporate mesh principles for domain ownership while providing shared platforms and standards.
Category: Opinion
What is a common mistake in Enterprise Data Architecture?
Over-specifying technology prematurely or ignoring operating model and governance, leading to shelfware.
Category: Mistakes
How is success measured?
Faster data product delivery, reduced duplication, improved quality/security, and higher platform adoption.
Category: Metrics
What artifacts should be produced?
Current/target state diagrams, standards catalogs, domain models, and a prioritized roadmap with guardrails.
Category: Best practices
How often should the architecture be updated?
Review quarterly and after major strategic, regulatory, or platform changes to remain aligned.
Category: Management
What risks arise without enterprise data architecture?
Data silos, compliance gaps, costly integration, and inconsistent semantics hinder initiatives.
Category: Risks