Data Architecture

Intro

Data architecture describes the structure and content of organizational data types, sources, and flows. It matters because reliable data underpins analytics, processes, and applications.

Key points:

• Defines models, integrations, storage, and lineage.
• Separates raw data from information derived by analysis.
• Common use cases: data modeling (EA), governance (Data), integration patterns (Tech), reporting schemas (Apps).
• Pitfall: conflating data and information, leading to unclear ownership and controls.

Examples:

• Canonical data model for customer across systems.
• Data lake ingestion patterns with metadata lineage.
• Master data domains with stewardship roles.

In practice:

Document data domains, models, and lineage, and align them with governance and usage needs.

Related terms: information-architecture; data-governance; master-data-management

FAQs:

Q: What’s the difference between data and information?
A: Data are raw facts; information is analyzed data with meaning.

Q: Who owns data architecture?
A: Typically enterprise and data architects with governance bodies.

‍