data-architecture-design

Data Architecture Design

Quick Start

Assume the Data Architect Agent role from agents/data-architect.md: clarify the canonical data object, ownership, source of truth, lifecycle, quality, privacy, retention, integration flows, and governance expectations.

Before creating or updating any data architecture design files, run a grill-me clarification session using ../grill-me/SKILL.md. Ask one question at a time until the target architecture link, data object meaning, ownership, lifecycle, integrations, quality expectations, privacy concerns, assumptions, and open questions are clear enough to avoid avoidable misunderstanding.

During that clarification session, use ../ubiquitous-language/SKILL.md whenever terms are vague, missing, overloaded, conflicting, or important enough to become shared domain language. Create or update <private-lab-root>/GLOSSARY.md inline as terms are clarified; do not batch glossary updates until the end.

Use templates/data-architecture-design-template.md as the output structure. Replace placeholders and drafting guidance with concrete content; mark unknown facts as TBD or open questions.

Store generated data architecture designs under the consuming repository's private lab root:

data-architectures/<data-object-slug>/<data-architecture-basename>.md
data-architectures/<data-object-slug>/diagrams/

Derive <data-architecture-basename> without repeating data:

• When <data-object-slug> ends in -data, use <data-object-slug>-architecture-design.
• Otherwise, use <data-object-slug>-data-architecture-design.
• Never generate a filename containing -data-data-.

Examples: customer-data becomes customer-data-architecture-design.md, customer-user-data becomes customer-user-data-architecture-design.md, and audiences becomes audiences-data-architecture-design.md.

Do not write real-company data architecture details into this public toolkit repository.

Required Inputs

• Target architecture document to link to
• Canonical data object name
• Data object description and business purpose
• Main capability or business process using the data object
• Source of truth and data owner
• Data classification
• Producing systems, consuming systems, and integration touchpoints
• Data lifecycle states, retention, privacy, and classification expectations
• Known quality rules, reconciliation needs, lineage needs, and governance constraints