/data-model

You are helping an enterprise architect create a comprehensive data model for a project that will guide database design, API specifications, and compliance requirements.

User Input

$ARGUMENTS

Instructions

Note: The ArcKit Project Context hook has already detected all projects, artifacts, external documents, and global policies. Use that context below — no need to scan directories manually.

1. Read existing artifacts from the project context:

   MANDATORY (warn if missing):

   • REQ (Requirements)
     • Extract: All DR (data requirements), NFR-SEC (security/privacy), INT (integration/data exchange), BR (data-related business requirements)
     • If missing: STOP and warn user to run /arckit:requirements first — data model MUST be based on DR-xxx requirements

   RECOMMENDED (read if available, note if missing):

   • STKE (Stakeholder Analysis)
     • Extract: Data owners from RACI matrix, governance stakeholders, data stewardship responsibilities
   • PRIN (Architecture Principles, in 000-global)
     • Extract: Data governance standards, privacy by design principles, data sovereignty requirements

   OPTIONAL (read if available, skip silently if missing):

   • SOBC (Business Case)
     • Extract: Data-related benefits and costs
   • RSCH (Research Findings)
     • Extract: Database technology recommendations, data platform choices

2. Identify the target project:

   • Use the ArcKit Project Context (above) to find the project matching the user's input (by name or number)
   • If no match, create a new project:
     1. Use Glob to list projects/*/ directories and find the highest NNN-* number (or start at 001 if none exist)
     2. Calculate the next number (zero-padded to 3 digits, e.g., 002)
     3. Slugify the project name (lowercase, replace non-alphanumeric with hyphens, trim)
     4. Use the Write tool to create projects/{NNN}-{slug}/README.md with the project name, ID, and date — the Write tool will create all parent directories automatically
     5. Also create projects/{NNN}-{slug}/external/README.md with a note to place external reference documents here
     6. Set PROJECT_ID = the 3-digit number, PROJECT_PATH = the new directory path

3. Read external documents and policies:

   • Read any external documents listed in the project context (external/ files) — extract entity definitions, relationships, data types, constraints, existing schemas, migration requirements
   • Read any enterprise standards in projects/000-global/external/ — extract enterprise data dictionaries, master data management standards, cross-project data architecture patterns
   • If no external docs exist but they would improve the data model, ask: "Do you have any existing database schemas, ERD diagrams, or data dictionaries? I can read PDFs, images, and SQL files directly. Place them in projects/{project-dir}/external/ and re-run, or skip."

4. Read the template (with user override support):

   • First, check if .arckit/templates/data-model-template.md exists in the project root
   • If found: Read the user's customized template (user override takes precedence)
   • If not found: Read ${CLAUDE_PLUGIN_ROOT}/templates/data-model-template.md (default)

   Tip: Users can customize templates with /arckit:customize data-model

5. Extract data requirements:

   • Read the project's requirements document (ARC-*-REQ-*.md)
   • Extract ALL Data Requirements (DR-xxx)
   • Also look for privacy/GDPR requirements in NFR section
   • Identify integration requirements (INT-xxx) that involve data exchange
   • Note any data-related business requirements (BR-xxx)

6. Load Mermaid Syntax Reference:

   • Read ${CLAUDE_PLUGIN_ROOT}/skills/mermaid-syntax/references/entityRelationshipDiagram.md for official Mermaid ER diagram syntax — entity definitions, relationship types, cardinality notation, and attribute syntax.

7. Generate comprehensive data model:

   A. Executive Summary:

   • Total number of entities identified
   • Data classification summary (Public, Internal, Confidential, Restricted)
   • PII/sensitive data identified (Yes/No)
   • GDPR/DPA 2018 compliance status
   • Key data governance stakeholders

   B. Visual Entity-Relationship Diagram (ERD):

   • Create Mermaid ERD syntax showing:
     • All entities (E-001, E-002, etc.)
     • Relationships (one-to-one, one-to-many, many-to-many)
     • Cardinality notation
   • Organise by logical domain/bounded context if possible
   • Use descriptive entity and relationship names

   C. Entity Catalog (E-001, E-002, etc.):

   • For each entity, document:

     • Entity ID: E-001, E-002, etc.

     • Entity Name: Customer, Transaction, Product, etc.

     • Description: What this entity represents

     • Source Requirement: Which DR-xxx requirement(s) drive this entity

     • Business Owner: From stakeholder RACI matrix

     • Technical Owner: Data steward or database team

     • Data Classification: Public/Internal/Confidential/Restricted

     • Estimated Volume: Initial records + growth rate

     • Retention Period: How long data is kept (GDPR requirement)

     • Attributes Table:

       | Attribute | Type | Required | PII | Description | Validation | Source Req |
       |-----------|------|----------|-----|-------------|------------|------------|
       | customer_id | UUID | Yes | No | Unique identifier | UUID v4 | DR-001 |
       | email | String(255) | Yes | Yes | Contact email | RFC 5322, unique | DR-002 |
       

     • Relationships: What other entities this connects to

     • Indexes: Primary key, foreign keys, performance indexes

     • Privacy Notes: GDPR considerations, data subject rights

   D. Data Governance Matrix:

   • For each entity, identify:
     • Data Owner: Business stakeholder responsible (from RACI matrix)
     • Data Steward: Person responsible for quality and compliance
     • Data Custodian: Technical team managing storage/backups
     • Access Control: Who can view/modify (roles/permissions)
     • Sensitivity: Public, Internal, Confidential, Restricted
     • Compliance: GDPR, PCI-DSS, HIPAA, etc.
     • Quality SLA: Accuracy, completeness, timeliness targets

   E. CRUD Matrix (Create, Read, Update, Delete):

   • Map which components/systems can perform which operations on each entity

   • Example:

     | Entity | Payment API | Admin Portal | Reporting Service | CRM Integration |
     |--------|-------------|--------------|-------------------|-----------------|
     | E-001: Customer | CR-- | CRUD | -R-- | -R-- |
     | E-002: Transaction | CR-- | -R-- | -R-- | ---- |
     

   • Helps identify unauthorized access patterns and data flows

   F. Data Integration Mapping:

   • Upstream Systems: Where data comes from
     • System name, entity mapping, update frequency, data quality SLA
   • Downstream Systems: Where data goes to
     • System name, entity mapping, sync method (API, batch, event), latency SLA
   • Master Data Management: Which system is "source of truth" for each entity

   G. Privacy & Compliance:

   • GDPR/DPA 2018 Compliance:
     • List all PII attributes across all entities
     • Document legal basis for processing (consent, contract, legitimate interest, etc.)
     • Data subject rights implementation (access, rectification, erasure, portability)
     • Data retention schedules per entity
     • Cross-border data transfer considerations (UK-EU adequacy)
   • Data Protection Impact Assessment (DPIA):
     • Is DPIA required? (Yes if high-risk processing of PII)
     • Key privacy risks identified
     • Mitigation measures
     • ICO notification requirements
   • Sector-Specific Compliance:
     • PCI-DSS: If payment card data (special handling requirements)
     • HIPAA: If healthcare data (US projects)
     • FCA regulations: If financial services (UK)
     • Government Security Classifications: If public sector (OFFICIAL, SECRET)

   H. Data Quality Framework:

   • Quality Dimensions:
     • Accuracy: How correct is the data? (validation rules, reference data)
     • Completeness: Required fields populated? (% target)
     • Consistency: Same data across systems? (reconciliation rules)
     • Timeliness: How current is the data? (update frequency, staleness tolerance)
     • Uniqueness: No duplicates? (deduplication rules)
     • Validity: Conforms to format? (regex patterns, enums, ranges)
   • Data Quality Metrics:
     • Define measurable targets per entity (e.g., "Customer email accuracy >99%")
     • Data quality monitoring approach
     • Data quality issue resolution process

   I. Requirements Traceability:

   • Create traceability table:

     | Requirement | Entity | Attributes | Rationale |
     |-------------|--------|------------|-----------|
     | DR-001 | E-001: Customer | customer_id, email, name | Store customer identity |
     | DR-002 | E-002: Transaction | transaction_id, amount, status | Track payments |
     | NFR-SEC-003 | E-001: Customer | password_hash (encrypted) | Secure authentication |
     

   • Show how every DR-xxx requirement maps to entities/attributes

   • Flag any DR-xxx requirements NOT yet modeled (gaps)

   J. Implementation Guidance:

   • Database Technology Recommendation:
     • Relational (PostgreSQL, MySQL) for transactional data
     • Document (MongoDB, DynamoDB) for flexible schemas
     • Graph (Neo4j) for highly connected data
     • Time-series (InfluxDB, TimescaleDB) for metrics/events
   • Schema Migration Strategy: How to evolve schema (Flyway, Liquibase, Alembic)
   • Backup and Recovery: RPO/RTO targets, backup frequency
   • Data Archival: When to move data from hot to cold storage
   • Testing Data: Anonymization/pseudonymization for test environments

8. UK Government Compliance (if applicable):

   • Government Security Classifications: OFFICIAL, SECRET, TOP SECRET
   • Data Standards: Use GDS Data Standards Catalogue where applicable
   • Open Standards: Preference for open data formats (JSON, CSV, OData)
   • ICO Data Protection: Reference ICO guidance for public sector
   • National Cyber Security Centre (NCSC): Data security patterns

Before writing the file, read ${CLAUDE_PLUGIN_ROOT}/references/quality-checklist.md and verify all Common Checks plus the DATA per-type checks pass. Fix any failures before proceeding.

9. Write the output:
   • Write to projects/{project-dir}/ARC-{PROJECT_ID}-DATA-v1.0.md
   • Use the exact template structure from data-model-template.md
   • Include Mermaid ERD at the top for quick visualization
   • Include all sections even if some are TBD
   • Create comprehensive entity catalog with ALL attributes

IMPORTANT - Auto-Populate Document Information Fields:

Before completing the document, populate document information fields:

Auto-populated fields

• [PROJECT_ID] → Extract from project path (e.g., "001")
• [VERSION] → Start with "1.0" for new documents
• [DATE] / [YYYY-MM-DD] → Current date in YYYY-MM-DD format
• [DOCUMENT_TYPE_NAME] → Document purpose
• ARC-[PROJECT_ID]-DATA-v[VERSION] → Generated document ID
• [STATUS] → "DRAFT" for new documents
• [CLASSIFICATION] → Default to "OFFICIAL" (UK Gov) or "PUBLIC"

User-provided fields

• [PROJECT_NAME] → Full project name
• [OWNER_NAME_AND_ROLE] → Document owner

Revision History

| 1.0 | {DATE} | ArcKit AI | Initial creation from `/arckit:data-model` command |

Generation Metadata Footer

**Generated by**: ArcKit `/arckit:data-model` command
**Generated on**: {DATE}
**ArcKit Version**: {ARCKIT_VERSION}
**Project**: {PROJECT_NAME} (Project {PROJECT_ID})
**AI Model**: [Actual model name]

10. Summarize what you created:

• How many entities defined (E-001, E-002, etc.)
• How many total attributes across all entities
• How many entities contain PII (privacy-sensitive)
• Data classification breakdown (Public/Internal/Confidential/Restricted)
• GDPR compliance status (compliant / needs DPIA / gaps identified)
• Key data governance stakeholders identified
• Requirements coverage (% of DR-xxx requirements modeled)
• Suggested next steps (e.g., "Review data model with data protection officer before proceeding to HLD" or "Run /arckit:hld-review to validate database technology choices")

Example Usage

User: /arckit:data-model Create data model for payment gateway project

You should:

• Check prerequisites (requirements document exists, stakeholder analysis recommended)
• Find project directory (e.g., projects/001-payment-gateway-modernization/)
• Extract DR-xxx requirements from the requirements document
• Generate comprehensive data model:
  • Mermaid ERD showing Customer, Transaction, PaymentMethod, RefundRequest entities
  • Detailed entity catalog with attributes, PII flags, retention periods
  • GDPR compliance: PII identified, legal basis documented, DPIA required
  • Data governance: CFO owns financial data, DPO owns PII, IT owns storage
  • CRUD matrix: Payment API can create transactions, Admin can read all, Reporting read-only
  • PCI-DSS compliance: Payment card data encrypted, tokenized, not stored long-term
  • Requirements traceability: All DR-001 through DR-008 mapped to entities
• CRITICAL - Token Efficiency: Use the Write tool to create projects/001-payment-gateway-modernization/ARC-001-DATA-v1.0.md
  • DO NOT output the full document in your response (this exceeds 32K token limit!)
• Show summary only (see Output Instructions below)

Important Notes

• Data model drives database schema, API contracts, and data governance policies

• GDPR compliance is MANDATORY for any PII - identify and protect it

• Every entity MUST trace back to at least one DR-xxx requirement

• Data ownership is critical - assign business owners from stakeholder RACI matrix

• PII requires special handling: encryption at rest, encryption in transit, access controls, audit logging, retention limits

• Use Mermaid ERD syntax for GitHub-renderable diagrams (not PlantUML or other formats)

• Data quality metrics should be measurable (not "high quality", use "99% accuracy")

• Consider data lifecycle: creation, updates, archival, deletion (GDPR "right to erasure")

• Reference architecture principles from any ARC-000-PRIN-*.md file in projects/000-global/ if they exist

• Flag any DR-xxx requirements that cannot be modeled (gaps for requirements clarification)

• UK Government data projects: The data model supports National Data Strategy alignment — Data Foundations pillar (metadata, standards, quality) and Availability pillar (data access, sharing). The Data Quality Framework section maps to the Government Data Quality Framework 6 dimensions. See docs/guides/national-data-strategy.md and docs/guides/data-quality-framework.md for full mappings.

• Markdown escaping: When writing less-than or greater-than comparisons, always include a space after < or > (e.g., < 3 seconds, > 99.9% uptime) to prevent markdown renderers from interpreting them as HTML tags or emoji

Integration with Other Commands

• Input: Requires requirements document (ARC-*-REQ-*.md) for DR-xxx requirements
• Input: Uses stakeholder analysis (ARC-*-STKE-*.md) for data ownership RACI matrix
• Input: References SOBC (ARC-*-SOBC-*.md) for data-related costs and benefits
• Output: Feeds into /arckit:hld-review (validates database technology choices)
• Output: Feeds into /arckit:dld-review (validates schema design, indexes, query patterns)
• Output: Feeds into /arckit:sow (RFP includes data migration, data governance requirements)
• Output: Supports /arckit:traceability (DR-xxx → Entity → Attribute → HLD Component)

Output Instructions

CRITICAL - Token Efficiency:

1. Generate Data Model

Create the comprehensive data model following the template structure with all sections.

2. Write Directly to File

Use the Write tool to create projects/[PROJECT]/ARC-{PROJECT_ID}-DATA-v1.0.md with the complete data model.

DO NOT output the full document in your response. This would exceed token limits.

3. Show Summary Only

After writing the file, show ONLY a concise summary:

## Data Model Complete ✅

**Project**: [Project Name]
**File Created**: `projects/[PROJECT]/ARC-{PROJECT_ID}-DATA-v1.0.md`

### Data Model Summary

**Entities**: [Number] entities modeled
- Core Entities: [List main entities, e.g., Customer, Order, Payment]
- Supporting Entities: [List supporting entities]
- Lookup/Reference Data: [List reference tables]

**Relationships**: [Number] relationships defined
- One-to-Many: [Number]
- Many-to-Many: [Number]
- One-to-One: [Number]

**Attributes**: [Number] total attributes across all entities
- PII Attributes: [Number] (GDPR-sensitive)
- Encrypted Attributes: [Number]
- Indexed Attributes: [Number] (for performance)

**GDPR Compliance**:
- PII Entities: [List entities containing PII]
- Legal Basis: [e.g., Consent, Contract, Legitimate Interest]
- DPIA Required: [Yes/No]
- Retention Periods: [Range, e.g., 6 months to 7 years]

**Data Governance**:
- Data Owners: [Number] stakeholders assigned as data owners
- CRUD Matrix: [Number] roles/systems defined
- Access Controls: [Summary of who can access what]

**Compliance Requirements**:
- [List: GDPR, PCI-DSS, HIPAA, SOX, etc. as applicable]

**Requirements Traceability**:
- Data Requirements Mapped: [Number] DR-xxx requirements
- Unmapped Requirements: [Number] (need clarification)

### What's in the Document

- Entity Relationship Diagram (Mermaid ERD)
- Detailed Entity Catalog (all attributes, data types, constraints)
- GDPR Compliance Matrix (PII identification and protection)
- Data Governance Framework (ownership, CRUD matrix)
- Data Quality Metrics (accuracy, completeness, timeliness targets)
- Data Retention Policy (by entity)
- Encryption and Security Requirements
- Requirements Traceability Matrix (DR-xxx → Entity mapping)

### Next Steps

- Review `ARC-{PROJECT_ID}-DATA-v1.0.md` for full ERD and entity details
- Validate with data owners and stakeholders
- Run `/arckit:research` to research database technologies
- Run `/arckit:hld-review` after HLD is created

Statistics to Include:

• Number of entities
• Number of relationships
• Number of PII attributes
• Number of data requirements mapped
• Number of data owners assigned
• DPIA required (yes/no)
• Compliance frameworks applicable

Generate the data model now, write to file using Write tool, and show only the summary above.