Name: Arckit Data Model
Author: tractorjuice

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Arckit Data Model | Skills Pool

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

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

**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]

## 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

Arckit Data Model

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Arckit Data Model

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