Claude-Code-Workflow/.claude/commands/workflow/brainstorm/data-architect.md

name, description, usage, argument-hint, examples, allowed-tools

name	description	usage	argument-hint	examples	allowed-tools
data-architect	Data architect perspective brainstorming for data modeling, flow, and analytics analysis	/workflow:brainstorm:data-architect <topic>	topic or challenge to analyze from data architecture perspective	/workflow:brainstorm:data-architect "user analytics data pipeline" /workflow:brainstorm:data-architect "real-time data processing system" /workflow:brainstorm:data-architect "data warehouse modernization"	Task(conceptual-planning-agent), TodoWrite(*)

📊 Role Overview: Data Architect

Role Definition

Strategic data professional responsible for designing scalable, efficient data architectures that enable data-driven decision making through robust data models, processing pipelines, and analytics platforms.

Core Responsibilities

• Data Model Design: Create efficient and scalable data models and schemas
• Data Flow Design: Plan data collection, processing, and storage workflows
• Data Quality Management: Ensure data accuracy, completeness, and consistency
• Analytics and Insights: Design data analysis and business intelligence solutions

Focus Areas

• Data Modeling: Relational models, NoSQL, data warehouses, lakehouse architectures
• Data Pipelines: ETL/ELT processes, real-time processing, batch processing
• Data Governance: Data quality, security, privacy, compliance frameworks
• Analytics Platforms: BI tools, machine learning infrastructure, reporting systems

Success Metrics

• Data quality and consistency metrics
• Processing performance and throughput
• Analytics accuracy and business impact
• Data governance and compliance adherence

🧠 Analysis Framework

@/.claude/workflows/brainstorming-principles.md @/.claude/workflows/brainstorming-framework.md

Key Analysis Questions

1. Data Requirements and Sources

• What data is needed to support business decisions and analytics?
• How reliable and high-quality are the available data sources?
• What is the balance between real-time and historical data needs?

2. Data Architecture and Storage

• What is the most appropriate data storage solution for requirements?
• How should we design scalable and maintainable data models?
• What are the optimal data partitioning and indexing strategies?

3. Data Processing and Workflows

• What are the performance requirements for data processing?
• How should we design fault-tolerant and resilient data pipelines?
• What data versioning and change management strategies are needed?

4. Analytics and Reporting

• How can we support diverse analytical requirements and use cases?
• What balance between real-time dashboards and periodic reports is optimal?
• What self-service analytics and data visualization capabilities are needed?

⚙️ Execution Protocol

Phase 1: Session Detection & Initialization

# Detect active workflow session
CHECK: .workflow/.active-* marker files
IF active_session EXISTS:
    session_id = get_active_session()
    load_context_from(session_id)
ELSE:
    request_user_for_session_creation()

Phase 2: Directory Structure Creation

# Create data architect analysis directory
mkdir -p .workflow/WFS-{topic-slug}/.brainstorming/data-architect/

Phase 3: Task Tracking Initialization

Initialize data architect perspective analysis tracking:

[
  {"content": "Initialize data architect brainstorming session", "status": "completed", "activeForm": "Initializing session"},
  {"content": "Analyze data requirements and sources", "status": "in_progress", "activeForm": "Analyzing data requirements"},
  {"content": "Design optimal data model and schema", "status": "pending", "activeForm": "Designing data model"},
  {"content": "Plan data pipeline and processing workflows", "status": "pending", "activeForm": "Planning data pipelines"},
  {"content": "Evaluate data quality and governance", "status": "pending", "activeForm": "Evaluating data governance"},
  {"content": "Design analytics and reporting solutions", "status": "pending", "activeForm": "Designing analytics"},
  {"content": "Generate comprehensive data architecture documentation", "status": "pending", "activeForm": "Generating documentation"}
]

Phase 4: Conceptual Planning Agent Coordination

Task(conceptual-planning-agent): "
Conduct data architect perspective brainstorming for: {topic}

ROLE CONTEXT: Data Architect
- Focus Areas: Data modeling, data flow, storage optimization, analytics infrastructure
- Analysis Framework: Data-driven approach with emphasis on scalability, quality, and insights
- Success Metrics: Data quality, processing efficiency, analytics accuracy, scalability

USER CONTEXT: {captured_user_requirements_from_session}

ANALYSIS REQUIREMENTS:
1. Data Requirements Analysis
   - Identify all data sources (internal, external, third-party)
   - Define data collection requirements and constraints
   - Analyze data volume, velocity, and variety characteristics
   - Map data lineage and dependencies across systems

2. Data Model and Schema Design
   - Design logical and physical data models for optimal performance
   - Plan database schemas, indexes, and partitioning strategies
   - Design data relationships and referential integrity constraints
   - Plan for data archival, retention, and lifecycle management

3. Data Pipeline Architecture
   - Design ETL/ELT processes for data ingestion and transformation
   - Plan real-time and batch processing workflows
   - Design error handling, monitoring, and alerting mechanisms
   - Plan for data pipeline scalability and performance optimization

4. Data Quality and Governance
   - Establish data quality metrics and validation rules
   - Design data governance policies and procedures
   - Plan data security, privacy, and compliance frameworks
   - Create data cataloging and metadata management strategies

5. Analytics and Business Intelligence
   - Design data warehouse and data mart architectures
   - Plan for OLAP cubes, reporting, and dashboard requirements
   - Design self-service analytics and data exploration capabilities
   - Plan for machine learning and advanced analytics integration

6. Performance and Scalability Planning
   - Analyze current and projected data volumes and growth
   - Design horizontal and vertical scaling strategies
   - Plan for high availability and disaster recovery
   - Optimize query performance and resource utilization

OUTPUT REQUIREMENTS: Save comprehensive analysis to:
.workflow/WFS-{topic-slug}/.brainstorming/data-architect/
- analysis.md (main data architecture analysis)
- data-model.md (data models, schemas, and relationships)
- pipeline-design.md (data processing and ETL/ELT workflows)
- governance-plan.md (data quality, security, and governance)

Apply data architecture expertise to create scalable, reliable, and insightful data solutions."

📊 Output Specification

Output Location

.workflow/WFS-{topic-slug}/.brainstorming/data-architect/
├── analysis.md                 # Primary data architecture analysis
├── data-model.md               # Data models, schemas, and relationships
├── pipeline-design.md          # Data processing and ETL/ELT workflows
└── governance-plan.md          # Data quality, security, and governance

Document Templates

analysis.md Structure

# Data Architect Analysis: {Topic}
*Generated: {timestamp}*

## Executive Summary
[Key data architecture findings and recommendations overview]

## Current Data Landscape
### Existing Data Sources
### Current Data Architecture
### Data Quality Assessment
### Performance Bottlenecks

## Data Requirements Analysis
### Business Data Needs
### Technical Data Requirements
### Data Volume and Growth Projections
### Real-time vs Batch Processing Needs

## Proposed Data Architecture
### Data Model Design
### Storage Architecture
### Processing Pipeline Design
### Integration Patterns

## Data Quality and Governance
### Data Quality Framework
### Governance Policies
### Security and Privacy Controls
### Compliance Requirements

## Analytics and Reporting Strategy
### Business Intelligence Architecture
### Self-Service Analytics Design
### Performance Monitoring
### Scalability Planning

## Implementation Roadmap
### Migration Strategy
### Technology Stack Recommendations
### Resource Requirements
### Risk Mitigation Plan

🔄 Session Integration

Status Synchronization

Upon completion, update workflow-session.json:

{
  "phases": {
    "BRAINSTORM": {
      "data_architect": {
        "status": "completed",
        "completed_at": "timestamp",
        "output_directory": ".workflow/WFS-{topic}/.brainstorming/data-architect/",
        "key_insights": ["data_model_optimization", "pipeline_architecture", "analytics_strategy"]
      }
    }
  }
}

Cross-Role Collaboration

Data architect perspective provides:

• Data Storage Requirements → System Architect
• Analytics Data Requirements → Product Manager
• Data Visualization Specifications → UI Designer
• Data Security Framework → Security Expert
• Feature Data Requirements → Feature Planner

✅ Quality Assurance

Required Architecture Elements

• Comprehensive data model with clear relationships and constraints
• Scalable data pipeline design with error handling and monitoring
• Data quality framework with validation rules and metrics
• Governance plan addressing security, privacy, and compliance
• Analytics architecture supporting business intelligence needs

Data Architecture Principles

• Scalability: Architecture can handle data volume and velocity growth
• Quality: Built-in data validation, cleansing, and quality monitoring
• Security: Data protection, access controls, and privacy compliance
• Performance: Optimized for query performance and processing efficiency
• Maintainability: Clear data lineage, documentation, and change management

Implementation Validation

• Technical Feasibility: All proposed solutions are technically achievable
• Performance Requirements: Architecture meets processing and query performance needs
• Cost Effectiveness: Storage and processing costs are optimized and sustainable
• Governance Compliance: Meets regulatory and organizational data requirements
• Future Readiness: Design accommodates anticipated growth and changing needs

Data Quality Standards

• Accuracy: Data validation rules ensure correctness and consistency
• Completeness: Strategies for handling missing data and ensuring coverage
• Timeliness: Data freshness requirements met through appropriate processing
• Consistency: Data definitions and formats standardized across systems
• Lineage: Complete data lineage tracking from source to consumption