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

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?

⚡ Two-Step Execution Flow

⚠️ Session Management - FIRST STEP

Session detection and selection:

# Check for active sessions
active_sessions=$(find .workflow -name ".active-*" 2>/dev/null)
if [ multiple_sessions ]; then
  prompt_user_to_select_session()
else
  use_existing_or_create_new()
fi

Step 1: Context Gathering Phase

Data Architect Perspective Questioning

Before agent assignment, gather comprehensive data architect context:

📋 Role-Specific Questions

1. Data Models and Flow Patterns

• What types of data will you be working with (structured, semi-structured, unstructured)?
• What are the expected data volumes and growth projections?
• What are the primary data sources and how frequently will data be updated?
• Are there existing data models or schemas that need to be considered?

2. Storage Strategies and Performance

• What are the query performance requirements and expected response times?
• Do you need real-time processing, batch processing, or both?
• What are the data retention and archival requirements?
• Are there specific compliance or regulatory requirements for data storage?

3. Analytics Requirements and Insights

• What types of analytics and reporting capabilities are needed?
• Who are the primary users of the data and what are their skill levels?
• What business intelligence or machine learning use cases need to be supported?
• Are there specific dashboard or visualization requirements?

4. Data Governance and Quality

• What data quality standards and validation rules need to be implemented?
• Who owns the data and what are the access control requirements?
• Are there data privacy or security concerns that need to be addressed?
• What data lineage and auditing capabilities are required?

Context Validation

• Minimum Response: Each answer must be ≥50 characters
• Re-prompting: Insufficient detail triggers follow-up questions
• Context Storage: Save responses to .brainstorming/data-architect-context.md

Step 2: Agent Assignment with Flow Control

Dedicated Agent Execution

Task(conceptual-planning-agent): "
[FLOW_CONTROL]

Execute dedicated data architect conceptual analysis for: {topic}

ASSIGNED_ROLE: data-architect
OUTPUT_LOCATION: .brainstorming/data-architect/
USER_CONTEXT: {validated_responses_from_context_gathering}

Flow Control Steps:
[
  {
    \"step\": \"load_role_template\",
    \"action\": \"Load data-architect planning template\",
    \"command\": \"bash($(cat ~/.claude/workflows/cli-templates/planning-roles/data-architect.md))\",
    \"output_to\": \"role_template\"
  }
]

Conceptual Analysis Requirements:
- Apply data architect perspective to topic analysis
- Focus on data models, flow patterns, storage strategies, and analytics requirements
- Use loaded role template framework for analysis structure
- Generate role-specific deliverables in designated output location
- Address all user context from questioning phase

Deliverables:
- analysis.md: Main data architect analysis
- recommendations.md: Data architect recommendations
- deliverables/: Data architect-specific outputs as defined in role template

Embody data architect role expertise for comprehensive conceptual planning."

Progress Tracking

TodoWrite tracking for two-step process:

[
  {"content": "Gather data architect context through role-specific questioning", "status": "in_progress", "activeForm": "Gathering context"},
  {"content": "Validate context responses and save to data-architect-context.md", "status": "pending", "activeForm": "Validating context"},
  {"content": "Load data-architect planning template via flow control", "status": "pending", "activeForm": "Loading template"},
  {"content": "Execute dedicated conceptual-planning-agent for data-architect role", "status": "pending", "activeForm": "Executing agent"}
]

📊 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