workflow:
  id: community-analytics
  name: Community Analytics Workflow
  description: >-
    Streamlined analytics workflow for community users with basic agent collaboration
    and standard validation. Supports exploratory and business analysis projects.
  type: greenfield
  project_types:
    - exploratory-analysis
    - business-intelligence
    - performance-analytics
    - customer-insights

  sequence:
    - step: requirements_gathering
      agent: data-product-manager
      action: gather-requirements
      creates: analysis-requirements.md
      duration: 0.5 days
      notes: |
        Gather business requirements and define analysis objectives:
        - Business context and objectives
        - Key stakeholders and success criteria
        - Data sources and availability assessment
        - Timeline and resource constraints
        SAVE OUTPUT: Copy final analysis-requirements.md to your project's docs/ folder.

    - step: data_profiling
      agent: data-analyst
      action: profile-data
      creates: data-profile-report.md
      requires: analysis-requirements.md
      duration: 0.5 days
      notes: |
        Conduct data profiling and discovery:
        - Data quality assessment
        - Schema understanding and relationship mapping
        - Missing data patterns and anomaly detection
        - Statistical summaries and distribution analysis
        - Data readiness evaluation for analysis goals

    - step: analysis_execution
      agent: data-analyst
      action: analyze-data
      creates: analysis-results.md
      requires: [analysis-requirements.md, data-profile-report.md]
      duration: 1-2 days
      notes: |
        Execute data analysis:
        - Statistical analysis and hypothesis testing
        - Pattern discovery and trend analysis
        - Business insights and recommendations
        - Data visualization and charts

    - step: dashboard_creation
      agent: data-analyst
      action: create-dashboard
      creates: analytical-dashboard
      requires: analysis-results.md
      duration: 1 day
      notes: |
        Create dashboard and visualizations:
        - Interactive charts and visualizations
        - Key metrics and KPI displays
        - User-friendly navigation and filters
        - Export and sharing capabilities

    - step: quality_validation
      agent: data-quality-engineer
      action: validate-data-quality
      validates: [data-profile-report.md, analysis-results.md, analytical-dashboard]
      duration: 0.5 days
      notes: |
        Quality validation of analysis outputs:
        - Data accuracy validation in analytical results
        - Calculation verification and testing
        - Dashboard functionality testing
        - Cross-validation with known business metrics

    - step: stakeholder_review
      agent: data-product-manager
      action: define-metrics
      requires: [analysis-results.md, analytical-dashboard]
      duration: 0.5 days
      notes: |
        Stakeholder validation and sign-off:
        - Present findings to business stakeholders
        - Validate business impact and actionability
        - Document success metrics and KPIs
        - Plan implementation and next steps

validation_gates:
  - gate: requirements_validation
    criteria:
      - Business objectives clearly defined and measurable
      - Data sources identified and accessible
      - Success criteria established with stakeholders
      - Resource requirements validated and approved
  
  - gate: analysis_validation
    criteria:
      - Statistical methods appropriate for data and objectives
      - Quality validation passes framework standards
      - Insights are actionable and business-relevant
      - Visualizations follow best practices
  
  - gate: delivery_validation
    criteria:
      - All deliverables pass quality checks
      - Stakeholder approval documented
      - Success metrics established
      - Implementation plan documented

success_criteria:
  business:
    - Key stakeholders can articulate value and next steps
    - Insights directly address business objectives
    - Recommendations are actionable and prioritized
    - Success metrics are established and trackable
  
  technical:
    - Dashboard performance meets requirements
    - Data accuracy meets business quality standards
    - Analysis is reproducible and documented
    - User adoption targets are achievable
  
  analytical:
    - Statistical analysis is methodologically sound
    - Business insights are supported by evidence
    - Visualizations effectively communicate findings
    - Documentation enables reproducibility

workflow_variants:
  exploratory_analysis:
    focus: Open-ended data exploration and hypothesis generation
    duration: 2-3 days
    additional_emphasis:
      - Pattern discovery and trend identification
      - Hypothesis generation and validation
      - Exploratory data visualization
  
  business_intelligence:
    focus: KPI monitoring and business performance analysis
    duration: 3-4 days
    additional_emphasis:
      - KPI definition and calculation
      - Performance dashboards
      - Business reporting automation
  
  customer_insights:
    focus: Customer behavior analysis and segmentation
    duration: 3-5 days
    additional_emphasis:
      - Customer segmentation analysis
      - Behavior pattern identification
      - Personalization opportunities

escalation_procedures:
  - condition: Data quality issues preventing reliable analysis
    action: Escalate to Data Quality Engineer and Data Engineer
    timeline: Within 2 hours
  
  - condition: Technical analysis beyond analyst capability
    action: Consider external expertise or simplified approach
    timeline: Within 4 hours
  
  - condition: Stakeholder requirement conflicts or scope changes
    action: Escalate to Data Product Manager and Business Sponsors
    timeline: Within 1 business day

post_completion_activities:
  - activity: Dashboard usage monitoring
    frequency: Weekly for first month, then monthly
    responsible: Data Analyst
  
  - activity: Business impact tracking
    frequency: Monthly for first quarter
    responsible: Data Product Manager
  
  - activity: Analysis quality review
    frequency: Quarterly
    responsible: Data Quality Engineer