agentic-data-stack-community/data-core/templates/value-mapping-tmpl.yaml

AI Agentic Data Stack Framework - Community Edition. Open source data engineering framework with 4 core agents, essential templates, and 3-dimensional quality validation.

# Value Mapping Template
# Purpose: Framework for mapping and quantifying data product value
# Version: 1.0.0
# Last Updated: 2025-01-23

metadata:
  template_id: "value-mapping-tmpl"
  version: "1.0.0"
  name: "Data Product Value Mapping Template"
  description: "Comprehensive framework for identifying, mapping, and quantifying data product value"
  category: "data-product-management"
  tags:
    - value-mapping
    - business-value
    - roi
    - value-proposition
    - impact-assessment
  owner: "Data Product Management Team"
  created_date: "2025-01-23"
  last_modified: "2025-01-23"

template:
  structure:
    - value_framework
    - stakeholder_value_analysis
    - value_drivers
    - quantification_methods
    - value_realization_tracking
    - communication_strategy

sections:
  value_framework:
    value_categories:
      financial_value:
        revenue_generation:
          - "New revenue streams from data monetization"
          - "Increased sales through better insights"
          - "Premium pricing for data-enhanced products"
          - "Subscription revenue from data services"
        
        cost_reduction:
          - "Operational efficiency improvements"
          - "Automated decision-making reducing manual effort"
          - "Reduced infrastructure costs through optimization"
          - "Decreased compliance and regulatory costs"
        
        cost_avoidance:
          - "Prevention of system failures and downtime"
          - "Avoiding regulatory penalties"
          - "Reducing customer churn"
          - "Preventing data security breaches"
      
      strategic_value:
        competitive_advantage:
          - "Market differentiation through unique insights"
          - "Faster time-to-market for new products"
          - "Superior customer experience"
          - "Innovation leadership in industry"
        
        market_positioning:
          - "Thought leadership in data-driven solutions"
          - "Partnership opportunities"
          - "Market expansion capabilities"
          - "Brand enhancement and reputation"
      
      operational_value:
        efficiency_gains:
          - "Faster decision-making processes"
          - "Improved resource allocation"
          - "Streamlined operations"
          - "Enhanced productivity"
        
        quality_improvements:
          - "Better data quality and reliability"
          - "Improved product quality"
          - "Enhanced customer satisfaction"
          - "Reduced error rates"
    
    value_measurement_framework:
      quantitative_measures:
        - "Return on Investment (ROI)"
        - "Net Present Value (NPV)"
        - "Internal Rate of Return (IRR)"
        - "Payback Period"
        - "Total Cost of Ownership (TCO)"
      
      qualitative_measures:
        - "Customer satisfaction scores"
        - "Employee engagement levels"
        - "Brand perception metrics"
        - "Innovation index"
        - "Competitive positioning"

  stakeholder_value_analysis:
    internal_stakeholders:
      executives:
        value_drivers:
          - "Strategic alignment and business growth"
          - "Competitive advantage and market position"
          - "Risk mitigation and compliance"
          - "Shareholder value creation"
        
        success_metrics:
          - "Revenue growth rate"
          - "Market share increase"
          - "Cost reduction percentage"
          - "ROI on data investments"
      
      business_units:
        value_drivers:
          - "Operational efficiency improvements"
          - "Better decision-making capabilities"
          - "Customer insights and engagement"
          - "Process automation and optimization"
        
        success_metrics:
          - "Time savings in decision-making"
          - "Increased sales conversion rates"
          - "Reduced operational costs"
          - "Improved customer satisfaction"
      
      it_organization:
        value_drivers:
          - "Infrastructure optimization"
          - "Reduced maintenance overhead"
          - "Improved system reliability"
          - "Enhanced security posture"
        
        success_metrics:
          - "System uptime improvements"
          - "Reduced support tickets"
          - "Infrastructure cost savings"
          - "Security incident reduction"
    
    external_stakeholders:
      customers:
        value_drivers:
          - "Improved product/service quality"
          - "Personalized experiences"
          - "Faster service delivery"
          - "Better value for money"
        
        success_metrics:
          - "Customer satisfaction scores"
          - "Net Promoter Score (NPS)"
          - "Customer retention rates"
          - "Usage and engagement metrics"
      
      partners:
        value_drivers:
          - "Enhanced collaboration capabilities"
          - "Shared insights and intelligence"
          - "Joint innovation opportunities"
          - "Improved supply chain efficiency"
        
        success_metrics:
          - "Partnership satisfaction scores"
          - "Joint revenue generation"
          - "Collaboration efficiency metrics"
          - "Innovation pipeline health"
      
      regulators:
        value_drivers:
          - "Improved compliance and transparency"
          - "Better risk management"
          - "Enhanced data governance"
          - "Faster regulatory reporting"
        
        success_metrics:
          - "Compliance audit scores"
          - "Regulatory reporting timeliness"
          - "Risk assessment ratings"
          - "Penalty reduction"

  value_drivers:
    primary_value_drivers:
      data_quality_improvement:
        description: "Enhanced data accuracy, completeness, and reliability"
        value_impact:
          - "Better decision-making leading to 15-25% improvement in business outcomes"
          - "Reduced data reconciliation efforts saving 20-30 hours per week"
          - "Decreased error rates in reports and analysis by 80-90%"
        
        measurement_approach:
          - "Data quality scores and trends"
          - "Time spent on data cleaning and validation"
          - "Error rate reduction in downstream processes"
      
      faster_insights_delivery:
        description: "Reduced time from data to actionable insights"
        value_impact:
          - "Accelerated decision-making by 60-80%"
          - "Increased market responsiveness and agility"
          - "Competitive advantage through faster time-to-insight"
        
        measurement_approach:
          - "Time-to-insight metrics"
          - "Decision-making cycle time"
          - "Market response time improvements"
      
      self_service_analytics:
        description: "Empowering business users with self-service data capabilities"
        value_impact:
          - "Reduced dependency on IT and technical teams"
          - "50-70% reduction in ad-hoc reporting requests"
          - "Increased user satisfaction and productivity"
        
        measurement_approach:
          - "User adoption rates"
          - "Self-service query volume"
          - "Reduction in support requests"
    
    secondary_value_drivers:
      compliance_automation:
        description: "Automated compliance monitoring and reporting"
        value_impact:
          - "Reduced compliance costs by 40-60%"
          - "Minimized regulatory risk and penalties"
          - "Improved audit readiness and confidence"
        
        measurement_approach:
          - "Compliance cost reduction"
          - "Audit preparation time"
          - "Regulatory penalty avoidance"
      
      data_monetization:
        description: "Creating new revenue streams from data assets"
        value_impact:
          - "New revenue streams generating 5-15% of total revenue"
          - "Enhanced product offerings and pricing"
          - "Market expansion opportunities"
        
        measurement_approach:
          - "Data-driven revenue generation"
          - "Product enhancement metrics"
          - "Market expansion success"

  quantification_methods:
    financial_quantification:
      revenue_impact_calculation:
        method: "Incremental Revenue Analysis"
        formula: "Revenue Impact = (Conversion Rate Improvement × Customer Base × Average Order Value)"
        example:
          scenario: "Personalized recommendations increase conversion by 2%"
          calculation: "2% × 100,000 customers × $150 AOV = $300,000 annual impact"
      
      cost_savings_calculation:
        method: "Process Efficiency Analysis"
        formula: "Cost Savings = (Time Saved × Hourly Rate × Number of Employees) × Frequency"
        example:
          scenario: "Automated reporting saves 4 hours per week per analyst"
          calculation: "4 hours × $75/hour × 10 analysts × 52 weeks = $156,000 annual savings"
      
      cost_avoidance_calculation:
        method: "Risk-Based Valuation"
        formula: "Cost Avoidance = (Probability of Issue × Cost of Issue) × Risk Reduction %"
        example:
          scenario: "Data quality improvements reduce compliance risk"
          calculation: "20% risk × $500,000 penalty × 80% reduction = $80,000 avoidance"
    
    roi_calculation:
      total_benefits:
        components:
          - "Revenue increases"
          - "Cost savings"
          - "Cost avoidance"
          - "Productivity gains"
        
        calculation_period: "3-year projection"
        discount_rate: "10% for NPV calculation"
      
      total_costs:
        components:
          - "Initial development and implementation"
          - "Ongoing operational costs"
          - "Training and change management"
          - "Maintenance and support"
        
        cost_allocation: "Spread over useful life of solution"
      
      roi_formula:
        simple_roi: "(Total Benefits - Total Costs) / Total Costs × 100"
        npv_calculation: "Sum of (Annual Net Benefits / (1 + Discount Rate)^Year)"
        payback_period: "Time required for cumulative benefits to exceed costs"

  value_realization_tracking:
    baseline_establishment:
      pre_implementation_metrics:
        - "Current process efficiency measurements"
        - "Existing cost structures and allocations"
        - "Quality metrics and error rates"
        - "User satisfaction and engagement levels"
      
      baseline_documentation:
        - "Historical performance data (12-24 months)"
        - "Current state process maps"
        - "Cost breakdowns and allocations"
        - "Stakeholder satisfaction surveys"
    
    tracking_methodology:
      short_term_tracking:
        timeframe: "First 3-6 months post-implementation"
        focus: "Early adoption indicators and quick wins"
        metrics:
          - "User adoption rates"
          - "Process completion times"
          - "Error rate reductions"
          - "Initial cost savings"
      
      medium_term_tracking:
        timeframe: "6-18 months post-implementation"
        focus: "Operational improvements and efficiency gains"
        metrics:
          - "Productivity improvements"
          - "Cost reduction achievements"
          - "Quality improvements"
          - "User satisfaction increases"
      
      long_term_tracking:
        timeframe: "18+ months post-implementation"
        focus: "Strategic value realization and ROI validation"
        metrics:
          - "Revenue impact measurement"
          - "Competitive advantage indicators"
          - "Market position improvements"
          - "Innovation acceleration"
    
    value_attribution:
      direct_attribution:
        method: "Clear cause-and-effect relationship"
        examples:
          - "Automated process reduces manual effort by X hours"
          - "Improved data quality reduces error rates by Y%"
          - "Faster insights lead to Z% improvement in decision speed"
      
      indirect_attribution:
        method: "Statistical correlation and business logic"
        examples:
          - "Better customer insights contribute to retention improvement"
          - "Data-driven decisions support revenue growth"
          - "Enhanced analytics capabilities enable innovation"
      
      proportional_attribution:
        method: "Allocate value based on contribution percentage"
        approach:
          - "Identify all contributing factors to outcome"
          - "Estimate relative contribution of data product"
          - "Apply percentage to total measured benefit"

  communication_strategy:
    value_story_development:
      narrative_structure:
        problem_statement: "Clear articulation of business challenge"
        solution_approach: "How data product addresses the challenge"
        value_proposition: "Specific benefits and outcomes delivered"
        proof_points: "Evidence and metrics supporting value claims"
      
      audience_customization:
        executive_audience:
          focus: "Strategic value and competitive advantage"
          metrics: "ROI, market share, revenue growth"
          format: "Executive summary with key insights"
        
        operational_audience:
          focus: "Process improvements and efficiency gains"
          metrics: "Time savings, error reduction, productivity"
          format: "Detailed operational impact analysis"
        
        technical_audience:
          focus: "Technical capabilities and performance"
          metrics: "System performance, data quality, reliability"
          format: "Technical specifications and benchmarks"
    
    ongoing_communication:
      regular_updates:
        frequency: "Monthly progress reports"
        content: "Value realization progress against targets"
        distribution: "Key stakeholders and sponsors"
      
      milestone_celebrations:
        events: "Quarterly value achievement announcements"
        purpose: "Recognize success and maintain momentum"
        format: "Success stories and testimonials"
      
      continuous_improvement:
        feedback_collection: "Regular stakeholder feedback on value delivery"
        value_optimization: "Identify opportunities to enhance value"
        communication_refinement: "Improve messaging based on feedback"

template_metadata:
  update_frequency: "Quarterly value assessment and annual comprehensive review"
  stakeholder_involvement: "Cross-functional value realization team"
  documentation_requirements: "Maintain baseline data and value tracking metrics"
  success_measurement: "Achievement of projected ROI and stakeholder satisfaction"