workflow: id: analytics-workflow name: Analytics Pipeline Development description: >- End-to-end workflow for developing analytics solutions from business requirements through dashboard deployment. Supports descriptive, diagnostic, predictive, and prescriptive analytics. type: greenfield project_types: - business-intelligence - operational-analytics - customer-analytics - financial-reporting - performance-dashboards sequence: - step: business_requirements_analysis agent: data-analyst action: analyze-business-requirements creates: analytics-requirements.md duration: 1-2 days notes: | Conduct comprehensive business requirements analysis: - Stakeholder interviews and needs assessment - Key performance indicators (KPI) definition - Success metrics and measurement criteria - Reporting and dashboard requirements - User personas and access patterns SAVE OUTPUT: Copy final analytics-requirements.md to your project's docs/ folder. - step: data_requirements_specification agent: data-product-manager action: create-analytics-data-contract creates: analytics-data-contract.md requires: analytics-requirements.md duration: 1 day notes: | Define data requirements for analytics: - Required data sources and datasets - Data granularity and aggregation needs - Historical data requirements and retention - Data quality requirements for analytics - Real-time vs batch processing requirements SAVE OUTPUT: Copy final analytics-data-contract.md to your project's docs/ folder. - step: analytical_design agent: data-scientist action: design-analytical-approach creates: analytical-design.md requires: [analytics-requirements.md, analytics-data-contract.md] duration: 1-2 days notes: | Design analytical methodology and approach: - Statistical methods and analytical techniques - Model selection and validation strategies - Feature engineering and data preparation - Hypothesis testing and validation frameworks - Performance metrics and evaluation criteria - step: data_architecture_design agent: data-architect action: design-analytics-architecture creates: analytics-architecture.md requires: [analytics-data-contract.md, analytical-design.md] duration: 1-2 days notes: | Design technical architecture for analytics: - Data modeling for analytics (dimensional, OLAP) - Processing architecture (batch, streaming, hybrid) - Storage optimization for analytical workloads - Query performance optimization strategies - Scalability and resource planning SAVE OUTPUT: Copy final analytics-architecture.md to your project's docs/ folder. - step: user_experience_design agent: data-experience-designer action: design-analytics-interface creates: analytics-ux-design.md requires: analytics-requirements.md duration: 1-2 days notes: | Design user experience for analytics consumption: - Dashboard wireframes and user interface design - Information architecture and navigation design - Visualization selection and design principles - Interactive features and drill-down capabilities - Mobile and responsive design considerations - step: data_model_implementation agent: data-engineer action: implement-analytics-data-model creates: analytics-data-model requires: [analytics-architecture.md, analytics-data-contract.md] duration: 2-3 days notes: | Implement analytical data models: - Dimensional model implementation (facts, dimensions) - Data transformation and aggregation logic - Calculated measures and KPI definitions - Data lineage and metadata documentation - Performance optimization and indexing - step: analytical_implementation agent: data-scientist action: implement-analytics creates: analytical-models requires: [analytical-design.md, analytics-data-model] duration: 2-3 days notes: | Implement analytical models and calculations: - Statistical model implementation and training - Business metric calculations and formulas - Trend analysis and forecasting models - Segmentation and classification algorithms - Model validation and performance testing - step: visualization_implementation agent: data-experience-designer action: implement-dashboards creates: analytics-dashboards requires: [analytics-ux-design.md, analytical-models] duration: 2-3 days notes: | Implement dashboards and visualizations: - Interactive dashboard development - Chart and visualization implementation - Drill-down and filtering capabilities - Export and sharing functionality - Performance optimization for large datasets - step: quality_validation agent: data-quality-engineer action: validate-analytics-quality validates: [analytics-data-model, analytical-models, analytics-dashboards] duration: 1-2 days notes: | Comprehensive quality validation: - Data accuracy validation in analytical models - Calculation verification and testing - Dashboard functionality and performance testing - Cross-validation with known business metrics - User acceptance testing coordination - step: performance_optimization agent: data-engineer action: optimize-analytics-performance requires: [analytics-data-model, analytics-dashboards] duration: 1 day notes: | Optimize analytical performance: - Query optimization and indexing strategies - Caching implementation for frequently accessed data - Resource allocation and scaling configuration - Response time optimization for interactive features - Load testing and capacity planning - step: user_acceptance_testing agent: data-analyst action: conduct-analytics-uat validates: [analytics-dashboards, analytical-models] duration: 1-2 days notes: | Business user acceptance testing: - Stakeholder validation of analytical outputs - Dashboard usability and functionality testing - Business logic verification and sign-off - Training material development and delivery - Feedback collection and issue resolution - step: deployment_and_rollout agent: data-engineer action: deploy-analytics-solution creates: production-analytics requires: [analytics-dashboards, analytical-models] duration: 1 day notes: | Deploy analytics solution to production: - Production environment configuration - Security and access control setup - Monitoring and alerting configuration - User provisioning and permission setup - Rollout communication and training - step: monitoring_and_governance agent: data-governance-owner action: setup-analytics-governance creates: analytics-governance-framework requires: production-analytics duration: 0.5 day notes: | Establish ongoing governance and monitoring: - Usage monitoring and analytics adoption tracking - Data lineage documentation and maintenance - Compliance monitoring and audit procedures - Change management and version control - Performance monitoring and optimization alerts validation_gates: - gate: requirements_validation criteria: - Business stakeholders approve analytical requirements - Success metrics and KPIs are clearly defined - Data requirements are feasible and available - User experience requirements are documented - gate: design_validation criteria: - Analytical approach is scientifically sound - Architecture supports performance requirements - User experience design meets usability standards - Integration points are well-defined - gate: implementation_validation criteria: - All analytical models meet accuracy requirements - Dashboard performance meets response time SLAs - Data quality validation passes all tests - User acceptance testing completed successfully - gate: deployment_validation criteria: - Production deployment completed without issues - All security and access controls are functional - Monitoring and alerting systems are operational - User training and documentation are complete success_criteria: business: - Key stakeholders actively use analytics solution - Decision-making speed improves measurably - Business insights lead to actionable outcomes - ROI targets for analytics investment are met technical: - Dashboard response times meet SLA requirements - Data accuracy meets business quality standards - System availability exceeds 99% uptime - User adoption reaches target thresholds analytical: - Model predictions meet accuracy thresholds - Statistical significance is maintained - Business metrics align with external benchmarks - Trend analysis provides actionable insights workflow_variants: descriptive_analytics: focus: Historical analysis and reporting additional_steps: - Historical data validation and cleansing - Trend analysis and pattern identification - Comparative analysis and benchmarking typical_duration: 2-3 weeks predictive_analytics: focus: Forecasting and prediction models additional_steps: - Machine learning model development - Model validation and backtesting - Prediction accuracy monitoring typical_duration: 4-6 weeks prescriptive_analytics: focus: Optimization and recommendation systems additional_steps: - Optimization algorithm development - Scenario modeling and simulation - Decision support system integration typical_duration: 6-8 weeks escalation_procedures: - condition: Analytical accuracy below thresholds action: Escalate to Data Scientist and Data Architect timeline: Within 24 hours - condition: Performance issues affecting user experience action: Escalate to Data Engineer and Infrastructure Team timeline: Within 4 hours - condition: Business requirement conflicts or changes action: Escalate to Data Product Manager and Business Sponsors timeline: Within 1 business day post_deployment_activities: - activity: Usage analytics and adoption monitoring frequency: Weekly for first month, then monthly responsible: Data Product Manager - activity: Model performance monitoring frequency: Daily for statistical models, weekly for business metrics responsible: Data Scientist - activity: User feedback and satisfaction surveys frequency: 30, 60, and 90 days post-deployment responsible: Data Experience Designer - activity: Business impact assessment frequency: Quarterly responsible: Data Product Manager and Business Stakeholders