workflow: id: simple-analytics-project name: Simple Analytics Dashboard Project description: "Small-scale analytics project: Create a customer segmentation dashboard from existing CRM data" complexity: simple project_size: small duration: "2-4 weeks" team_size: "2-3 people" metadata: use_case: "Business Intelligence Dashboard" industry: "E-commerce/Retail" data_volume: "< 1M records" data_sources: 1 stakeholders: 3 compliance_level: basic context: business_scenario: | A small e-commerce company wants to understand their customer base better. They have customer data in their CRM system and want to create segments for targeted marketing campaigns. The marketing team needs a simple dashboard showing customer segments, purchase patterns, and basic metrics. success_criteria: - Customer segmentation based on purchase behavior - Interactive dashboard with key metrics - Automated daily data refresh - Marketing team can self-serve insights constraints: - Single data source (CRM database) - Limited technical team (1 data analyst, 1 developer) - Small budget for tools and infrastructure - 4-week deadline for campaign launch agents_involved: - data-product-manager - data-analyst - data-quality-engineer workflow_stages: - stage: project_initiation name: "Project Kickoff and Planning" duration: "2 days" description: "Define requirements, scope, and initial planning" tasks: - task: stakeholder_alignment agent: data-product-manager description: "Meet with marketing team to understand requirements" deliverables: - Business requirements document - Success criteria definition - Timeline and resource planning activities: - "Interview marketing stakeholders" - "Define customer segmentation goals" - "Identify key metrics and KPIs" - "Establish project timeline and milestones" - task: data_discovery agent: data-analyst description: "Explore CRM data to understand structure and quality" deliverables: - Data profiling report - Initial data quality assessment - Segmentation feasibility analysis activities: - "Connect to CRM database" - "Profile customer and transaction data" - "Identify data quality issues" - "Assess segmentation variables availability" - task: technical_planning agent: data-analyst description: "Plan technical approach and tool selection" deliverables: - Technical architecture overview - Tool selection rationale - Development environment setup plan activities: - "Evaluate dashboard tools (Tableau, Power BI, etc.)" - "Plan data extraction and transformation approach" - "Design simple ETL process" - "Set up development environment" - stage: data_preparation name: "Data Analysis and Preparation" duration: "1 week" description: "Clean data and develop segmentation logic" tasks: - task: data_cleaning agent: data-analyst description: "Clean and prepare CRM data for analysis" deliverables: - Cleaned dataset - Data transformation scripts - Data quality report activities: - "Handle missing values and duplicates" - "Standardize data formats" - "Create calculated fields for analysis" - "Document data transformation rules" quality_gates: - "Data completeness > 95%" - "No duplicate customer records" - "All required fields populated" - "Data types properly formatted" - task: segmentation_development agent: data-analyst description: "Develop customer segmentation logic" deliverables: - Customer segments definition - Segmentation algorithm/rules - Segment validation report activities: - "Analyze purchase behavior patterns" - "Define segmentation criteria (RFM, demographics, etc.)" - "Create segmentation rules or model" - "Validate segments with business stakeholders" quality_gates: - "Segments are mutually exclusive" - "Each segment has meaningful business interpretation" - "Segment sizes are actionable for marketing" - "Stakeholder approval of segment definitions" - stage: dashboard_development name: "Dashboard Creation and Testing" duration: "1 week" description: "Build and test the analytics dashboard" tasks: - task: dashboard_design agent: data-analyst description: "Design and build interactive dashboard" deliverables: - Interactive customer segmentation dashboard - Dashboard documentation - User guide for marketing team activities: - "Create dashboard mockup and get approval" - "Build visualizations for each customer segment" - "Add filters and interactive elements" - "Implement key metrics and KPIs" quality_gates: - "Dashboard loads within 5 seconds" - "All visualizations display correctly" - "Interactive filters work as expected" - "Data refreshes without errors" - task: data_pipeline_setup agent: data-analyst description: "Set up automated data refresh pipeline" deliverables: - Automated ETL pipeline - Data refresh schedule - Pipeline monitoring setup activities: - "Create daily data extraction job" - "Set up data transformation pipeline" - "Configure dashboard data source refresh" - "Implement basic error handling and alerts" quality_gates: - "Pipeline runs successfully without manual intervention" - "Data refreshes complete within 1 hour" - "Error notifications sent to appropriate team members" - "Data lineage is documented" - stage: validation_deployment name: "Validation and Deployment" duration: "3-5 days" description: "User acceptance testing and production deployment" tasks: - task: user_acceptance_testing agent: data-product-manager description: "Conduct UAT with marketing team" deliverables: - UAT results report - Bug fixes and improvements - Final stakeholder approval activities: - "Train marketing team on dashboard usage" - "Conduct hands-on testing sessions" - "Gather feedback and implement minor changes" - "Get formal sign-off from stakeholders" quality_gates: - "Marketing team can navigate dashboard independently" - "All critical functionality works correctly" - "Performance meets user expectations" - "Stakeholders approve for production use" - task: production_deployment agent: data-analyst description: "Deploy to production and monitor" deliverables: - Production deployment - Monitoring setup - Handover documentation activities: - "Deploy dashboard to production environment" - "Set up production monitoring and alerts" - "Create operational documentation" - "Conduct knowledge transfer to support team" quality_gates: - "Production deployment successful" - "All security and access controls configured" - "Monitoring and alerting functional" - "Support documentation complete" project_deliverables: primary: - "Interactive customer segmentation dashboard" - "Automated daily data pipeline" - "Customer segment definitions and business rules" supporting: - "Data profiling and quality report" - "Technical documentation" - "User training materials" - "Operational procedures" technical_stack: data_source: "CRM Database (PostgreSQL/MySQL)" etl_tool: "SQL scripts + scheduled jobs" dashboard_tool: "Power BI/Tableau Public" scheduling: "Database scheduler or simple cron jobs" quality_framework: data_quality: completeness: "> 95% for key fields" accuracy: "Customer data validated against business rules" consistency: "No duplicate customer records" timeliness: "Data refreshed daily before 8 AM" technical_quality: performance: "Dashboard loads within 5 seconds" availability: "99% uptime during business hours" usability: "Marketing team can use without technical support" business_quality: relevance: "Segments actionable for marketing campaigns" insights: "Clear differentiation between customer segments" adoption: "Marketing team actively uses dashboard weekly" risk_management: technical_risks: - risk: "CRM database performance impact" mitigation: "Schedule data extraction during off-peak hours" probability: low impact: medium - risk: "Dashboard tool licensing costs" mitigation: "Use free/open-source alternatives if needed" probability: medium impact: low business_risks: - risk: "Marketing team adoption resistance" mitigation: "Involve team in design process and provide training" probability: low impact: high - risk: "Changing requirements during development" mitigation: "Implement core features first, enhancements later" probability: medium impact: medium success_metrics: technical: - "Zero critical bugs in production after 1 week" - "Data pipeline 100% success rate for first month" - "Dashboard response time < 5 seconds" business: - "Marketing team uses dashboard at least 3x per week" - "Customer segmentation drives at least one campaign" - "Positive feedback from marketing stakeholders" adoption: - "100% of marketing team trained on dashboard" - "Self-service usage without IT support requests" - "Request for additional features/expansions" lessons_learned_template: what_worked_well: - "Simple scope enabled quick delivery" - "Close collaboration with business stakeholders" - "Early data discovery prevented major surprises" challenges_faced: - "Data quality issues required additional cleaning time" - "Tool selection required balancing features vs. cost" - "Business stakeholder availability for feedback sessions" improvements_for_next_time: - "Allocate more time for data quality assessment" - "Create data dictionary upfront" - "Set up regular check-ins with stakeholders" recommendations: - "Consider this pattern for other simple analytics projects" - "Document reusable components for future projects" - "Plan for scaling if project proves successful"