- Sprint Duration: 2 weeks
- Project Timeline: 8 weeks (4 sprints)
- Start Date: April 14, 2025
- End Date: June 6, 2025
- Data Team (3 members)
- 1 Data Engineer (Senior)
- 1 Business Intelligence Specialist
- 1 Data Science Intern
- Web Development Team (4 members)
- 1 Full-Stack Lead Developer
- 2 Frontend Developers
- 1 Backend Developer
- CRM Team (3 members)
- 1 CRM Specialist
- 1 Business Analyst
- 1 Integration Engineer
- AI Team (2 members)
- 1 ML Engineer
- 1 NLP Specialist
- Platform Development Team (5 members)
- 1 Software Architect
- 2 Backend Developers
- 2 Frontend/Mobile Developers
- Project Management (2 members)
- 1 Project Manager
- 1 Scrum Master
Resources: Data Team (100%)
- Day 1-2: [CRITICAL] Dashboards requirement analysis and tool selection
- Evaluate Grafana, Tableau, and Power BI for dashboard implementation
- Document specific KPI requirements and data visualization needs
- Day 3-5: Data lake inventory and processing architecture
- Map existing data sources and formats (SQL, NoSQL, CSV, API endpoints)
- Design ETL pipelines for each data source
- Day 6-8: Initial dashboard prototyping
- Create wireframes for dashboard layouts
- Set up development environments for the selected tools
- Day 9-10: Data lake processing implementation start
- Begin building data connectors for primary data sources
- Implement data validation and cleaning scripts
Technical Dependencies:
- Access to all data source APIs and credentials
- Cloud storage infrastructure (AWS S3 or similar)
- Dashboard tools licensing
Resources: Web Development Team (100%)
- Day 1-3: [CRITICAL] Auth service analysis and planning
- Review current authentication architecture
- Identify security vulnerabilities and performance bottlenecks
- Design improved JWT-based authentication flow with refresh tokens
- Day 4-6: Auth service infrastructure setup
- Configure OAuth providers (Google, Facebook, Email)
- Set up secure token storage and validation mechanisms
- Day 7-8: Property recommender service planning
- Define recommendation algorithms (collaborative filtering vs. content-based)
- Map data requirements for recommendation engine
- Day 9-10: Auth service prototype development
- Create API endpoints for authentication flows
- Implement basic version of token management
Technical Dependencies:
- Current website codebase access
- Production database read-access
- Cloud hosting environment for prototyping
Resources: CRM Team (100%)
- Day 1-3: [CRITICAL] Module identification and analysis
- Audit current CRM capabilities and limitations
- Map business processes requiring CRM support
- Identify integration points with website and data platform
- Day 4-7: [CRITICAL] Customization requirements gathering
- Conduct stakeholder interviews (sales, support, management)
- Document custom field requirements, workflow rules, and automation needs
- Prioritize customization features
- Day 8-10: [CRITICAL] Pipeline architecture design
- Design lead capture, qualification, and conversion flows
- Map data transformation requirements
- Create technical specifications for APIs and integrations
Technical Dependencies:
- Selected CRM platform access (admin-level)
- API documentation for existing systems
- Business process documentation
Resources: AI Team (100%)
- Day 1-4: Scope and use case identification
- Identify high-value conversation points for AI assistance
- Map conversation flows and decision trees
- Define success metrics for AI interactions
- Day 5-7: Initial NLP framework selection
- Evaluate language models (GPT, BERT, custom models)
- Test sample conversations against candidate frameworks
- Document technical requirements for deployment
- Day 8-10: Conversation flow prototyping
- Create sample dialogues for primary use cases
- Implement basic intent recognition logic
- Begin knowledge base structuring
Technical Dependencies:
- NLP framework access and licensing
- Sample conversation data
- Computing resources for model testing
Resources: Platform Development Team (50%), UI/UX Specialist (contract, 10%)
- Day 1-3: Initial scope definition workshop
- Define data products to be listed
- Map user journey for data buyers and sellers
- Document monetization strategy
- Day 4-6: Competitive analysis
- Analyze competitor platforms
- Document feature sets and pricing models
- Identify differentiation opportunities
- Day 7-10: High-level architecture planning
- Create conceptual architecture diagram
- Identify key technical components
- Document security and compliance requirements
Technical Dependencies:
- Market research tools
- Whiteboarding/design tools
- Compliance requirements documentation
- Daily (9:00 AM): Stand-up meetings for each workstream
- Monday/Wednesday/Friday (4:00 PM): Cross-team sync meetings
- Sprint Start (Day 1): Sprint planning and goal setting
- Sprint Mid-point (Day 6): Progress review and adjustment
- Sprint End (Day 10): Demo, retrospective, and next sprint planning
- Initial dashboard structure
- Success: At least 3 dashboard layouts approved by stakeholders
- Data lake processing plan
- Success: Complete data source inventory with 90% coverage
- Auth service architecture
- Success: Security review approved, performance projections meeting 2x current capacity
- CRM modules identified
- Success: All business processes mapped to CRM modules with no gaps
- AI use cases documented
- Success: Minimum 5 high-value conversation flows documented and validated
- Data listing platform scope document
- Success: Requirements document approved by all stakeholders
Resources: Data Team (100%)
- Day 1-3: [CRITICAL] Data warehouse schema design (Depends on: Data lake processing architecture)
- Define fact and dimension tables
- Design star/snowflake schema for analytical queries
- Document data transformation rules
- Day 4-6: ETL pipeline implementation
- Develop data extraction routines from identified sources
- Build transformation logic for business rules
- Create loading procedures for warehouse
- Day 7-8: Dashboard configuration and customization (Depends on: Dashboard prototyping)
- Implement dashboard templates with real data connections
- Create custom visualizations for specific KPIs
- Set up automated refresh schedules
- Day 9-10: Data quality monitoring setup
- Implement validation rules and quality checks
- Set up alerting for data anomalies
- Document data governance procedures
Technical Dependencies:
- Data warehouse infrastructure (Snowflake/Redshift/BigQuery)
- ETL tools (Airflow/DBT)
- Data quality frameworks
Resources: Web Development Team (100%)
- Day 1-4: [CRITICAL] Auth service implementation
- Develop JWT token management system
- Implement OAuth providers integration
- Create user session management
- Day 5-6: Auth service testing and security audit
- Penetration testing of auth endpoints
- Load testing of authentication system
- Security vulnerability assessment
- Day 7-8: Property recommender service development
- Implement recommendation algorithms
- Create API endpoints for recommendations
- Develop caching strategy for performance
- Day 9-10: Website loading optimization analysis
- Perform website performance audit
- Identify critical rendering path issues
- Document optimization strategies
Technical Dependencies:
- JWT library
- OAuth provider SDKs
- Recommendation engine framework
- Performance testing tools
Resources: CRM Team (90%), Web Development Team (10%)
- Day 1-3: [CRITICAL] Pipeline implementation planning (Depends on: Module identification)
- Detailed workflow mapping
- Custom object and field specification
- Integration touchpoint documentation
- Day 4-6: CRM platform configuration
- Configure custom objects and fields
- Set up workflow rules and triggers
- Create user roles and permissions
- Day 7-8: API integration development
- Develop API connectors for website integration
- Create webhook handlers for real-time updates
- Build authentication mechanisms for secure access
- Day 9-10: Pilot preparation
- Develop test scenarios and data
- Create user training materials
- Set up monitoring and logging
Technical Dependencies:
- CRM platform development environment
- API development tools
- Integration testing framework
Resources: AI Team (90%), CRM Team (10%)
- Day 1-3: Matrices definition & classifier implementation (Depends on: Scope identification)
- Define intent classification matrix
- Design entity recognition system
- Develop confidence scoring mechanism
- Day 4-6: Training data preparation
- Collect and annotate conversational data
- Create synthetic training examples
- Build data augmentation pipeline
- Day 7-8: Model training infrastructure setup
- Configure GPU/TPU environments
- Set up model versioning and experiment tracking
- Implement evaluation metrics
- Day 9-10: Initial model training
- Train base models for intent classification
- Perform hyperparameter optimization
- Evaluate model performance
Technical Dependencies:
- Machine learning framework (PyTorch/TensorFlow)
- Training infrastructure (cloud GPU instances)
- Model versioning tools (MLflow/DVC)
Resources: Platform Development Team (70%), UI/UX Specialist (contract, 30%)
- Day 1-3: UI/UX design exploration
- Create mood boards and design language
- Define color schemes and typography
- Design component library
- Day 4-6: Flowchart development
- Map user flows for all personas
- Document state transitions
- Define API contracts between components
- Day 7-8: Wireframe development
- Create low-fidelity wireframes for key screens
- Document interaction patterns
- Define responsive behavior
- Day 9-10: Technical architecture detailing
- Design database schema
- Define API endpoints
- Document authentication and authorization strategy
Technical Dependencies:
- Design tools (Figma/Sketch)
- Wireframing tools
- Database modeling tools
Resources: Platform Development Team (30%), External Design Agency (contract)
- Day 1-3: Initial wireframing planning
- Define screen requirements
- Document user stories for each screen
- Create information architecture
- Day 4-7: Web application wireframes
- Develop wireframes for customer-facing portal
- Create component specifications
- Document responsive breakpoints
- Day 8-10: Admin panel wireframes
- Design admin dashboard layout
- Create CRUD interface wireframes
- Document permissions model
Technical Dependencies:
- Wireframing tools
- Design system documentation
- User story repository
- Daily (9:00 AM): Stand-up meetings for each workstream
- Monday/Wednesday/Friday (4:00 PM): Cross-team sync meetings
- Sprint Start (Day 1): Sprint planning and goal setting
- Sprint Mid-point (Day 6): Progress review and adjustment
- Sprint End (Day 10): Demo, retrospective, and next sprint planning
- Data warehouse structure
- Success: Schema supports all required analytical queries with <2s response time
- Dashboard configuration specs
- Success: All dashboards can be generated automatically from data sources
- Auth service MVP
- Success: Authentication system passes security audit and handles 1000 concurrent users
- Property recommender service specs
- Success: Recommendation algorithm achieves >70% relevance in blind tests
- CRM pipeline plan
- Success: Plan covers 100% of required business processes
- AI classifier specs
- Success: Classifier achieves >85% accuracy on test dataset
- Data platform design themes
- Success: Design system approved by stakeholders and ready for implementation
- Platform flowcharts
- Success: Complete coverage of all user journeys with no dead ends
Resources: Data Team (100%)
- Day 1-3: [CRITICAL] Sharing checklists and SOPs for maintaining data flow (Depends on: Configuring dashboard)
- Document data pipeline maintenance procedures
- Create troubleshooting guides
- Develop data quality monitoring procedures
- Day 4-7: [CRITICAL] Integration testing of data infrastructure
- End-to-end testing of ETL pipelines
- Performance testing under full load
- Validation of dashboard data accuracy
- Day 8-10: Knowledge transfer to operations team
- Training sessions for operations staff
- Documentation handover
- Establish support procedures
Technical Dependencies:
- Completed ETL pipelines
- Configured dashboards
- Testing environment with production-like data
Resources: Web Development Team (100%)
- Day 1-4: [CRITICAL] Property recommender service implementation
- Implement backend recommendation engine
- Create caching layer for performance
- Develop API endpoints for website integration
- Day 5-7: [CRITICAL] Website loading optimization planning
- Run comprehensive performance audit
- Profile critical rendering path
- Identify optimization opportunities
- Day 8-10: Analytics service planning
- Define key metrics and events to track
- Evaluate analytics platforms (Google Analytics, Mixpanel, custom)
- Create data collection plan
Technical Dependencies:
- Recommendation algorithm specification
- Performance testing tools
- Analytics requirements
Resources: CRM Team (100%)
- Day 1-5: [CRITICAL] Pipeline implementation development (Depends on: Pipeline planning)
- Configure CRM platform according to specifications
- Develop custom objects and fields
- Implement workflow rules and automation
- Day 6-7: [CRITICAL] Integration development with website
- Create API endpoints for lead capture
- Implement authentication and authorization
- Develop data transformation services
- Day 8-10: [CRITICAL] Prepare for pilot run
- Create test data and scenarios
- Develop pilot user training materials
- Set up monitoring and logging
Technical Dependencies:
- CRM platform development instance
- API development framework
- Integration testing tools
Resources: AI Team (80%), CRM Team (20%)
- Day 1-4: Scripting and guard railing (Depends on: Matrices definition)
- Develop conversation scripts for primary use cases
- Implement fallback mechanisms
- Create safety filters and guardrails
- Day 5-7: Web hooks planning for CRM integration
- Design API contract for AI-CRM communication
- Document data flow between systems
- Define security requirements
- Day 8-10: Preliminary model validation
- Test scripts against sample conversations
- Validate guardrail effectiveness
- Measure intent recognition accuracy
Technical Dependencies:
- Conversation design toolkit
- API specification tools
- Testing framework for conversational AI
- Data infrastructure SOPs
- Success: Operations team can run procedures independently
- Auth service fully implemented
- Success: Production-ready authentication system
- Property recommender service MVP
- Success: Recommendations delivered with <200ms latency
- Website optimization plan
- Success: Plan identifies optimizations for 50% load time reduction
- CRM pipeline MVP
- Success: Complete lead capture to assignment workflow
- AI conversation scripts
- Success: Scripts handle 80% of expected customer inquiries
Resources: Data Team (80%), Platform Team (20%)
- Day 1-3: [CRITICAL] Dashboard deployment to production
- Deploy dashboard infrastructure to production environment
- Configure user access and permissions
- Implement data refresh schedules
- Day 4-7: [CRITICAL] Final data quality validation
- Comprehensive data quality audit
- Validation against source systems
- Performance testing under peak load
- Day 8-10: Handover to business users
- Conduct training sessions for business users
- Create user documentation
- Establish feedback channels for improvements
Technical Dependencies:
- Production environment access
- User accounts and permissions
- Training materials
Resources: Web Development Team (100%)
- Day 1-5: [CRITICAL] Website loading optimization implementation
- Implement asset minification and bundling
- Configure CDN for static assets
- Optimize critical rendering path
- Implement lazy loading for non-critical resources
- Day 6-8: Property recommender service integration
- Integrate recommendation API with website
- Implement UI components for recommendations
- Create A/B testing framework for recommendation effectiveness
- Day 9-10: Analytics service implementation
- Implement tracking code
- Configure event tracking
- Create initial dashboards for key metrics
Technical Dependencies:
- Web performance optimization tools
- CDN configuration access
- Analytics platform account
Resources: CRM Team (90%), Business Team (10%)
- Day 1-4: [CRITICAL] Pilot run execution (Depends on: Pipeline implementation)
- Launch pilot with selected users
- Monitor system performance and issues
- Provide real-time support to pilot users
- Day 5-7: [CRITICAL] Feedback collection and analysis
- Conduct user interviews
- Analyze system logs and performance data
- Document critical issues and enhancement requests
- Day 8-10: High-priority fixes and adjustments
- Implement critical fixes identified during pilot
- Adjust workflow rules based on feedback
- Prepare for expanded rollout
Technical Dependencies:
- Monitoring tools
- Feedback collection system
- Development environment for rapid fixes
Resources: AI Team (90%), CRM Team (10%)
- Day 1-4: Web hooks & API connections implementation (Depends on: Scripting)
- Develop webhook endpoints for CRM integration
- Implement authentication and authorization
- Create data transformation services
- Day 5-7: [CRITICAL] Limited pilot preparation
- Set up test environment for AI agent
- Create test scenarios and scripts
- Develop monitoring tools for conversation quality
- Day 8-10: Limited pilot execution with internal users
- Run pilot with controlled group of internal users
- Collect conversation logs and feedback
- Identify critical improvement areas
Technical Dependencies:
- API gateway
- Webhook management tools
- Conversation monitoring system
- Production-ready dashboards
- Success: Dashboards provide all required KPIs with real-time data
- Optimized website
- Success: Page load time reduced by 40%, First Contentful Paint under 1.5s
- CRM pilot results
- Success: 90% of pilot users rate system as "highly usable"
- AI integration MVP
- Success: AI system successfully communicates with CRM for basic scenarios
- Analytics implementation
- Success: Complete funnel tracking from acquisition to conversion
| Team | Sprint 1 | Sprint 2 | Sprint 3 | Sprint 4 | Average Load |
|---|---|---|---|---|---|
| Data Team | 100% | 100% | 100% | 80% | 95% |
| Web Development | 100% | 100% | 100% | 100% | 100% |
| CRM Team | 100% | 90% | 100% | 90% | 95% |
| AI Team | 100% | 90% | 80% | 90% | 90% |
| Platform Team | 50% | 70% | 0% | 20% | 35% |
| Project Management | 100% | 100% | 100% | 100% | 100% |
Critical Resource Constraints:
- Web Development
- Analytics service implementation
- [CRITICAL] Integration testing of website services
- [CRITICAL] Feedback analysis & iterations (Depends on: Pilot run)
- Begin legacy data migration planning
- [CRITICAL] AI pilot run execution (Depends on: Web hooks & API connections)
- Begin feedback collection
- Sprint board creation
- [CRITICAL] Server architecture finalization
- Web-app advanced features mockups
- Admin panel essentials mockups
- Server architecture implementation planning (Depends on: Server architecture)
- Mockup breakup for development
- Analytics service MVP
- CRM iteration plan
- Legacy data migration plan
- AI pilot results
- Platform sprint board
- Advanced web mockups
- Admin panel mockups
- Server implementation plan
- [CRITICAL] Implementation iterations (Depends on: Feedback analysis)
- Legacy data migration execution
- [CRITICAL] Feedback analysis & iterations (Depends on: AI pilot run)
- Begin model training preparation
- Mobile app advanced features mockups
- Admin panel advanced mockups
- [CRITICAL] Essentials static implementation
- Essentials dynamic planning
- Iterated CRM pipeline
- Legacy data migration progress
- AI feedback analysis report
- Complete mockup suite
- Platform essentials static implementation
- Dynamic features plan
- Complete legacy data migration
- [CRITICAL] Deployment preparation
- [CRITICAL] Model training execution (Depends on: Feedback iterations)
- Deployment preparation
- [CRITICAL] Essentials dynamic implementation
- Admin panel essentials stage 1 development
- Mobile app static development planning
- Completed CRM data migration
- CRM deployment plan
- Trained AI model
- AI deployment plan
- Dynamic platform features
- Admin panel stage 1
- Mobile app development plan
- [CRITICAL] Full deployment (Depends on: Deployment preparation)
- Post-deployment monitoring
- [CRITICAL] Deployment execution (Depends on: Model training)
- Post-deployment monitoring
- Admin panel essentials stage 2 development
- [CRITICAL] Mobile app static implementation
- Mobile app dynamic planning
- Deployed CRM system
- Deployed AI system
- Complete admin panel essentials
- Mobile app static version
- Mobile app dynamic plan
- [CRITICAL] Mobile app dynamic implementation
- Mobile app - admin panel integration
- Web-app beautification
- CI/CD implementation planning
- Dynamic mobile app features
- Integrated mobile/admin systems
- Beautified web application
- CI/CD planning document
- Mobile app beautification
- [CRITICAL] User testing preparation
- [CRITICAL] CI/CD implementation
- Phase 1 deployment preparation
- Beautified mobile application
- User testing plan
- CI/CD pipeline
- Phase 1 deployment plan
- [CRITICAL] Phase 1 deployment
- User testing execution
- Web-app advanced features implementation
- Admin panel advanced features implementation
- Mobile app advanced features implementation
- Deployed Phase 1 platform
- User testing results
- Advanced feature implementations
- CRM & 3rd party integrations
- [CRITICAL] AI/ML functions deployment
- Final user testing
- [CRITICAL] Phase 2 deployment
- Project handover documentation
- Maintenance plan development
- Full system integration
- Complete platform deployment
- Project documentation
- Maintenance procedures
- Knowledge transfer
- [CRITICAL]: Tasks that are on the critical path and must be completed on schedule
- Depends on: X: Tasks that cannot start until the referenced task X is completed
- MVP: Minimum Viable Product
-
Data Infrastructure
- Quantifiable Metrics:
- Dashboard tool selection completed with documented decision matrix
- 100% of data sources identified and documented
- ETL pipeline designs reviewed and approved
- Quality Gates:
- Dashboard wireframes must pass UX review
- Data source inventory must be validated by business stakeholders
- User Acceptance Criteria:
- Dashboard layouts approved by at least 2 key business stakeholders
- Data processing plan approved by technical leadership
- Technical Debt Thresholds:
- No more than 5 "TODO" items in initial implementation
- Technical documentation coverage must exceed 80%
- Quantifiable Metrics:
-
Website Optimization
- Quantifiable Metrics:
- Auth service architecture document with 100% security requirements covered
- Performance benchmark showing projected 2x capacity improvement
- Quality Gates:
- Security review sign-off by security team
- Architecture review sign-off by technical leadership
- User Acceptance Criteria:
- Auth flow mockups approved by product team
- Property recommendation strategy approved by business stakeholders
- Technical Debt Thresholds:
- No critical or high security vulnerabilities in design
- Architecture must support future scaling without redesign
- Quantifiable Metrics:
-
CRM Pipeline
- Quantifiable Metrics:
- 100% of business processes mapped to CRM modules
- Requirements document with explicit coverage of all use cases
- Quality Gates:
- Business process mapping reviewed by operations team
- Technical specifications reviewed by CRM vendor consultant
- User Acceptance Criteria:
- Sales team approves CRM workflow
- Support team validates accessibility of customer data
- Technical Debt Thresholds:
- No custom development that replicates out-of-box CRM functionality
- Integration design must use supported APIs only
- Quantifiable Metrics:
-
AI Implementation
- Quantifiable Metrics:
- Minimum 5 high-value conversation flows documented
- Initial NLP framework evaluation with benchmark results
- Quality Gates:
- Use case validation by product management
- Framework selection criteria reviewed by AI team lead
- User Acceptance Criteria:
- Sample dialogues approved by sales and support teams
- Technical approach validated with vendor if using commercial framework
- Technical Debt Thresholds:
- Must support at least 2 languages in future without architecture changes
- Framework must have active maintenance or support
- Quantifiable Metrics:
(Similar detailed metrics provided for each workstream in Sprints 2-4)
| ID | Risk | Severity | Probability | Business Impact | Technical Impact | Mitigation Strategy | Early Warning Indicators | Technical Debt Implications |
|---|---|---|---|---|---|---|---|---|
| R1 | Data migration complexities | High | Medium | - Delayed dashboard deployment - Inaccurate business reporting - Poor decision making |
- Complex transformation logic - Performance issues with large datasets |
- Early proof-of-concept for complex migrations - Staged migration approach - Parallel run of old and new systems |
- Excessive time spent in data mapping sessions - Data quality issues in source systems - Inconsistent data models across sources |
- Quick fixes may result in brittle transformation code - May require re-architecture if data volume grows |
| R2 | Integration challenges between systems | High | High | - Features delivered in silos - Poor user experience - Business process breaks |
- API compatibility issues - Performance bottlenecks - Security vulnerabilities at integration points |
- Early integration testing - API-first design approach - Service contract definitions before implementation - Integration monitoring |
- Delayed API specifications - Excessive API changes during development - Integration tests failing |
- Temporary workarounds become permanent - Point-to-point integrations instead of API gateway - Insufficient error handling |
| R3 | User adoption of AI features | Medium | Medium | - Low ROI on AI investment - Negative user feedback - Lost competitive advantage |
- Overengineered solutions - Underutilized infrastructure |
- Early user involvement in conversation design - Progressive rollout strategy - Extensive user testing - Clear success metrics |
- Negative feedback in initial user tests - Low accuracy in early model testing - Resistance in stakeholder meetings |
- "Quick wins" may create inflexible models - Technical shortcuts to improve early accuracy |
| R4 | Performance issues with the platform | High | Medium | - Poor user experience - Increased operational costs - Scalability limitations |
- System instability - Excessive infrastructure costs - Complex bottleneck troubleshooting |
- Performance testing from sprint 1 - Infrastructure monitoring - Performance budgets for all features - Scaling plan |
- Slow performance in dev environment - Increasing response times with growing data - Resource utilization spikes |
- Performance optimizations may increase code complexity - Quick fixes may not address root causes |
| R5 | Timeline slippage in critical path | High | High | - Delayed market entry - Increased project costs - Reduced feature set |
- Technical debt accumulation - Rushed testing - Insufficient documentation |
- Buffer sprints built into timeline - Clear prioritization framework - Regular progress tracking - Contingency planning |
- Tasks consistently taking longer than estimated - Increasing bug backlog - Team working overtime |
- Shortcuts in implementation - Deferred testing - Incomplete documentation |
| R6 | Resource constraints in Web Development | Medium | High | - Critical website features delayed - Reduced quality of implementation |
- Rushed code reviews - Inconsistent coding standards - Missing test coverage |
- Identify cross-training opportunities - Potential contractor backup - Clear prioritization of activities - Optimize parallel workstreams |
- Increasing task backlog - Missed deadlines - Quality issues in deliverables |
- Inconsistent implementation patterns - Reduced test coverage - Minimal documentation |
| R7 | Data quality issues | Medium | Medium | - Inaccurate reporting - Low user trust in dashboards |
- Complex data cleansing logic - Performance overhead for validation |
- Data quality assessment in Sprint 1 - Data validation rules - Data quality monitoring |
- Initial data audit reveals inconsistencies - High error rates in data processing |
- Hardcoded data cleansing rules - Exception handling for specific data cases |
- Weekly Risk Review: Each sprint includes a dedicated risk review session
- Risk Ownership: Each identified risk has a designated owner responsible for monitoring and mitigation
- Contingency Budget: 20% time buffer allocated for critical path items
- Escalation Path: Clear escalation procedure for when risk factors exceed thresholds
- Debt Tracking: Technical debt items explicitly logged in project management system
- Remediation Windows: Specific time allocated in each sprint for addressing high-priority technical debt
- Definition of Done: Includes technical debt thresholds that must not be exceeded
- Architecture Reviews: Regular reviews to prevent architectural drift
The following sprints represent planned work after the initial 8-week delivery phase and will be refined based on outcomes from Sprints 1-4:
- Successful completion of CRM pilot with positive user feedback
- Authentication service fully implemented and validated in production
- Data infrastructure providing reliable dashboard solutions
- AI conversation flows validated with stakeholders
-
Go Criteria:
- All critical path items in Sprints 1-4 successfully completed
- No high-severity issues remaining unresolved
- Business stakeholder sign-off on delivered components
- Infrastructure capacity meets projected requirements
-
No-Go Criteria:
- Unresolved critical bugs in core functionality
- Performance metrics not meeting minimum thresholds
- Significant user experience issues identified during testing
- Technical debt exceeding defined thresholds
- Alternative deployment strategies if full scope cannot be delivered
- Phased rollout options to minimize business disruption
- Resource adjustment plans for high-demand teams