Employee Social Proactivity Analysis - Data Integration and AI
TL;DR
Objective: Develop a two-phase system that first collects and aggregates data from public Discord channels, then uses analytics and LLMs to evaluate employee social engagement patterns, providing insights into community participation and impact.
Key Points
- Public channel data aggregation
- Engagement pattern analysis
- Contribution quality assessment
- Impact measurement
- Knowledge sharing tracking
- Response patterns
- Community support metrics
- Leadership indicators
Core Requirements
The system should collect data from public Discord channels and analyze employee participation patterns using both quantitative metrics and qualitative LLM analysis to understand engagement effectiveness and impact.
Technical Specifications
Collection Scope
- Message content
- Channel activity
- Thread participation
- Reaction patterns
- Response times
- Mentions
- Problem solving
- Knowledge sharing
- Event participation
- Initiative taking
- Collaboration patterns
- Leadership moments
Data Points to Analyze
- Message frequency
- Response quality
- Help provided
- Knowledge shared
- Questions answered
- Initiatives started
- Collaboration instances
- Community impact
- Topic expertise
- Engagement consistency
- Project discussions
- Mentorship activities
Privacy Measures
- Analyze only public channels
- Follow Discord terms of service
- Implement rate limiting
- Store aggregated metrics
- Hash employee identifiers
- Respect privacy settings
Implementation Details
Pipeline Phases
Phase 1 - Data Collection
- Channel data aggregation
- Message history collection
- Interaction pattern tracking
- Metadata gathering
- Context preservation
Phase 2 - Analysis
- Engagement pattern analysis
- LLM-based quality assessment
- Impact measurement
- Proactivity scoring
- Trend identification
Key Features
- Real-time data collection
- Engagement scoring
- Quality assessment
- Impact tracking
- Pattern recognition
- Leadership identification
- Expertise mapping
- Contribution analysis
Processing Stages
Collection
- Channel monitoring
- Message aggregation
- Context gathering
- Pattern tracking
Analysis
- Engagement scoring
- Quality assessment
- Impact evaluation
- Pattern detection
Processing
- Score calculation
- Trend analysis
- Insight generation
- Recommendation creation
Reporting
- Engagement visualization
- Quality mapping
- Impact assessment
- Growth tracking
Deliverables
- Data collection system for Discord
- Analytics and LLM pipeline
- Proactivity scoring system
- Interactive dashboard showing:
- Engagement metrics
- Quality scores
- Impact measurements
- Topic expertise
- Leadership moments
- Growth patterns
- Contribution trends
- Documentation covering:
- System architecture
- Collection methodology
- Analysis algorithms
- LLM implementation
- API documentation
Success Metrics
- Data completeness
- Analysis accuracy
- Pattern detection
- Impact assessment
- Scoring fairness
- Insight quality
- Trend identification
- Resource efficiency
Additional Considerations
- Handle varying activity levels
- Adapt to channel growth
- Scale with message volume
- Provide timely insights
- Support custom analyses
- Generate fair assessments
- Track improvement patterns
- Identify growth opportunities
Conclusion
This bounty aims to create a comprehensive system for understanding employee social proactivity through data-driven analysis. The focus should be on providing fair and actionable insights about engagement patterns while maintaining high standards for privacy and assessment quality.