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.