Internal Tech Radar - Data Analysis and Science

TL;DR

Objective: Develop an AI-powered system that analyzes internal communications and documentation to automatically maintain and update an internal Tech Radar, providing insights into technology adoption, success patterns, and challenges within the organization.

Key Points

• Multi-source internal data analysis (Slack, Discord, Notion, Git, Jira)
• LLM-powered content understanding
• Automated technology sentiment analysis
• Usage pattern detection
• Internal adoption tracking
• Technology success metrics
• Integration challenges identification

Core Requirements

The system should leverage LLMs to analyze internal communications and documentation, extracting meaningful insights about technology usage, adoption patterns, and success metrics to maintain an accurate, real-time internal Tech Radar.

Technical Specifications

Data Sources

• Slack conversations
• Discord channels
• Notion documentation
• Internal wikis
• Git repositories
• Jira tickets
• Team postmortems
• Architecture documents
• Engineering blog drafts
• Technical RFCs

Analysis Scope

• Technology mentions
• Usage contexts
• Implementation challenges
• Success stories
• Integration patterns
• Developer sentiment
• Adoption velocity
• Migration efforts
• Performance impacts
• Maintenance burden

Privacy Measures

• Process data internally only
• Restrict LLM output to non-sensitive insights
• Hash project identifiers
• Aggregate insights across teams
• Focus on technology patterns

Implementation Details

LLM Pipeline

The LLM pipeline will:

• Collect text data from internal sources
• Preprocess and clean content
• Extract technology references
• Analyze usage patterns
• Determine sentiment and success metrics
• Generate radar placement recommendations

Key Features

• Prompt engineering for technology extraction
• Context-aware analysis
• Pattern recognition across sources
• Sentiment aggregation
• Success metric calculation
• Adoption trend analysis
• Challenge identification
• Integration impact assessment

Processing Stages

Collection

• Source integration
• Content extraction
• Metadata gathering
• Context preservation

Analysis

• LLM processing
• Pattern extraction
• Sentiment analysis
• Impact assessment

Synthesis

• Insight aggregation
• Trend identification
• Recommendation generation
• Radar placement

Reporting

• Visualization updates
• Movement tracking
• Success stories
• Challenge areas

Deliverables

• Data collection system with internal source integration
• LLM processing pipeline
• Automated radar update system
• Interactive dashboard showing:
  • Current internal radar
  • Technology movements
  • Team adoption patterns
  • Success metrics
  • Challenge areas
  • Integration impacts
• Documentation covering:
  • System architecture
  • LLM methodology
  • Analysis patterns
  • Prompt engineering
  • API documentation

Success Metrics

• Coverage of internal tech usage
• LLM analysis accuracy
• Pattern recognition quality
• Movement prediction accuracy
• System uptime
• Processing success rates
• Insight actionability
• Resource efficiency

Additional Considerations

• Handle varying communication styles
• Adapt to new data sources
• Scale with team growth
• Provide real-time updates
• Support custom analyses
• Generate actionable insights
• Track emerging patterns
• Identify early signals

Conclusion

This bounty aims to create an intelligent system for understanding internal technology usage through AI-powered analysis. The focus should be on providing accurate insights about technology adoption and success patterns while maintaining high standards for data quality and privacy.