Open-Source Change Management

Chairs: Ning Leng, Nate Mockler

The Foundation: Why Before How

Risk Management: Change management is fundamentally about managing risk
Breaking the IBM Rule: “No one gets fired for buying IBM” - need to show upsides of change to overcome conservative mindset
Value Proposition: Emphasize collaboration benefits, AI integration opportunities, and modern capabilities

Grassroots vs. Top-Down: Grassroots efforts have limits; executive support and repeated messaging from leadership essential
Innovation Teams: Trend toward creating dedicated innovation groups rather than making change management a side job
Hiring vs. Training:
- Just-in-time training more effective than advance training
- Trend toward hiring R/Python programmers from day one
- Removing SAS requirements from job descriptions
Standards Influence: Good opportunity to influence internal standards and reset limitations from legacy tools

Git Foundation: Essential foundation for open source adoption and AI tool integration
Environment Management: Focus on Statistical Computing Environment (SCE) setup
Containerization: Important for managing package versioning issues (R+ENV, Docker)

Version Control: Git as key process improvement
Reporting Innovation: Opportunities to rethink clinical trial reporting (e.g., using Shiny to reduce static TLFs)
Communication Strategy: Right people delivering right messages, expect pushback, avoid mandate-style approaches

R Validation Hub: Valuable resource for demonstrating process and risk metrics
Specific Use Cases: Show concrete examples of complex tables/graphs generated easily
Environment Comfort: Ensure users feel comfortable in the new environment
Package Management: Education about dynamic nature of open source vs. traditional single-package updates
Trial Periods: Implement trials before major investments