SCEs in 2025
Chairs: David Thiriot, Phil Bowsher and James Black
See also: 2023 Discussion, 2024 Discussion
SCE Components (5 Core Elements)
- IDE: Development environment interface
- File and Version Management: Source control and file handling
- Data and Access: Data storage and access management
- Governance: Policies and compliance frameworks
- Compute: Processing and computational resources
Scope and Architecture Challenges
- Historical Separation: Legacy approach had separate SCEs for GXP work vs. everything else
- Unified Direction: Most companies moving toward single SCE for all work
- User Diversity: Modern SCEs serve PK, Biostatistics, Stats Programming, and other groups on same platform
Vendor Considerations
- Ecosystem Promises: Vendors offer packaged solutions but create vendor lock-in concerns
- Missing Integrations: Gaps in vendor offerings, sometimes strategically maintained
- Cost Management: Total cost of ownership concerns when using multiple vendors
- Integration Overlap: Redundancy between different vendor products increases costs
Validation Challenges
- Timeline: Validation processes take months (up to 3 months reported)
- Test Suite Maintenance: Significant burden maintaining test suites as platforms change
- IDE Validation Debate: Whether to validate IDE as part of environment or keep separate
- System vs. Process: Best practice is qualifying systems while validating processes
Technology Evolution
- Python Integration: Coming into clinical reporting, mainly for transformations
- Python Validation Gap: Lacks standardized documentation and testing frameworks compared to R
- Data Format Shift: Parquet replacing SAS7bdat as standard data format
- Package Governance: Some companies using opinionated lists of approved R packages
Environment Management
- Update Cycles: 3-6 months common window for base environment updates
- Package Coupling: Most companies couple R packages with environments (some decouple)
- Environment Tracking: Tools to flag whether environments are managed/production-ready
- Consistency Requirements: Containers must be accessible across SCE, Shiny apps, HPC, etc.
Version Control and Data Access
- Git Adoption: Standard direction but significant change management challenges
- Learning Curve: Git presents training challenges for traditional SAS users
- Data Access Complexity: Challenges with data access in Shiny applications
- Technology Solutions: Denodo and AWS Data Zone mentioned for data access management