Enterprise R adoption in life sciences requires more than package access. GSK's STAR program moved 3,000 biostatisticians from SAS to R by building an infrastructure that prioritized real-time support and role-specific workflows over generic training software.
Users stall when they don’t know the specific commands to write to produce standard trial deliverables. Success requires role-specific, end-to-end workflows that map directly to their pipeline tasks.
GSK established a dedicated "Starburst" team that was tasked with answering every pipeline question without exception. Resolving basic execution errors rapidly is the fastest way to build trust.
Instead of keeping solutions siloed in private threads, the support team published one microblog post per answered question every day. Answering an issue once allows hundreds of other programmers with the same blocker to self-serve.
GSK deployed small, cross-disciplinary specialist teams to solve high-friction bottlenecks. They used this framework to build R-to-SAS methodology mappings, streamline code environment sharing with external contractors, and create debugging checklists.
Self-sustaining pharma open source adoption requires closing skills gaps, deepening proficiency, continuous learning, and open source culture. GSK built community forums, expert networks, and mentorship programs designed to meet those criteria after the STAR program ends.
GSK has a mandate already in motion: transition 3,000 biostatisticians and statistical programmers to open source and hit a senior leadership commitment of 70% open-source code across R&D. While the organization had experimented with open source since 2017 and validated foundational GxP tools, scaling that momentum across thousands of users introduced friction. Programmers frequently didn’t know how to translate their deeply ingrained routines into an unfamiliar, multi-package R ecosystem.
Ben Arancibia, Director of Data Science within the Statistical Data Science Innovation Hub, details how GSK confronted this challenge. The organization realized that true adoption isn't about tooling. It’s a workflow problem that requires a high-touch, human response. They launched a three-month experiment centered on a high-touch support group called "Starburst," operating under a strict mandate to never say no to any user request, regardless of its complexity. The team bypassed standard ticketing queues, connecting directly with users via email to mentor them through error screens and build code fluency in real time.
To capture this localized knowledge, the Starburst team published a daily microblog post with every solution, creating an active internal repository that quickly reached over 800 unique viewers. As patterns emerged from these queries, GSK began building internal data products, such as a standardized library of tables, figures, and listings (TFLs). Ben details the scaling arc that followed:
This architecture successfully shifted GSK’s programming practice into a self-sustaining open-source culture before the formal program reached its conclusion.
