Cloud-native is often discussed as a technical concept, but for pharma SCE leaders it has very practical implications. This article explains what cloud-native means in the context of a Statistical Computing Environment, how technologies like containers, Kubernetes, infrastructure as code, and CI/CD reduce manual work, and why they can make change management faster without weakening compliance. It also outlines key questions to ask when evaluating whether an SCE is built for reproducibility, auditability, and future modernization.

A practical guide for pharma leaders deciding whether to build their statistical computing environment (SCE) on-premise or in the cloud. The piece argues that while on-prem still suits stable, predictable workloads and large legacy datasets, modern SCEs are no longer stable workloads — they need elastic compute, multi-language support, and AI/ML-ready hardware that cloud delivers natively. It addresses the security objection head-on (citing Gartner's finding that 99% of cloud security failures are customer misconfigurations, not platform weaknesses), highlights how cloud democratizes access to the same infrastructure powering Novo Nordisk and Eli Lilly's NVIDIA partnerships, and lands on a pragmatic recommendation: build on cloud-native patterns (containers, IaC, automated deployments) regardless of where you deploy today, so a future migration is incremental rather than a rewrite. Hybrid is positioned as the enterprise default, with 90% of organizations expected to adopt it by 2027.

Where R, Python, and SAS each fit in a modern pharma SCE, and why the AI era turns this from a language choice into an architecture decision.

In this article, we'll cover why R package validation is harder than most teams expect, the two main ways pharma organizations go about it today, and what we built at Appsilon to solve it.

Most statistical computing environments in pharma were designed when SAS was the default language, studies were simpler, timelines were longer, and "open source in regulated settings" was still a controversial idea.

If your statistical computing environment was designed before R became a viable language for submissions, before cloud infrastructure had the prominent position it has today, and before "data engineering bottleneck" entered everyone's vocabulary you're probably already feeling the pressure. It's obvious that to overcome some of these challenges, a serious modernization effort needs to ensue. The questions that remain are how, how fast, and whether to build, buy, or partner. To answer those questions, Appsilon interviewed statistical computing leaders at top pharmaceutical companies. We coupled this with internal expertise and released a comprehensive report last year: The Anatomy of Modern Statistical Computing Environments in Pharma.

The conversation around AI readiness typically centers on data quality, talent acquisition, and executive buy-in. While these factors matter, they overlook a more fundamental question: does the organization have the technical and cultural infrastructure to operationalize AI at scale? 

Understand how Cloud-Native and Infrastructure as Code enhance scalability, ensure compliance, and improve efficiency.

Whether you’re an individual just starting out with Shiny or a CTO of a Fortune 500 company, you need your Shiny apps up and running. These five deployment options have you covered.

Posit and Databricks have joined forces to revolutionize data science, explore what this means for the R community, data scientists and data engineers.