Is your R and Python data science work reaching the people who need it?

This ebook covers how organizations across pharma, healthcare, financial services, and insurance used Posit Connect to get their R and Python into the hands of decision makers safely and at scale while still using their preferred open source tools and frameworks.

Data science teams build diverse content: dashboards and reports in R, Python apps, even agentic AI apps. As the scale grows, the same question always pops up. How to keep open source flexibility while also managing their content so it stays current, secure, and provides value? Posit Connect answers both. It gives teams the flexibility to work in their preferred languages while providing IT the governance and ease of management they need to scale.

This ebook explores what’s needed to ensure data scientists have the tools they need, IT the governance it requires, and business leaders the access and insights to scale value from their analytics work.

What you'll learn

How practitioners stay in control of their work. When a model needs updating or access needs to change, who handles it? Connect gives practitioners visibility and control at the content level: usage analytics, access management, and iterative updates, without routing requests through IT.
What enterprise governance looks like in practice. Platform owners need more than a secure repository. They need SSO, RBAC, audit trails, and a self-managed environment that meets compliance requirements. The ebook covers what that infrastructure looks like and how it coexists with practitioner flexibility rather than restricting it.
How organizations across industries have scaled. Regeneron reduced a three-week mentorship matching process in three days. NMDP scores 42 million stem cell donors daily to help physicians identify the best match for patients who need a transplant. Gen Re-cut per-submission review from 30 minutes to 5, across 1,500 broker submissions a day. Each story is grounded in the specific deployment challenges the team faced and what changed when they solved them.
The path from first deployment to organizational capability. Most data science hubs don't start with a finished platform. They start with a specific use case. The ebook follows that progression: from getting critical work into production reliably, to reaching broader audiences, to building a central hub where analytical work is published, discovered, and iterated across the organization.

If your team builds in R or Python but struggles to get that work into the right hands, this ebook covers how other organizations solved that problem.