Premier Analytics Consulting at the Pharmaceutical Software User Group (PharmaSUG) 2026 U.S. Conference

Data scientists, statistical programmers, machine learning engineers, and researchers are increasingly leveraging a growing number of open-source tools, libraries, and programming languages that can enhance and seamlessly integrate with their existing data workflows. One of these integrations, built into SAS® Viya®, is its pre-configured Python runtime integration, PROC PYTHON, that gives SAS programmers access to Python's open-source data science libraries for wrangling and modeling structured and unstructured data alongside the validated procedures provided in SAS. This paper demonstrates how to install and import external Python libraries into their SAS Viya sessions; generate Python scripts containing methods that can import, process, visualize, and analyze data; and execute those Python methods and scripts using SAS Viya's PYTHON procedure. By integrating the added functionalities of Python's libraries for data processing and modeling with SAS procedures, SAS programmers can enhance their existing data workflows with Python's open-source data solutions.

Generative AI is rapidly reshaping how organizations search, reason over, and contextualize information, yet deploying these systems on private, confidential, and sensitive data introduces distinct architectural, performance, and governance challenges. This paper presents a practical roadmap for the 2026 enterprise generative AI stack, with emphasis on agentic AI architectures built on retrieval-augmented generation (RAG), vector embeddings, and secure model deployment. Core concepts including encoding, similarity search, and vector databases are introduced to explain how knowledge is stored, retrieved, and reused across AI agents, alongside API-driven patterns that enable tools for search, reasoning, and contextualization. The discussion contrasts proprietary large language models (LLMs) and open-source small language models (SLMs), highlighting trade-offs in output quality, performance, and deployment, while examining how ecosystems such as Hugging Face support localized inference and domain-specific adaptation. Implementation considerations are presented primarily in Python, with extensions to R and SAS® Viya®, focusing on secure, reproducible analytics workflows. This paper concludes with prompt engineering strategies and ethical considerations for responsible generative AI use on sensitive enterprise data in the life sciences, pharmaceutical, and healthcare domains.

This paper presents a practical framework for building reliable and collaborative data science workflows using Git, GitHub, and Data Version Control (DVC). It begins by explaining the importance of version control in data science and introduces Git as the foundation for tracking code changes. GitHub is then presented as a collaboration layer for shared repositories and team-based development. DVC is introduced as an extension to version control that enables efficient tracking and versioning of datasets alongside code. The paper presents practical examples demonstrating structured branching strategies, meaningful commit practices, managing work-in-progress (WIP) safely, and reducing merge conflicts in team environments. DVC workflows are demonstrated to show how dataset changes can be tracked, compared, and restored without storing large files directly in Git repositories. Together, these tools provide a structured and reproducible workflow for collaborative data science, machine learning, and analytics projects. The paper concludes with industry applications in regulated environments where reproducibility, auditability, and collaboration are essential.

Organizations worldwide are undergoing a fundamental shift in how software is developed, deployed, and sustained. The rapid emergence of new technologies, combined with the accelerating adoption of open-source solutions – has created an ecosystem where innovation thrives alongside increased complexity. As diverse tools, platforms, and communities converge, organizations must balance opportunity with risk. This presentation examines the benefits, challenges, and emerging opportunities of open-source technologies in the 21st century. Participants will explore how organizations and user communities are addressing critical considerations, including system integration and compatibility, security vulnerabilities, intellectual property concerns, licensing and warranties, and the variability of development and support practices. Attendees will gain practical insight into the evolving roles of Python, R, SQL, modern database platforms, cloud computing, and open software standards, as well as the collaborative culture that fuels global open-source innovation. Join us for a forward-looking discussion on how open-source software is reshaping technology strategy, collaboration models, and innovation across industries.