From SAS 9 to SAS Viya: Modernize analytics without starting over

For many organizations, SAS 9 is more than just an analytics platform. It is an integral part of how critical decisions get made every day, from banks managing credit risk and detecting fraud, to insurers analyzing claims and pricing, to health care organizations identifying fraud, waste and abuse, to government agencies improving public programs, and manufacturers using data to improve operations and safety.

SAS 9 is trusted because teams rely on its consistency, accuracy and governance for decisions that matter. It is familiar because users know how to work with it and deeply embedded in the workflows, reports and processes that turn data into insight.

SAS Viya is the next generation of the SAS platform. It helps organizations evolve beyond SAS 9 by modernizing the data and AI life cycle, from data management and analytics to AI development, deployment and operational decisioning. Teams can work faster, collaborate more easily and move analytics into production with less friction. They also have the flexibility to deploy across cloud, hybrid and on-premises environments while keeping the governance and analytical rigor they value from SAS.

The question for many SAS 9 customers is not whether to modernize, but where to begin and how to do it in a way that builds on the SAS programs, data pipelines, reports and user expertise already in place.

Modernize with purpose

Modernizing SAS Viya does not have to be an all-or-nothing decision. It can begin with a specific business need, such as accelerating an AI use case, scaling a high-value analytics workflow, or helping SAS, Python and R users collaborate more easily in one governed environment.

Four areas show where Viya can make that difference: AI assistance that helps people work faster, agentic AI that connects insight to action, open collaboration across SAS, Python and R, and scalability for meeting data and AI demands.

1. Work with AI assistance across the data and AI life cycle

SAS Viya Copilot helps make analytics more approachable and productive by bringing natural language assistance into the way people already work. Users can ask questions, get guidance in context and move from data to insight without relying on every step being manual or highly specialized. For example, Copilot can help users code, explore data, generate explanations, identify next steps and complete common tasks more efficiently.

Figure 1. SAS Viya Copilot supports model pipeline development

That matters for teams trying to do more with limited time and resources. In Viya, Copilot is connected to the data and AI life cycle, so users can work faster, build confidence, and stay within a governed SAS environment instead of moving work into disconnected tools.

2. Move from insight to governed action with agentic AI

AI is evolving from systems that generate answers to systems that can help analyze, decide and act within business workflows. In SAS Viya, agentic AI can connect data, analytics, decisioning and governance so AI-supported actions are transparent, controlled and aligned with business rules.

That can help teams move beyond recommendations to action, such as prioritizing a fraud alert, triggering a next-best offer, routing a service issue or applying a policy decision, while keeping humans in the loop where judgment and accountability matter. For organizations, the value is not simply automation. It is trusted automation, with oversight where it matters.

3. Bring SAS, Python and R teams together

Modern analytics teams rarely use one language or tool. SAS, Python and R users often need to work on the same problems, such as preparing data, testing models, comparing methods or sharing results, even when they start from different tools and workflows.

SAS Viya brings those ways of working together in one governed environment. Teams can use familiar tools, reuse existing SAS assets, collaborate on common data and models, and move work from experimentation to production with more consistency, control and scale.

Figure 2. SAS Viya Workbench supports SAS, Python and R development in a governed environment.

4. Scale confidently as data and AI demands grow

Analytics workloads rarely stay the same. Data volumes are growing; more users need access to insight and AI projects can require significant compute. SAS Viya gives organizations the flexibility to scale analytics as demand changes, instead of being limited by environments that were not designed for today’s data and AI workloads.

With Viya, teams can support more users, run more workloads and choose deployment options that fit their environment, including cloud, hybrid and on-premises. For example, organizations can expand capacity for model training, support seasonal demand spikes and give more teams access to shared analytics resources.

SAS Managed Cloud Services can also help reduce the operational burden of managing the platform, giving teams another way to modernize while focusing more on analytics outcomes than infrastructure management.

What this means for SAS 9 customers

Together, these areas point to a bigger shift. Instead of treating analytics as a set of separate tasks, organizations can manage data, AI and decisions as a connected, governed life cycle. AI assistance, agentic AI, open collaboration and cloud scale support each shift by helping teams work faster, move from insight to action, collaborate more easily and create room for future growth and innovation. The opportunity is to make modernization focused, achievable and tied to business value. Start with the work that matters most, then build from there.

Ready to explore your Viya path?

If your team is already delivering value with SAS 9, Viya can help you build on that foundation. Start with the opportunity that matters most, whether that is faster AI development, smoother collaboration, easier deployment or greater scale. Connect with us to start the conversation.

Additional resources

To go deeper, explore these resources:

SAS Viya Copilot: Learn how natural language assistance can help users work faster across the data and AI life cycle.

Agentic AI: Explore how AI agents can support governed decisions and actions.

SAS open-source integration: See how SAS, Python and R users can work together in one governed environment.

SAS Viya Workbench: On-demand computing power and self-service flexibility for fast SAS, Python or R coding.

Move to SAS Viya webinar series: On-demand sessions showing clear migration pathways, product walk-throughs and demos from SAS Enterprise Guide® and SAS Analytics Pro transitions to data integration, visual analytics and modeling.

SAS Managed Cloud Services: Learn how managed services can reduce operational complexity.

From SAS 9 to SAS Viya: Modernize analytics without starting over was published on SAS Users.