SAS has updated its data management capabilities as enterprises struggle to scale artificial intelligence beyond pilot projects, citing fragmented data environments as being a major barrier to AI adoption.
Announced at SAS Innovate in Dallas, the refreshed SAS Viya data management portfolio is designed to help organisations prepare, govern and activate data for analytics, automation and AI – embedding lineage, transparency and governance directly into data workflows.
It comes as SAS Viya continues to be heavily used by marketing teams to drive personalised customer experiences, optimise campaign performance, and deploy AI-driven decisioning at scale.
“A modern data platform is now a mission-critical requirement as organisations move toward agentic AI workflows with less human oversight,” said Alyssa Farrell, senior director of data specificallyand AI strategy at SAS.
“SAS is redefining data management for the AI era by helping organisations optimise modern data estates, reduce complexity and unlock AI value, with governance and trust engineered directly into the foundation.”
Rather than requiring organisations to move or duplicate data across systems, SAS is also promoting a “bring AI to the data” approach, allowing analytics and AI models to run directly where data already resides. The company says this reduces latency, lowers cost and improves governance by limiting unnecessary data movement.
SAS highlighted tools including high-performance in-place analytics and cloud-native acceleration capabilities designed to help enterprises run AI closer to their data sources, while maintaining auditability and control.
The company is also expanding support for AI agents and copilots that operate within governed data workflows, including tools for natural language data discovery, code assistance and synthetic data generation.
For marketing teams, the impact is big.
Modern marketing systems rely heavily on large, distributed datasets to power customer segmentation, personalisation and campaign optimisation. Improvements in data quality, governance and lineage can translate into more accurate targeting, more reliable AI-driven recommendations and stronger measurement of campaign performance.
Industry research cited by SAS suggests that poor data environments remain one of the biggest blockers to AI success, with analysts warning that a majority of AI initiatives fail due to insufficient AI-ready data and governance gaps.
SAS said the updated platform is intended to address that gap by treating governance not as a compliance layer, but as a built-in part of how data is accessed, prepared and used across enterprise AI systems.
