SAS has introduced a major refresh of its Data Management portfolio, placing a strong emphasis on preparing and governing data for analytics, automation, and artificial intelligence. Notably, the updated portfolio operates as a cloud-native solution built on the SAS Viya platform, enabling businesses to streamline how they manage and utilize data across environments.

As organizations increasingly push to transition AI initiatives from experimental stages into real-world deployment, they face persistent challenges such as fragmented data ecosystems, labor-intensive engineering processes, and governance frameworks that fail to scale efficiently. Consequently, SAS highlights that weak data foundations continue to delay AI adoption and elevate operational risks, even as AI tools become more accessible.

Moreover, research conducted in collaboration with IDC reinforces this concern. The study reveals that 49% of organizations identify poorly optimized or non-centralized cloud data environments as a primary barrier to AI progress. In addition, 44% cite inadequate governance processes as a critical limitation. Supporting this trend, forecasts from Gartner predict that 60% of AI initiatives could fail due to the absence of AI-ready data.

At the core of this portfolio refresh lies a shift toward embedding governance directly into everyday data workflows. Instead of treating governance as a separate compliance layer, SAS integrates lineage tracking, transparency, and control mechanisms into how data is accessed, prepared, and analyzed. As a result, organizations gain improved audit trails and stronger oversight capabilities.

Furthermore, SAS aims to reduce unnecessary data movement, a common issue that increases costs and complicates governance. By enabling analytics to run closer to where data resides, the company minimizes duplication and enhances efficiency. For instance, SAS SpeedyStore allows analytics and AI workloads to operate alongside distributed data while preserving auditability. Similarly, SAS Data Accelerator enables analytics to run within existing cloud data warehouses and lakehouse systems, eliminating the need for data replication.

In addition, the platform supports embedded analytics engines such as DuckDB, allowing users to analyze formats like Parquet, CSV, and JSON within governed workflows.

Another significant aspect of the update focuses on pre-deployment data processes. SAS introduces AI-driven agents and copilots designed to assist users throughout the data lifecycle. These tools help reduce manual effort while maintaining transparency and human oversight. For example, SAS Viya Copilot for Data Discovery leverages natural language prompts to help users explore and validate governed data assets. Likewise, SAS Viya Copilot for Code Assistance supports developers by simplifying SAS and Python coding tasks within a governed environment.

Additionally, SAS Data Maker enables teams to generate synthetic datasets that replicate real-world characteristics without exposing sensitive information, thereby supporting secure development and testing.

“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 and 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,” Farrell said.

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