Singapore — SAS has announced an update to its SAS Data Management portfolio, introducing new and expanded capabilities aimed at helping organisations prepare, govern and activate data for analytics, automation and artificial intelligence (AI).
The cloud-native portfolio, built on the SAS Viya data and AI platform, is designed to address challenges around fragmented data environments, manual data engineering and governance as organisations scale AI initiatives.
According to SAS, the updated portfolio incorporates governance, lineage, and performance capabilities into data workflows, with a focus on supporting trusted AI deployments.
“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,” she continued.
The company cited recent research from IDC and SAS, which found that 49% of organisations identified non-centralised or poorly optimised cloud data environments as their top barrier to AI progress. Furthermore, 44% pointed to insufficient data governance processes.
SAS also referenced Gartner‘s prediction that 60% of AI initiatives will fail because of a lack of AI-ready data.
The updated Data Management offering is centred on several areas, including AI-ready data management, governance, agentic AI and copilots, and cloud-native analytics acceleration.
Bringing analytics closer to data
SAS said its approach is intended to reduce the need to move data between platforms when deploying analytics and AI. The company argues that keeping analytics capabilities closer to where data resides can reduce latency and costs while helping organisations maintain governance and auditability.
One component is SAS SpeedyStore, a cloud-native analytical data platform integrated with SAS Viya. The platform is designed to run analytics and AI alongside distributed data while reducing unnecessary data movement.
SAS Data Accelerator extends this approach to external cloud data environments, allowing SAS analytics to run within data warehouses and lakehouse architectures. SAS Viya also supports embedded analytics engines such as DuckDB for local analysis of open formats including Parquet, CSV and JSON within governed workflows.
AI assistance for data management
The company is also expanding its use of agents and copilots across the data life cycle. SAS said these capabilities are intended to help organisations understand, prepare, and use data before it is applied to analytics, automation or AI applications.
SAS Viya Copilot for Data Discovery enables users to explore governed data and analytics assets using natural language, while SAS Viya Copilot for Code Assistance provides AI-assisted development within SAS Studio for SAS and Python code.
SAS Data Maker, meanwhile, generates synthetic data designed to reflect statistical, relational and temporal characteristics of real datasets. The company said the capability is intended to support development, testing and collaboration while reducing exposure of sensitive information.
SAS said the broader portfolio is designed to help organisations scale AI initiatives while retaining governance, transparency and human oversight.
