SAS Named a Leader in 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

SAS Named a Leader in 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

SAS is recognized as a Leader in 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. This is the eighth consecutive year for SAS to be recognized as a Leader in this Magic Quadrant.

“Across the globe, organizations are aspiring to operationalize analytics and accelerate artificial intelligence (AI) adoption to facilitate better, more intelligent business decisions,” said Susan Kahler, AI Strategist at SAS. “SAS advanced analytic solutions, which offer innate integrations with popular open source tools, give users at all levels of an organization access to agile, powerful advanced machine learning to help master the complex data that’s in cloud environments.”

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The 2021 Gartner report evaluated SAS for its completeness of vision and ability to execute. SAS believes this recognition can be attributed to SAS Visual Data Mining and Machine Learning, a comprehensive visual and programming interface running on the SAS® Viya® engine. SAS Visual Data Mining and Machine Learning supports the end-to-end data mining and machine learning processes for team members of all skill levels to handle all tasks in the analytics life cycle in a simple, powerful and automated way.

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According to the Gartner report: “To achieve fully mature, advanced analytic capabilities, organizations must plan for and invest in the end-to-end data science life cycle. The life cycle includes processes for accessing and transforming data, conducting analysis and building analytic models, operationalizing and embedding models, managing and monitoring models over time to reassess their relevancy, and adjusting models to reflect changes in the data and business environment.”

The latest release of the SAS Viya platform debuted in February and is designed to be delivered and updated continuously. The cloud-native software enables customers to efficiently democratize analytics throughout their organizations, while seamlessly managing analytic workloads and building SAS into a variety of applications.

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