SAS has announced updates to its data analytics platform, highlighted by enhancements to its machine learning, computer vision, and natural language processing capabilities. The updates, which were unveiled at the SAS Global Forum in Dallas, bring new analytic model building and data preparation functionality to the SAS Platform. The company is also adding automation for model deployment and retraining tasks to the product.
SAS is a notable player in enterprise BI and analytics software. The company’s advanced and predictive analytic technologies, which include forecasting, text analytics, and decision trees, are excellent. Data scientists can extend these capabilities using the integrated Visual Statistics tool. SAS caters to a wide range of verticals, and users particularly enjoy ease of use in accessing Hadoop and NoSQL data.
The platform now includes a new project insight area that offers a high-level narrative summary that explains what and how an analysis was performed (for non-technical users). This is supposed to provide the user with increased trust in SAS’s AI. Users can also automatically generate explanations of analytics results in layman’s terms. Explanations are there to assist analysts in understanding analytic results and encourage real-world use of AI and advanced analytics.
New open APIs enable users to access data and create custom web applications to help technical personas leverage machine learning, natural language processing and other SAS AI capabilities, even those without a background in code or statistics. There’s also more content embedded in the SAS portfolio, allowing users to access information from their mobile device and input data from text, documents or photos. Real-time data can also be used to update analytical models and run risk assessments based on new information.
These SAS Analytics developments are part of the company’s recently announced $1 billion investment in AI to drive the technology on a global scale. Over the next three years, SAS will build on its foundation in AI, as well as invest in education programs to equip data and analytics leaders with new skills and technology.