Recent adopters of SAS Viya analytics products include the American Red Cross; Cisco; Klingel Group, the second-largest traditional online fashion and apparel company in , represented in 12 European countries; and Munich Re, one of the world’s leading reinsurers.
- extracts value from unstructured data using the combined power of natural language processing (NLP), machine learning and linguistic rules. It addresses business challenges across industries, including managing and interpreting notes, assessing risk and fraud, and using customer feedback for early detection of problems. Capabilities of SAS Visual Text Analytics include text mining, contextual extraction, categorization, sentiment analysis and search within a modern and flexible framework. The software allows users to prepare data for analysis, visually explore topics, build text models and deploy them within existing systems or business processes. Users can quickly analyze large volumes of data using predefined templates and integrate the output of text analytics with other machine learning and forecasting techniques.
- The new release of
- offers an end-to-end visual environment that covers all aspects of machine learning and deep learning – from data access and data wrangling to sophisticated model building and deployment. In-memory, distributed processing provides faster answers to critical business questions and more efficient use of valuable data and skilled staff. It also supports programming from popular open source languages like Python and R.
- One of the hallmarks of this release of the SAS Platform is a web interface that unifies the entire analytics life cycle, helping cross-divisional teams collaborate. Now, all activity – from data preparation, to interactive discovery and exploration to model building and deployment – is supported from a single, visual interface. This tightly integrated environment speeds the production of modern machine learning algorithms that can, for example, build more profitable customer relationships, fight fraud and effectively manage risk.
“With SAS combining advanced analytics, model deployment, data preparation and visualization into one platform, users are now able to go through all the stages of the analytics process without the need to switch from software product to product,” said Dan Vesset, Group Vice President for Analytics and Information Management Research at IDC. “After previewing the SAS Platform updates, incorporating the latest statistical, machine learning, deep learning and text analysis algorithms and supporting open source languages, it’s apparent that SAS is going to continue as a leading analytics as well as cognitive/AI platform provider for years to come.”
Munich Re enables more users, expands deep learning capabilities
“As an early adopter, Munich Re has played a key role in helping us shape our new solutions and platform,” said Saurabh Gupta , Director of Analytics Products at SAS. “While working with them, we heard that the new release unifies their analytics infrastructure and enables users of varying skill sets to collaborate to solve the organization’s challenges faster. Customer feedback has always been instrumental in helping SAS release world-class products.”
As a reinsurer with worldwide operations, Munich Re has access to massive amounts of data that are pulled into a centralized environment. Being able to use SAS to run sophisticated machine learning algorithms on big data within a collaborative user interface will allow the company to gain analytic insights to quickly address business challenges and serve clients. Plus, having access to embedded AI capabilities and the latest deep learning algorithms helps the company to stay at the leading edge of what is possible with analytics.
“The newest version of SAS allows all our users to quickly get started and collaborate with a unified and visual interface,” said Wolfgang Hauner , Chief Data Officer at Munich Re. “We like that it allows those who aren’t as familiar with SAS to code in Python and R and run the same actions in the same platform. It is both in tune with the end-to-end needs of an advanced data scientist and is also convenient for beginners. This ability to appeal to data scientists and non-coders will allow multiple users and teams to explore and analyze the same data, making the data discovery and model-building process more collaborative.”
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