According to the report,“Gartner defines advanced analytics as the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) such as query and reporting are unlikely to discover.”
“We believe we offer the broadest set of advanced analytics in the industry,” said SAS Executive Vice President and Chief Marketing Officer Jim Davis. “Customers continue to invest in SAS® to gain a competitive edge, understand their customers, manage risk, detect fraud or achieve a multitude of other goals.”
Recent SAS advanced analytics initiatives seize on two trends among customers: increasing desire to move analytics closer to the data, and rising demand for advanced analytics that doesn’t require trained data scientists. In addressing the former, SAS has moved processing into the distributed big data Hadoop infrastructure for several products, including SAS Visual Analytics, SAS Visual Statistics and SAS In-Memory Statistics for Hadoop. And as demand for data science skills exceeds supply, enterprises seek analytics simple enough for business professionals. With highly graphic, point-and-click interfaces, SAS Visual Analytics and SAS Visual Statistics address this need.
“Our customers prize the wide range of SAS Analytics capabilities, along with SAS’ reliability, domain knowledge, support, scalability and documentation. SAS software thoroughly supports the data-to-decision process. Big data is only useful when advanced analytics is applied to it,” Davis said.
Evolution of analytics
Data-driven enterprises must modernize their analytics infrastructures for additional competitive benefit. SAS supports them with advanced analytics techniques that exploit the power of in-memory processing for faster, more complete and more accurate insight:
• Machine learning is increasingly popular among data scientists. This technology offers automated model building using self-learning, iterative algorithms to enhance predictive power. Since implementing its first machine learning algorithm in the 1980s, SAS has added new, improved techniques yearly.
• Deep learning involves advanced neural networks with many hidden layers. It performs modeling in growing areas such as pattern, image and facial recognition. SAS delivers a comprehensive set of neural network techniques to support deep learning.
• Text analytics explores unstructured data and plays a core role in many big data initiatives. The more complex functions, such as applying natural language processing to extract insight, require the advanced text analytics algorithms that SAS continues to innovate.
• Mass-modeling and management quickly builds thousands of predictive models in an automated process and manages them in a real-time operational environment. This advanced analytics technology makes the most ambitious visions of 1-to-1 marketing a reality. SAS expects to provide this later in 2015.
Please take the five-minute SAS Business Analytics Assessment to pinpoint your organization's analytical strengths and areas for improvement. (sas.com/baaonline)
About the Magic Quadrant
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SAS (sas.com) is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW®.