SAS Cited as a Leader in Big Data Text Analytics by Independent Research Firm

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Forrester research named SAS a leader in its first Wave report on text analytics platforms

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SAS is a leader in The Forrester Wave™: Big Data Text Analytics Platforms, Q2 2016. With text analytics implementations doubling over the last three years, this new report places SAS among vendors that “lead the pack.”

SAS is one of six leaders in a broad, diverse and complex landscape that includes more than 200 potential players, according to Forrester. The comprehensive Forrester study applied 20 criteria across three categories: current offering, strategy and market presence.

Forrester evaluated SAS® Contextual Analysis alongside nine other products, with SAS holding the highest possible score (5.0) for technical architecture; scalability; industry and business domain expertise; partnership ecosystem; installed base; and global presence.

“Most of SAS’ information management technologies, including SAS Visual Analytics and SAS Data Management, are seamlessly integrated and reuse numerous components,” reports Forrester. “SAS also leverages its expertise in text mining/text analytics to build on its strength as a leader in ‘The Forrester Wave: Big Data Predictive Analytics Solutions, Q2 2015.’”

Text analytics rising
Forrester research in 2012 showed 35 percent of surveyed enterprises were not interested in text analytics. Just 20 percent were using it. Three years later, companies uninterested dropped to 18 percent, while those deploying the technology doubled to 40 percent.

“Interest in text analytics and text mining is soaring,” said Robert Moreira, SAS Senior Analytics Product Marketing Manager. “SAS Contextual Analysis automates some of the more tedious manual processes, so more enterprises can quickly and easily find insight from unstructured data. No matter how big that data.”

With SAS Contextual Analysis, organizations can uncover emerging issues, patterns and trends from unstructured data without knowing up front what the content contains. Combining machine learning with domain-specific rules makes the content extraction more accurate. Eliminating the need to manually create taxonomies and write categorization rules significantly accelerated the process for customers.

SAS’ point-and-click interface guides users through developing the initial taxonomy and defining taxonomy rules from raw text inputs, which streamlines text model building for data analysts.

To learn more about text analytics, please read Text Analytics 101: Improve Decision-Making by Incorporating Unstructured Data, Words and Images into Analytic Processes.

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