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11 market trends in advanced analytics

Thor Olavsrud | July 9, 2014
In today's business environment, organizations are increasingly demanding advanced analytics that allow them to use large volumes and diverse types of data to discover patterns and anomalies and predict outcomes.

"For example, SAP is focusing on automating the predictive process, while Angoss offers a very visual interface for decision and strategy trees," Kaufman and Kirsch write. "SAS and IBM have released specific offerings aimed at business users. For example, SAS' Visual Analytics offering and IBM's Analytics Catalyst are both aimed at business users."

6. Real-Time Data Streams and the Internet of Things Are Hot

The demand for analytics on real-time data streams is increasing rapidly as more and more devices connect to the Internet. By applying advanced analytics to streaming data, organizations can respond with much greater agility, whether that means providing personalized recommendations as you shop online, or monitoring a jet engine's key metrics to identify signs of failure before maintenance crews notice.

"Traditionally, the airline would rely on manually set thresholds and visual inspections," Kaufman and Kirsch say. "These thresholds might send an alert if the engine overheated, but will be unable to identify potential problems that result from the occurrence of several normally innocuous factors that when combined are problematic. Vendors are responding to the need to provide analytics on real-time data. SAS' Event Stream Processing Engine and IBM's InfoSphere Streams allow users to run analytics while data is in motion."

7. Data Visualization Is Becoming a Business Requirement

Data visualization is taking on an even more important role within organizations as they become flooded with streaming data, social media data, machine data and other large volumes of structured, semi-structured and unstructured data. Visualizations are necessary to help analysts uncover insights that would simply be impossible to spot in a vortex of data tables, spreadsheets and charts.

"Visualization might be the primary interface for the business users and might be a first step for the data scientist," Kaufman and Kirsch say. "To help bridge the gap between business users and data scientists, vendors are offering more visualization capabilities. Visualization capabilities can be customized for different user groups so that they can easily understand them. Some vendors are offering complex visualization products. For example, SAS has an in-memory based interactive visualization tool, SAS Visual Analytics. IBM's Rapidly Adaptive Visualization Engine (RAVE) is built into SPSS Analytic Catalyst and gives users suggestions for visualizations based on the data set. Other vendors, such as Megaputer, RapidMiner and StatSoft rely on visualization capabilities that are built into the core offering."

8. Organizations Are Infusing Big Data Analytics into All Decision-Making Activities

It is no longer enough for analytics to be managed solely through a statistics or data analysis department. Organizations want to make analytics part of the decision-making process across all areas, including marketing, sales, operations, finance and human resources.

"In order to improve customer engagement and optimize outcomes across all these functional areas, companies want to include more varieties of data in their analysis," Kaufman and Kirsch say. "For example, data types ranging from machine-generated and other sensor data to mobile and financial data feeds and social media data are typically included in big data analysis. These companies are looking to their vendors to support very large data sets."


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