SAS® Event Stream Processing Unleashes Value from Internet of Things Data
Improved customer experience, fraud prevention and cyber defense from streaming analytics applied to fast-moving big data in real time
With few data centers equipped to handle the mass of data generated by the Internet of Things (IoT), many organizations are missing out on the opportunity to transform this data into improved customer experience, asset performance, fraud prevention, cyber defense and more.
To help transform this constant stream of data into tangible value, analytics leader SAS has added more punch to SAS® Event Stream Processing. The latest version empowers organizations to derive fact-based value from fast-moving streaming data thanks to operational transactions, sensors, devices, transmissions, and more – defining continuous queries in an interactive and intuitive interface.
IDC predicts that organizations will accelerate their adoption of technology to continuously analyze streams of events in 2015. The worldwide market for IoT will grow from $1.3 trillion in 2013 to $3.04 trillion in 2020 with a compound annual growth rate of 13 percent.
“Streaming analytics is extremely important in helping organizations identify conditions in real-time that can help them better respond to problems or opportunities. A key problem is the ability to leverage the models and algorithms learned in discovery to the runtime models used in real-time analysis,” said IDC Analyst Maureen Fleming. “Approaches that solve this problem are required to speed transition from reactive to predictive systems.”
SAS Event Stream Processing unites discovered insights with real-time streaming analytics, and continuously assesses streaming data as it is generated. The software transforms and analyzes data in real-time, detecting patterns of interest while data is in motion, before it is stored. The system determines what information should be retained and triggers necessary actions or alerts. And it does so with lightning-fast processing speeds, limited only by hardware restrictions.
For example, a manufacturer is analyzing data streaming from sensors on critical assets to identify sudden changes in the equipment that indicate a high likelihood of failure. The system triggers an alert, sending engineers to investigate and intervene as needed to prevent an expensive stoppage and with enough lead time to schedule maintenance that avoids disrupting operations.
“The constant stream of data from the Internet of Things creates major challenges for companies in industries such as financial services, energy and utilities, retail, and transportation,” said Fiona McNeill, Global Product Marketing Manager for SAS Event Stream Processing. “The genius of SAS Event Stream Processing is its ability to delineate the signals from the noise – based on deep insights from analytics. Users can quickly transform and assess data while it’s in motion, before it’s stored – to determine what actions must be taken – if any at all.”
Whether the incoming data is machine-readable, human-readable or both, SAS Event Stream Processing scales to analyze millions of events per second. Embedded within SAS Event Streaming continuous queries are SAS Data Quality and SAS’ unparalleled analytics. These allow the solution to pinpoint deviations from patterns of interest and existing, real-time conditions. Detected anomalies and patterns trending outside of desired norms can be further investigated by filtering the desired streaming data directly to big data stores like Hadoop. Such data-at-rest analysis then defines any new patterns to monitor, and is loaded into SAS Event Stream Processing for condition monitoring.
Learn more about SAS Event Stream Processing in the free white paper, “Understanding Data Streams in IoT.”
D. Lund et al., Worldwide Internet of Things 2014-2020 Forecast: Forecast Update and Revenue by Technology Split, Nov 2014 IDC #252330
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