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ScaleOut Software 2017 Predictions: Operational Intelligence Goes Mainstream with New Focus on Live Systems

VMblog Predictions 2017

Virtualization and Cloud executives share their predictions for 2017.  Read them in this 9th annual series exclusive.

Contributed by Dr. William L. Bain, CEO and founder, and Chris Villinger, vice president, business development and marketing, ScaleOut Software

Operational Intelligence Goes Mainstream with New Focus on Live Systems

Traditional business intelligence alone can no longer keep pace in today's data-driven market. Companies also need real-time analysis of their data streams to capture perishable business opportunities and make mission-critical decisions in industries like manufacturing, e-commerce and finance. In 2017, we foresee the following shifts as companies discover competitive advantages through gaining real-time insight into their data:   

In 2017, the need for "operational" intelligence to capture highly dynamic business opportunities will shift the focus of big data from the data warehouse to live systems. 

For the last several years, the big data revolution has popularized Hadoop and other technologies that capture business intelligence in the data warehouse. While there continues to be a place for business intelligence to perform "after-the-fact" analysis of historic data and inform strategic decision making, businesses also need to analyze live streams of fast-changing data in order to generate immediate feedback that boosts ROI. We call this "operational intelligence," and it picks up where business intelligence leaves off. The need for operational intelligence to maximize competitiveness will drive its adoption in a wide range of industries, including e-commerce, finance, manufacturing,  patient-monitoring, transportation, and utilities. In 2017, we expect to see widespread integration of this exciting capability into live systems.

In 2017, in-memory computing will enter the mainstream as the enabling technology for adding operational intelligence to live systems, and it will supplant legacy streaming technologies. 

In 2017, the adoption of in-memory computing technologies, such as in-memory data grids (IMDGs), will provide the enabling technology to capture perishable opportunities and make mission-critical decisions on live data. Driven by the need for real-time analytics, the IMDG market alone - currently estimated at $600 million - will exceed $1 billion by 2018, according to Gartner

Unlike big data technologies, such as Spark, created for the data warehouse and legacy streaming technologies, in-memory computing enables the straightforward modeling and tracking of a live system by analyzing and correlating persistent data with live fast-changing data in real time, and it provides immediate feedback to that system for automated decision making. Gartner has recently elevated the term "digital twin" in its recent Top 10 strategic technology trends for 2017 to describe the shift in focus from data streams to the data sources which produce those streams. In-memory computing technology enables applications to easily create and manage digital representations of real-world devices, such as Industrial Internet of Things (IIoT) sensors and actuators, and this enables real-time introspection for operational intelligence.

In-memory computing techniques will leverage the power of machine learning to enhance the value of operational intelligence. 

The year 2017 will see an accelerated adoption of scenarios that integrate machine learning with the power of in-memory computing, especially in e-commerce systems and the Internet of Things (IoT). E-commerce applications benefit by offering highly personalized experiences created by tracking and analyzing dynamic shopping behavior. IoT applications, such as those associated with windmills and solar arrays, benefit by delivering predictive feedback based on rapidly emerging patterns. In both of these applications, machine learning techniques can dramatically deepen the introspection and enhance operational intelligence.

Once only practical only on supercomputers, machine learning techniques have evolved to become increasingly available on standard, commodity hardware. This enables IMDGs to apply them to the analysis of fast changing data and specifically to dynamic digital models of live systems. The ability of IMDGs to perform iterative computation in real-time and at extreme scale enables machine learning techniques to be easily integrated into stream processing which provides operational intelligence.


About the Authors

Dr. William L. Bain, CEO and founder, ScaleOut Software

ScaleOut Software was founded in 2003 by Dr. William L. Bain. Bill has a Ph.D. (1978) in electrical engineering/parallel computing from Rice University, and he has worked at Bell Labs research, Intel, and Microsoft. Bill founded and ran three start-up companies prior to joining Microsoft. In the most recent company (Valence Research), he developed a distributed Web load-balancing software solution that was acquired by Microsoft and is now called Network Load Balancing within the Windows Server operating system. Dr. Bain holds several patents in computer architecture and distributed computing. As a member of the screening committee for the Seattle-based Alliance of Angels, Dr. Bain is actively involved in entrepreneurship and the angel community.

William Bain 

Chris Villinger, vice president, business development and marketing, ScaleOut Software

Chris has over 18 years' experience at global, high-tech multinationals, including Microsoft and Philips, in software marketing, marketing technology, content management, global digital marketing and commercial sales networks, systems integration consulting, and business planning and forecasting. Chris has a multicultural US and European background with fluency in four major languages. He holds a BSEE degree from Tulane University and a Bilingual International MBA from the IESE Business School of the University of Navarra in Barcelona Spain.

Chris Villinger 

Published Monday, December 19, 2016 9:05 AM by David Marshall
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