Enhancing the Value of Machine Data with the Fourth V: Visualization

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Here are some additional examples of how GE is applying disciplined analytics to data many of its machines have been producing for years:

  • GE is a major manufacturer of jet engines, turbines and medical scanners. It is using operational data from sensors on its machinery and engines for pattern analysis.
  • GE-developed software now directs a Canadian electricity supplier in pruning trees cost-effectively along its electricity distribution lines. Strategic pruning can prevent vegetation from falling on power lines, the biggest cause of electricity outages.
  • GE’s wind farm division already claims a 2%-5% efficiency improvement by adjusting the turbine blades in real time to account for changes in the wind patterns.  Real-time blade adjustments, particularly to turbines in front, reduce vibrations that cause stress fractures, especially in turbines toward the rear.

What many of the innovators in this new M2M space, including GE, found as they become more rigorous with their monitoring is that traditional integration and BI technologies couldn’t adequately deliver the real-time visual analytics solution they require.

Anatomy of a Real-Time Visual Analytics Solution for M2M

If you’re shopping for a platform for real-time M2M analytics, you should be sure your solution includes:

  • Connected products, sensor monitoring or other M2M applications
  • Real-time visual exploration of the big data that M2M produces, without ETL delays
  • Analytic snapshots for instant visual event trending
  • Insights from Key Performance Indicators users specify
  • Apps that visualize live and historical enterprise data mashed with M2M data
  • Dashboards that business users can easily assemble quickly and share

Machine-generated sensor data is the poster child for big data and its three “Vs”: Volume, Velocity and Variety. But what M2M data requires is a fourth “V” (Visualization) to convert its big data into value by giving users the ability to identify data patterns through real-time analytics. Visual dashboards combining real-time sensor data with other enterprise data result in a 360-degree view, not only of the complete business operations but also the impact on their customers. Innovative companies like GE connect machine data with business system data, mashing these together for fast integration.  And it’s only by using robust application programming interfaces that allow organizations to analyze data and react in real time that M2M gets converted into profitable efficiency gains.


About the author:

As CTO of JackBe, John Crupi is entrusted with understanding market forces and the business drivers that impact JackBe’s product innovation and customer success. Crupi has over 20 years of experience in OO and enterprise distributed computing. Prior to JackBe, he was with Sun Microsystems, serving as a Distinguished Engineer and CTO for Sun's Enterprise Web Services Practice. He is co-author of the highly popular Core J2EE Patterns book, his articles have been published in a variety of technology publications and he is a well-known blogger and spea

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