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March 1, 2016

Digital, Data Science and Risk Sharing Contracts @ GE

by Ravi Kalakota

We are transforming GE into the world’s premier digital industrial company using our scale and diversity to drive outcomes for customers.   The real opportunity for  change…surpassing the magnitude of the  Consumer  Internet…is the Industrial  Internet.” –  Jeff Immelt,  ex-CEO GE

Recently, a client asked me a thought provoking question – which large firm is executing the most interesting and complex data driven transformation today.  Took me a while to process this question.  The usual familiar list of suspects –, Facebook, Google etc. – ran through my mind.  But one firm – GE – stood out in terms of the boldness of their digital vision and complex multi-year digitization they are executing across various industrial businesses.

Industrial Data has strategic value. Reducing operational risk of jet engines, wind turbines, locomotives, gas turbines, healthcare equipment and oil & gas equipment has economic value.  


GE is taking a data-driven approach to digital transformation (Industrial Internet project).  Specifically, their objective is to drive a “Better Customer Outcomes Using Innovative Data-Driven Apps On a Integrated Platform.”

The business outcomes being targeted include:

  • Asset optimization – optimize performance with minimal downtime
  • Operations optimization – Increased system and people efficiency
  • Process optimization – lower waste (material and cycles)

GE calls this strategy “the power of 1% efficiency improvement”.  The context for this improvement is a world where the machines are not just intelligent but self-aware, predictive, reactive and even social.  The goal is to wring small improvements and the ensuing savings that they can share with customers.

To understand this data-centric optimization strategy better consider the following “What-If” use cases across Aviation, Energy and Transportation.


GE  Aviation Use Case

GE Aviation Goal
  • Improve jet engine efficiency and increase service profitability
  • Selling thrust, not engines
  • 1% fuel savings = 100,000,000 gallons of fuel
  • Store & analyze massive amounts of engine data for analytics


  • Large data sets ingested via batch
  • Store 100s tb of engine data in Hadoop
  • Open doors for industrial engineers to poke at data
  • Fast machine learning based algorithms (2000x faster, 10x cheaper)
  • Customer portals for visibility

GE  Energy Use Case

GE Energy Goal


  • Failing gas turbines causing issues with power generation
  • Unable to store & process fire-hose of data
  • 1% Fuel Effiency in Utilities = $5 Bln annual impact


  • HIGH VELOCITY data ingestion from Gas Turbines
  • Store 10 TB of turbine data in memory
  • VERY LOW LATENCY and HIGH SPEED data access
  • “Predictive” Asset  Maintenance

GE  Transportation Use Case

GE  Transportation Goal
  • Help rail companies manage locomotives better
  • 1% uptime = $1.2Bln in impact
  • Data from tracks, equipment from sensors in locomotives fed into asset management systems


  • Machine learning model built from combined data set (madlib)
  • Real time scoring of rail sensor data (spring xd)
  • Real-time alerting of critical events via email & a real time dashboard (spring)

GE Data Foundation – Predix

  • Every pair of GEnx engines, which are installed on Boeing’s 787 Dreamliner, generate a terabyte of information every day.
  • A single blade in a gas turbine, with sensors on it, can generate 500 gigabytes of data [each day]. That’s just one blade.

When GE began outfitting its machines with a large number of sensors, the company realized it would be generating more data than the company or its clients knew what to do with. Hence the need for a Big Data analytics platform called Predix, built on Pivotal Cloud Foundry that can:

  1. Connect & Collect
  2. Analyze & Predict
  3. Monitor & Manage

GE’s Predix platform helps build, deploy and operate industrial applications.  In addition to data ingestion and aggregation, Predix has capabilities such as natural language technology, artificial intelligence, advanced data visualization and enterprise application integration.


Executing the Data Science Vision @ GE

Machines that talk, machines that react, machines that constantly update their status – vision is clear. It is all about the disruptive power of data.

How do you execute this across the firm? GE has put the following execution framework in place since 2013 across each business unit.


I will be covering more of this DT case study in subsequent postings.

Additional References

For more see Power of 1% Improvement





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