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June 25, 2013


Power of 1% Improvement – ROI & Use Cases for Industrial Big Data

by Ravi Kalakota

RiseOfTheIndustrialInternetJust like Business to Consumer (B2C)  e-commerce and Business to Business (B2B) e-commerce…we are seeing a similar distinction emerge in Big Data.

The “real meat and potatoes” use cases behind big data actual adoption might be around B2B machine data management and Industrial analytics enabled by wireless, battery-free sensor platforms.

While social, consumer, retail and mobile big data get a lot of PR, the big data business cases around industrial machine data analytics or “things that spin” actually make economic sense.  These projects tend to show tangible Return on Investment (ROI).

The concept of Internet-connected machines that collect telemetry data and communicate, often called the “Internet of Things or M2M” has been marketed for several years:

–       I.B.M. has its “Smarter Planet” initiative

–       Cisco has its “Internet of Everything” initiative

–       GE has its “Industrial Internet” initiative.

– has its “Internet of Customers” theme

To compete with GE….Hitachi, United Technologies, Siemens, Bosch, Schneider Electric, Phillips and other industrial giants are all getting on the band-wagon as the vision of M2M is now viable with advances in microelectronics, wireless communications, and microfabricated (MEMS) sensing enabling platforms of rapidly diminishing size.

The Bosch Group has embarked on a series of initiatives across business units that make use of data and analytics to provide so-called intelligent customer offerings. These include intelligent fleet management, intelligent vehicle-charging infrastructures, intelligent energy management, intelligent security video analysis, and many more. To identify and develop these innovative services, Bosch created a Software Innovations group that focuses heavily on big data, analytics, and the “Internet of Things.”

Similarly, the Schneider Electric focuses primarily on energy management, including energy optimization, smart-grid management, and building automation. Its Advanced Distribution Management System, for example, handles energy distribution in utility companies. ADMS monitors and controls network devices, manages service outages, and dispatches crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use analytics to monitor the network.

Industrial Internet – making smart use of sensors, networked machines and data analytics – is the big vision, but the business driver is in no unplanned downtime for customers.

Industrial Analytics

The industrial B2B value chain is shown below.

Industrial Value Chain

The focus of industrial firms which produce power plant turbines, rail locomotives, aircraft engines, efficient power distribution, medical imaging equipment, medical diagnosis, smart energy management, sensor rich home automation etc. is different than the Consumer Big Data.

The business case is centered on boosting efficiency gains and productivity improvements rather than a new innovation or experience.  However the underlying ingredients are the same — tools from data science,  machine learning, visualization and management of vast streams of new floods of data from smaller, more powerful and cheaper sensors.

Caterpillar, Komatsu and other equipment giants are also pushing the machine data use cases. Take for instance John Deere, which uses sensors + GPS to assist farmers in managing their agri fleet. The goal is to decrease downtime of their tractors, combines as well as save on fuel. The information is combined with historical and real-time data regarding weather prediction, soil conditions, crop features and many other data sets. The information is presented in the platform as well as on the iPad and iPhone app Mobile Farm Manager in order to helper farmers figure out which crops to plant where and when, when and where to plough, where the best return will be made with the crops and even which path to follow.  The target goal – higher production and revenue for farmers.

The fundamentals behind these initiatives and projects are pretty much the same.   If the analytics around machine data achieves just a 1% efficiency improvement in cost reduction or efficiency gain then the business case for Machine or Sensor Data capture, harvesting and analytics is truly impressive.

Here are a few ROI examples from a 1% improvement in productivity across different industries:

  • Commercial aviation industry — a 1% improvement in fuel savings would yield a savings of $30 billion over 15 years.
  • Utilities — In global gas-fired power plant fleet a 1% improvement could yield a $66 billion savings in fuel consumption.
  • Global health care industry —  A 1% efficiency gain from reduction of process inefficiencies globally could yield more than $63 billion in health care savings.
  • Railway Networks — Freight moved across the world rail networks, if improved by 1% could yield another gain of $27 billion in fuel savings.
  • Upstream Oil and Gas Exploration – a 1% improvement in capital utilization upstream oil and gas exploration and development could total $90 billion in avoided or deferred capital expenditures.

The convergence of intelligent devices, intelligent networks and intelligent decisioning (Insight vs. Hindsight analytics) is definitely paving the foundation for the next growth spurt or productivity gains.   Here is a great graphic from GE that I found interesting.



1% Machine Data Analytics Use Cases

So what are some use cases that are pertinent to Machine Data Analytics that will enable the 1% improvement?

TurbinesAsset management and predictive maintenance is a universal use case.  Machines (such as Wind Turbines) tend to fail eventually so optimal, low-cost, machine maintenance across fleets facilitated by intelligent systems is a standard use case.  An aggregate view across machines, components and individual parts provides a line of sight on the status of these devices and enables the optimal number of parts to be delivered at the right time to the correct location. This minimizes parts inventory requirements and maintenance costs, and provides higher levels of machine reliability. Intelligent system maintenance optimization can be combined with network learning and predictive analytics to allow engineers to implement preventive maintenance programs that have the potential to lift machine reliability rates to unprecedented levels.

Supply Chain optimization. To illustrate the supply chain and logistics challenge being tackled with sensors and machine data, consider the perishables supply chain like bananas. Bananas are a popular item in any grocery store—nearly everybody buys them—and both the price and the ripeness must be just right. To ensure that no bunch is too green or too brown, bananas are picked and packed so they ripen in transit.  Goal for every shipment is to arrive at the store delicious and ready to buy. But this creates a logistics nightmare—timing is everything, and being too early is as bad as being too late.  When bananas arrive in bad condition the whole truckload has to be rejected and disposed safely.   

So how does the instrumentation actually work? Temperature sensors could be attached or embedded inside the package.   Such sensors must be small  and inexpensive to the point of being disposable. The temperature can be monitored via readers or a storage capacitor (roughly the size of a pea) can be attached to the RFID sensor to log data for up to a day when out of range of a reader.

Health Care Network Optimization: The operation of interconnected machines within a system can be coordinated to achieve operational efficiencies at the network level. For example, in health care, assets can be linked to help doctors and nurses route patients to the right care stations more quickly. Information can then be seamlessly transmitted to care providers and patients resulting in shorter wait times, higher equipment utilization, and better quality care.

Transportation Optimization. Intelligent systems are also well suited for route optimization within transportation networks or delivery networks like FedEx or UPS. Interconnected vehicles will know their own location and destination, but also can be alerted to the location and destination of other vehicles in the system— allowing optimization of routing to find the most efficient system-level solution.

The following figure from GE Aviation illustrates the data flow.  The scale is staggering… GE Aviation captures Engine Sensor Snapshot data – captured during takeoff, climb & cruise from ~ 34,000 engines in field, every 2 seconds, an aircraft with GE engine technology is taking off  somewhere in the world. An average of 1000 engine  parameters are captured. 


All these use cases – store, manage and deliver value from fast, massive data sets – tend to have a similar data collection, harvesting and analytics structure. See also complementary blog post – Big Data Use Cases


Industrial Analytics Project Phases

BigDataAdoptionStagesThere are four main stages of any industrial analytics project   –  Educate,  Explore, Engage and Execute.

  • In the Educate stage, the primary focus is on awareness and knowledge development.
  • In Explore stage the focus is on developing an organization’s roadmap for big data development.
  • In the Engage stage, organizations begin to prove the business value of big data, as well as perform an assessment of their
  • technologies and skills.
  • In the Execute stage, big data and analytics capabilities are more widely operationalized and implemented within the organization.

However, getting the solution architecture via throw-away pilots is a major first step.

GE Industrial Analytics

  • $2B initiative in software, analytics, and  “Industrial Internet”
  • Offering new industrial data platforms and brands like “Predictivity” and “Predix”
  • Primary focus on data-based products and services from “things that spin”
  • Will reshape service agreements for locomotives, jet engines, turbines
  • Gas blade monitoring in turbines produces 600  gigabytes/day—7 times Twitter daily volume

Basic Architecture of What GE Industrial Internet is building off


Classic data warehouses and batch analytics is not viable for the high frequency and high velocity industrial internet.

An infrastructure that scales between always-on, real-time and near real-time, in-memory analysis and machine-learning, batch analytics at different locations from machine to cloud – private, public, or hybrid – is a necessary foundation.

Innovative strategies require regular collection and aggregation of high-quality data.

GE has recently invested$105 M in new spinout from VMware and EMC named Pivotal.  Pivotal One is a  Enterprise Platform-as-a-Service (PaaS) that makes it possible to create consumer-grade big data or analytics applications.

Pivotal PaaS is attempting to provide a  data fabric that is a convergence of vFabric, Channel Partnerships, Hybrid Storage Strategies, and Appliance Manufacturing and Distribution, and vSphere/vCloud Optimization.



The Internet of Things (or Industrial Internet) and the rise of a machine-to-machine (M2M) ecosystem have been long anticipated. As this ecosystem converges with cloud computing and big data, it is creating several opportunities but also creating challenges. The amount of data generated by connected devices in the Internet of Things is orders of magnitude larger than anything IT has ever seen. Managing this volume of structured and unstructured data and extracting value from it is difficult—but the rewards can be huge.

It is important to stay focused on the ROI and business value.  In addition to the 1% improvement target, the ultimate target is to make it easier for millions of hyper-connected customers to engage, transact, converse and interact (see figure below).


Big data in industrial setting is going to be where some significant innovation is taking place. The full power of the Industrial Internet is clearly being realized with a third element—data and analytics.   Intelligent decisions occur when data  from devices and systems is analyzed to facilitate data-driven learning, which in turn enables machine and network-level operational functions to be automated.  Intelligent feedback control systems.

According to GE research below…the economic impact of this is pretty significant and sizable.


Notes and References

  • Checkout MIT’s Institute for Soldier Nanotechnologies, for some amazing trends in the fusion of RFID and sensors.  Also checkout  for more use cases.
  • GE seems to be ahead of everyone else in executing the vision. GE is making a push around the “Industrial Internet” — bringing digital intelligence to the physical world.  It created a new software and analytics division staffed with a 1000+ work force of computer scientists and software developers, investment of $2 billion by 2015.
  • John Deere uses the open source programming language R for data analysis. R helps John Deere to forecast demand correctly, to forecast crop yielding, to determine land area and usage as well as help John Deere forecast the demand for (spare) parts of the combines. They use Open Database Connectivity to port the multiple data sources and data types. R is then used to export this data into different channels.
  • GE estimates that technical innovations of the Industrial Internet could find direct application in sectors accounting for more than $32.3 trillion in economic activity. As the global economy grows, the potential application of the Industrial Internet will expand as well. By 2025 it could be applicable to $82 trillion of output or approximately one half of the global economy.
  • Also check out a complementary blog post…  Splunk and Machine Data Analytics
  • GE is taking a 10 percent stake and investing $105 million in Pivotal, the spin-out from EMC and VMware.  GE has also partnered with Accenture to create Taleris – a joint venture to provide airlines and cargo carriers around the world with intelligent operations services, focused on improving efficiency by leveraging aircraft performance data, prognostics, recovery and planning optimization solutions.
6 Comments Post a comment
  1. Jun 30 2013

    Thanks for sharing your insights. Hard to find good information on construction equipment in blogs usually, so I am happy to find your website. I agree with you 100%.

    I look forward to reading more in the future and if there is anything I can ever do for you please don’t hesitate to call or email my friend.

    Truly yours,

    Stephenson Equipment


  2. Ravi Kalakota
    Oct 30 2013

    GE announced it has added 14 new industry modules to its Predictivity line of software, adding to the 10 it had to manage and analyze industrial data. It sells Predix, a data management platform (much of it using NoSQL approaches) designed to store and use machine-generated data. It also announced new partnerships with AT&T, Cisco and Intel to advance the networks, sensors and on-machine computing needed for the growth of industrial data.



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