Industrial IOT Data and Analytics Platforms

By pjain      Published July 19, 2020, 10:29 p.m. in blog Invest   

5G , IOT , Robots

Major IOT Cloud, Analytics Platforms

Why Industrial Big Data

Operations e.g. if yield of a chemical factory can be optimized even improve yield of 0.1% in a Billion dollar per year output factory is worth tens of millions.

But to achieve such optimizations in the production line of any big manufacturing client needs large quantities of data processed near real-time.


The Industrial Internet of Things includes low-cost sensors and a flood of data and clever software that should deliver insights to cut costs, conserve fuel and design better products, faster.

IOT heightens significant challenges that includes privacy fears, surveillance concerns, legal and other developing challenges that hold many organizations back when it comes to its deployment. This is when Microsoft felt the need to introduce Azure IoT suite to help companies thrive in the era of digital businesses.


Problems in Industrial IoT

Underestimated Depth, Complexity of Software Stack

GE Digital and Predix suffered from this.

PROBLEM: Hard to produce industrial software

Huge difficulties of producing modern software for industrial businesses as they adopt digital technology.

Software outside Circle of Competence

G.E.’s technical prowess, they said, lies in designing and manufacturing big machines like power-plant turbines, jet engines and medical-imaging equipment. Its traditional software skills have been in the specialized programs that control the machines and factory operations. GE Digital was a striking departure into cloud-based internet software, data analytics and artificial intelligence tools like machine learning.

Massive Investment of Billions needed!

GE Digital "became a financial black hole” of several billions of dollars.

In HBR Immelt wrote that just in 2016 “we put about $4 billion into developing analytics software and machine learning capabilities.”

Unclear Benefits - no clear business case

GE Digital> The problem was that the immediate benefits of these actions haven’t been of the scale needed by the company. Centralizing IT for a company the size of GE is a huge undertaking that didn’t provide the head-count reduction many expected. And essentially building an entirely new class of software-hardware integration is an immense undertaking, one that requires massive engineering and design talent, huge amounts of input from thousands of people across dozens of time zones, and every business in GE.

Complex Engineering Organization, Scale needed

To achieve something that hasn't been done before needs a HUGE engineering capacity with a team of 1000+. To achieve goals like GE Digital had in only a few years would require engineering operations on the scale of Microsoft or Google. It’s literally impossible, unless you are willing to pay $250k per year and double that for senior scientists, to just spin up an organization of that size and unique talent sets.

Need for GREAT top-down Management

During the buildup, G.E. recruited veteran software managers who had worked at leading tech companies, including Google, Microsoft and Apple. However, the challenge is vast, as these managers have to create a real time OS from scratch - a very tough challenge.

The problem is when engineering capacity is smaller that Microsoft or Google. In that situation, yes, more rigid, top-down management is important because a large goal will require 100% of an organization’s abilities.

Small teams help FEEL OUT for DARPA style Innovation needed!

Instead in new undefined areas, being unfocused and running teams with a fair amount of independence is actually a good idea, because that can lead to innovations and insights that don’t come from rigid, top-down management and direction.

When the endpoint is unclear, it is better to run multiple SMALL exploratory groups, with POCs and scope out projects and deliver real value in real business cases.

Those early wins were not part of the plan at GE Digital.

On the other hand, Azure IoT was a MUCH SMALLER team, with incremental investment on top of a HUGE Cloud organization. AWS and Google have far far less investments than these.

Industry 4.0, IOT Use Cases

Level 1 : Early Warning to predict product failures

From 2011 to 2013, the charger of GE Digital's San Ramon center was writing data analysis and modeling software for applications like predicting when a gas turbine or jet engine would need maintenance, before the machine failed. In 2013, G.E. called that software Predix as it seemed to be succeeding.

Level 2: ETL for Manufacturing data

  • Then GE Digital expanded the product plan later to include a cloud-based software platform for handling all kinds of sensor and machine data, and secure communications from the factory floor to data centers. G.E. even built its own data centers.

Level 3 : Real time OS for Predicting to Action Planning

The software platform under development was often referred to as an operating system for the industrial internet.

IoT Architecture

As conceptualized by Microsoft in its Azure IoT it can be split up into - Things - Data - Insights - Actionables

IoT Things - Devices or "Points" to Monitor

Things refers to first, critical step, which is the need to connect, monitor, and manage devices. Every IoT solution will need a remote monitoring or asset management solution

IoT Data and Storage

Data refers to storage and analysis as well as stream analysis tools that are needed to the evaluate information and telemetry from devices and software systems.

IoT Insights

Insights includes cognitive services such as machine learning are required.

IoT Action

To take action, the ability to automate processes, command and control of systems, and engage with people is the fourth step.

IoT Platforms - Gather, Reliability, Low-Latency

To enable IoT solutions, a company will need a platform.

Industrial Apps for Verticals

GE is focussed on selling products for specific industrial applications, sold as offerings in the “Predix portfolio,” and tailored for G.E.’s roster of existing industrial customers

OnPremises IoT Platforms

Some may prefer the perceived advantages of speed and security that come with a local solution,

Cloud IoT Platforms

AWS, Azure, and even Predix are device cloud services, used by enterprises to capture and analyze vast amounts of data from the ‘things’ in the IoT.

While some may prefer the perceived advantages of speed and security that come with a local solution, the vast majority of businesses are better served by the ubiquity, power, low cost, and flexibility of cloud services to address FULL FEATURED out of box with all four areas of the IoT; Things, Data, Insights, and Action. Cloud computing an indispensable and critical part of the Internet of Things. Which is why every IoT solution provider has embraced cloud in one form or another.

Cloud IoT Packaged Applications

For GE Predix such as APM and OPM (built by GE Digital), and FlightPulse and IntelligiStream (built by GE businesses such as Power, Aviation, Oil & Gas). Those apps are quite widely deployed across customers / verticals.

Special-purpose applications built for specific use cases

E.g. using Predix platform capabilities. Some case studies have been published, but most deployments are company internal.

Custom or Adhoc Project based solutions, e.g. to solve specific analytic problems

These could be adhoc tests without ongoing need for operationalization. Eg there are many proof-of-concepts and even hackathon-developed apps.

AppStores and Portfolio of apps

There are hundreds of industrial applications built and successfully deployed e.g. using the Predix platform.

As an example is GE Predix Portfolio of apps.

GE Digital Market Expansion and Predix

Overall, G.E. Digital INCLUDES its traditional industrial software, and the unit’s global work force is more than 4,000. However, the core prior Predix now sold as consulting arm GE Predix Portfolio of apps has $600m+ of revenue.

Predix is an Industrial Internet technology (IIOT),with a tag line of Machines Can Speak.

GE Digital Market Expansion and Digital Transformation Strategy

Consulting Model - Building and Applying Custom Applications not All-in-One

Predix Portfolio Apps - GE Selling to Industrial Customers

The opportunity for G.E., analysts said, centers on its longstanding relationships with customers and selling to them. And so far, only 8 percent of its industrial customers are using Predix portfolio products, the company said.

Case Study of New York Power Authority, the nation’s largest state-owned utility is working with GE Digital to build apps that improve the efficiency of its power generation and distribution network. In pilot projects, it saved $3 m in costs and goal is $500 m over the next decade. But even for this large customer, to actually reduce electricity consumption for its customers, it preferred a startup C3 IoT not GE.


  • 2011 G.E. set up a center in San Ramon, Calif., to push into the digital market, hired thousands, and invested billions. Immelt recruited William Ruh, an executive at Cisco Systems.
  • 2015 Setup GE Digital as separate top level unit - and set goal to become a top 10 software revenue company by 2020
  • 2016 PEAKed “we put about $4 billion into developing analytics software and machine learning capabilities.” Immelt - indicating HUGE BLACK HOLE
  • 2016 Sept bought Meridium, equipment-tracking software, $495m
  • 2016 Nov bought ServiceMax manage industrial field service workers, for $915 m.
  • 2017 Nov - New CEO Flannery told investors that it would be cutting its expenses in digital by 25% or $400m/year, and "we want a much more focused strategy.”
  • 2018 Revenue from Predix Portfolio Apps reached $550m. Basically, GE Digital didn’t fail. It did exactly what it set out to do, which was centralize technical jobs within GE and set GE’s industrial concerns onto a path of high-tech software as well as hardware. Both of these goals were achieved.
  • 2020 GE ran out of time last year. GE was near its 10-year peak at the end of 2016, but had lost nearly half that value by the end of 2017. Ten year plans were no longer viable.

  • Evolution of Predix

    • Early Warning to predict product failures 2011-13
    • IoT ETL for Manufacturing data - 2013-2015
    • Real time OS - Peak 2016
    • All-purpose layers for Predicting to Action Planning - failed in this
    • Scale down to software tools to help write specific applications
  • G.E. Makes a Sharp ‘Pivot’ on Digital - The New York Times

  • G.E. Rolls Back the Breadth of Its Ambitions - The New York Times

Predix Strategy and Architecture

Predix strategy is to use internally within GE major industrial data sources - airplane and power generation turbines. Implementing with their biggest clients convincing them showing stats they acquired implementing in their own manufacturing companies(GE Motors,GE Aviation)

Predix is a broad edge to cloud platform. You can use part or all of the platform depending on your needs.

Predix Cloud services - Discovery and Catalog of Services

  • It lets any equipment that has connectivity or connected to a gateway and can communicate with services over standard HTTPS / WSS could send data to Predix services.

Which services are valuable for your needs can vary which is why there is a catalog of microservices and a rapidly growing partner ecosystem to help you build out a solution.

Data Aggregation Features

  • Aggregates Data sources. Predix allows machines and its individual components seamlessly communicate between each other and record performance using Predix cloud.

Adapters for External systems

Predix has a wide range of customizations and integrations with 3rd party systems.

Some manufacturers may also not allow you to install software on low end devices like microcontrollers like Arduino at all, so it really will depend on the equipment you want to use as to what can be supported. There is equipment manufactured by GE and other OEMs that may come “Predix Ready” which will make the process easier, but that is not a requirement for using Predix.

Custom adapters possible

You would need to install, manage, update, etc. your own software on that equipment though in order to make that data exchange possible if you were not using the Predix Machine SDK.

Device Registration, Management, Protocols and Security support

Predix Machine SDK for Programming

It runs within a containerized Java Runtime Environment. If you have a constrained device (typical of embedded systems or microcontroller like Arduino) there may be insufficient processing power to run the full Predix Machine software suite. You may lose some of the benefits of security, device management, protocol support, etc. in that case so it will depend on your use case whether that is acceptable or not.


Major Industry 4.0 Firms and IoT Cloud Majors




Bluemix, IBM Cloud

The bluemix solution from IBM is a mature integration platform within the IBM stack and is highly effective.


The Amazon solution of the ones listed is the least mature at this point. That said, potentially the largest market of solutions could be built in the AWS world, so that may change quickly.

Azure IOT Cloud

Azure IoT suite is an integrated set of its Azure services. It connects a broad range of devices and operating systems, capture and generates the big data, integrate and manage the flow of that data, so that the improved decisions can be made to automate operations.

The Azure solution integrates well with existing 95%+ installed base of PCs and Windows 10 - giving a slight advantage for PC-knowing consumers.

However, factory floors are different, giving no real advantage for integration within that family of sensors

Google Cloud


SAS Institute is a leading enterprise AI software provider for building enterprise-scale AI applications and accelerating digital transformation. A platform as a service for developing and operating big data, predictive analytics, AI, and IoT software applications.

Year Founded 2009 by Tom Siebel of Siebel Systems fame HQ: Seaport Boulevard 1300, Redwood City, United States Number Of Employees 251-500 Estimated Revenue $50M-$100M


FogHorn is a leading developer of “edge intelligence” software for industrial and commercial IoT application solutions. FogHorn’s software platform brings the power of advanced analytics and machine learning to the on-premises edge environment enabling a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance and operational intelligence use cases. FogHorn’s technology is ideally suited for OEMs, systems integrators and end customers in manufacturing, power and water, oil and gas, renewable energy, mining, transportation, healthcare, retail, as well as smart grid, smart city, smart building and connected vehicle applications.

Year Founded 2014 Number Of Employees 51-100 Industry Application Software Headquarters Mountain View, United States

Smaller Companies

OSIsoft, LLC is a manufacturer of application software for real-time data management, called the PI System. Delivers the PI System, the industry standard in enterprise infrastructure, for management of real-time data and events. Year Founded 1980 Number Of Employees 1750 Estimated Revenue $500M-$1B Headquarters: San Leandro, United States

ThingWorx radically transforms application development for the the connected world, allowing companies to achieve a step change in operational innovation and resource efficiency.

ThingWorx model-based development environment allows enterprises to do just that: rapidly build and evolve IoT applications, no matter where the data resides. ThingWorx has announced its open platform strategy, with pre-built integrations to major cloud platforms, starting with AWS IoT, Azure IoT Hub. The ThingWorx platform architecture is completely open, giving companies the freedom to evolve their devices, clouds, databases, and enterprise systems as those evolve over time.

Year Founded 2009 Number Of Employees 101 Industry Application Software Website Headquarters Needham, MA


Provides software solutions. The Company develops solutions to help manufacturing companies improve their asset availability, asset utilization, and utilities consumption. Rttech Software serves customers worldwide.

Sight Machine

Sight Machine specializes in manufacturing analytics and used by Global 500 companies to make better, faster decisions about their operations. Sight Machine's analytics platform, purpose-built for discrete and process manufacturing, uses artificial intelligence, machine learning, and advanced analytics to help address critical challenges in quality and productivity throughout the enterprise. The platform is powered by the industry’s only Plant Digital Twin, which enables real-time visibility and actionable insights for every machine, line, and plant throughout an enterprise. Founded in Michigan in 2011 and expanded to the Bay Area in 2012, Sight Machine fuses the spirit of Silicon Valley technology innovation with rock-solid Detroit manufacturing. Our team includes the founders of Slashdot along with leadership from early Yahoo, Palantir, Tesla, Cisco, IBM, McKinsey, and Apple.

Year Founded 2012 Number Of Employees 85 Industry Application Software Website


Nokia's machine learning analytics and industrial IoT applications optimize operations in motion, in context, and in real time.

Year Founded 2008 Number Of Employees 65 Industry Application Software Website Headquarters San Mateo


There are no comments yet

Add new comment

Similar posts

IOT Systems - Business, Uses and Key Factors

IOT Systems

Smart 4.0 - post Industry/Info/Services to Society 5.0

App Store Marketing