I admit that in past blogs I have vented about the state of insight being demonstrated on IoT. It would be far easier to provide a list of dumb things said and offered in the space than a list of smart things. In fact, up to late last week I couldn’t put anything on the “smart thing” list at all. Fortunately, things have changed because of information I’ve received from and discussions I’ve had with GE Digital.
Everyone who lives in even a semi-industrial society has likely been touched by GE at some point; the company is an enormous industrial and consumer conglomerate. They created a new unit, GE Digital, to handle their growing software business, and it’s GE Digital that’s offering Predix, which they call “a cloud platform for the industrial Internet”. Yes, it is that, but under the covers Predix is how the IoT should have been visualized all along.
If you recall my blogs on IoT, particularly the most recent one, you know that I’ve advocated visualizing IoT not as some “Internet extension” or “LTE opportunity” or even as a “network” but as a big data and analytics application. My model of three ovals—big in the middle and a little one on top and at the bottom, reflects a view that real IoT will be a repository/analytics engine (the middle oval) connected to sensors and controllers at the bottom oval, and accessed by applications at the top. This is essentially what Predix creates.
The central piece of Predix, the “Industrial Cloud”, is the repository and cloud platform plus a set of supporting applications that include analytics. It’s fed from sensors and connected to controllers through a software element called a Predix Machine. These can interface with (using proper adapters) any sensor/controller network technology, so this is my bottom oval.
You can have a hierarchy of Predix Machines, meaning that you can have one controlling a single device, a collection of them, a floor, a factory. Each machine can do local analytics and respond to events based on locally enforced policies. They can also generate events up the line, and this structure keeps control loops short and reduces central processing load, but the central repository can be kept in the loop through events generated or passed through.
Speaking of events, they could be generated by analytics operating on stored data, or on real-time streams through event recognition or correlation. Events can change the repository and also change the state of the real systems, and in all cases they are processed by software that then decides what to do. As I noted, some of that software can be strung along a Predix Machine hierarchy, some can be inside the Industrial Cloud, and some could be in external applications linked by APIs.
The top oval is a set of APIs available to GE Digital and developer partners to build either general or industry-specific applications. There’s a Predix Design System that provides reusable components, developer frameworks to support specific developer types and goals, and a UI development environment based on Google’s Polymer (designed to build highly visual, interactive, and contextual user experiences).
Inside Predix there’s the concept they call the “Digital Twin”. This is a kind of virtual object that’s a representation of a device, a system of functionally linked devices, a collection of devices of a given type, or whatever is convenient. A model or manifest describes the elements and relationships among elements for complex structures, and the Digital Twin links (directly or through a hierarchy) to the sensors and controllers that actually represent and alter the real-world state the Digital Twin represents. You can kind of relate a Digital Twin to social networks—you have individual profiles (Digital Twins of real humans or organizations that collect real humans) and you have any number of ad hoc collections of profiles representing things like demographic categories. A profile would also be a network of “friends”, which means that it’s also representing a collection.
GE builds the “Digital Twin” of all its own technology, and you could build them for third-party elements or anything else as long as you provide the proper model data that describes what’s in the thing and how the innards relate to each other. The Digital Twin provides a representation of the real world to Predix, collecting data, recording relationships, and providing control paths where appropriate.
One of the benefits of this Digital Twin approach is that Predix understands relationships or object context explicitly, and also correlations among objects. If you look at a given profile in social media, you can see who it relates to. Same with Digital Twins but in more dimensions. A piece of an engine is part of the engine and part of a broad collection of that particular piece in whatever other things it’s also part of. You can then gather information about that specific thing and from how it’s behaving elsewhere, and predict what might happen based on the current state of that single real thing and on the behavior of what’s related to it. GE Digital has a blog about this.
You can analyze things in a time series too, of course. You can correlate across classes of things to follow Digital Twin paths, project conditions from the general class to specific objects, and project the result out into the future for essentially any period where the asset you’re talking about has value. The modeling used to define the Digital Twins lets you contextualize and data and policies let you define access and usage of information for security and governance.
Another interesting principle of Predix that directly relates to the popular vision of IoT is the notion of closed-loop operation. The concept of “M2M” has been extended by IoT enthusiasts to apply to the idea that refrigerators talk to sinks, meaning that two machines could interact directly. Even a cursory look at that notion should demonstrate it’s not practical; every device would have to understand how to interpret events sourced from another and how to act on them. In Predix, closed-loop feedback from sensor to control is handled through a software process intermediary that does the analysis and applies policies.
The notion of closed-loop feedback also introduces yet another Predix concept that I think should be fundamental to IoT, which is “Outcome as a Service”. OaaS says that in “thing systems” like IoT would generate, the consumer of the system is looking for an outcome. “Get me to the church on time!” is an example of an outcome, and it would be dissected into routes, traffic analysis, vehicle condition, size, and speed, driver proclivities based on past history, etc. OaaS is probably the most useful single concept to come along in IoT because it takes the dialog up to the top where the value to the user lives.
In an implementation sense, Predix is a cloud application. Everything is implemented as a microservice that combine to create an evolving PaaS that future elements (including those developed by third parties) can support. There are also DevOps tools to deploy applications and microservices, and “BizOps” tools to manage the cloud platform itself. To say that Predix is an IoT cloud is no exaggeration.
Even in a blog over a thousand words long, I can’t do more than scratch the surface of Predix, and I don’t have any specific insight into what GE Digital might do to promote it widely or apply it to generalize IoT problems. Their specific application is the “Industrial Internet” remember. But this application, which includes transportation, healthcare, energy, and manufacturing, has enormous IoT potential and could generate enormous benefits (in fact, it already has to early customers). All of that would make Predix a great on-ramp to a broad IoT success.
IoT is like a lot of other things in our age, including SDN and NFV. You can nibble at little pieces and control your risk, cost, and effort, but the results will be little too. The trick is to find early apps that are so beneficial they can justify significant infrastructure. In the four key verticals GE Digital is targeting, you can see how a Predix deployment around the core (GE-supplied and third-party) technologies could build a lot of value and deploy a lot of technology. The incremental cost of adding in peripheral (and yes, pedestrian) things like thermostats and motion and door sensors would be next to nothing. These applications then don’t have to justify anything in the way of deployment, and they are all pulled into a common implementation framework that’s optimized for hardware and software reuse and for operations efficiency.
I think GE Digital under-positions Predix, and that the material is far too technical to be absorbed by the market overall. This reflects the “industrial” flavor of the offering. GE Digital is also seeing this more as a service than as a product, which would make it difficult to promote broadly—to network operators to offer, for example. All these issues could be resolved, and most very easily, of course. In any event, even the success of one rational IoT framework could change the dialog on the IoT topic.
We need that. There might not be more IoT hype than for technologies like SDN or NFV, but there’s darn sure worse hype, and IoT is a newer concept. The barriers to become a strident IoT crier are very low; anything that senses anything and communicates. We’ve made a whole industry out of nonsense, when the real opportunity for IoT to reshape just about everything in our lives is quite large, and quite real. I hope Predix will change things.
