The emergence of mobile, cloud, and other digital technologies that make it easy for people to access Information Technology (IT) systems has transformed the economy. Driven particularly by Software as a Service (SaaS) applications built on top of AWS, Microsoft Azure, and other cloud services, this digital transformation has fundamentally changed entire markets, including sales (Salesforce), retail (Amazon), entertainment (Netflix), and travel (Airbnb).
Yet, one market has been slow to jump on the digital transformation train – the industrial market. Despite all the talk we have heard about a Fourth Industrial Revolution, with a few notable exceptions, companies still purchase and use industrial Operation Technology (OT) equipment without using IoT technologies to connect it to their own or the manufacturer’s IT systems.
This is true despite the fact that Original Equipment Manufacturers (OEMs) and their customers realize that if they were able to collect data from industrial OT equipment and then transmit this data to cloud-based IT systems, they could transform their businesses with new predictive maintenance, equipment-as-a service (EaaS), asset tracking, smart factory process control, and other Industrial IoT (IIoT) applications.
So why have we not seen the proliferation of a variety of new IIoT applications? The problem is that when companies have launched projects designed to connect industrial OT equipment to IT systems, these projects have often hit a dead end – as demonstrated by a Cisco survey that found that as many as three-fourths of IoT projects fail.
The reason? Either during the IIoT application proof of concept (POC) development process, or later as these companies tried to move their IIoT application POC out of the lab and into the field, they experienced difficulties that halted development or deployment of the application. One of the main difficulties leading to the failure of these projects is the complexity involved in building the IIoT infrastructure needed to bridge the gap separating OT from IT.
With mobile and web applications, companies can use common programming skills to build an application in the cloud and then deliver it to users on their already connected smart phones and laptops. The bridge between cloud-based IT systems and users’ endpoints is already there.
However, to develop and deploy an IIoT application, companies have to build their own bridge between IT systems and OT equipment endpoints. To do this, they have to:
• Use a variety of different communications protocols to integrate embedded modules, gateways, and other edge devices into their industrial equipment.
• Connect these edge devices to wireless networks and then manage these connections in ways that minimize data transmission costs and, for battery-powered edge devices, energy use.
• Build or find APIs that integrate the edge device data into cloud-based IT systems.
• Ensure all of this data is secure.
Moreover, companies need to do this with an infrastructure that allows them to update firmware and rules in the edge devices if they hope to upgrade or improve their applications’ security and functionality over time. This infrastructure also needs to be able to scale to support thousands of pieces of equipment around the world if they hope to have an application they can commercialize. In addition, the teams tasked with these IoT projects are usually IT departments with little OT domain knowledge. Faced with all this complexity, it is no surprise that IIoT application development has been slow and risky, with a long time to revenue even for successful projects.
Fortunately, new data orchestration technologies are emerging that finally enable companies to bridge the gap between OT and IT. These technologies orchestrate the extraction of data from OT equipment (with built-in equipment protocols), the transmission of data over wireless networks (through a single management interface), and the integration of this data into cloud-based IT systems (via a set of pre-built APIs). In addition, they orchestrate the IoT infrastructure’s security by ensuring that edge firmware is updated, security keys rotated, and the entire infrastructure is constantly monitored for new threats.
In this way, data orchestration technologies enable the tight integration of edge devices, wireless networks, and cloud APIs into single, all-in-on Industrial IoT solutions able to collect data from a wide variety of OT equipment and then securely transmit this data to the cloud. With these solutions, companies do not have to acquire deep levels of expertise in embedded software design, manage dozens of wireless networks, or build their own cloud APIs in order to develop and deploy IoT applications. IIoT is no longer a special use case that requires entirely different tools and skill sets than other enterprise software. In fact, these solutions allow companies to use the same skills they employ to build, deploy, and refine cloud applications for IIoT applications.
In many ways, data orchestration technologies are similar to the computing, network, storage, and other technologies that enabled the development of the cloud. In both cases, these technologies make most of the underlying infrastructure (like computing, network, and storage for cloud; edge devices, wireless networks, and cloud APIs for IIoT) not just scalable, but also practically invisible to application developers. So rather than focusing on building and managing infrastructure, companies can instead focus on how their application collects, analyzes, and acts on application data.
For too long, industrial companies have had to sit back and watch as companies in other markets used cloud and other digital technologies to transform the way they did business. For these companies, the bridge between the cloud-based IT systems and users’ smart phones, laptops, and other endpoints came to them practically pre-built. They could focus all of their efforts on creating applications that would collect and use the data that would allow them to cross it.
Industrial companies, on the other hand, found themselves having to collect, tie together, and manage edge devices, wireless networks, and cloud APIs if they hoped to bridge the gap that separated their OT equipment from cloud-based IT systems. Some, after investing a lot of time and effort, managed to cobble together a bridge, leaving few resources to invest in building applications to get the right OT data to the right IT systems. Others built a bridge able to work as a proof of concept, but not able to support a commercialized application connected to thousands of industrial assets around the world. Many companies found they lacked the expertise to build infrastructure that could bridge the gap between their OT and IT at all. Now, with data orchestration technologies, all these companies no longer have to build the IoT infrastructure they need to connect industrial OT equipment to cloud-based IT systems themselves.
The OT to IT bridge is here. With it, expect The Fourth Industrial Revolution to finally break out as industrial companies race to build transformative new IIoT applications that cross it.
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