As originally published in IoT Agenda
In all the excitement over the IIoT (Industrial Internet of Things), the domain of computing activity that may do most to translate IIoT technology into lasting business value has been somewhat overlooked. Yes, we’re talking about Edge Computing.
Of course, computing on the Edge – technology infrastructure that’s located on or near production operations, for data collection, data analysis, and data storage – has been going on for decades. Processes like keeping an assembly line running smoothly, delivering clean water continuously, and making trains run on time have long depended on Edge data being gathered efficiently, with only limited connectivity to data centers. But from a computing standpoint, the Edge has often been seen as something of a sleepy backwater.
All that has changed recently, thanks to secular (industry-agnostic) trends that have driven dramatically more investment in computing infrastructure at the Edge, followed by increased reliance on Edge-gathered data for cutting-edge applications. These trends include the criticality of data to business success; the demand for real-time analysis of data, in order to make better business decisions; and the increasing interconnection of “things” of all kinds, in order to gather ever more and higher-quality data.
As a result, analysts estimate that 5.6 billion IoT devices owned by enterprises and governments will utilize Edge Computing for data collection and processing in 2020 – up from 1.6 billion in 2017. And by 2019, 40 percent of all IoT-collected data is expected to be stored, processed, analyzed, and acted upon close to or at the Edge of the network.
Real Benefits, Real Opportunities
These trends present the opportunity to reap significant benefits, for those organizations that can take advantage of them.
Consider the case of a manufacturer looking to improve decision-making and overall productivity. Most manufacturers are already operating at the Edge. While their plant operations may be centralized, the data gathered by unmanned machinery or unattended workstations may be only minimally connected to their data centers and business networks. As a result, the time it takes to gather, process, and analyze data on machine performance makes it difficult to identify problems, diagnose them, and respond to them promptly.
With today’s Edge Computing infrastructure, in contrast, manufacturers can now automate the collection of large volumes of machine data (available from IoT sensors), compare it to their own historic performance or industry-wide standards, and derive usable analysis right on the shop floor. This approach drives predictive maintenance to maximize machine uptime, streamline production processes, and reduce costs.