Recent research says revenue from the IoT edge will increase by 81 percent in 2018. In other words, the
market is awakening to the opportunity for distributed data processing and analytics closer to where IoT data is actually generated.
The promise of edge computing in distributed environments is inextricably tied to making asset-generated data consumable by human operators and managers in near real-time, so they can make faster decisions, improve asset utilization, minimize downtime and simply gain a more granular level of control over the processes they oversee.
While there is no debate that market awareness—even a sense of urgency—is on the upswing for the value of data analysis closer to the edge, the trend is contingent on two important factors. First, static assets at the edge need to be able to share information without the assistance of a cloud-based intermediary. For example, the safety equipment in an airliner would talk directly to the pre-flight inspection crew. Or, the hydraulic control valve on an excavator on site tells a maintenance technician when the hoses were last serviced or replaced.
Secondly, there needs to emerge a much simpler, safe, efficient and cost-effective approach to building edge infrastructure. On this point, there is huge opportunity to embrace both iOS and Android as dominant OS options for edge system equipment. Over the last decade, society has come to a place where we walk around with a tablet or mobile phone that contains levels of computing power and connectivity unheard of even just 15 year ago. Consequently, virtually anyone now has the power in their pocket to simply walk up to an operational asset and have a smart interaction with it. You can query the asset’s lifecycle history, download information about its installation or write dynamic condition updates to it. Essentially, modern consumer devices need to gain their due respect as the missing link to analytics at the edge.
Once an asset-heavy organization makes this mental leap, assets can start to be seen for more than their pure operational purpose. Now, they are as much repositories for information as they are endpoints that flow information to the cloud. Information can be retrieved and updated on any given asset throughout its lifespan, without requiring cloud connectivity of any kind.
It is here the vision of edge analytics as an antidote to the deluge of unstructured IoT data becomes clearer. Edge devices are getting smarter and generating more minute-by-minute data; that horse has left the stable. But, with iOS and Android in place to intercept and interpret the most prescient of this data, only a subset must now be sent to the cloud for the more resource-intensive processing, analytics or parsing to inform cognitive platforms. There’s a streamlining effect on the data, while the path to vital decisions takes a less circuitous route.
There’s a sizable workforce benefit to be gained, as well, one that materializes at both ends of the experience spectrum. For workers new to the job, edge analytics provides a built-in means toward job training and skill development. If an asset has been embedded with the right data, it can actually dictate its maintenance or operating instructions to employees as they interact with it. The asset becomes the informed trainer.
On the other end, more experienced workers have a way to transfer the “tribal knowledge” they’ve accumulated over the years more broadly across the organization. In fact, edge data provides a way for these workers to understand that technology isn’t going to replace them. Rather, they are more empowered to show people how smart they are by embedding certain tricks of the trade into the assets they work with every day, to be absorbed by the next colleague down the line.
Functionally, the concept shapes up as a distributed model similar to the chip-hardware/bus-OS stack that is at the heart of consumer-device computing. Without a wide-scale perspective shift that envisions how an OS-layer on iOS and Android becomes the node of direct interaction with static assets, the steep ramp everyone’s predicting for edge data may not come to pass as quickly as we hope.
Timothy Butler is founder and CEO with Tego, Inc.