Digital transformation needs insight from the edge to the cloud
With edge computing and cloud computing it’s not an either/or scenario
Data insight is seen as a primary driver of digital transformation–the process of bringing technology to bear on virtually any type of enterprise or industry in an effort to maximize efficiency, identify cost savings and create new revenues. But gaining actionable data insight isn’t easy given the myriad sources of that data.
Consider an oil drilling operation. Some data is collected on-site, at the edge, where it is processed and analyzed. Other data is transported to a centralized cloud infrastructure where analytics are applied. Factor in a wide range of internet of things-type sensors and devices, plus the varying connectivity schemes used to transmit information, and you’re left with a fairly difficult equation to solve. But it can be done, GE says.
Speaking at the Minds + Machines event in San Francisco, GE Digital CEO Bill Ruh said industries “that unlocks data to master asset productivity will be positioned to lead. To power digital transformation, industrial organizations must understand every stage of the asset lifecycle. The company that designs, builds, operates, maintains and services industrial assets is best equipped to help others with their industrial IoT journeys. And only GE offers the combination of industrial apps, underpinned by an industrial platform, to ultimately drive customer outcomes.”
To that end, GE Digital has expanded its portfolio of edge and cloud solutions that are part of its Predix platform, which is used to unlock the value of the industrial IoT. Predix Edge is a new edge computing product that integrates with the Predix platform and the Microsoft Azure cloud computing offering.
In a blog post, Corey Olfert, Predix Machine product marketing leaders, described a balancing act between the edge and the cloud. “It’s not an either/or scenario,” he wrote. “Think of your two hands. You go about your day using one or the other or both depending on the task. The same is true in industrial internet workloads. If the eft hand is edge computing and right hand is cloud computing, there will be times when the left hand is dominant for a given task, instances where the right hand is dominant, and some cases where both hands are needed together.”
He noted that edge is needed when “low latency, bandwidth, real-time/near real-time actuation, intermittent or no connectivity, etc…” is the case. Cloud comes into play with “compute-heavy tasks, machine learning, digital twins, cross-plant control, etc…”