The case of moving Intelligence to the Edge

There is a reason why DCS and SCADA systems in Industrial environments stayed on-prem, it’s the same reason that will drive IoT Intelligence closer to the Edge. There are thousands of scenarios where speed is the operational imperative from Asset management, critical production setup, command and control of high-value assets with predictive intelligence capabilities. The term “Edge” has been around, and architecture has existed. Internet of Things has expanded the scope and blended OT with IT datasets for better insights and outcomes.

Gartner defines edge computing as solutions that facilitate data processing at or near the source of data generation. It’s the same definition that DCS and SCADA systems applied in the past which are still in use to this day.

So what’s the real noise about? It’s the cloud which is perceived as a backbone of all architectures that enable digital transformation. Just that it doesn’t seem to address several core challenges of the Industrial setup:

Reliability- This is the core consideration in the mind of shopfloor manager. Do I have access to my data and insights, here and now in a deterministic way?

Latency – What’s the use of the insight if it’s not actionable in time?

Network Connectivity and bandwidth costs – There are so many blind spots through the connectivity chain and bandwidth costs keep adding up unless there is a way to limit the use and prioritize the data and optimal payloads.

Security – Industrial shopfloor assets and data are susceptible. Most of the devices are not IP-routable. Manufacturers have a higher preference if the data never leaves their premises.

There are several other considerations, but these four are crucial to solving for. Intelligence at the Edge has gained more investments and actions from most IoT vendors. It will continue to grab more mindshare.

#EdgeComputing #IIoT #EdgeAnalytics

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