Get started Bring yourself up to speed with our introductory content.

Rethinking data through edge computing: Four phases for IIoT infrastructure

As industrial enterprises continue to move toward fully embracing the industrial internet of things, however tentatively that may be, we can observe an evolution in how operational technology (OT) teams are thinking.

Until more recently, OT teams have been reluctant to change and caught viewing their environment as a flat line — focused solely on their automation equipment and tools within their environment that made automation possible — not as having the potential for growth and adaptation.

However, a major shift in mindset is starting to take place. We’re now seeing engineers at more and more organizations place higher importance on data produced by their automation systems than on the tools needed to make them happen. This change in priority reflects the increasing potential that data and advanced analytics offer enterprises in untapped business value.

So, what’s driving this change of focus from applications to data? It’s not only due to the tremendous growth in data, but also due to the increase in soft computing at the edge, which is closer to production processes.

Four phases to true IIoT infrastructure

  1. Currently, most industrial enterprises are in the “informed” stage, where they are starting to understand and realize the potential of IIoT, but have not made strides in tapping its potential. However, many are beginning to look ahead and think more tactically about progressing to the next phases.
  2. The next step in the progression to a true IIoT infrastructure is the “insightful” step, where enterprises are implementing business analytics to drive new insights and increased efficiencies in their business models.
  3. Once companies have implemented business analytics, they can then advance to the “intelligent” phase, where linking elements in the infrastructure enables real-time optimization.
  4. The ultimate and final phase is the “invisible” stage, where artificial intelligence processes all of the data and makes real-time decisions with no human interaction.

One major component stalling multiple industries from beginning this transition is their perceived ROI. Many “old fashioned” industries are hesitant to change, as they’re not realizing the full potential or believe the costs associated with implementing new infrastructure to be too high. Consequently, most industrial enterprises are still in the informed stage.

However, there are many industries, like food and beverage, which are actively embracing IIoT technologies. They are already realizing the value of data through production efficiency and improved product safety and quality — critical components for protecting brand reputation. As technology progresses, I predict these holdouts will begin to recognize real-time analytics as an essential component of a modern industrial enterprise — or find themselves playing catch-up.

Those enterprises that already understand the value of data have an opportunity to use it as a catalyst to modernize infrastructure. Going forward, that infrastructure will need to be scalable and flexible to accelerate data growth and allow for new ways to use real-time data analytics. As enterprises become increasingly dependent on data, it will be important for them to consider how to protect their business’ valuable data. That will mean incorporating data availability as a key business requirement, not as an afterthought.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.