Industrial Internet of Things and Big Data: The (R)evolution Continues

The industrial ecosystem increasingly is turning to the Industrial Internet of Things (IIoT) in pursuit of quality control, efficiency and supply-chain improvements. But as sensors get cheaper, Big Data grabs a bigger footprint, and the technology gets ever more ubiquitous, complexities emerge with the broadening scale. With all this ability to measure and monitor, businesses risk drowning in a sea of data during the digital transformation. This challenge demands a strategy for structuring information, applying analytics and extracting knowledge to harness data’s value.

If you think of an industrial machine as a living organism, IIoT is the central nervous system that enables feedback and intelligence, allowing that organism to prosper. Thriving through instrumentation and analytics is no longer a cost-prohibitive proposition. And it’s only going to get cheaper and more practical for more discreet elements of information to be collected, analyzed and used. IIoT has fostered the massive proliferation of low-cost sensors that — while occasionally susceptible to giving incomplete or false readings — collect richer amounts of information, gracing operators with a far wider understanding of their systems, chunks of information to create actionable insights, and better opportunities to optimize against broader domains and to make quicker decisions.

Operators  of wind turbines, for instance, have been able to leverage data from those towering devices to prevent component failures and make minor design tweaks that make them more efficient, often at no additional expense. 

While the fact that IIoT drives efficiencies and optimizes processes isn’t necessarily new, the scale is. With the transformational ability to capture numbing blasts of data through sensors, structuring and finding ways to distill something usable from the collected information becomes a significant, even daunting proposition.

That’s increasingly the case as advancing technology — evidenced by things such as smartphones, drones and self-driving cars — grabs an ever-expanding foothold in the industrial workplace. Businesses are spurred to digitize with more analytic platforms involving such things as sensors, machine learning and artificial intelligence, increasing the opportunity for intelligent operations and planning. 

Such departures from traditional business applications hold the promise of paring operational costs and furthering sustainability by using data analytics to better understand  our environment and drive greater decisions about costs, materials and the amount of energy used — or more importantly, wasted — in creating something.

The increasing affordability of technology is fueling the revolution, with no shortage of examples: Drones, once costing consumers thousands of dollars to build from scratch not many years ago, now fetch just a few hundred dollars in retailers’ electronics sections. More new automobiles these days come equipped with rear-mounted backup cameras. Cloud computing technology also allows massive amounts of data to be processed quickly and made available to more people than just programmers or analysts. Wireless is the pervasive norm, not the exception.

As industries strive for cost-cutting efficiencies and rightsizing workforces through gadgetry, downsides of the disruptive technologies are drawing scrutiny, most visibly when it comes to the societal fallout on staffing levels and the resulting blurring of where any new jobs will come from in such workplaces. In the IIoT sphere, businesses are taking their energy destiny into their own hands, turning to renewables and microgrids rather than relying on power utilities. This move from centralized to distributed reinforces the need for data collection and analysis to ensure proper operation and maintenance of these often unmanned assets. 

In addition, businesses are exploiting technology well beyond sensors, turning to such analytics platforms as Black & Veatch’s cloud-based ASSET360®, which gathers, integrates and crunches data from infrastructure systems, assets and devices to help users make more informed, quicker decisions. That technology also offers modeling capabilities and predictive analytics, identifying issues well before a human’s situational awareness could discern that something is wrong. 

With the marrying of IIoT efforts and datastructuring analytics picking up steam, businesses that master that union and nimbly collect and process vast amounts of data hold the edge.