The Internet of Things (IoT) is a double-edged sword, especially in the industrial realm where the Industrial Internet of Things (IIoT) reigns supreme. Collabro’s insights shed light on a pressing issue: the sheer volume of data generated by these networks can be overwhelming. “As we see it the challenge with IoT is the amount of data points it will collect. Often all you need is just one piece of insight from all those data points in order to make the decision or make a calculation, yet it’s grabbing so much information,” Collabro notes. This sentiment resonates deeply in a world where data is often viewed as the new oil, but too much of it can lead to inefficiency and confusion.
Andy Hancock, Global VP at the Centre of Excellence for SAP Digital Supply Chain, echoes this sentiment with a cautionary tale about the perils of overloading systems with data. Hancock highlights that the powerful capabilities of 5G technology can inadvertently lead to a data deluge. “The trouble is that when you scale-up this approach to enterprise level you soon end up with 50 million data points that flood the network, making it inefficient.” This is a wake-up call for businesses that might be tempted to throw more technology at the problem rather than stepping back to reassess their strategy.
The crux of Hancock’s argument lies in the notion that less is often more when it comes to data collection. He emphasizes the importance of focusing on exceptions rather than the mundane. “Think of a temperature gauge on a piece of equipment that is feeding back data. As long as everything is running okay, the equipment will always be roughly the same temperature. You don’t need to keep feeding back data about that piece of equipment. The only data you want to capture is if something changes – for example, if the thermostat fails.” This perspective shifts the focus from quantity to quality, urging businesses to refine their data-gathering techniques.
Hancock’s advice to treat IIoT networks like the early days of the internet is particularly poignant. “Think back to the days of dial-up modems,” he suggests. “Where everyone minimised the amount of data transmitted, because if you didn’t then the whole thing just hung.” This analogy serves as a reminder that efficiency and effectiveness often hinge on smart data management rather than sheer volume.
Moreover, the integration of edge computing into IIoT operations could be a game-changer. By processing data closer to its source, businesses can react swiftly to anomalies without the lag of sending information to the cloud. Hancock points out the critical advantage of local processing: “If a machine is going out of tolerance and there is a delay in sending this data to the cloud and back, you could have big problems by the time the machine is switched off.” The prospect of machine-learning tech autonomously recalibrating equipment underscores the potential for a more responsive and resilient industrial ecosystem.
As the IIoT landscape continues to evolve, these insights challenge industry norms and provoke thought on how businesses can navigate the complexities of big data. The future will likely favor those who can distill actionable insights from the noise, ensuring that technology serves as a powerful ally rather than an overwhelming burden.