Despite concerns about return on investment (ROI), skill availability, and upgrading, the iot has brought useful insights to businesses. Edge computing products are one way to address these challenges.
When it comes to the realities of the Industrial Internet of Things (IIoT), many businesses must consider cost, time, and disruption of new facilities. The prospect of having to phase out and replace new infrastructure to support the Internet of Things is not a viable option for many businesses.
Addressing the challenges of iot implementation
Edge iot and analytics can provide a powerful mechanism to transform complex data sources into streamlined, low-cost platforms with faster ROI and higher value. However, businesses face five key challenges when considering iot implementation.
1. Investment
The transformational potential of the Internet of Things across multiple industries is staggering, and there has been much discussion about its ability to revolutionize business models. But while the possibilities in the market sector are very exciting, the reality of these industry iot products is that they are designed for a large number of use cases - intricate Settings, incredibly powerful networking capabilities, and requiring significant investment and skill to execute.
Major players in the iot space, including AWS and Microsoft, require substantial upfront investments in the iot stack and other hardware integrated into data centers, as well as people who can code, write, and build solutions, which can cost hundreds of thousands of dollars even before an organization has access to the underlying data or insight.
Return on investment is lacking in the iot space, which leads to the failure of proof of concept. An early use case for the Internet of Things, smart meters, is an example of an easy ROI to calculate. Because businesses don't have to send meter readers to the site, and it's directly cost effective.
But for the industrial Internet of Things, it's much more than that. There may be some cost savings and less machine maintenance may be required. Savings are hard to pin down in the first place. Therefore, in this case, it does not make sense to make a large upfront investment in this solution.
2 Changing
In many industrial situations, existing machinery to be monitored includes large, complex and expensive structures. These machines are designed and built for the task at hand, so they should be monitored in a non-intrusive way.
Many facilities cost billions of dollars to design and build, and companies can't start dismantling and replacing components because the benefits cloud technology offers haven't been quantified.
Conversely, many iot products on the market rely on building iot into the infrastructure from the start, a concept that can lead to significant business disruptions and outages.
3 Skills
The skills required to manage these types of complex Settings are also a significant hurdle for many businesses. A large proportion of iot customers in manufacturing are not necessarily as IT-savvy as traditional database users. Since many vendors need people who can effectively manage these platforms, this is an issue that hurts adoption opportunities in this space.
Businesses need a way to get data out of iot devices without the complexity of the surrounding ecosystem through simplified platforms that only require browser access. That means companies have to figure out if they can afford to hire a dedicated iot professional and how that role provides value.nfrastructure
Another obstacle for many iot projects is that infrastructure has not been developed, such as locations in inconvenient places and no reliable Wi-Fi -- the only available clouds are those floating in the sky. In this case, having an iot solution that collects all the data and analyzes it at the point of collection enables fast and reliable visibility into what is happening. This can have a huge impact and will be a more practical solution, both in large factories and in distant locations. This is the difference between the original vision of the Internet of Things and the reality.
4. The edge of the Internet of Things
The vision and reality of the Internet of Things are very different. The "yes" or "no" response of a sensor is not the same as determining whether a complex mechanical part is working as it should and at an optimal level of efficiency. This is not only about the opportunity to collect data, but also having the ability to modify the data collection and add additional sensors to further extend the data collected.
For example, a setup might monitor temperature and speed, but then vibrations must be measured. This requires another sensor, so the platform must be adaptive and scalable. In the current industry sector environment, IT teams must be flexible and ready to respond to scale changes, including the size and complexity of the data they collect.
As edge computing gains momentum, enterprises are discovering how to quickly access the most valuable data critical to their business in real time.
Like smart meters, this type of iot deployment involves millions of identical devices with identical data and a single purpose. It's still an investment, but the rationale is simply to connect multiple devices of the same kind together, which is different from today's industrial environment. In today's industrial environment, there may be a few or even tens of thousands of different devices, all performing slightly different tasks in different ways.
Therefore, this dedicated device requires an iot edge solution that can accurately convert, measure, and analyze different data formats as they arrive, without removing and replacing the internal electronics of the machine.
Edge allows data processing to be performed on edge nodes and then only aggregated data to be transferred to a central server. Depending on the measurement use case, this can be reduced to a few messages every five minutes, instead of transferring large amounts of data every minute.
This leads to a significant reduction in bandwidth, so cellular networks become cost effective, thereby reducing infrastructure costs and creating faster ROI and value.
For enterprises that decide to start with the Internet of Things, edge computing eliminates the need for large-scale, complex and expensive deployments. Edge computing can provide a way for projects to get up and running, providing data points and insight into how enterprises can further leverage iot through data-led strategies.
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