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It is no secret that IoT offers limitless capabilities, with the ability to transform industries, reform productivity and offer new levels of insight that once seemed unattainable. The IoT revolution is undeniably intriguing, but challenges such as high price points, connectivity issues and infrastructural obstacles can be major stumbling blocks for businesses.
The vision of IoT and the hard reality are very different. The overall time, cost and disruption of building a new facility aren't realistic for some businesses, as many can't remove existing infrastructure to support this new technology. But, with so many companies faced with the same recurring issues, is there an alternative way? Explore how edge IoT and analytics can transform complex data sources into modernized, smooth-running platforms with lower costs, higher value and faster ROI.
IoT boasts astonishing potential across numerous sectors, ultimately holding power to revolutionize and transform business models. The opportunities are endless; however, they are far more accessible for major players in the field. The infrastructure can be elaborate and complicated with vast capabilities that require substantial investment and an extensive skill set to effectively implement. IoT giants, such as Microsoft and AWS, require immense backing and investment into technology stacks, as well as a workforce with the necessary expertise. These costs can amount to hundreds of thousands of dollars, even before an organization obtains any complex data or insights.
The lack of ROI within the IoT space has been widely discussed, fundamentally causing widespread doubt and leading proof of concepts to fail. When considering some of the earlier use cases for IoT, it is far simpler to calculate ROI. For example, there is an immediate cost benefit of using smart meters because the data can be easily accessed without the expense of sending meter readers to sites. However, with industrial IoT, there are many aspects to consider. Savings are more difficult to establish from the outset, meaning a huge investment upfront is difficult to justify and obtain.
Many facilities have been meticulously designed and built at the cost of millions, if not billions, of dollars. Large, complicated and expensive structures are often composed of state-of-the-art machinery and complex components that cannot just be ripped out and replaced by cloud technology with no concrete evidence of reasonable ROI. Yet, it's still the case that many of the existing IoT solutions from market leaders heavily depend on being built in from the start, which would likely result in significant business downtime and disruption.
If the infrastructure behind an IoT project is not extensively developed or does not have reliable Wi-Fi access, businesses may face connectivity hurdles that are difficult to overcome. Organizations must have IoT solutions in place that collect all of the essential data, analyze it at the point of collection, and boast the ability to provide fast and reliable visibility in both remote locations and large factories. Connectivity challenges highlight the true differences between the original vision of IoT and reality.
Complex and intricate setups require a specific skill set. Sourcing the necessary expertise can be an issue for many businesses. Many IoT systems in the manufacturing sector are not tech-savvy in the same way that more traditional database users are. Can a company realistically justify hiring a dedicated IoT professional with so many doubts surrounding the cost benefit? It's crucial that organizations truly consider all options and explore how they can access data from IoT devices without the complex ecosystem surrounding them, such as through a streamlined system that can be accessed easily using just a browser.
IoT at the edge
IoT at the edge is about much more than the opportunity to collect data. The concept quickly gains momentum as organizations discover how to access the most valuable and relevant data instantaneously. Businesses now can analyze and modify data at the point of collection and add additional sensors to expand even further on the data. For instance, the solution could be monitoring speed and temperature; however, you may also need to measure vibration. This measurement would require another sensor, making an adaptable and scalable platform essential.
In the smart meter example, certain IoT deployments involve millions of identical devices with one shared purpose. While undeniably still an investment, the principle is simple with regards to connecting multiple similar devices. It is possible to connect tens of thousands of different devices that work differently in today's industrial environment. In these circumstances, organizations must have an IoT edge solution to correctly translate, analyze and measure data in various formats without completely ripping and replacing the existing technology.
Ultimately, edge computing enables data processing on the edge nodes before transmitting the collected data to the central server. This process effectively reduces the volume of data transmitted each minute, reducing the bandwidth and infrastructure costs to create quicker ROI.
The capabilities of IoT deployments are limitless; however, many businesses are unaccustomed to the availability of straightforward, cost-effective solutions that provide data analysis at the edge. IoT at the edge ensures that only the most valuable and relevant data collected is shared in real time, making the entire data collection process more affordable for the business.
While the likes of Microsoft and AWS have claimed their place, most organizations lack use cases to justify the time and dedicated attention. Instead, small-scale solutions with the ability to incorporate big data, edge and IoT must be considered that can also deliver easily scalable opportunities without the need to rip and replace existing infrastructure.
About the author
Peter Ruffley is the founder of Zizo with more than 40 years of broad experience in the IT industry. He worked with some of the biggest data technology companies, such as Oracle, IBM and Ingres. His keen interest in cloud analytic technologies meant Ruffley understood the move to cloud analytics was underway. He assembled a team to create a new type of technology suited to deliver big data analytics and pattern database services at scale in the cloud. That technology is Zizo software. With recent developments to the platform, Zizo is moving into the IoT and edge space to deliver insight on any device, wherever it may be.