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IoT Strategy for Industrial Equipment and Healthcare Technology
In the evolution of the Internet of Things (IoT), there has been a shift from devices being data consumers to data producers and consumers. What was once a one-way street has now become a cyclical relationship where the loop between devices and people has been closed.
This closed loop is generating an enormous amount of data, and in order to make sense of it all, there needs to be a level of processing and computation of data at some point in the loop. For industrial equipment and healthcare technology companies, this data overload problem can be addressed with edge computing.
Edge computing is pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network. It enables analytics and data gathering to occur at the source of the data, moving the computation of device-generated data to the “edge of the network”. “Edge of the network” is a broad definition encompassing the concept of moving computing out of a centralized location (the Cloud) and closer to the location of the end user, whatever that may look like for a specific company.
Edge computing explained.
IE and HHiT devices (machines, wearable health monitors, etc.) are now producing data at a rate greater than ever before. The intent of the Internet of Things (IoT) is to capture this data and distill it into usable insights than can then enable humans or the devices themselves to take action, addressing the ever-increasing demand for outcome-based services.
You can think of edge computing like our own conscious mind. The human mind is capable of inputting 11 million data points/second, but we are only consciously aware of 40 of those data points. The act of distilling 11 million data points down to the most relevant 40 is our mind’s equivalent of edge computing.
The 40 data points that we are aware of are based on the particular outcome we are trying to achieve. For example, when you are looking for your keys, you are filtering out all irrelevant data and only looking for “keys”. You aren’t consciously looking at the dishes in the sink, or the laundry you need to do, although your conscious mind is picking up on that as you go. The subconscious is storing all of that information, and only feeding the relevant information to your conscious mind when you make a “query” for particular information.
This is the same concept for edge computing. Devices are capable of producing more data than is needed to achieve a particular outcome. By intentionally designing outcome-based services, a manufacturer can identify the specific outcome and embed the computation at the edge, and transmit only the actionable insights offsite.
Putting computation capabilities at the edge of the network and on the devices themselves allows for an added layer of control to the customer for what data is remotely transmitted offsite and what information remains stored onsite. As discussed in a previous blog, addressing security concerns is one of the major barriers to remote connectivity for industrial equipment and healthcare technology customers.
The psychological underpinning behind resistance to remote connectivity and the IoT-enabled insights could very simply be an information overload. Including a level of edge computing into your devices and service offerings can help prevent the data overload to the ecosystem, at both the device and the human level.
Edge computing is essential to not only address security concerns, but also to ensure the device-generated data is able to be translated into usable insights for both the manufacturer and the customer. Very often, the information that the customer is worried about being transmitted for intellectual property concerns isn’t even data that the manufacturer is concerned with collecting. By keeping the IP level data at the edge, and transmitting only the data relevant to deliver on the services expected, you can address this security concern and enable yourself as the manufacturer to design outcome-based services by collecting only the actionable insights.
Establishing remote connectivity is an essential step to harnessing the full potential of IoT capabilities, and adding in a level of edge computing will make those insights more powerful while also addressing security concerns. To contribute to the conversation about remote connectivity and edge computing, please participate in this quick poll by TSIA.
In the meantime, please be sure to read other posts in my “IoT Strategy for Industrial Equipment and Healthcare Technology” blog series:
"IoT Strategy for Industrial Equipment and Technology Companies"
"Addressing Cybersecurity Concerns with the Internet of Things"
“The Role of Blockchain in Healthcare Technology and Industrial Equipment IoT Strategies”
Post Date: April 2, 2019
Sarah Swanson is a former research analyst for TSIA and was part of the company's "A-Team", which works to collect and analyze technology and services industry data for the benefit of TSIA members. She holds a Masters in Social Science Research from University of Chicago and has worked in the analytics field for 5 years applying research methodologies and quantitative analysis to various data sources. She has a passion for using data-driven processes to improve efficiencies and optimize performance.
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