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4 edge computing use cases delivering value in the enterprise

Research shows that the move toward edge computing will only increase over the next few years, and we've identified four areas where it is already proving to be of great value.

The use of edge computing in the enterprise is set to dramatically expand. In a 2019 report, Gartner predicted that more than 50% of large enterprises will deploy at least one edge computing use case to support IoT or immersive experiences by the end of 2021, which is up from less than 5% in 2019.

The number of edge computing use cases will jump further after that, with Gartner predicting that more than half of large enterprises will have at least six edge computing use cases deployed by the end of 2023. In 2019, only 1% of large organizations had six or more edge computing deployments.

The rise in edge computing corresponds to the growing organizational need for real-time analysis at endpoints, as well as the explosion of data being created at the edge. "The rapid development and adaptation of IoT, sensors, mobile devices and other connected devices mean more data and more widespread needs for computing," said Bruce Guptill, chief strategist at Addressable Markets and a member of The Analyst Syndicate, a community of senior independent analysts. "But, with the volumes of data in use, the vast range of data types and formats, the increasing need to protect more types of data in more ways and the need to utilize the data on so many devices of so many types, it's really become inefficient to go back and forth to multiple clouds. And network bandwidth is not keeping up with the trend, so response times get worse."

The reasons for edge computing use cases and deployments vary based on enterprise requirements and industry trends, but experts pointed to several categories of use that are driving adoption. Here's a look at four areas where edge computing is already delivering value and showing increased potential.

1. Enhanced workplace safety

Panic buttons have been around for decades, letting employees call for help with the push of a button. But, for much of their lifespan, these panic buttons simply emitted noise to draw attention. Modern employee safety devices (ESDs) are part of integrated platforms processing locational data at the edge to relay the location of a worker in distress, said Scott Likens, who leads advisory firm PwC's New Services and Emerging Tech practice. Likens cited the use of such devices to help enhance the safety of hotel housekeepers as they move from room to room.

Advancements in sensors, computer vision and AI are further expanding workplace safety applications. Running these technologies at the edge can help organizations better monitor conditions at remote worksites, such as oil rigs, or at temporary and outdoor locations, such as construction sites, to identify and alert to dangerous conditions in real time -- a capability only possible in such places where connectivity limits and latency issues would make near-instantaneous processing of the data impossible. For example, companies can deploy the technology to ensure employees follow set safety protocols or stay out of restricted areas.

Organizations can also opt for edge computing to enhance safety even when connectivity and latency aren't problems, said Dan Miklovic, founder and principal analyst at Lean Manufacturing Research LLC and also a member of The Analyst Syndicate.

Another example is companies using locational data from employees on-site to enforce the new social distancing requirements during the COVID-19 pandemic, alerting them if they move and stay too close together. Miklovic, who pointed to the pilot that Ford Motor Co. is using in its facilities as an example, explained that, because such locational data has no value beyond that moment, the information can be collected and processed on the edge rather than moved and stored in the corporate data center.

Edge computing market growth

2. Improved healthcare

According to a 2019 Dell Technologies report, the volume of data being managed by healthcare providers has exploded, growing by 878% from 2016 to 2018 -- a faster rate of data growth than in financial services, manufacturing and media. At the same time, the healthcare industry has had to adopt a growing list of technologies -- from automation to machine learning -- to make the most of all that data. Add edge computing to that list, as clinicians seek to understand more about the state of their patients in real time.

Healthcare data is coming from a variety of devices, including in doctor's offices, in hospitals and on patients themselves. Moving all that data to a central location for analysis could create bandwidth congestion, and all the data doesn't even need to be moved to centralized servers. Every single normal heart rate reading from a patient's medical device, for example, may not need to be retained. However, some pieces of data are so critical that the need to analyze and understand them can't be subject to any delay due to low latency or unreliable network connectivity.

Here's where edge computing comes into play, according to Likens. Edge computing can take and process data coming from endpoint medical devices in real time and identify what data points aren't critical -- i.e., the normal heart rate readings -- yet also identify, process and react to the critical data points, thereby alerting clinicians to act on those as quickly as possible.

3. Transportation advances

Autonomous vehicles are another prime edge computing use case, as they can only operate safely and reliably when they're able to analyze in real time all the data required to drive. The volume of data that these vehicles amass is staggering. Industry experts estimated that the data generated by a single autonomous car could be between 5 TB and 20 TB a day. And, while 5G will certainly be able to handle more capacity, the existing 4G network is nowhere near capable of being able to handle all that data at sufficient speeds.

"Autonomous vehicles must aggregate and process huge amounts of data of different types in different ways from multiple different sources -- including other vehicles -- more or less instantaneously while in motion," Guptill said.

As such, onboard computing power and edge data centers will be needed for mission-critical processing for navigation, vehicle-to-vehicle communications and integration with emerging smart cities. The need is significant enough that an industry association, Automotive Edge Computing Consortium, has formed to support the network buildout needed to support connected cars.

Edge computing will also help civic authorities, such as traffic agencies, public transformation departments and private transportation companies, better manage their vehicle fleets and overall traffic flow by enabling rapid adjustments based on real-time, on-the-ground conditions, Miklovic said. For instance, edge computing platforms deployed to handle vehicle data could determine which areas are experiencing congestion and then reroute vehicles to lighten traffic.

4. Equipment monitoring

The volume of data coming from IoT devices continues to grow at a rapid clip. IDC estimated that there will be 41.6 billion connected IoT devices generating 79.4 zettabytes of data in 2025.

However, all that data doesn't need to be handled in centralized servers; similar to the healthcare edge computing use cases, every temperature reading from every connected thermometer, for example, isn't important. Rather, most organizations only need to bring aggregate data or average readings back to their central systems, or they only need to know when such readings indicate a problem, such as a temperature on a unit that's out of normal range.

Edge computing enables organizations to take and understand the data near those endpoint devices, thereby limiting the cost and complexity of sending reams of often unneeded data points to central systems, while still gaining the benefits of understanding the performance of its equipment.

The ROI of this is critical: The insights into the data generated by endpoint devices enable remote monitoring so organizations can identify performance problems and safety issues early, even when no one is on-site. Using edge computing with predictive and prescriptive analytics can deliver even bigger ROI, as they enable organizations to predict the optimal time to service their equipment.

"Software and systems running supply chain management and optimization will depend much more on edge computing in order to mitigate or even prevent at least some of the resource and output availability issues we've all been experiencing," Guptill added.

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