Much has been said about the growth of IoT devices and the sheer volume of data they generate. Taking the analysis further ahead, IDC predicts that “every connected person in the world will have at least one digital data interaction every 18 seconds — likely from one of the billions of IoT devices, which are expected to generate over 90 ZB of data in 2025.”
The explosive growth of sensor data is creating an unprecedented need for technologies like edge computing and AI to realize the value of IoT data that help analyze and apply the insights back to the system for optimization.
Let’s revisit the definition of edge computing and understand how the technology is helping companies in mission-critical processes. Edge is a distributed computing model that brings computation, data storage, and power closer to the point of action or occurrence of an event. Processing data where it is created—at the edge—allows for more immediate application of analytics and AI capabilities.
Powering real-time decisions at the edge
In short, edge computing helps companies act on insights closer to where IoT data is created.
Because much of the IoT data created by edge devices or edge gateways need not traverse over a network to a cloud or datacenter to be processed, latency – the delay between transfer of data following a transfer instruction, is significantly reduced. Further, the real-time data processing at the edge allows for more immediate insights from connected devices and systems. Powered by edge and AI, devices and machines can interpret, learn, and make decisions instantaneously.
Let us consider other scenarios where IoT and edge computing are driving value through real-time insights for organizations:
- Mining, Energy & Utilities, Construction: In this sector, companies strive to optimize processes and production, which requires functional equipment. The key is to keep equipment up and running and avoid unplanned maintenance. Companies are deploying predictive maintenance solutions with edge computing based on IoT data and smart sensor analysis. These solutions use AI machine learning algorithms to analyze the equipment sensor data at a component level, enabling organizations to better predict and prevent equipment breakdowns in real-time. Predictive maintenance is one the areas that edge computing is expected to have a high impact.
- Manufacturing: With IoT devices and edge computing, monitoring asset performance in real-time using predictive analytics can determine component availability, predict potential equipment failures, and provide next best action for managing any disruption. When deviations occur, AI models can deliver key control settings to bring the manufacturing process back to optimum operating parameters. This can help avoid quality problems, improve throughput, and improve energy efficiency.
- Automotive: Many automotive manufacturers are essentially making cars into edge devices, equipping them with internal and external sensors that generate data. Edge computing can power decisions and actions in real time for individual vehicles— from braking to steering and lane change.
- Sustainable agriculture: In this space, companies are equipping crops with IoT-enabled sensors and using edge computing to monitor the growth needs and ideal harvest time for individual plants
Why edge computing is so important for connected operations
As IoT adoption across industries continue to increase, placing AI or analytical applications powered by edge computing, will drive a huge impact on cost and other parameters.
In collaboration with Oxford Economics, IBM Institute of Business Value conducted a survey among 1,500 executives across the C-Suite roles with direct knowledge of their organizations’ strategies, investments, and operations concerning edge computing. We found:
- 91% organizations will implement edge computing
- 84% believe edge computing applications to have a positive impact on their operational responsiveness
- 75% say they will invest in AI in the next three years to create new business models at the edge, combining intelligent workflows, automation, and edge device interconnectivity
- 54% will use edge computing applications for energy efficiency management
- Most edge disruptors expect ROI of over 20% in the next three years.
Edge computing can play a key role in these strategies, in different ways for different industries. In summary, those companies say edge computing can help:
- Drive operational responsiveness: Edge-induced responsiveness can lead to significant business benefits – reduce operating costs, automate workflows and accelerate decision making.
- Increase energy efficiency: Edge can help organizations manage energy efficiency and reduce power consumption. As more data is processed on the edge, less moves to and from the cloud, thus decreasing data latency and energy consumption.
- Drive business model innovation: Edge computing enables new business models that will capture untapped value from machine data.
Executives across industries are building strategies to generate faster insights and actions, maintain continuous operations and personalize customer experiences. To learn more on how companies across industries are planning to adopt edge computing, we invite you to read the IBV Study – Why organizations are betting on Edge Computing.
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