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Organizations are increasingly focusing on reducing lag time in data transmission and processing as well as reducing the amount of data transmitted and stored in the cloud. Enterprises are feeling the need to transform the way they handle computing and are embracing Edge Computing to accelerate their Digital Transformation initiatives.
The traditional cloud-based architecture is built around a centralized data-processing warehouse, where data is gathered on the outermost edges and subsequently transmitted to the main servers for processing. Edge Computing essentially eliminates the need to transmit raw data to a centralized data center. It offers a distributed IT architecture, wherein it processes data close to the edge, where it is generated and consumed (it could be a retail store, a factory floor, a sprawling utility, etc) and also enables more immediate application of analytics and Artificial Intelligence (AI) capabilities.
The role of Edge Computing in Digital Transformation initiatives can enable faster, less restrictive data analysis – thus, creating the opportunity for deeper insights, faster response times, and improved customer experiences. Devices and machines harnessing the power of edge and AI can interpret, learn, and make decisions instantaneously.
Edge Computing on IoT devices can substantially reduce latency, enhance performance, and facilitate improved decision-making, paving the way for a streamlined IT infrastructure. Further, the arrival of the 5G technology combined with the power of Edge Computing and IoT can offer limitless opportunities going forward.
According to a study conducted by Gartner, 75% of enterprise data will be created and processed at the edge by 2025. Further, another study conducted by International Data Corporation (IDC) revealed that the global Edge Computing market will reach $250.6 billion in 2024 at a CAGR of 12.5% over the 2019–2024 forecast period.
Let us understand how businesses can leverage the benefits of Edge Computing and IoT and become more productive, more efficient, and more effective.
Manufacturing & Operations
Edge Computing help manufacturing businesses to enable better predictive maintenance, reduce costs and energy consumption while maintaining better reliability and productive uptime. Edge Computing can help enterprises make faster and more accurate business decisions concerning their facility and operations. Edge Computing can be exceedingly beneficial for businesses operating in areas, where bandwidth is low or non-existent. For example, offshore oil rigs can leverage the edge architecture to gather, monitor, and process data on a variety of environmental factors without having to rely on a distant data center infrastructure.
Supply Chain
A lot happens on the edge of a supply chain and a lot could go wrong. But by digitally connecting and automating the processes at the edge, organizations can extend the reach and visibility of their supply chains by dividing processes into sets of smaller, more manageable actions. Backed by AI and automated tools, the insights obtained from the edges of supply chains will enable businesses to effectively react to market conditions, identify long-term trends ahead of competitors and adjust strategies right down to the local level.
Workplace safety
Edge Computing can be the enabler for enhancing workplace safety across organizations. This Edge technology can combine and analyze data from on-site cameras, employee safety devices, and various other sensors to help enterprises keep a tab on workplace conditions or ensure that employees are adhering to established safety protocols, especially when a workplace is remote or unusually dangerous. For instance, sensors in valves at a petroleum refinery detect dangerously high pressure in the pipes, wherein shutoff needs to be triggered as quickly as possible. And if analysis of pressure data takes place at distant processing centers, the automatic shutoff instructions may come too late, but with processing power placed closer to the end devices, there is less latency and round-trip time (RTT) can be significantly reduced potentially saving downtime, damage to property, and even lives.
Autonomous Vehicles
Autonomous vehicles to operate safely will need to gather and analyze vast amounts of data pertaining to their surroundings, directions, weather conditions, communicating with other on-road vehicles, etc. Edge Computing will enable autonomous vehicles to collect, process, and share data between vehicles and to broader networks in real-time with almost no latency. Further, a network of edge data centers geographically positioned to collect and relay critical data to municipalities, emergency response services, and auto manufacturers, will ensure autonomous vehicles unparalleled reliability without crippling network infrastructures.
Retail
Edge Computing can help retail businesses maximize the use of IoT devices and transmit a plethora of data such as surveillance, stock tracking, sales data, etc in real-time. This technology can power AI and machine learning technologies to enhance the efficiency of operational processes as well as identify business opportunities such as an effective endcap or campaign, predict sales, optimize vendor ordering, etc.
Healthcare
The healthcare industry has been witnessing an exponential growth of patient data that are generated from devices, sensors, and other medical equipment. Edge Computing helps enterprises access data, especially problem data so that immediate action can be initiated by clinicians to help patients avoid health incidents in real-time. This edge architecture can drive an improved patient experience – IoT smart apps can help patients check their appointments as well as the schedule of doctors. These devices can also be leveraged when if any patient feels lost in the hospital and act as a guide through the different departments.
Conclusion
Although Edge Computing is yet to witness large-scale adoption, the promise of this digital technology cannot be overlooked. Edge Computing can help businesses fast-forward their Digital Transformation focus as it is the most viable architecture for deploying computing and storage resources closer to the data source. The relevance of this technology will be widely felt in coming times because it can effectively address emerging network problems associated with moving enormous volumes of data that organizations produce and consume today. It is no more just a problem of amount but also a matter of latency since applications depend on processing and responses that are increasingly time-sensitive.
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