Edge computing is a distributed computing application that helps bring organisations closer to important data sources, such as IOT devices or local edge servers. This proximity and enhanced access to data at its source can deliver a wide array of business benefits: quicker insights, enhanced response time, and increased bandwidth availability.
The robust advancement of innovations like the Internet of Things (IoT) devices, along with the increasing computing power and technological capabilities of these devices, has resulted in unprecedented volumes of data to use. Data volumes will persistently grow as 5G networks raise the number of connected mobile devices.
Over the past few years, the assurance of cloud and artificial intelligence (AI) was supposed to lead to a new era of automation and speed innovation by driving actionable insights from data. But the unforeseen scale and intricacy of data that’s developed by connected devices has greatly exceeded the network and infrastructure functionalities.
Transmitting all the device-generated data to an orthodox centralised data centre or to the cloud results in significant bandwidth and latency issues. Edge computing provides a more productive solution: data is worked on and analysed in closer proximity to the place where it is created. Since data need not be transmitted over a network to a cloud or data centre in order to be processed, the disadvantages of latency are significantly reduced. Edge computing—and mobile edge computing on 5G networks—facilitates faster and more comprehensive data recording analysis, while simultaneously creating the opportunity for deeper insights, better response time, and greater customer experiences.
This new era of analytics capability in edge devices can power innovation to improve quality and enhance value. It allows businesses to take action in uncovering new business opportunities, enhance operational efficiency, and lastly, offer quicker, safe and consistent experiences for your customers. The most relevant edge computing models can help users speed up performance by analysing data locally. A systematic angle to edge computing can keep workloads up-to-date depending on predefined policies, can enable greater privacy and adherence to data residency laws and regulations.
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