Learn how edge computing can reduce latency, boost performance, and improve data security.
The business landscape constantly runs on data generated in high volumes from computers, applications, and Internet of Things devices, putting data at the centerpiece of every transaction.
With the evolution and adoption of more computing devices, it’s expected that higher volumes of data will continue to be generated, stored, and processed. According to Statista, the total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. It stood at 149 zettabytes in 2024. Up until 2028, global data creation is projected to grow to more than 394 zettabytes.
With this expected rise, there is a common fear that businesses will struggle with how to reduce latency and inefficiencies in data processing. This is where edge computing comes into play. It makes it possible for businesses to optimize their systems by moving data processing to the sources where the data is created, rather than depending on data centers for processing and analyzing.
SEE: What Is Predictive Analytics? (TechRepublic)
Edge computing is a framework that supports the generation, storage, and processing of data in the location where it is created without resorting to a data center or central data computing environment.
Under this framework, there is no need for data gathered from endpoints to make it back to centralized data services to be processed and analyzed. Rather, data is processed immediately within the same environment in which it is created.
SEE: What Is Data Storage? (TechRepublic)
Hosting applications and data on centralized hosting platforms or centers can create latency when users try to use them over the internet. The process of requesting data from these data centers can get slow when there are internet connectivity issues. Edge computing solves this issue by keeping the data on the edge of the devices for easier access.
It provides more data security and privacy protection because data is processed within the edge rather than from central servers.
However, this does not suggest that edge devices are not vulnerable by any means. Not at all. It only suggests that there is less data to be processed from the edge, so there is hardly a complete collection of data that hackers can pounce on.
In other words, privacy can easily be compromised when data hosted on centralized servers is hacked because they contain more comprehensive information about people, locations, and events. In contrast, because edge computing creates, processes, and analyzes just a set of data needed at an instant, other pieces of data that might compromise privacy in the event of a hack are not tampered with.
Moving data around on cloud hosting services costs businesses a lot of money.
However, with edge computing, organizations spend less on operational costs due to the minimal need to move data to the cloud. In addition, since data is processed in the same location where it’s generated, there is also a reduction in the bandwidth needed to handle the data load.
Meeting regulatory and compliance requirements can be made more difficult when data is hosted and managed by different data centers or hosting providers. This is because each data center has its own privacy and regulatory requirements.
However, this is not the case with edge computing because data is created, stored, and processed in one place.
Data can still be fetched and processed with little or no hindrances, even when there is a poor internet connectivity issue. In addition, when there is a failure at one edge device, it won’t alter the operation of other edge devices in the ecosystem, facilitating the reliability of the entire connected system.
AI/ML applications work by fetching and processing huge volumes of data, which can suffer latency and connectivity issues when the data is hosted on a centralized server.
In contrast, edge computing facilitates AI/ML applications because data is processed close to where it’s created, making it easier and faster for AI/ML to obtain results.
SEE: What Is Data Quality? (TechRepublic)
For more information on this topic, check out TechRepublic’s articles on data.
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This article was originally published in September 2022. It was updated by Antony Peyton in June 2025.
Franklin Okeke is an author and tech journalist with over five years of IT experience. Coming from a software development background, his writings span cybersecurity, AI, cloud computing, IoT and software development. In addition to pursuing a Master's degree in Cybersecurity & Human Factors from Bournemouth University, Franklin has two published books and four academic papers to his name. His writing also appears regularly in Enterprise Networking Planet, Techopedia, ServerWatch, The Register and other leading technology publications.
