The proliferation of devices and sensor-laden applications driven by the Internet of Things (IoT) is constantly generating a deluge of data, but traditional data processing methods and network architectures are struggling to keep up. Globally, the number of IoT devices is expected to more than double by 2030, reaching a staggering 32 billion, according to data from research firm Transforma Insights.
To process and analyze the data influx from these devices effectively in real time, edge computing is the only realistic solution. Edge computing brings the processing power closer to the source of the data, at the very “edge” of the network.
For enterprises, consumers, and the operators providing the infrastructure, the question isn’t whether to adopt edge computing, but when. A recent industry survey on enterprise edge adoption reinforces this sense of urgency, with 40 percent of data center professionals globally citing the need for low latency and high bandwidth as the primary driver for deploying edge data centers.
Edge computing offers significant advantages beyond lower latency, including reduced data transfer costs, enhanced data security, compliance with national privacy and sovereignty regulations, and uninterrupted business operations. This inevitable shift to edge computing, however, necessitates a significant change in data center leasing and location strategies.
Why Location Matters
Unlike traditional data centers, one of the defining features of edge data centers is the need to be within close proximity to the end users utilizing the data. Existing data centers, often concentrated in urban environments, struggle to meet the needs of secondary and tertiary markets where mobile and Internet penetration is rising. Edge facilities can help to cater the rising demand for digital services, particularly in underserved areas and rural regions. For instance, edge computing can facilitate real-time consultations between doctors and patients in remote locations or allow for faster, local processing and analysis of data from medical devices like diagnostic equipment. Similarly, real-time processing of data from vehicle sensors can help with safe navigation for autonomous vehicles in these regions.
