By Anand JK Jain, Vice President – Solutions Engineering, MSys Technologies
By reducing data transmission costs and losses, edge computing helps businesses achieve high efficiency, reliable storage, and real-time insights into critical business parameters.
The enviable growth of edge computing is very much evident today. Just a cursory look at the numbers and one immediately realizes the growth potential the category has. From a market cap of $15.96 bn in 2023, edge computing is estimated to grow 36.3% CAGR to reach $139.58 bn by 2030, as per Fortune Business Insights. This spectacular growth is the transformative shift edge computing has enabled in how businesses store, analyze, and manage data today. Moreover, the ability of edge computing to extract information and help organizations make superior data-based decisions sets it apart from the rest.
Edge Computing: A Paradigm Shift
The widespread adoption of IoT has necessitated the shift from the centralized data storage approach to more agile, nimble, decentralised networks. Computing models in the past relied on centralized data, although the rising prominence of real-time applications today has warranted the shift to a decentralized philosophy. Edge computing is one manifestation of decentralisation, and by processing data on the “Edge” with the help of local devices and servers, edge computing offers enhanced performance, better efficiency, reduced latency, and decreased transmission cost, among others.
Data Storage Revolution
The most crucial impact of edge computing is on the way data is stored across servers and networks around the world. Unlike before, when the data storage was largely centralized, with big servers in specific geographic areas offering access, decentralised edge computing distributes the data in small clusters across several regional locations. The decentralised approach fortifies the safety and dependability of data storage, mitigating the risks of cyberattacks or interruptions and ensuring a resilient and secure system.
a) Edge-to-Cloud Synchronisation: With the help of edge computing, organizations can create a hybrid model to synchronize data exchange between edge and centralized storage systems. This makes the data available at both ends, offering firms the flexibility to use either method subject to specific requirements of projects, applications, or use cases.
b) Reducing Bandwidth Costs: By enabling data storage and analysis near the local devices, edge computing can significantly reduce the need for data transmission, thereby reducing the costs associated with data storage and bandwidth requirements. This saving is especially prominent for companies that deal with massive data and offer real-time capabilities to their users.
c) Enhanced Security: Edge computing allows storage and analysis of data at the source, thereby eliminating the need to transmit data from central servers to analysis centers. This minimizes transmission losses and boosts safety by reducing data hacks or breaches during the transmission process. Further, by decentralizing the data storage and access over many data points, edge computing makes the entire system robust, safer, and more secure than others.
d) Real-time analysis: Edge computing facilitates the real-time response to data inputs, allowing organizations to make fast and reliable decisions. Further, edge computing and filtration mechanisms will enable firms to sift pertinent data that can be analyzed and implemented swiftly in matters of strategic interests. This selective capability, in turn, reduces the costs associated with data collection, storage, and analysis while equipping organisations with effective decision-making capabilities.
e) Improved reliability: Central data stores are vulnerable to various risks, making them a less reliable option in this day and age of high uncertainty. These data centers can fall prey to infrastructure failures, natural calamities, hostile wars, and unpredictable situations. On the other hand, Edge computing is a more reliable option as it ensures redundancy by storing and distributing data through many nodes. This helps the systems to maintain operations even when a data node or two fails.
Application of Edge Computing: Real-world Cases
a) Healthcare Sector: By allowing real-time monitoring of patients’ data on crucial health indicators, edge computing is revolutionising the healthcare sector like never before. The wearable devices equipped with edge computing can collect and analyze the data at the source, and by transferring the vital information to professionals, the technology is contributing to enhancing the efficacy of the healthcare industry.
b) Optimising Manufacturing: Edge computing in conjunction with the Industrial IoT is helping the manufacturing industry to rewrite its success story. Factory floors equipped with IoT devices collect the data at the source, which is then processed and evaluated by edge computing technology. The results on crucial indicators such as equipment status, production efficiency, and maintenance requirements are then passed on to managers, who can use this information to enhance efficiency, reduce downtime, and run proactive maintenance schedules.
c) Personalised Retail Experience: By embedding smart sensors in shelves and aisles, IoT devices and edge computing can offer end users personalised and immersive retail experiences. These technologies also help retailers better understand their customers and innovate products/services aligned with the activities, interests, and opinions of the target market. No wonder many retailers are adopting edge computing today to boost their customer base and business profitability.
Conclusion
Edge computing offers multiple benefits across the value chain – from reducing cost to increasing efficiency to security data transfers. The technology allows the collection and analysis of relevant data at the source, which helps reduce latency and bandwidth costs while significantly enhancing the redundancy factor and efficiency of the computing processes. In sum, edge computing is a crucial prerequisite for realising the full potential of the data revolution that smart sensors, IoT devices, and allied technologies, among others, have heralded.
