What is edge computing? Gartner defines edge computing as solutions that facilitate data processing at or near the source of data generation. This is done by placing data centers closer to the point of use. Edge data centers will complement existing public cloud or colocation deployments in a decentralized paradigm to improve response times, save bandwidth, and provide real-time analytics on edge devices.
Edge computing provides the benefits of cloud computing and big data processing while minimizing the use of an organization’s IT infrastructure. And given the business value IoT analytics can bring to banks and financial institutions, edge analytics is growing as an alternative to big data analytics where data resides onsite, to maintain security and control of user data and reduced risk of data loss or theft while financial transactions are processed on edge devices through seamless omnichannel banking tools.
The absolute quest for edge analytics is to have data computed in real-time on edge devices instead of streamlining the data back and forth the cloud for computing. Not only does edge analytics improve the customer’s experience, but it also reduces the mounting congestion on the cloud infrastructure as institutions and enterprises rely on it for storing and processing massive amounts of data.
With the increasing adoption of the internet of things (IoT), the improved computing capacity of edge devices and 5G on the horizon, edge analytics is being widely adopted across all industries especially in the financial sector. Business Insider Intelligence forecast that there will be more than 64 billion IoT devices by 2025, up from about 10 billion in 2018, and 9 billion in 2017. According to Market Research Future’s (MRFR) recent report, the global edge analytics market is predicted to reach $11 billion by 2023.
The rise of edge analytics in the financial sector to analyze important data in near real-time will soon extend to manufacturing, health care, and telecommunications industries who will tap edge computing for IoT analytics.