One of the most common questions we get from customers is: how can edge computing improve and accelerate our decision-making process and our overall business performance?
To help answer these and other related edge computing questions, the product experts at Scale Computing have built SC//Insights, an educational resource designed to help explain the ins and outs of edge computing, virtualization, and hyperconverged infrastructure.
But before diving into how edge computing can transform the velocity and quality of critical business decisions, it’s helpful to take a step back to understand how edge computing works, the most common use cases, and how it will impact your operating budget.
In our Benefits of Edge Computing article, we explain how running applications in proximity to where they are used and where data is being generated can yield a number of diverse benefits. With massive volumes of data from devices such as IoT, video systems, and environmental sensors, placing computing resources at the edge enables all of this data to be collected and processed locally, making data transfer more cost-effective and practical to the people who need it.
At the edge, applications can run in a more efficient and predictable manner, even without a reliable connection to the Internet. By running applications closer to where they’re being used, edge computing also reduces latency and improves performance. Finally, the use of edge computing makes complying with data security and privacy regulations easier, as it minimizes the need for data movement – such considerations have become increasingly important for heavily regulated industries such as healthcare, financial services, and government agencies.
Data Data Everywhere
Data is the lifeblood of the modern enterprise. But as the volume of data continues to mushroom, the ability to process and analyze all of this data – and do so at scale – becomes increasingly challenging. Yet, it is this challenge that businesses must overcome to extract valuable insights, make informed decisions, and gain a competitive advantage.
In Edge Computing Solutions Revolutionize How Data is Processed and Analyzed, we detail how a modern edge computing architecture can help streamline complex data processing requirements and provide a foundational framework that enables the efficient and decentralized processing of data.
This post also provides several examples of how edge computing is being applied across an array of industries – from real-time inventory management and personalized customer experiences in the retail sector and traffic and energy monitoring in smart cities to precision farming in agriculture. It also catalogs the many different types of edge computing devices currently in use today, such as edge servers, gateways, routers, appliances, accelerators, and sensors, each serving a specific purpose in this decentralized approach to computing.
Of course, edge computing is particularly beneficial for real-time data processing and for handling large volumes of data. In our article, Edge Computing Technology Enables Real-time Data Processing and Decision-Making, we take a deep dive into how edge computing has become a fundamental operating requirement for a growing number of time-sensitive applications such as autonomous vehicles, industrial automation, and remote healthcare monitoring. By enabling data right at the source, edge computing also enables real-time analysis where up-to-the-minute data is critical for effective real-time decision-making.
One of the reasons why so much data is being generated at the edge is due to the explosion of connected devices in the industrial enterprise. From manufacturing and retail to healthcare, IoT sensors are being used to capture a wealth of real-time information to improve operational efficiency, predict equipment failures, personalize customer experiences, and enhance patient care. In our IoT Edge Computing post, we explore how edge computing has become instrumental in the domain of connected devices as it enables data processing and analysis to occur directly on the devices themselves or on nearby edge servers, reducing network congestion and enhancing data privacy and security.