What is edge computing?

The explosive growth and increasing computing power of IoT devices has resulted in massive volumes of data. And data volumes continue to grow as 5G networks increase the number of connected mobile devices.

In the past, the promise of cloud and AI was to automate and speed up innovation by driving actionable insight from data. But connected devices create data at an extraordinary scale and complexity, outpacing network and infrastructure capabilities.

Sending all device-generated data to a centralized data center or to the cloud causes bandwidth and latency issues. Edge computing offers a more efficient alternative—data is processed and analyzed closer to the point where it's created. Because data does not traverse over a network to a cloud or data center to be processed, latency is reduced.

Edge computing—and mobile edge computing on 5G networks—enables faster and more comprehensive data analysis, creating the opportunity for deeper insights, faster response times and improved customer experiences.

IoT devices, from smart vehicles to factory bots, generate more data than ever, yet most remains unused. For example, a McKinsey & Company study found that an offshore oil rig generates data from 30,000 sensors—but less than 1% of that data is actively used to determine actions.¹

Edge computing harnesses growing in-device computing capability to provide deep insights and predictive analysis in near-real time. This increased analytics capability in edge devices can power innovation to improve quality and enhance value.

It also raises important strategic questions: How do you manage the deployment of workloads that perform these types of actions in the presence of increased compute capacity? How can you use the embedded intelligence in devices to influence operational processes for your employees, your customers and your business more responsively?

In order to extract the most value from all those devices, significant volumes of computation must move to the edge.

Edge computing unlocks the potential of vast untapped data generated by connected devices. You can uncover new business opportunities, increase operational efficiency and provide faster, more reliable and consistent experiences for your customers.

The best edge computing models can help you accelerate performance by analyzing data locally. A well-considered approach to edge computing can keep workloads up-to-date according to predefined policies, can help maintain privacy and adhere to data residency laws and regulations.

But this process is not without its challenges. An effective edge computing model should address network security risks, management complexities and the limitations of latency and bandwidth. A viable model should help you:

• Manage your workloads across all clouds and on any number of devices
• Deploy applications to all edge locations reliably and seamlessly
• Maintain openness and flexibility to adopt to evolving needs
• Operate more securely and with confidence

No matter which variety of edge computing interests you—cloud edge, IoT edge or mobile edge—be sure that you find a solution that can help you accomplish the following goals.

Reduce unnecessary administrators, save the associated costs and deploy software where and when it’s needed.

Use an edge computing solution that nurtures the ability to innovate and can handle the diversity of equipment and devices in today’s marketplace.

Know that the right workloads are on the right machine at the right time. Make sure there’s an easy way to govern and enforce the policies of your enterprise.

Find a vendor with a multicloud platform and a comprehensive portfolio of services designed to increase scalability, accelerate performance and strengthen security in your edge deployments. Ask your vendor about extended services that maximize intelligence and performance at the edge.

Chief information officers (CIOs) in banking, mining, retail or just about any other industry are building strategies designed to personalize customer experiences, generate faster insights and actions and maintain continuous operations. This approach can be achieved by adopting a massively decentralized computing architecture, otherwise known as edge computing. However, within each industry there are particular use cases that drive the need for edge IT.

Banks might need to analyze ATM video feeds in real-time in order to increase consumer safety. Mining companies use their data to optimize operations and enhance worker safety. They also reduce energy consumption and boost productivity through data-driven insights. Retailers can personalize the shopping experiences for their customers and rapidly communicate specialized offers.

Companies that use kiosk services can automate the remote distribution and management of their kiosk-based applications. This method helps ensure continued operation, even when they aren’t connected or have poor network connectivity.