Edge computing is one of the latest technologies sweeping enterprises across the globe. Indeed, Gartner predicts that businesses will create and process over 75 percent of enterprise-generated data outside of the cloud or centralized data centers by 2025.

If you want to maintain a competitive edge and extract the most value from your data, you’ll want to seize this opportunity too.

In this blog, we’ll define what edge computing is and how it can underpin your wider digital transformation initiatives.

What do we mean by edge computing today?

In short, edge computing enables you to collect, process, and act upon data at or closer to the source. This happens through decentralized edge devices, such as routers, gateways, smart cameras, and other IoT devices that can perform computational tasks locally.

Edge computing isn’t new, but it is evolving rapidly. For example, many businesses are now blending their edge systems with 5G networks (where possible) to improve connectivity and achieve ultra-low latency. The technology is also adapting to specific industry needs, allowing extremely remote operations — such as mining sites — to deploy robust edge controllers miles underground in order to track ore characteristics and monitor vibrations.

4 edge computing use cases that complement DX initiatives

Edge computing and digital transformation go hand-in-hand. Why? Because real-time insights enable your business to work towards your strategic goals with agility, rather than wait and risk your competition overtaking you.

This is particularly useful in industries such as manufacturing and product development, where it’s common to run multiple factories in distributed locations. By deploying local edge devices, these organizations can collect and act upon insights on the factory floor at speed.

Here are four specific use cases that complement DX initiatives:

1. Get real-time process insights

Edge computing devices make insights readily available with low latency. This is crucial for automated workflows and machine learning processes which rely on reliable intelligence to complete tasks.

Some of these machine learning processes will pass data through a centralized data center, but most sit on the edge. Even models that you develop centrally may end up on an edge computing network, running on local devices such as phones. Consider retail employees working on the shop floor — through a mobile application, they may be able to quickly see whether an item is in stock in their very shop, online or in another shop location.

2. Monitor errors and boost efficiency

Edge computing can also monitor performance through advanced robotics, automation, and machine-to-machine communication.

Consider Formula 1 cars. During a race, every tenth of a second matters — the quicker a pit crew can determine which components need maintenance, the quicker they can get the car back onto the track. Using advanced processors, these cars continually collect, process, and send data back to the relevant engineers and pit crew teams mid-race. That means there’s no stopping or starting; with real-time insights, teams can spend precious seconds resolving problems rather than diagnosing them.

In a manufacturing environment, edge computing devices can identify faults, such as flow problems or motor breakages, and either fix them or route them to a human supervisor. This means you can understand what you need to fix, as well as analyze why it failed in the first place. Down the line, this can help you to limit downtime, reduce waste and harmful emissions, and improve product quality.

3. Manage warehouse stock and tracking

Often sitting close to the factory floor, warehouses are continuously shifting products in and out for assembly or shipping. As a result, there’s a lot of data to keep track of, including stock levels and item status.

Edge computing can monitor each product or product part in real time. This helps you make better decisions when ordering more stock, streamlining your assembly processes, or communicating with your shipping partners.

4. Improve data security

Beyond improving business intelligence and operational efficiency, edge computing can also bolster your security posture. With no single point of entry, it’s harder for cybercriminals to break into the network.

What’s more, the devices process data onsite and typically discard it once they complete an action, which reduces the risk of a data breach. While some data goes to the centralized cloud server for further processing, this is often less sensitive.

Digital transformation on the edge

Data is fundamental to any digital transformation initiative; it’s the connective tissue that meshes your processes together. But it’s difficult to make the most of this data when you cannot scale your efforts — or you suffer from latency issues.

Edge computing, supported by automation, allows you to process and act upon data where you can use it most effectively — at the source. With powerful insights at your fingertips, you can make real-time decisions that’ll improve your operations and deliver strategic value. This is crucial for supporting your digital transformation efforts, not only now but years down the line.

Of course, configuring edge computing can be tricky. This is because distributed systems may have hundreds of thousands of points that all require regular careful monitoring and updating by IT operations.

At Success Software Services, we provide IoT and edge computing practices within our product development services. So, if you require some expert advice, please get in touch with our team.