Lately, more and more industries are moving their technology closer to the edge. At this rate, Gartner’s prediction that by 2025, 75% of enterprise data will be generated and processed at the edge might even be exceeded.
So what, you might ask, is all the fuss about?
Edge computing is not only fast and reliable, it’s also proving essential to keep up with the explosion of IoT devices in the workplace. Years ago, organizations could send all their data to the cloud or a corporate data center for processing, analysis, and storage. Today, the steady increase in the volume of data collected has made this approach impractical—and businesses still need to keep operating smoothly. This is where edge computing comes into play.
By bringing data collecting and analytics to the edge, enterprises can process data in real-time while operating more efficiently and securely.
Here are a few ways enterprises are harnessing the power of edge computing to their advantage.
But First: What is Edge Computing?
Edge computing is a distributed IT model that brings data storage and processing closer to the data source, rather than sending it to the cloud. When data stays closer to the source, you experience reduced latency and bandwidth usage, enhanced security, improved response times, and real-time insights.
5 Impactful Edge Computing Use Cases
Now that we’re all in sync on the benefits edge computing offers, let’s check out a few real-world use cases that prove how edge computing can positively impact your business.
1. Predictive Maintenance
Manufacturers want to be able to detect and analyze changes in their products before a failure occurs—and edge computing lets them do just that. Predictive maintenance allows users to anticipate any potential service disruptions, handle them, and proceed with confidence that their operations will go smoothly.
With edge technology, manufacturers can use sensor data collected on the shop floor to inform their decisions in real-time—there’s no lag in receiving or processing the information. Edge computing enables proactive maintenance, improving productivity, production quality, and operational efficiency.
For example, a multi-auto parts manufacturer avoided operations shutdown using RapidMiner for predictive maintenance. They leveraged sensor data and log entries to drastically reduce the risk of shutdown—likely avoiding 1-2 shutdowns per year, each of which could cost upwards of $20 million dollars per day.
2. Remote Monitoring
Wouldn’t it be great if you could monitor what was going on with your operations from afar?
Remote monitoring is especially critical for oil and gas companies for two reasons—if they experience a failure (see: the Deepwater Horizon oil spill), it can have disastrous consequences, and their plants are often in remote locations.
If oil and gas companies are only using the cloud to store and transmit data from the plant to whoever is remotely monitoring the plant, they’re dealing with slower speeds and worse connection—both of which are critical if something is awry.
By leveraging edge computing, oil and gas companies can have their data processed locally, meaning they can access real-time analytics that rely less on strong connectivity to keep things running smoothly.
A report from Cisco estimated that the average oil platform generates up to 2TB of data per day. As Jane Ren, CEO and founder of software company Atomiton said, “Many offshore facilities [are] working on satellite communications at a speed of around 2Mbps… it’s still not practical. The obvious solution would be to deal with that data on site as close as possible to where it is generated.”
3. Fraud Detection in Financial Services
Because edge computing takes place closer to the source device, it enables financial institutions to carry out real-time fraud detection at the transaction level.
Running AI-enabled analytics at the edge allows banks and financial institutions to identify fraudulent patterns as they happen, rather than after the fact—so they can proactively address these issues and reduce their negative financial impact. This also protects the institution’s reputation, ensures regulatory compliance, and increases customer satisfaction.
For instance, ATM cameras can use facial recognition technology at the edge to detect if the person trying to take out money from an account is really the account holder and alert the bank and police if they suspect criminal activity.
4. Enhanced Customer Support
Today, customers expect quick, accurate answers to their questions. IDC estimates that by 2025, the average person will interact with connected devices approximately 4,800 times a day—having on-demand support is necessary for most businesses to keep up with such a high volume of interactions.
By implementing edge computing, businesses can not only deliver real-time customer service, but also provide a hyper-personalized, omnichannel experience for their customers. By processing customer data—location, time of day, past shopping history, etc.—and reacting appropriately with targeted messaging or offers, organizations can provide a better experience for their customers in the moment.
5. Reimagining the Retail Experience
We’ve already established that customer expectations are higher than ever before. One thing retailers can do to keep up with them is using edge computing to process and analyze data from point-of-sale systems, customer interactions, and security cameras in real time.
Digitally, edge computing can help online retailers identify and monitor sales trends, how products are displayed on the site, and effective promotions and coupon codes. In the post-COVID world, customers also expect a seamless experience from brick-and-mortar retailers, requiring shops to leverage edge computing in combination with IoT to manage inventory, contactless checkout, virtual dressing rooms, staffing needs, and smart shelves.
A recent MIT report detailed how Gap Inc. is implementing edge technology to process transactions in-store, remotely monitor devices, and enable teams to build intuitive mobile user experiences using real-time sales data.
Bonus: Where Do Edge Computing and AIoT Intersect?
AIoT, or artificial intelligence of things, is just what it sounds like—a combination of AI and IoT. When devices are AIoT-enabled, they can gather and analyze data, and use that data to make decisions without human involvement.
Adding edge computing to the mix means that these decisions can be made locally in real-time—reducing communication latency between IoT devices, improving response time, and processing the data that matters as close to the source as possible.
For example, RapidMiner’s partnership with Hivecell, the Edge-as-a-Service company, allows Hivecell users to pool together data and use it to build centralized RapidMiner models. These models can then be pushed out at the edge, allowing for swift implementation, lower operating costs, and better customer experience.
Today’s enterprises have more data than ever before—and edge computing can help them use it to deliver better experiences to customers, faster. Who wouldn’t want that?
Edge computing is just one piece of the puzzle. Check out 50 Ways to Impact Your Business with AI to see concrete ways data science can help propel your organization forward today.