A new year is on the horizon, and IT planners are already thinking about their initiatives and priorities for 2021. We’ve all heard about how the global pandemic has accelerated technology trends like remote work, healthcare, and of course e-commerce. What do these diverse use cases have in common? They are all enabled by a wide variety of connected devices, including laptops, phones, and increasingly IoT devices like point-of-sale and medical equipment. These different endpoints connect to applications that are deployed everywhere, both on premises and over different types of cloud services.
An SD-WAN environment creates vast amounts of data about everything from network activity to distributed application usage and application performance. You can visualize and explore this data, but without the right tools, manually analyzing it to get the actionable insights you need simply isn’t practical.
How can you assure that all these IoT devices and other endpoints are delivering the performance, high availability, and security you require? If you’re an IT professional, you need a new approach that lets you proactively manage IoT devices and other endpoints at the network edge.
Dive deeper with AIOps and analytics
An AIOps approach can take network insights a step further, using AI, automation, and machine learning to define what’s normal for network activity and application performance—and spot anomalies. When you add data analytics powered by VMware Edge Network Intelligence, you open up the doors to the real-time analysis you need to ensure the best possible performance for your endpoints.
For example, in a healthcare setting, recognizing IoT devices and classifying them according to their type and location can provide valuable context that could make a big difference in patient outcomes. Consider an IoT device like an infusion pump in an emergency room, delivering critical medication to a patient over an IV. If a pump had problems connecting to the hospital’s server, it would be unable to identify the proper medicine and dosage—which could put patients at risk. Traditional network monitoring tools looking at a MAC or IP address could provide only limited insights. They would not be able to identify the device as a critical infusion pump, and would simply report that a device using the address is malfunctioning.
With its ability to recognize and classify IoT devices, AIOps could identify these devices as infusion pumps critical to patient healthcare. The network could understand their importance, and elevate alert levels in the event of a connection failure.
Leverage big data for proactive protection
Getting out in front of potential issues is key in IoT environments. AIOps and analytics can help IT teams take advantage of more data, more quickly, to respond to potential problems proactively. For example, suppose the hospital’s infusion pumps were to be hijacked by a bad actor. Without AIOps, the hospital’s IT team might not even spot the issue until healthcare delivery began to suffer. Even after discovering the problem, it could take time for IT to correlate infusion pump failures with network failures. On the other hand, an AIOps approach would have established a performance baseline for the devices and understood that a cluster of pumps should not be talking to a rogue endpoint. IT staff would have been alerted before problems started to happen, and patient outcomes were impacted.
With a cloud-native AIOps solution like VMware Edge Network Intelligence, collective insights about device behavior and threat intelligence on one network can also be shared anonymously across many customers. For example, if analysis showed that many pumps in different hospitals were experiencing similar types of failures, AIOps could alert IT teams about a potential hardware problem with all pumps from a specific manufacturer.
Sharpen your edge intelligence at our webinar
Ready to learn more about how you can realize the IoT outcomes your business needs? Sign up for our free webinar, “Operational Assurance for IoT Devices with VMware Edge Network Intelligence.” Join VMware’s Jay Thontakudi, Senior Product Marketing Manager and Anand Srinivas, Senior Staff Engineer to learn how VMware Edge Network Intelligence uses AI/ML and big data to proactively manage IoT device performance. They’ll show you how network intelligence ensures performance, security, and self-healing for IoT devices at the edge of a distributed enterprise while accessing cloud applications.
Register for our live online event, December 4 at 10:00 am Pacific time, or view it on demand.
Getting out in front of potential issues is key in IoT environments. AIOps and analytics can help IT teams take advantage of more data, more quickly, to respond to potential problems proactively. For example, suppose the hospital’s infusion pumps were to be hijacked by a bad actor. Without AIOps, the hospital’s IT team might not even spot the issue until healthcare delivery began to suffer. Even after discovering the problem, it could take time for IT to correlate infusion pump failures with network failures. On the other hand, an AIOps approach would have established a performance baseline for the devices and understood that a cluster of pumps should not be talking to a rogue endpoint. IT staff would have been alerted before problems started to happen, and patient outcomes were impacted.
