What were once revolutionary words when talking about enterprise WLAN – monitoring, visibility, single-pane-of-glass management – now sound conventional to the ears of network managers. Today, for many, the features those words describe have evolved into what is now known as the “cloud console.”
As WiFi networks have moved from merely supporting the business to serving as its backbone, WLAN management must evolve as well. Yesterday's security, scalability, and remediation are inadequate for the digitally transformed enterprise in which WiFi is the conduit to customers, suppliers, and mission-critical business applications.
IDC has observed that several innovation accelerators are uplifting capabilities across the enterprise IT stack. Machine learning is one accelerator that holds tremendous promise for the WLAN. IDC defines machine learning as a toolbox of computer algorithms that, through constant analysis of collected data, creates a model that enables systems to learn, discover, predict, and improve automatically without explicit programming. Another related innovation accelerator with implications for the enterprise WLAN is cognitive computing, which combines data mining, pattern recognition, and natural language processing to achieve intuitive process automation that resembles human thought patterns. In tandem, these accelerators can simplify and revolutionize enterprise WLAN management.
The power of machine learning and cognitive computing in WLAN management is the ability of the network to become self-healing, reducing mean time to remediation (MTTR) and allowing enterprise IT to focus on proactive tasks that further IT's enablement of business objectives. When paired with an intuitive, user-friendly management dashboard, cognitive computing reduces the learning curve for new staff and provides greater accessibility to actionable insights. Enterprise IT can use these insights to understand the "baseline" of the network (meaning what is normal and what is of concern) and how to make improvements to network design, policies, and security. Sounds good, right? But what if the network could understand its own baseline and make necessary adjustments, without human intervention?
Manually identifying problems on the network is not enough when one can take advantage of the benefits of machine learning. The cognitively-enabled network will learn what "normal" is and how to return itself to that status in the case of a problem. It will drill down into the details of what is causing an issue (such as a user, device, application, or something else) in an easy-to-understand way. Meanwhile, in public-facing organizations, machine learning and cognitive computing also can help the network glean customer analytics that can be shared with the lines of business. The ability for the network management platform to take network and customer analytics and create robust reports on critical KPIs is also a must-have.
The intuitiveness that machine learning and cognitive computing bring to enterprise WiFi management is an extension of the advances that cloud management has brought to enterprise WiFi accessibility, manageability, and scalability. Cloud and Big Data form the foundation for these innovation accelerators to usher in the next era of wireless network intelligence.
I encourage anyone evaluating a new WiFi solution to consider the value of machine learning and cognitive computing capabilities. Wireless technologies are leading the mobile device and application revolution, so it is important that the network be equipped with next-generation capabilities that maximize the power of the cloud and Big Data. What’s even more important is that the network retains the highest uptime, service levels, security, and reliability.
Nolan Greene is a Sr. Research Analyst with IDC’s Network Infrastructure group covering Enterprise Networks. In this role, he is responsible for market and technologytrends, forecasts, and competitive analysis in the Ethernet switching, routing, wireless LAN, and adjacent networking markets. While contributing to quarterly and yearly forecast and market share updates, he also assists in survey design, end-user interviews, and contributes to custom projects for IDC’s Consulting and Go-To-Market Services practices.
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