Enterprise networks generate a lot of data. A lot. Imagine a network with 6000+ access points, 10 wireless controllers, a data center, dozens of branch offices, and over 10,000 roaming wireless devices covering an area the size of a small city. Every AP collects telemetry on its operating environment, radio performance, interference statistics, and the identities of devices that are connecting to them. The SD-WAN fabric connects distributed branch offices and remote workers to cloud applications and data center resources, managing thousands of connections and traffic flows over the course of a work day.

Trying to manually analyze and troubleshoot the traffic flowing through thousands of APs, switches, and routers is a near impossible task, even for the most sophisticated NetOps team. In a wireless environment, onboarding and interference errors can crop up randomly and intermittently, making it even more difficult to determine probable causes. How long does it take for devices to onboard as they are carried from segment to segment? Is taking 5 seconds to connect to an AP satisfactory or unacceptable performance? Is onboarding time consistent regardless of device density or does it vary unpredictably? How do you measure and compare application performance from SaaS providers to distributed branch offices and remote workers?

The irony of having mountains of telemetry and activity logs awaiting analysis by overworked IT teams is that there is too much noise from too much data for humans to deal with in a timely manner. Machine learning (ML) and applied artificial intelligence (AI) automates the analysis of trillions of bytes of telemetry, radio fingerprints, and network access points to uncover patterns in the chaos, and turn the findings into actionable insights or automated mitigation actions. Where is the nexus of AI/ML for enterprise network analytics? In the Cisco DNA Center and the Cloud.

Cisco AI Network Analytics in the Cloud

For years now, Cisco has been integrating AI/ML into many operational and security components, with Cisco DNA Center the focal point for insights and actions. Now we are adding new capabilities withCisco AI Network Analyticsin the Cloud. AI Network Analytics collects massive amounts of network data from Cisco DNA Centers at participating customer sites, encrypts and anonymizes the data to ensure privacy, and collates all of it into the Cisco Worldwide Data Platform. In this cloud, the aggregated data is analyzed with deep machine learning to reveal patterns and anomalies such as:

• Highly personalized network baselines with multiple levels of granularity that define