Cisco’s Acquisitions of Viptela and Saggezza Highlight Value of Analytics in SD-WAN
Cisco made aggressive moves in software-defined wide area networking (SD-WAN) and analytics recently with the acquisitions of Viptela as well as the advanced analytics team from Saggezza. Given the amount of fanfare around these one-two punch announcements, it appears that the rest of the IT world is catching up to what SDxCentral readers figured out a long time ago — SD-WAN is here to stay. And the network knows all. When you apply analytics to network data on an SD-WAN, it offers insights on applications, users, devices, business processes, and almost everything IT needs to know to ensure employee productivity flows and the user experience is always superior.
As Cisco said in its blog announcing its move to acquire Saggezza assets, “As applications are moving to the cloud and billions of things are connecting to the network, our customers need a way to see and manage these increasingly complex networks.”
As enterprises upgrade their networks to handle more data intensive workloads and manage the complexities of cloud applications through an SD-WAN, they’re discovering that a centralized platform offers deeper insights into the network. These insights introduce a level of intelligence that uncovers hidden issues such as shadow IT and other data anomalies that often go unnoticed. It can also reduce or eliminate false positives by applying contextual relevance and situational awareness to the users and devices on the network.
For example, one of the nation’s top 10 banks deployed an SD-WAN to support its growth, phase out an aging WAN, and meet the demands for an increasingly mobile customer base. As the network continued to get more complex, the bank’s need for deeper analytics became more apparent. While the bank had network management tools in place to monitor and spot issues, they lacked visibility across the entire network, which made them reactive to network issues.
Their SD-WAN solution made it easier to get visibility across the entire network, including site-to-site traffic analysis. Then, by applying analytics to their network data, the bank was able to predict where and when additional network resources were needed to cover spikes in usage. This enables the bank’s network engineers to predict issues based on trends and take action before employees or customers experience slow response times or outages. It sounds simple until you realize the sheer volume of transactions, devices, and applications of hundreds of thousands of users on the network — not to mention the security demands.