Three principles for building data-defined networks
Over the next decade, the complexity and load placed upon enterprise networks will rapidly outpace our ability to manage them effectively. This will constrain business growth, create performance bottlenecks, and undermine the experiences network teams are working to deliver.
The leading indicators of this crisis are easy to measure. In the Cisco 2021 Global Networking Trends Report, a survey of over 600 enterprise networks found an average of 4,400 monthly events impacting wireless networks. As organizations rush to enable a hybrid workforce, we expect to see that flood of issues rapidly increase.
For hybrid organizations, connectivity is a core network service, and interruptions caused by authentication, DHCP, DNS, and other errors can’t be allowed to grind the business to a halt. But when IT spends the majority of its time on troubleshooting hundreds of events a day, it’s impossible to find time to do the important work needed to drive business growth. The capacity of the team—and the network they support—suffers as a result.
The coming storm: preparing for the Great Resignation
Compounding these capacity challenges is the looming threat of the Great Resignation, the fear that a newly empowered remote workforce will pick up sticks and seek greener career grass. In another 2021 survey, Enterprise Strategy Group found that 55% of IT leaders indicated that their biggest skill gaps were in network architecture, administration, and planning. That’s a capacity problem that’s very hard to remediate.
To break this bottleneck and avoid the single points of failure that threaten network resilience, IT teams need better tools to help them adapt faster, troubleshoot with more precision, and free up time to properly plan for future capacity needs.
Putting network data to work for customers
At Cisco Meraki, we see a future where intelligent, data-defined networks give CIOs the power to readily adapt to change, effectively increase capacity, and find better ways to plan for the future. This is at the heart of a data-defined network, an architecture that not only can quantify the impact of an event, but provides precise evidence about the root cause and quickly points toward a recommended remediation.
There are three key elements that can unlock these kinds of data-driven improvements:
- Using data to drive outcomes. Going beyond basic analytics and insight, any investment in AI and ML must come in the service of a desirable customer outcome.
- Depth and breadth of data. When it comes to making relevant, contextual recommendations, more data beats a better model every day of the week. The best platforms can learn from data captured not just from your network, but from millions of other networks like yours. That breadth and depth is critical to informing effective decisions.
- Upholding trust. Having the data and the models is not sufficient. A data-defined platform must overdeliver on its ability to protect and govern the data it captures, continually improve the quality of its recommendations, and always keep control in the customers’ hands. Think autonomous, not 100% automated.
We’ll be talking more about our outcome-focused approach to AI and ML systems at Meraki in the coming months. In the meantime, we’d love to hear from you in the Meraki Community about which network challenges are impacting your team’s capacity and how more intelligent services might help define the future of your network.