Enterprise networks and the organizations that support them are more complex than ever. With an abundance of task-specific devices and issue-specific tools, it’s become nearly impossible to understand the true state of network topology and behavior.
There is no single person, technology, or circumstance to blame for the current state of affairs. For decades, enterprise networks have grown organically. They evolved to support new challenges as the business expanded, acquired, and offered new products. Public clouds, IoT, Zero Trust, and edge computing further complicate network operations and management.
As organizations struggled to understand their network, they added new tools and departments, each with specific remits. While this helps address particular issues or concerns, the lack of data portability and transmissibility between tools and teams has made it harder than ever to ensure the network is constantly in policy and behaving as intended.
A digital twin creates a single source of truth that all teams within the organization can consult to prevent and resolve issues. How might this impact the operations of global IT teams? To answer this question, we partnered with 451 Research to conduct a survey that examined the prevalence and effectiveness of shared data models and digital twins.
451 Research compiled the data and incorporated their industry knowledge to deliver a paper titled “Examining the Effectiveness of Digital Twins in Network Modeling.” The Pathfinder report examines how data sharing approaches impact each role and the interaction between job functions from the perspective of cloud operations, network operations, and security operations.
We invite you to read this paper and see what your peers have to say about digital twin technology and how they believe it will improve their effectiveness.
Interested in seeing what digital twin technology can do for your network needs? Request a demo with our technical team.
The average network is a collection of configuration settings that exist in their own little island. They interact with each other and create situations where that interaction causes systemic issues in other places. Half of the job of a network engineer is figuring out those interactions and anticipating how they will impact other parts of the steady-state machine that we build to operate our applications. It’s hard enough to learn where all the switches are. Asking for anything more complicated is taxing for any engineer.
With the rise of networks that need to be more reliable for things like cloud applications and important use cases for financial or medical, it’s not enough to guess about the network state any longer. We can’t just hope that a configuration was done and that it was made in such a way as to lessen the impact on other systems. We can’t wish that things were configured correctly. We have to go one step further and actually verify that everything is done correctly. Adding that verification step into our routine is a source of contention, though. It’s a lot of extra work. It requires extra steps to get the information and make sure it’s accurate. It’s not what the standard network was built to provide. There needs to be a better tool out there to give us the info we need.