Network operations, or NetOps, teams have traditionally used performance monitoring tools to manage the health and performance of corporate networks. However, the growth in network usage combined with disaggregated network deployments has led many to seek alternative methods of performance monitoring, including the use of AI for IT operations, or AIOps.

This article compares traditional performance monitoring methods found in NetOps practices and explains why teams will ultimately turn to adopting AIOps.

What is NetOps?

NetOps is derived from DevOps, a set of computer software development principles that leverage operational feedback to power rapid application development and implementation. The goal of DevOps is to provide more useful, reliable, and secure digital services to end users.

The NetOps principles embrace the Agile nature of DevOps and apply it to the processes of managing, growing, and scaling in network management practices. Using this methodology, NetOps tools automatically collect network health data. NetOps personnel can analyze the data to make informed decisions on how to tune network components and achieve the best possible network performance.

NetOps relies heavily on the use of automation to streamline network management and troubleshooting tasks. These automations speed up the time needed to identify areas of the network that could be modified to increase performance in critical areas. NetOps administrators manually analyze this collected and curated data to make informed decisions.

The Next Step for NetOps: AI

While NetOps principles drive advances in network performance agility, expansion into public and private clouds and edge computing has increased overall complexity. This increasing complexity can create bottlenecks in the network analysis phase of the NetOps process.

AI can analyze network health data using tools with built-in data analysis functionality.

Until recently, the only option IT decision makers had to eliminate this bottleneck was to increase the size of the NetOps team. With an increased number, more people could see the network analysis data which would be translated into actionable tasks. Thus, the ability to actualize network performance improvements is often offset by significantly higher costs required to implement said improvements.

To strike a balance between money spent and performance gains achievable, a second option has emerged in the form of AIOps. AI can analyze network health data using tools with built-in data analysis functionality. It can provide detailed, granular advice on network changes teams need to make to improve performance for the entire network or for identified critical applications.

NetOps staff will seek AIOps tools or risk falling behind

As more NetOps teams realize that the lack of profitability of dedicating more administrative human resources to network health analysis is a losing proposition, they will begin to push for AIOps tools. which will greatly reduce the data analysis phase of the process.

Most NetOps practitioners realize that as more and more applications, services and devices are added to the network – and networks expand further into various devices and clouds – the amount of data collected on health and performance will soon become so large that it will overwhelm their teams. So, the ability to rely on AIOps tools that can automatically analyze data and provide fixes for network performance issues is something that will become highly desirable.