How does predictive analytics help network operations?
Predictive analytics can project network traffic flows, predict future trends and reduce latency. However, tools continue to evolve, so teams should use caution in their selection.
AI's integration into network operations has become hard to ignore. Enterprises have started to consider how AI can improve business outcomes. Meanwhile, network professionals have begun to consider how predictive analytics can support operations with its machine learning capabilities.
Predictive analytics tools apply ML analysis techniques to large sets of operational and behavioral network data. The tools develop an internal model of how the network and its equipment behave. Once they collect enough data and equipment over time, these tools can understand the current network state and predict its future state.
Network operations professionals can use predictive analytics tools in a variety of ways. These tools can provide useful insight into the network, such as predicting traffic patterns and detecting anomalous activities before they occur. Network operations professionals can use predictive analytics tools to improve network performance, reliability and availability.
How does predictive analytics help network operations?
Predictive analytics tools can help optimize network efficiency by analyzing patterns to predict future network behavior and performance. In addition, predictive analytics can go a step further and identify potential issues and suggest ways to fix problems that have occurred.
Predictive analytics can help network operations professionals with the following:
- Project network traffic flows.
- Predict future network usage.
- Reduce network latency.
- Improve network operations and maintenance.
Project network traffic flows
Predictive analytics tools can make useful projections about network traffic. With enough data, they can project how much traffic to expect at a given place in the network at a given time. Predictive analytics tools can also predict the mix of traffic types that will affect the network.
For example, predictive analytics could determine the amounts of traffic that will exist on the network simultaneously, such as real-time communications traffic and latency-insensitive bulk data transfer traffic.
These predictions also enable the tools to anticipate where and when congestion and packet loss will create problems in the network. Some tools can also suggest possible remedies, such as adjusting quality of service settings and redistributing connections among switches.
Predict future network usage
Enterprise network usage data also enables predictive tools to project future usage trends. For example, predictive analytics can project far enough into the future to support comprehensive network capacity planning.
If network operations teams have a reasonable map of the types of traffic they expect to change, as well as the location, they can better identify where networks need higher-speed services or devices. They can also see where to allocate capacity and ports.
Reduce network latency
While network latency is more of a matter of distance, predictive analytics tools can offer insight into where professionals can reduce latency. One way predictive analytics tools accomplish this is by identifying alternate traffic routes. Another way, specific to data centers, is by highlighting equipment with high device latency.
Improve network operations and maintenance
Network engineers can use predictive analytics tools that are pretrained with databases of operational data for common network equipment. Pretrained analytics tools work out of the box and are more accessible to enterprises without AI engineers to train tools from scratch. These tools already understand what to expect from normal operations, which means they can provide more accurate data.
Predictive analytics tools can spot the warning signs of impending equipment failure based on ongoing operational data and behavior from equipment in the enterprise's network. They can also predict when equipment will need updates, repairs or replacements.
For example, a predictive analytics tool could detect variations in transmission failure rates on a switch port or gradual increases of internal router temperatures. It then suggests the device either be serviced or replaced before it can fail enough to disrupt the network.
Network predictive analytics tools continue to evolve
ML-driven predictive analysis is not new to network management tools. Developers have embedded some form of the technology into these tools for the past decade. The advent of AI has accelerated rapid growth and change in the field.
For example, developers are adding large language models into the tools, typically to create virtual assistants. This new AI layer can help improve the accuracy of predictions, but it has additional use cases as well. Developers intend for AI to help staff understand data so they can make better use of findings. AI will aid network teams with management rather than solely improve core predictive functions.
The accuracy of predictions is highly variable across tools and environments. Results can fluctuate even when network teams use the same tool in the same organization. No one tool is ideal for everything. Some tools are better at traffic prediction, while others better suit tasks like hardware reliability assessments.
Although enterprises can benefit from predictive analytics and other AI tools, network teams should remain cautious in selection and deployment. Network teams should deploy predictive analytics tools once they ensure they can fully use the functionality.
John Burke is CTO and a research analyst at Nemertes Research. Burke joined Nemertes in 2005 with nearly two decades of technology experience. He has worked at all levels of IT, including as an end-user support specialist, programmer, system administrator, database specialist, network administrator, network architect and systems architect.
