The workload for IT and network management teams has never been so high. Networks are more complex, convoluted, and chaotic than ever before, and tolerance for down time or slowdown is dwindling towards the vanishing point.
Network operations management pulls employees in several directions at once, requiring them to continuously monitor performance metrics, maintain and update security systems, and streamline configuration and orchestration for all the networks under their purview. At the same time, they need to think ahead with change management and capacity planning, and detect and resolve faults, failures, and incidents as close to instantly as possible.
NetOps and IT employees need help from advanced tools, and predictive analytics is a new entrant to their toolbox. Predictive analytics involves using artificial intelligence (AI) and machine learning (ML) to crunch masses of data from network devices such as routers, switches, access points and servers, deriving insights into network conditions, identifying patterns and anomalies, and forecasting and anticipating future events or behavior.
Predictive analytics is already in widespread use in many industries such as manufacturing, retail, finance, and healthcare, and it offers value to network management teams too.
There are many ways that predictive analytics applications can assist with NOM. By identifying patterns and anomalies in network behavior, predictive analytics tools can spot and raise an alert about potential issues that are too subtle for human analysis to detect. Network administrators can then address issues while they are still relatively quick and cheap to resolve.
Predictive maintenance, a subset of predictive analytics, can likewise detect the earliest signs of impending equipment or system failures before they occur, enabling NetOps teams to schedule maintenance activities, replace faulty components, or take corrective actions. Predictive analytics tools can also pick up on suspicious activities or deviations from normal behavior which could indicate a breach or a cyber attack, enabling a faster response and improved mitigation. In these ways, predictive analytics can reduce downtime and associated costs.
Predictive analytics can go beyond minimizing downtime to improve overall network performance and availability. By analyzing data on network traffic, latency, packet loss, bandwidth utilization, and other performance metrics, such tools can identify congestion or inefficiencies. With this information, network operators can optimize routing, allocate resources appropriately, and prioritize critical traffic, leading to improved network performance and reduced latency.
Network managers also benefit from actionable insights provided by data-driven predictions to make better decisions about network configuration, upgrades, capacity planning, and investments, leading to more efficient and effective network operations.
By identifying potential bottlenecks or capacity constraints, network administrators can enhance capacity planning and proactively scale their infrastructure to meet increasing demands, thereby optimizing resource allocation and improving customer experience. Overall, predictive analytics offers many ways for NOM teams to ratchet up their service delivery and customer satisfaction while lowering costs.
Predictive analytics uses AI and ML capabilities to apply a number of specific analysis techniques. Statistical modeling and analysis collects network performance data like network traffic, latency, packet loss, and throughput, and deploys methodologies like time series analysis, regression analysis, and correlation analysis to identify patterns, trends, and anomalies in network performance.
Statistical models help establish baseline performance metrics and detect deviations from expected behavior, enabling proactive identification of performance issues and potential bottlenecks. They can also be used to analyze historical data on network traffic and usage patterns, to forecast future network capacity requirements, and to build predictive maintenance models that notify netops teams about potential network failures or equipment malfunctions.
Another important technique is anomaly detection, which examines network data to identify abnormal behavior or deviations from expected patterns. By leveraging ML algorithms, organizations can detect anomalies in network traffic, system performance, or security events.
Additionally, network management teams rely on root cause analysis to help investigate and resolve incidents that occur. Predictive analytics tools can examine data from multiple sources, including logs, network devices, and performance metrics, to uncover hidden patterns and relationships that could have contributed to the issue. This allows network managers to understand the underlying causes of network problems and take proactive measures to address them.
Predictive analytics is not a magic wand. The insights, alerts, and predictions you receive will only be as reliable and accurate as the data with which they are generated. Network operations generate vast amounts of data, but that data may not be entirely accurate, consistent, and/or complete. It can also be challenging to handle, process, and integrate the enormous volumes of diverse data that arrive from different sources.
Additionally, real time insights require real time data, but it’s not always easy to collect, process, and analyze so much data in real or near-real time. Delays in these activities or in model training can limit the effectiveness of predictive analytics.
Network management teams need to learn to trust the guidance and forecasts produced by complex predictive models, which can operate as “black boxes.” It doesn’t help that models need to constantly adapt and evolve to stay relevant to ever-changing network environments. Organizations might also struggle to successfully integrate new AI and ML based predictive analytics solutions into their existing systems. There are certain technical and logistic requirements that teams need to meet before they can implement predictive analytics.
Finally, predictive analytics models aren’t always able to take every factor into account. Networks involve dynamic and interdependent components which can be unpredictable and sometimes unstable. External issues such as the weather, geopolitical events, or cyber threats can also influence network behavior, but may not be reflected in internal network data. NetOps teams need to find ways to incorporate these factors.
Before you head out to adopt predictive analytics for your network operations management teams, it’s useful to know some best practices that can guide you towards success.
Like everything in network management, it’s vital to start with setting clear goals and objectives for predictive analytics. Do you hope to shorten time to resolution for network incidents? Improve specific performance metrics? Decide what success looks like, and establish the KPIs you’ll track to measure progress.
You also need to ensure that you have the right human resources available. Predictive analytics for network management requires a skilled and diverse team that includes data scientists, network engineers, data analysts, IT infrastructure experts, data analysts, and business stakeholders who can provide insights into business challenges, requirements, and priorities of the project.
It’s just as important to choose the right tools and technologies that align with your objectives and dataset characteristics. Common models used in network operations management include regression, time series analysis, classification, clustering, and anomaly detection algorithms. Evaluate different models to find the best fit for your specific use case.
Another step is to identify and gather the necessary data. This includes network performance data, device logs, historical incident records, configuration data, and any other relevant information. Ensure that the data is of high quality, consistent, and representative of the network environment.
Finally, as you implement predictive analytics, include a feedback loop for continuous improvement. Foster collaboration between data scientists, network engineers, and other stakeholders involved in network operations, and regularly seek feedback, insights, and domain expertise from network operators to improve the predictive models. It’s best to start small and iterate across the organization, so you can validate the effectiveness of predictive analytics and make necessary adjustments.
Bear in mind that if you use an all-in-one predictive analytics solution for network operations management like NetOp.cloud, you will benefit from having most of the best practices built into the solution – the necessary technologies will already have been selected and implemented, the solution will continuously improve, and so on.
As network operations management continues to grow more pressured and complex, netops teams rely more heavily on advanced tools like predictive analytics. The value that predictive analytics applications can bring to NOM situations is immense, promising to radically speed up detection and resolution of network and security issues, slash downtime and its associated costs, and improve network performance, network operational efficiency, and service delivery, resulting in increased customer satisfaction.
But much depends on successful implementation of predictive analytics solutions. Organizations need to prepare the right skill sets, data pipelines, tools and technologies, and be aware of the challenges of poor data quality, delays in real time data management, integration with existing systems, and other issues which can slow adoption.
NetOp’s AI-powered network management solution analyzes your ongoing network performance along with historical data to enable predictive analytics for your network. It includes anomaly detection, automated configuration, predictions for capacity planning and bandwidth allocations, and more.
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