With powerful predictive analytics tools, enterprises can now leverage historical data to prevent network problems before they occur.
Enterprises already understand the crucial importance of network monitoring, and many have begun to deploy cutting-edge tools designed to do more than simply show how a network is performing. However, not all enterprises understand the distinct advantages of predictive network analytics (PNA), which is finally starting to make its way from big data centers into enterprise networks.
In many ways, PNA offers a jump step in capabilities, combining traditional network monitoring tools with the power of artificial intelligence (AI). PNA allows enterprises to actually predict network performance, head off potential issues, anticipate the impact of numerous network variables, and use historical data to generate actionable insights for use in the future.
Typical network monitoring can do a lot — provide threshold alerts, find network anomalies, identify root causes, and even help resolve issues. But predictive analytics goes beyond this, gathering as much high-quality historical data as possible to create an actionable overview of the network’s future — weeks and months down the line. With so much foresight, potential bottlenecks and breakdowns are easier to catch, and these tools are increasingly built to contain such problems on their own.
For example, modeling seasonal and special events helps administrators plan ahead to match behavioral trends. When used to its full potential, PNA can improve efficiencies and reduce costs, allowing IT teams to focus on managing and planning rather than constantly putting out fires.
Predictive capabilities are leaps and bounds ahead of the “proactive” networks that currently dominate enterprise IT. While proactive networks do collect and analyze network data to detect problems, they typically spin up only in response to an issue that has already impacted network performance. While such capabilities can help network administrators manage issues as they crop up, they don’t do much in the way of helping IT teams anticipate major disruptions or plan ahead for changes.
Integrating predictive network analytics into network management protocols takes time. This is due in part to the learning curve that comes with implementing new and complex tools. But even more importantly, in order for PNA to actually make accurate and useful predictions about the future, it needs a large volume of data. That data can take several months to acquire, and even then, it isn’t quite as simple as throwing data at the platform. Quality of data matters, and administrators may need to be prepared to structure and process data before it makes it into the system — which in turn may require the use of additional tools.
With large volumes of high-quality data, predictive analytics tools are able to take a broader perspective on the network, using machine learning to predict events and plan for changes. In this way, network issues can be detected and prevented well before they impact performance. Predictive forecasting can point to just about any infrastructural issue, including CPU usage, routing events, and performance thresholds. And by incorporating AI, the platform will be able to keep pace with ever-more-sophisticated hackers, anticipating future attack vectors and security gaps.
Predictive forecasting can provide relief for IT teams already spread thin with the problems of the day-to-day. Many IT teams are charged with predicting future network capacity based on gathering past metrics and performing complex calculations. The AI built into PNA tools can do these calculations on its own, drawing on data around usage and traffic to anticipate future use patterns and adjust the network accordingly.
PNA has traditionally been most commonly deployed in large data centers where efficiencies of scale and huge volumes of data have supported the investment. However, in recent years, PNA tools have progressed to the point where they are now viable options for large and mid-sized enterprise networks.
In data centers, predictive analytics solves configuration and interoperability issues that threaten to delay data delivery and cause storage redundancies. In this case, the goal is to predict data generation, store data correctly, avoid power waste or failures, and prevent performance bottlenecks. Shifting data has many potential consequences, and predictive simulations can use a host of data points to predict how changes — and even disasters — could impact the system overall.
Clearly, these are valuable capabilities. Enterprises have thus become eager to leverage tools that can in essence explore and learn the network on their own. That being said, deploying these tools can still pose challenges, especially if an enterprise is struggling with low-quality or poorly managed data, or simply doesn’t have a clearly defined set of objectives.
Although network performance monitoring is powerful, it often provides only proactive or post-facto insights into bottlenecks, outages, and other issues. For enterprises hoping to leverage the latest advancements in network management, their best bet will be to work with a managed service provider (MSP). A knowledgeable MSP can streamline the implementation process and make sure your network is properly set up to make use of these tools.
The sooner enterprises are able to incorporate PNA into their operations, the sooner they can put their valuable data to use, improve network performance, and take some pressure off their in-house IT teams. If your enterprise is interested in taking advantage of new developments in predictive network analytics, talk to the experts at Turn-key Technologies. With three decades of experience designing, building, and managing cutting-edge enterprise networks, we have the expertise and know-how to make sure your network achieves peak performance.
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