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How can network analytics tools be used to improve performance?

With pressure and expectations for network and enterprise performance rising, IT professionals are turning to network analytics tools that use machine learning to remain ahead of the game.

Today's virtualized enterprise networks present a complex challenge to IT professionals. IT organizations now operate in increasingly heterogeneous environments. The current mix of cloud-based and legacy, on-premises applications makes administering the IT environment an even more daunting task.  Add in sky-high corporate expectations for enterprise performance, and the need for more advanced network analytics tools becomes even more apparent.

Enterprise network management tools have not always kept pace with the rapidly evolving infrastructures they are designed to manage. Visibility across decentralized, multivendor environments has been one of the more difficult issues to solve. Cobbling together administrative tools from multiple vendors has often led to a very limited view into network activity.

The benefits of network analytics

Analytics -- parsing traffic and application data for both real-time incident information and longer-term trending -- is often promoted as a critical element in the quest for enterprise network optimization. Vendors are investing in cognitive technologies to both improve accuracy and accelerate troubleshooting. 

While there are vendors that offer specialized network analytics tools, enterprise buyers don't necessarily have to invest in a new discrete product. Networking vendors continue to improve the analytics capabilities of their network monitoring and management by using tools like machine learning to process high volumes of traffic and, in some cases, application data in a shorter time frame.

Network analytics tools can also make recommendations about the best ways to resolve a performance issue. Alert fatigue can be a problem when IT is flooded with alarms that often focus on relatively minor issues. However, networking vendors are making progress on consolidating and prioritizing events, knowing that having too many alarms puts IT at risk of missing a critical incident.

IT can use route analytics to identify protocol anomalies and structuring issues that can interfere with packet delivery. This information helps IT limit latency and avoid potential service disruptions.

Through ongoing trend analysis, network analytics tools promise to help identify service interruptions before they actually happen. These tools can also assist IT organizations in longer-term capacity planning by providing better insights into current bottlenecks. IT professionals can use this data to more accurately estimate future requirements. IT can also use analytics to make more incremental modifications to improve performance.

Even as network analytics continues to improve, some of the legacy challenges remain. Unifying data across a multivendor and increasingly virtualized environment can still be a difficult problem. IT needs to be aware of both the benefits and limitations of all the network analytics tools it has. The key to success is finding a way to effectively manage all of the information. As automation begins to play a larger role in enterprise management, IT will really begin to reap the benefits of analytics.

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