Network analytics, in its simplest definition, involves the analysis of network data and statistics to identify trends and patterns. Once identified, operators take the next step of ‘acting’ on this data—which typically involves a network operation or a set of operations.
Data is the currency on which digitalization runs – it is ever growing in quantity and quality, across all parts of the network. More
and more devices become available all the time, all collecting ever greater volumes of data.
At the same time, new technologies are always developing that can harvest and harness this data for the benefit of the business. Service providers cannot expect to increase their efficiency without it.
Customer experience depends on service delivery, which can only be guaranteed when the network is performing at its best. Through utilizing the insights provided by network analytics to maximize network uptime, vendors can help customers achieve their business goals – when the network performs, so does the business.
Effective network analytics can help us in achieving more efficient customer acquisition, optimizing marketing strategies and in determination of up-sell opportunities.
The modern network needs equally modern analytics tools that focus on the user experience and aid in troubleshooting. Network analytics must be capable of analyzing and correlating data from a wide range of devices and applications. Most network analytics solutions, however, are only capable of analyzing one part of the user experience, such as connectivity or application performance. Then it’s up to humans to query and correlate data and determine what action needs to be taken.
Machine learning is being added to network analytics to not only troubleshoot issues, but to diagnose the root causes, and recommend corrective actions. A form of artificial intelligence, machine learning uses algorithms to enable technology to learn, identify patterns, make decisions, and perform tasks without being programmed to do so. The more data a machine learning program consumes, the more intelligent the program becomes.
To retain customers and gain a competitive edge over peers, Communications Service Providers (CSPs) collect and analyze vast amounts of data from switches and networking equipment, as well as customer internet usage, applications, billing systems and more,. CSPs analyze billions of usage records to understand the overall health of their network, and identify areas to improve the performance and reliability of their networks.
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. 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.
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References:
https://www.vertica.com/solution/communication-and-network-analytics/
https://www.sequeldata.com/why-you-need-network-analytics-with-machine-learning-capabilities/