What is network analytics? It is a software engine that analyzes the results that are pooled together through data collection. These results can come from many different sources. For example routers, servers, or a configuration database.
These processes are also automated and can be used for many different devices. They can furthermore improve the experience of the user and do not drastically increase costs in doing so.
Collecting Data
Data can be collected in three different ways. The first way is “deep packet inspection.” This uses a software-designated application that can perceive the correspondence that is being used. The second is “streaming telemetry” which cuts down the lag in the data compilation.
Telemetry has the breakdown of anything, it can be complex, or have application-specific numbers. The more telemetry a system can stream the better the results that are used to make more accurate decisions.
The third is “context.” This is when a certain circumstance in which a system irregularity occurs. Similar irregularities in different circumstances can require a different setup. When this happens the analytics engine should be arranged with the variables for the context. For instance the network type or the application.
Data Scanning
After the data is collected from all the different sources, the software program then begins to analyze it. This analysis can correlate the present state with the model of perfect performance. When there are discrepancies the program might suggest improvements, or show its findings to an upgraded program.
Most of these analytical engines offer advice on how well the program carries out the tasks through “Decision Trees.” The decision tree measures the best network device alteration to advance the performance of that variable.
The decision tree expands depending on the number of sources pouring through the telemetry and the number of choices for creating better performance.
Furthermore when comparing the cloud to local analytics, consider the power for processing and the communication between other networks. The cloud-hosted analytics advance from the updated algorithms in the data.
When the analytics engine is local, it is displayed as more insightful, and has more useful advice for performance as well. Both processes should be considered, including the cloud for larger calculating sources, then the local analytics for huge increases in performance.
It can also detect what apps are being used, so you can determine which apps can be cleaned off to be using your network the most efficiently.
Benefits
All the information collected from network analytics can be serviceable in several different tasks. For example, identifying connected endpoints or evaluating the health of devices.
Furthermore, when a data source is detected as a discrepancy the analytics engine provides improvements to solve the problem.
Another benefit is when an endpoint is identified, the network analytics looks inside the traffic to pick up on the protocols. Then it will correspond with the data from other sources and build a profile for the endpoint.
Additionally, when it comes to catching incoming security issues, the network analytics watches the endpoint route to pick up on anomalies. This can display that the endpoint could be compromised. All of these are just some of the great benefits that come from network analytics.
