Network analytics, or more correctly network data analytics, is any process where network data is collected and analyzed to improve the performance, reliability, visibility, or security of the network.
This can be as simple as using network data to identify an endpoint, or as complicated as using big data principles and tools to build detailed user profiles which reveal malicious intentions.
What is network data?
Network data is literally just the data that travels over the network. It doesn’t matter what type of networking technology is being used, or how sophisticated the devices are.
Also Read: What is Subscriber Data Management System?
The data coming over the wire is what network data analytics needs to collect.
Network analytics is a phenomenon in which the network data is collected and analyzed to improve the performance, reliability, visibility, or security of the network.
How does Network Data Analytics Work?
Network analytics works by identifying the devices within the network. With these devices identified, they then inspect the traffic to and from each of them.
This process is called “packet inspection“. From there, they use AI to correlate this information with other data in the system, like
- Historical network performance data (for example, when did the network get congested?)
- Customer relationship data (is the user in question in an enterprise or residential location?)
- Customer transactional data (has he purchased in this store before? Is he making a recurring purchase?).
This information is either correlated by AI or supplied by the administrator.
After correlation, the analysis produces “Network Insights” for quick action by administrators. These insights are based on up-to-the-moment facts about what is happening on the network, rather than guesses or estimates that would be based on historical trends.
How can I benefit from network Data analytics?
The visibility & insights presented by network analytics can be used for several procedures, such as spotting bottlenecks, evaluating the health of devices, root-cause analysis, issue remediation, identifying connected endpoints, and probing for potential security lapses.
Malware infections can be detected by monitoring for traffic anomalies, such as outbound connections to known malicious hosts or domains, self-propagating behavior, or the use of kernel or administrative level functions.
In order to improve performance, Network Analytics compares incoming data with pre-programmed models to detect anomalies. Real-time telemetry data is provided as a model of ideal network performance.
- When the data source detects less performance than ideal or deviates from the functional benchmarks, analytics may recommend adjustments and actions that can improve engine performance.
- Network analytics can recommend corrective actions for problems identified in the network. These steps include guided remediation, where the engine network administrator sets out the steps to perform.
- On more advanced systems it can perform closed-loop remediation, where it sends instructions to the network controller’s automation component to make changes automatically.
- Optimized utilization of network resources: Analytics can help you understand your network and optimize network performance and low-cost structures in balancing and utilizing resources as optimally as possible.
- New Revenue Flows: Analyzes play a key role in mining insights that can identify new revenue streams and generate data-based business cases for quick action.
- Faster time to market: Analytics can help simplify capacity planning for new services. As a result, the resources needed for a new or expanded service can be calculated and provided much in advance for live streaming, ensuring that products reach the market on time.
How does Network Analytics Collect Data?
Network analytics tools are no longer confined to the environment where they are deployed.
The Data is collected from numerous sources, including from servers such as
- DHCP,
- Active Directory
- RADIUS,
- DNS, and
- Syslog, and from network devices using protocols such as NetFlow, traceroute, and SNMP.
They can use telemetry and deep packet inspection (DPI) to build a rich database from which contextual information can be derived.
