Despite innovations, today’s networks are complex and somewhat difficult to manage. Network operators find it rather challenging to come together and understand the broad information from various network elements. As a result of the lack of critical insights, companies experience a hard time assuring their compliance with various company and government policies. Network data analytics have become a necessity to address network and business concerns.
In simple terms, network data analytics uses various types of network data to identify trends and patterns. The information you derive from these trends helps to influence better decisions that will result in beneficial outcomes.
What is network data analytics?
Network data analysis provides insight into the area of the devices on the network and how they communicate with each other. Network analytics solutions shouldn’t just take in different types of network data; they should also compare and correlate it with other devices’ data.
It helps network operators and administrators better understand how their network functions. A network enterprise is quite diverse, with various devices from multiple vendors.
It also lets the operator get relevant data, analyze trends, and predict future behavior.
Why network data analytics is important
Network data analytics helps to give deeper insight into the IT network, aiding administrators in making business decisions that are well informed.
It helps prevent, detect, and respond to threats to security. It plays a vital role in detecting any abnormality in the network, predicting and notifying the administrator to prevent or respond to breaches in security.
It can also help industries to transform in all areas. It can be critical in boosting network efficiency and reducing operational expenditure.
Benefits of network analytics
- Ability to learn about the current health status of the network through real-time statistics.
- You can predict and prevent service disruptions
- Monitor and manage the process of compliance and assurance
- It improves the reliability of the network and guarantees good network performance
Some solutions grant some form of insight, but most of them operate within a data silo. The answer may only look at the traffic that passes through one type of device, or it may only take the report in one area of the user experience, like wireless connectivity.
Solution in-network data analytics can take all the dispersed data and handle the legwork to correlate it, after which it becomes possible to determine the root cause and the next step to take action.
Requirements for network data analytics
To benefit from the merits of network data analytics, enterprises need to have the infrastructure capability to generate performance and usage data for the network at low and high levels.
The data comprises the following:
- Bit rates through a specific physical network port.
- Collision and packet drop rates at a particular port.
- Latencies to build traffic through a port
- Number of packets or flows that are affected by specific security policies
- The number of flows that come from a given entity irrespective of location, device, application, or identity.
Enterprises also need to have a centralized system to collect, store and analyze voluminous information.
Using network data analytics for predictive analysis
Predictive network analytics is of interest to both network and security operations. By harnessing the techniques associated with Artificial Intelligence, particularly machine learning, network analytics tools gradually improve to predict future issues based on the current environmental data. These newer techniques enable devices to absorb and then correlate more data of more types from more sources. In turn, this acts as a powerhouse for significant improvements in the ability of the tools to understand the expected behavior of network entities, whether hardware, software, or human. And to elaborate on changes that are likely to occur. They supplement historical trends by projecting future trends.
In conclusion, network analytics helps predict networks’ performance through well-defined trends, providing an understanding of the technicalities involved in the functionality of networks and how to improve them to get the best result from using them.
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