Advanced Analytics

Network Analytics: How Algorithms Save the Day

Data is increasingly becoming a reliable source of business growth now. But the previous network protocols cannot cope with the evolving use cases. So, artificial intelligence and machine learning are helping operators to defuse the issues that threaten their network performance. With network analytics, you can monitor the behavioral pattern of systems and solve problems quicker. You can also predict new vulnerabilities. In this article at IT Pro Portal, Vishwas Puttasubbappa shares how network analytics benefits companies.

How Network Analytics Helps

Instead of spending resource hours on repetitive manual tasks, you can implement automation. Advanced network analytics also helps to penetrate further into the day-to-day network performance.

AIOps provides five layers of algorithms and analytics that further refine the network operations. What are they? Let’s find out below:

Selecting Data Set/ Source

Without a good source of data, the work you do is redundant. Using algorithms, you can collect good data. However, the complexity of using these algorithms often stops vendors from using analytics.

Pattern Discovery

Once you apply pattern discovery algorithms, you can find any prevalent patterns in the data in question. You can delve deeper into network analytics and procure valuable insights.

Drawing Inference

These algorithms help you get granular visibility in the datasets available to you. With the help of ‘what-if’ experiments, you can even connect two seemingly disconnected events.

Interactions and Coordination

After the algorithms draw out results, they make the data readable by machines. This further results in greater network transparency for actionable insights.


AIOps workflow has automation as the pinnacle of its efficiency. However, this is not an easy task because of the myriad complexities networking analytics can run into.

Benefits of Network Analytics

  • Reduction in operation costs and adherence to standards
  • Better collection of ‘proof of attack’ data
  • Improved in-depth performance analysis
  • On-the-spot network data visualization—flow visualization and QoS monitoring

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