Predictive network technology promotes proactivity

Thanks to predictive network technology, network problems can be anticipated and significantly reduced before they occur.

What is predictive network technology?

This new technology is based on a combination of calculations andArtificial Intelligence (AI) algorithms and machine language(ML). Early on, it issues warnings about potential network problems. And then, it submits corresponding solutions to the cases.

Titus Msenior analyst withEverest Group explains the logic behind predictive network technology. ” The predictive network technology operates by learning. It trains networks to learn from past situations by mobilizing huge masses of data from predictive analysis. This process involves three phases. First, the collection of network telemetry data. Second, trend recognition. And finally, the prediction and resolution of potential network anomalies that could influence the user experience.. “

Sam Halabihead of the technology consulting skills section at EY, also has his say. ” This technology can provide solutions for automatic or manual implementation. This depends on how it is used, and the IT network team is particularly responsible for this.

Proactivity: the sure value of predictive network technology

This technology is distinguished by the fact that it is a proactive technology in IT problem-solving. It has gone beyond the purely reactive model. This gives us a head start on the difficulties of the future. Halabi specifies the general sources of network disruption. ” Several factors come into play when the network is problematic. There may be transport network or bandwidth failures, lack of routing optimization, outages and so on. However, these factors create problems that corrupt business operations. Repeatedly, financial losses could result.

The potential risk of predictive network technology

Like all technologies, this one also presents a risk. The first concern with this technology is that the system will only make decisions based on the options available to it. “The system must first recognize a situation before it can resolve it. If the situation has not been foreseen or recognized in advance through learning, the system won’t be able to do anything about it. The problem would then persist”, warns Chuck Everette. The latter is Deep Instinct’s director of cybersecurity defense.

Everette was able to experiment with certain situations that were still problematic. The system made automatic decisions without actually solving the supposed problem. This was because the network was constantly trying to repair itself automatically.

This new technology is not the only one to mobilize the resources of AI and Machine Learning as problem-solving strategies. Society can currently use AI to solve these 15 social problems.