Thanks to predictive network technology, network problems can be anticipated and reduced significantly before they occur.
Predictive network technology: what is it?
This new technology is based on calculations andArtificial Intelligence (AI) algorithms and of machine language (ML). Very early on, she issued warnings about potential network problems. And then it submits corresponding solutions to the cases.
Titus MSenior Analyst atEverest Group explains the logic of predictive network technology. ” The predictive network technology operates by learning. It trains networks to learn from past situations by mobilizing huge masses of data drawn from predictive analysis. This process goes through three phases. First, the collection of network telemetry data. Second, the recognition of trends. And finally the prediction and resolution of potential network anomalies that would influence the user experience. »
Sam Halabi, head of technology consulting skills at EY, also speaks. ” This technology can advance solutions for automatic or manual implementation. It depends on its use and the computer network team is particularly responsible for it. »
Proactivity: the safe bet of predictive network technology
This technology is distinguished by the fact that it is a proactive technology in solving computer problems. She knew how to go beyond the purely reactive model. This allows you to be one step ahead of the difficulties of the future. Halabi specifies the general sources of network disturbance. ” Several factors come into play when the network is problematic. There may be transport network or bandwidth failure, lack of routing optimization, outages, etc. However, these factors create problems that corrupt the works within the companies. 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 solely on the options that present themselves. “The system must first recognize a situation before it can resolve it. If the situation has not been foreseen or recognized in advance by learning, the system will not be able to do anything about it. The problem would then persist. » warns Chuck Everette. The latter being director of cybersecurity defense within Deep Instinct.
Everette was able to experiment with some still problematic situations. The system made decisions automatically without actually solving the supposed problem. And that 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.