What you need to know about predictive network technology | Knowledge of the data center

It’s almost magical, but predictive networking technology is anything but a trick.

Using artificial intelligence (AI) and machine language (ML) mathematical models and algorithms, predictive network technology alerts an organization to network issues as early as possible and offers problem-solving solutions . “The technology allows networks to learn from past instances using massive amounts of data through predictive analytics,” says Titus M, principal analyst at technology and business research firm Everest Group. “It collects network telemetry data, recognizes trends and predicts network difficulties that could negatively impact user experience, and suggests potential solutions to the problem.”

Predictive network technology can also suggest network remediation solutions for automatic or manual implementation, depending on the use case, at the discretion of the IT networking or operations team, explains Sam Halabi , head of technology consulting skills at business consulting firm EY.

The value of predictive network technology is that it helps network operations move from a reactive to a proactive model when it comes to resolving potential issues. “Network issues can occur due to many factors, such as transport network degradation, bandwidth congestion/traffic loss, suboptimal routing, network outages, etc.,” explains Halabi. “Such issues are very disruptive to the business and can have a major negative financial impact when they arise.”

Challenges and Opportunities

Although a powerful and beneficial tool, predictive network technology comes with serious risks. One concern is that the system can only make decisions based on the available options. “If you didn’t plan for it or train it for certain situations, the system might not be able to respond appropriately,” says Chuck Everette, director of cybersecurity defense at Deep. Instinct, a cybersecurity technology company. Everette reports that he has witnessed situations “where automated decisions were happening at such a rate that you couldn’t deal with the root cause due to constant changes in network adaptation trying to repair or recover. heal”.

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