Predicting the future is getting easier. While it’s still not possible to accurately forecast tomorrow’s winning lottery number, the ability to anticipate various types of damaging network issues — and nip them in the bud — is now available to any network manager.
Predictive analytic tools draw their power from a variety of different technologies and methodologies, including big data, data mining and statistical modeling. A predictive analytics tool can be trained, for instance, to use pattern recognition — the automated recognition of patterns and regularities in data — to identify issues before they become significant problems or result in partial or total network failures.
“Relying on multiple sources of clean data, along with built-in redundancies to deliver good, accurate information, visibility in the network can prevent issues rather than simply reacting to them,” says Richard Piasentin, chief strategy officer at network performance specialist Accedian. He notes that analytics can even be integrated into closed-loop orchestration systems to provide network self-correction for many common problems. “Ultimately, predictive analytics … helps companies save on operational costs and prevents issues from going unnoticed — issues that usually culminate in complete outages,” he says.
Analyzing network behavior, infrastructure thresholds
When properly designed and deployed, predictive analytics can deliver deep insights into an array of commonplace and unique network issues, helping operators handle everything from policy setting and network control to security, says Rahim Rasool, an associate data scientist with Data Science Dojo, a data science training organization. To tackle security issues, for instance, predictive analytics can use anomaly detection algorithms to sniff out suspicious activities and identify possible data breaches. “These algorithms scan the behavior of networks working in the transfer of data and distinguish legitimate…