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In a time where end users have little tolerance for downtime or subpar performance, keeping network operations running at an optimal level is essential.
Outages are not only costly from a productivity perspective, but they can also expose an enterprise to security risks. Maintaining peak service levels in increasingly complex, highly distributed and virtualized enterprises requires effective network monitoring. But the capture of performance data from the physical layer to the application layer can be a complicated process hampered by a lack of visibility into end-to-end network activity.
While many organizations are benefiting from advances in network monitoring, fueled in part by AI network monitoring, most organizations are still relying on outdated tools. Eighty-six percent of enterprises are still using at least one legacy network or application monitoring tool, according to a study commissioned by network monitoring vendor ScienceLogic.
The management of these tools can become a problem if too many discrete offerings are used simultaneously. Indeed, 33% of the surveyed 207 IT decision-makers said their organizations are using 20 or more distinct monitoring tools.
With that many separate monitoring applications, it can be virtually impossible to get a complete and accurate picture of network performance. IT professionals struggle to understand how the performance of separate elements affects business operations. What's more, the multitude of tools can make prioritizing remediation difficult.
Arrival of AI to change network monitoring
IT organizations are eager to invest in more modern and comprehensive options to improve their network monitoring. Enter AI. AI network monitoring is a particularly promising discipline that could contribute valuable insights to infrastructure operations. Sixty-eight percent of organizations surveyed in the ScienceLogic research plan to invest in AI-based monitoring products in the next year.
Advanced monitoring tools are incorporating AI and machine learning-based analytics to correlate data from across myriad network assets. These tools use AI to discern what are often hidden infrastructure performance issues, and they can do so in real time. In addition, by applying AI to network monitoring and management, remediation can often be automated, thus freeing up operations staff to focus on more strategic activities.
AI's promise, however, comes with some caveats. It requires time to track traffic in order to distinguish normal patterns from anomalous behavior and to flag activity indicative of a service issue. And, as long as organizations take an ad hoc approach to monitoring by relying on too many distinct tools instead of a unified methodology, AI's positive effects will be diminished.
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