{"id":5270,"date":"2023-06-30T15:07:40","date_gmt":"2023-06-30T19:07:40","guid":{"rendered":"https:\/\/solutionsreview.com\/network-monitoring\/?p=5270"},"modified":"2023-06-30T15:09:27","modified_gmt":"2023-06-30T19:09:27","slug":"aiops-finding-relief-from-alert-fatigue","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/network-monitoring\/aiops-finding-relief-from-alert-fatigue\/","title":{"rendered":"AIOps: Finding Relief from Alert Fatigue"},"content":{"rendered":"

\"AIOps\"<\/p>\n

Solutions Review\u2019s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. Nitin Kumar of Selector<\/a> examines AIOps network monitoring, and what it takes to find relief from alert fatigue.<\/b><\/em><\/p>\n

\"ExpertWhen you run a large-scale business infrastructure, diagnosing connectivity problems can feel like trying to find a needle in a haystack. Except it\u2019s not just one haystack, it\u2019s dozens. And the tools you use to search one haystack won\u2019t work for others; each requires a specialized approach. Also, you don\u2019t own many of those haystacks, other people do, and there\u2019s no way to know ahead of time if it\u2019s your job to find the needle or someone else\u2019s. For that matter, most of the time, you don\u2019t know you\u2019re looking for a needle until after it\u2019s found.<\/p>\n

Sound frustrating? Then you can understand why so many businesses struggle to maintain reliable, high-performing infrastructures. But the problem goes deeper. In many network operations centers (NOCs), outdated monitoring strategies add to the challenge. Too often, they function as if all that\u2019s needed to uncover problems is more information, and still more, until engineers get flooded with a firehose of data. Understanding which alerts among thousands require further investigation becomes almost impossible. To the point that NOC engineers get \u201calert fatigue\u201d and start tuning them out.<\/p>\n

Now, a new generation of solutions known as Artificial Intelligence for Operations (AIOps) offers a machine learning (ML)-based approach to monitoring and alerting. Algorithmic approaches can be incredibly powerful, turning mountains of telemetry data into actionable insights to help engineers understand, prioritize, and fix problems more quickly. But ML is not magic. If AIOps platforms don\u2019t collect the right data \u2014 or correlate the right signals, or surface the right alerts \u2014 they can end up perpetuating problems instead of solving them.<\/p>\n

In the market for network monitoring solutions? Check out our free Solutions Buyer’s Guide<\/strong><\/a>!<\/p>\n

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AIOps and Finding Relief from Alert Fatigue<\/strong><\/h2>\n
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Anatomy of an Alert Storm<\/strong><\/h3>\n

Modern networks incorporate more components and functions than ever before, making them exponentially more complex. These sprawling, highly dynamic infrastructures enable amazing new capabilities, but they also introduce countless new interdependencies\u2014 and new opportunities for connectivity issues to degrade or knock out services. And monitoring approaches that worked in simpler environments just can\u2019t keep up.<\/p>\n

The problem isn’t the lack of telemetry data, but the opposite. With so many metrics, logs, and alarms to interpret in real-time, engineers find themselves in an almost impossible position. Somehow, they need to analyze mountains of data while navigating \u201calert storms\u201d and correlating anomalies. And they need to do it quickly enough to take action before connectivity issues disrupt users and customers. It\u2019s a tall order, requiring engineers to overcome multiple barriers, including:<\/p>\n