Spotting anomalies is one thing. Making sense of them is another.
In Part 3 of our guide, we focus on how to rank and correlate anomalies to uncover what really matters — and cut through alert fatigue in large-scale data environments.
“For every anomaly found in a metric, there is a notion of how far it deviates from normal as well as how long the anomaly lasts. These notions are called deviation and duration, respectively.”
What’s inside:
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How to assign significance scores to anomalies using Bayesian models
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Techniques for correlating related anomalies into a single, actionable incident
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The role of behavioral topology learning and metric relationships
