IT operations are more complex than ever owing to heterogeneous environments and increasing tool stack. It requires a breadth of IT monitoring capabilities to quickly identify and resolve critical issues before they wreak havoc on the business. But alert volumes captured from different monitoring tools become overbearing. IT teams become frustrated with "alert fatigue" because they have to sort through and triage individual events manually. It causes alert floods, which lead to distraction and cost valuable time, which could be utilized remediating the actual root-cause of events.
Algorithmic or machine-driven alert correlation is a smart way to make sense out of this deluge of data and separate right signals from the noise. It identifies incident patterns in a customizable manner to isolate critical issues. Alert correlation can be automated by creating system-generated patterns through machine learning. The generated data is normalized into a digestible format. Further data enrichment is performed via configuration information, custom tags, and operational categories.