Traditional monitoring data like metrics, events, logs, and traces are vital for AIOps, but real-time high-quality data is needed for effective utilization, as poor data leads to inaccurate insights and missed opportunities.
Challenges in data quality for AIOps include managing vast diverse data, data silos, noise and redundancy, data integrity, and error-prone manual processes.
Observability provides in-depth insights beyond traditional monitoring, helping understand the internal states of systems and the reasons behind occurrences.
AIOps solutions offer enhanced efficiency, reduced downtime, and predictive capabilities, but their success significantly depends on the quality and completeness of the data.
Collection
[
|
...
]