Category: Data, containerization, automation

According to both the Puppet State of DevOps Report and the Dynatrace Autonomous Cloud Survey, that is still the approach 90% of organizations are taking.

This is almost certainly doomed to fail, if 90% of these organizations continue to rely on manual troubleshooting, remediation and root-cause analysis. Organizations have begun to tap into the potential for AIOps to reduce this level of manual work and provide faster, automated solutions to get more precise insights into the performance and security of their applications, microservices and infrastructure.

In this article, and an accompanying article I’ll post later this month, I will describe what it looks like to deploy AIOps “the right way,” to ensure that you’re deriving maximum value from your AIOps solutions and identify where older iterations may have gone awry.

The first wave of AIOps solutions provided observability by ingesting data, including logs, metrics and traces, and analyzing this data for possible correlations to explain the root cause of technical problems or changed user behavior.

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