AIOps uses AI/ML and analytics to consolidate alerts, events, issues, trouble tickets, etc., and provides actionable insights to the IT team or takes corrective action automatically on their behalf. In this sense it provides focus, eliminating the noise and fatigue that comes with triaging a never-ending barrage of alerts, and instead looking below the surface to identify what is actually going on with the underlying systems. In many cases the AI/ML is actually better and faster than humans in detecting anomalies, recognizing patterns, predicting events, and narrowing down root causes – hence providing critical insights for taking corrective actions.
It also provides actionable context, giving IT teams the ability to act quickly and decisively. Taken to its limit, this contextual insight from correlated and analyzed data can ultimately enable automated optimizations and corrections…in short, “autonomous network operation”. It can also help baseline the system’s performance and compare key metrics as changes are made to identify improvements – resulting in an ongoing process of correcting, assessing and tuning based on “closed-loop” automation. By continuously and automatically crunching through reams of data, AIOps eliminates a lot of guesswork, biases and finger-pointing while reducing mean time to resolution (MTTR) and improving operational efficiency.
Data, however, is AIOps’ Achilles Heel, because without the right data in sufficient quantity at the appropriate frequency and high quality, the AI/ML can’t provide accurate analysis. In fact, it can potentially have a detrimental effect by reaching erroneous conclusions.