As disparate apps proliferate in enterprise environments, so have the number of uncorrelated data sources, the sheer amount of data produced and the number of false alarms and operational problems whose causes are opaque.
Visibility is the big problem AIOps attempts to remedy. Operations teams often cannot correlate data from multiple monitoring systems. Today, the objective is prediction, not simply observation; application performance, not simply hardware status; the ability to ingest and make sense of apps that produce huge volumes of data.
Two decades ago, for example, operations staff could not monitor an app server's performance inside a Java virtual machine (JVM).
Today, the issue is resiliency and performance issues of containerized microservices, analysts at Forrester say.
AIOps applies artificial intelligence, machine learning or other advanced analytics to business and operations data, providing correlations, prescriptive and predictive answers in real time.
Enterprise monitoring solutions using an AIOps approach include CA Technologies, Zenoss and others, Forrester notes.
Intelligent analytics overlay apps provided by firms such as BigPanda, FixStream, and Moogsoft have developed solutions that provide an intelligent overlay of AI or ML algorithms that ops teams can apply across multiple monitoring solutions.
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