Mounting IT complexities render traditional, domain-centric monitoring and IT operations management inadequate because they can’t correlate the onslaught of data created by various IT domains. They’re also unable to provide the insights that IT operations teams need to proactively manage their environments.

IT organizations need a new class of technology to modernize their processes by applying machine learning to automatically detect patterns and reduce noise, but that’s not all. If the IT inventory data is inaccurate, or without an application-centric view of the entire stack correlated with potentially disruptive operational events, powerful machine learning or artificial intelligence algorithms cannot work their magic.

Effective AIOps technology must first automate the discovery process across hybrid environments, create an accurate inventory – automatically updating the CMDB – and then correlate millions of data points across all IT domains. AIOps has the smarts to apply machine learning to detect patterns and reduce noise. It presents that information through an intuitive UI, so organizations can easily view and gain insights into what’s happening, both in the present and future.

Gartner is cited for coining the term AIOps, defined as “the application of machine learning (ML) and data science to IT operations problems. AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operations functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation.”