Transform Your IT Operations with AIOps

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.

Unify and Modernize Increasingly Complex IT Operations with AIOps

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.”

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Open Data Ingestion

An AIOps platform collects data of all types from various sources. This may include data on faults, logs, performance alerts, and tickets. The ability to ingest data from the most diverse data sources is critical. It allows for an accurate, real-time view of all the moving parts across hybrid IT environments.

Auto-Discovery

Given the incredibly dynamic nature of modern IT environments, an auto-discovery process is necessary to automatically collect data across all infrastructure and application domains. This includes on-premises, virtualized, and cloud deployments; it identifies all infrastructure devices, the running applications, and the resulting business transactions.

Correlation

Once data is ingested and devices are discovered, then it’s time for the AIOps platform to correlate this data in a contextual form. Automatic dependency mapping determines the relationships between infrastructure elements, such as the physical and virtual connections at the networking layer; between an application and its infrastructure, for instance, by mapping application flows to the supporting infrastructure; and between the business transactions and the applications.

Visualization

Once the end-to-end correlation process is completed, the insights need to be presented in an easy-to-use format. That’s what visualization is all about. Data is typically visualized in topology maps, application maps, business and operations dashboards, and other formats. Visualization is important because it allows IT operations to quickly pinpoint issues and take corrective actions.

Dependency Mapping

Finding the root cause of a problem is key, but it’s even more critical to determine recurring patterns and predict likely future events. AIOps uses supervised and unsupervised machine learning to determine patterns of events in a time-series. It also detects anomalies from expected behaviors and thresholds to predict outages and performance issues.

Machine Learning

Automation

All of these insights can then be turned into a wide range of intelligent actions performed automatically, from expediting service desk requests to end-to-end provisioning and deployment of network, compute, cloud and applications, to incident diagnostics and resolution.