What is AIOps?

AIOps, or Artificial Intelligence for IT Operations, is the next generation of IT operations analytics.

AIOps helps enterprises address complex IT challenges, including:

Dynamic Increases in the Nature of IT Architectures
Digital Business Transformation
Siloed IT Operations
Exponential Growth of Uncorrelated Data

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 to modernize their processes by applying a new class of technology that uses 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

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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

AIOps uses automated dependency mapping to build and track dynamic, multi-layer relationships between hybrid infrastructure entities, creating service and application topology maps across technology domains and across data center and cloud environments. Dependency mapping allows IT teams to accelerate incident response, quickly quantify the business impact of outages to ensure stakeholders are informed and issues are correctly prioritized, and improve uptime of business-critical applications, which helps ensure customer satisfaction.

Machine Learning

Root cause identification 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 capabilities to determine patterns of events in a time-series. It also performs anomaly detection based on expected behaviors and thresholds to predict outages and performance issues.

Anomaly Detection

Unsupervised machine learning is leveraged to learn the digital enterprise environment, recognize expected behavior, and set dynamic thresholds across multiple data sources and performance metrics. This data collection is drawn on to analyze event patterns in real time and compare those to expected behavior, alerting IT teams when a sequence of events or groups of events demonstrate activity that indicates anomalies are present.

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.