AIOps uses artificial intelligence to simplify IT operations management and accelerate and automate problem resolution in complex IT environments. AIOps uses big data, analytics, and machine learning capabilities to collect and aggregate data generated by multiple IT, applications, and performance-monitoring tools, and autonomously analyze the data to detect significant events and patterns related to system performance and availability issues for rapid remediation.

The massive digital transformation of the last decade has rendered traditional approaches for IT management irrelevant. With the growing volume and velocity of digital data generated by IT, networks and applications, ITOps has reached a tipping point: tracking and managing IT complexity through human oversight, using manual or offline methods, became non-feasible. The need to cope with the exponential complexity of the IT network and with the increasing speed in which infrastructure problems must be addressed has given rise to a new set of tools, based on machine learning capabilities. These are the new platforms of AIOps — Artificial Intelligence for IT Operations.

Robust AIOps platforms help IT operations teams monitor, detect and mitigate irregular and anomalous behavior on IT infrastructure and services by leveraging advanced machine learning techniques to analyze and correlate every business parameter, providing real-time alerts and lowering mean time to detection and resolution. IT and DevOps teams harness AIOps capabilities to dramatically improve network oversight, enabling them to go from reactive to proactive management of issues, decrease time to detection and remediation, and drive efficiencies and cost reduction.

The Elements of AIOps

AIOps is predicated on bringing together diverse data from both IT operations management (ITOM) (metrics, events, etc.) and IT service management (ITSM) (incidents, changes, etc.). Breaking down data silos enables a holistic, correlative view of the network that supports next level analytics in real-time (as opposed to offline). ML methodologies are used on the live data streams to automate existing, manual analytics and enable new modalities of correlation and contextualization. These are the main pillars of leading AIOps platforms:

Full data coverage

A prerequisite of every AIOps platform is the ability to consolidate and analyze all business data. AIOps platforms must be able to ingest historical and real-time data by aggregating inputs from multi-cloud environments, containerized applications, storage, databases, events and logs, APIs and SDKs, APM, monitoring, and data streams etc. Regardless of the business’s original data architecture and silos, data is streamed into one centralized analytics platform to analyze 100 percent of data streams and metrics.

AI and predictive capabilities

Big data enables the application of ML to analyze vast quantities of diverse data. ML automates existing, manual analytics and enables new analytics on new data—all at a scale and speed unavailable without AIOps. AIOps platforms vary widely in their approaches to AI and ML, running the gamut from statistical and probabilistic analysis, to automated pattern discovery and prediction, unsupervised learning for anomaly detection and topological analysis, to any amalgamation of these techniques. Advanced AIOps employs ML to learn the unique behavior and seasonality of every metric using a library of model types for different signal types.

Advanced correlations

Correlations are crucial for understanding metrics in context. To transcend the mere detection of outliers, events must be correlated across metrics and dimensions with potential business impact and other concurrent processes. Abnormal correlation, naming correlation, graph correlation, and implicit analytics topology — or any combination thereof – are some of the derivatives used by AIOps solutions for granular correlation between metrics in real time.

Root cause analysis

For the patterns that AIOps platforms detect to be relevant and actionable, a context must be placed around any outlying event. Pruning down the network of correlations established by the automated pattern discovery to define causality chains linking cause and effect is key to reducing time to resolution. Root cause analysis ties between different events by providing probabilistic indications and establishing the context that enables rapid remediation.

Reduction of TTD & TTR

While AIOps systems can and do provide valuable insights about infrastructure, operations, application etc., at their heart they are geared at helping the business bounce back from events and glitches as rapidly as possible, thus reducing damages to the minimum. AIOps reduce MTTR through real-time analysis, event correlation, and root cause analysis. Fast remediation reduces downtime and associated loss of ROI, reputation and customers.

Engage-and-act capabilities

The “action” phase of AIOps relies on automated, closed-loop processes referred to as ITSM or “self-driving ITOM”. Currently, AIOps act capabilities are applicable for low level tasks such as automating “bounce the server” or an “open a ticket” type of script. But as the technology matures autonomous remediation is likely to become a dominant feature for leading platforms that can effectively communicate granular data and insights to both IT stakeholders and other IT systems for use in the remediation phase.

AIOps trends

AIOps can be used to enhance a broad range of IT operations processes and tasks, such as automatic alerts, automatic remediation (auto healing), A/B testing and more. This is enabled by applying anomaly detection, correlations and performance analysis.

Gartner anticipates that by 2023, 40% of DevOps teams will augment application and infrastructure monitoring tools with AIOps platform capabilities, and that over the next five years, wide-scope AIOps platforms will become the form-factor for the delivery of AIOps functionality. Their research also concluded that IT organizations have already started exploring AIOps in a DevOps context as part of the CI/CD cycle, to better predict potential problems prior to deployment as well as detect potential security issues that organizations often face. Use cases are already expanding beyond IT to include business monitoring, digital experience monitoring (DEM), and third party services.

Written by Anodot

Anodot is the leader in Autonomous Business Monitoring. Data-driven companies use Anodot's machine learning platform to detect business incidents in real time, helping slash time to detection by as much as 80 percent and reduce alert noise by as much as 95 percent. Thus far, Anodot has helped customers reclaim millions in time and revenue.

alert decoration image

What can Autonomous Business Monitoring do for you?

Book a discovery call. It’s short and painless, and a quick way to see what we've done for businesses like yours.