The rise of AIOps
AIOps is the application of machine learning and data science to enhance and partially replace primary IT functions like availability and performance monitoring, event correlation and analysis, IT service management and automation.
Enterprises have been increasingly AIOps as the traditional IT management techniques have struggled to cope with the accelerated pace of digital business transformation amidst the COVID-19 pandemic.
In this article, we discuss about the growth drivers of the AIOps market, its industry landscape and the way forward.
Factors driving growth of AIOps:
According to Mordor Intelligence, the AIOps market was valued at $13.5bn in 2020 and is projected to grow at c.21.05% CAGR to reach $40.9bn in 2026. The rapid shift of workloads to cloud will continue to be the largest contributor to this growth.
As companies continue to adopt a hybrid cloud approach involving multiple vendors, AIOps will offer clear visibility into these interdependencies, reduce operational risks of cloud migration and improve productivity.
Factors like increasing complexity of modern IT infrastructure, growing data volume and need to spot issues in real time for quick incident resolution will also contribute to the growth of AIOps.
The AIOps solutions market is led predominantly by managed services and enterprise solution providers who are seeking to offer comprehensive solutions to enterprises who require modern operations solutions to embark on their digital transformation journey.
Companies like Dynatrance, Devo, ScienceLogic, AppDynamics(Cisco), BMC Software, SignalFx(Splunk), Microsoft, Sumo Logic, Datadog, Zenoss, Broadcom, LogicMonitor, New Relic and Moogsoft dominate the AIOps solutions market.
Companies are developing AI algorithms that can handle multiple data sets together to save time through early warnings systems, filter signals from noise and increase automation of operations.
As remote work becomes the status quo for IT companies, AIOps vendors are looking to integrate AI more closely to detect anomalies faster and handle processing of large quantities of disparate data from multiple sources.
AIOps will play an important role in threat detection as IT infrastructure as security systems can leverage AIOps’ ML algorithms and AI’s self-learning capabilities to help the IT teams detect data breaches and violations.