AIOps, the latest application of artificial intelligence for IT operations, is used to automate and improve IT operations. It combines AI algorithms and human intervention to provide full visibility into the performance of the IT systems. AIOps leverages the existing sources to glean information such as logs, monitoring systems, application alerts and performance test outcomes from different channels. As the ongoing digital revolution is taking over the conventional market space, the digital data available to process will be enormous. Deriving value from the voluminous amount of data available from sources like cloud infrastructures, third party systems, IoT devices, and mobile systems, among others is no longer possible by utilizing simple automation or manual intervention. This is where AIOps comes into the rescue.
AIOps works with machine learning and data science, so that they can give IT operations teams a real-time understanding of issues affecting the availability or performance of their systems. To address business complexities and drive innovation, interest and adoption of it has increased exponentially. According to Gartner, 40 percent of companies will be switching to AIOps by 2020. Several key reasons are enabling them to make this shift, including the volume of data they receive every day, deriving value from these large data in a shorter time, identifying and responding issues in real-time, and more.
AIOps also have the potential to improve automation by enabling workflows to be triggered with or without human intervention. As having two major components, big data and machine learning, it requires a move away from siloed IT data that can aggregate observational data, alongside engagement data inside a big data platform. Afterward, it implies inclusive analytics and machine learning strategy against the combined IT data.
As AIOps drives a significant development of IT operating models, businesses must consider some vital measures while adopting it. The first and foremost thing is the business culture change. It is imperative even when AIOps is constrained solely to monitoring operations. In this context, business leaders and staff as well need to make a transition away from traditional ways and views of roles in the workplace. In doing so, this will help make a success of digitalization.
Implementing AIOps will empower IT operations and observability teams in order to lessen event noise and prioritize business-critical issues, support the pace of application releases and DevOps processes, proactively spot issues and quickly respond to it, and model and envisage workload capacity requirements to optimize resource usage and cost.
Gartner identifies a list of AIOps functionalities that help improve the IT operations across a business. These include historical data management, streaming data management, Log data ingestion, wire data ingestion, metric data ingestion, document text data ingestion, automated pattern discovery and prediction, anomaly detection, root cause determination, on-premises delivery, and software-as-a-service.
Deploying an AIOps strategy delivers better analytics for existing data. In this way, leveraging AIOps platforms provide the capabilities that are best suited to manage the intricacy and scale of the digital transformation initiative of modern businesses. Moreover, making use of AIOps tools primarily delivers value by aggregating data, then mining information, and, eventually acting upon this intelligence. This will present a shift toward a modern IT strategy which combines machine intelligence to better support digital transformation.