This article discusses:
How AIOPs can play a crucial role in the increasingly complex IT management landscape
IT management has come far from the days of monolithic architectures. IT operations (ITOps) now involve dealing with increasingly distributed systems in virtualised and hybrid cloud environments coupled with exponential growth in data management requirements. These systems are further developing at a rapid pace and in doing so, becoming progressively more complex. A strategy to cope with this challenge is to infuse ITOps with the power of Artificial Intelligence (AI) and machine learning, thereby resulting in Artificial Intelligence for IT Operations (AIOps).
In the current environment of increasing competition from traditional and non-traditional competitors and the resulting need for faster product iterations, ITOps are tested with the dual challenge of managing increasing complexity while reducing costs. IT infrastructure and applications are generating an increasing variety of data at an unparalleled pace. This is further complicated by the need to contextualise the data per business requirements. Existing monitoring tools won’t be capable of managing this complexity. Further, they are not equipped to effectively cut across different types of data and metrics, including; sentiment data, transaction data, sensor data and logs. With the rise in new digital businesses and the evolution of existing business as digital enterprises, the speed with which IT needs to act is also increasing.
So, IT managers have started supporting IT operations tools with machine learning to enable monitoring or observation of IT infrastructure, or application behaviour. As per Gartner, there are four stages of monitoring – data acquisition, aggregation, analysis and action. While the current tools look to cater to the first three stages, technology needs to start focusing and enabling the action stage. This is where AIOps come in to enable a “right shift” from just monitoring tools to AIOps platforms (see figure).