AIOPS
The importance of AIOps within value stream management IT organisations today are charged with both running the business as well as reinventing it. BY GAURAV REWARI, CTO AND GM OF AI AND VSM AT DIGITAL.AI THE FORMER is usually managed by the infrastructure and operations (I&O) teams, whereas the latter is done by the application development and delivery (AD&D) function. As 70 percent of IT budget goes towards keeping the lights on (KTLO), artificial intelligence for IT operations (AIOps) began as a way to drive cost reduction through greater automation of operations and infrastructure activities, shifting the burden of KTLO from humans to machines – the ultimate goal for AIOps. However, for modern day organisations, applying AIOps for I&O teams alone is not enough. For organisations to reap all the benefits of AIOps, it needs to be part of the application development organisation’s strategy to reinvent the business. Enter Value Stream Management, or VSM – an approach which encompasses every step in the software delivery process and which is centred around helping companies move faster with higher quality, whilst
helping them to align technology activities with business outcomes. Incorporating AIOps into VSM provides all streams of the business with data-driven insights that help organisations to simultaneously manage risk and deliver high quality digital services more efficiently. The development of and key roles of AIOps Recently AIOps has been applied to many use cases. The first wave involved the use of AIOps for event noise suppression by filtering out unnecessary alerts generated by application, infrastructure, and network monitoring tools. AIOps techniques then extended to automating the process of understanding the root cause of issues to enable the swifter remediation of major incidents thus helping to reduce the downtime of critical business services. AIOps has also found applicability in the area of service management. AIOps techniques have led to the development of tools such as chatbots to help provide an answer to an employee’s query by leveraging the company’s knowledge base system as well as historical incident and service request patterns. Another compelling AIOps use case in this area is the application of machine learning techniques to predict whether an incident that seems relatively benign right now has the propensity to become a major incident based on patterns detected from historical IT service management (ITSM) and monitoring data. Additionally, AIOps and process mining techniques can help uncover bottlenecks in service delivery such as recurring patterns of ticket reassignments, clusters of incidents that may have similar underlying characteristics even though they are not tagged as such, etc. These insights can help drive process optimisation and automation decisions improving the cost, quality and effectiveness of service delivery. As AIOps matures as a field, we are seeing more extensive capabilities such as change risk prediction and release schedule risk prediction come to the fore,
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ISSUE II 2021
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