FEATURE: AIOPS

According to CASA Software the demands of today’ s digital economy augmented by the increasing complexity of modern application architectures have made the role of IT operations more challenging. Michael Brink, CTO, CASA Software, explains why AIOps is emerging as the future of IT operations. business service or application and it makes it near impossible to be able to quickly correlate and analyse multiple application performance metrics to solve complex emerging problems before they impact enduser experience.

According to Gartner, domain-based monitoring tools provide insight into issues within their own realm but typically are unable to present a holistic view across a digital service. Infrastructure and operations leaders need to use AIOps and digital experience monitoring to deliver a primary, single pane of analysis across all domains underlying the service.

Michael Brink, CTO, CASA Software, notes AI and Machine Learning have emerged as a means of relieving some of the manual interventions required.“ A recent survey of more than 100 IT professionals, found respondents overwhelmingly believe AI for IT Operations( AIOps) is the future of IT operations, with increased automation and faster remediation among the key benefits. It should be noted that these days the term AIOps is used interchangeably with observability,” he says.

Brink lists the research findings here:

Reason # 1: Proliferation of monitoring tools makes analytics challenging

New distributed and microservice-style architectures are expected to introduce more complexity and present new monitoring challenges. The use of disparate tools makes it extremely difficult to obtain end-to-end visibility across the entire

Collecting and correlating data from disparate sources so it can be effectively analysed is the first step in enabling AIOps. Having these end-to-end insights across the entire application stack, from back-end infrastructure to customer behaviour and performance will help you to ensure the optimal customer experience every time.

Reason # 2: The sheer volume of alerts is becoming unmanageable

With thousands of monthly alerts that need to be managed, coupled with dwindling resources, it is no wonder the use of AI and Machine Learning is becoming a necessity. AIOps can help reduce the impact of these issues by decreasing downtime, monitoring tool sprawl as well as the time expended on analysing alerts.

Reason # 3: Delivering superior user experiences requires predictive analytics

The reality is that in the digital age, every business is one poor user experience away from a lost customer, making it understandable why businesses place such a premium on supplying a superior customer experience.

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