The business benefits of AIOps

Faced with the increasing complexity of enterprise systems, driven by multicloud and working in hybrid environments, IT professionals must now tap into the massive amounts of data at their fingertips. Applying Machine Learning (ML) to this data gave rise to AIOps. Just as AIOps evolved to meet the needs of IT operations teams, they evolved to meet the needs of your business.

Automation, a necessity to simplify processes

Automation is most effective when applied to well-defined, manual, and repetitive processes and workloads. Thus, AIOps reduces the time that highly skilled engineers spend on these tasks and allows them to focus on activities with greater added value for the organization. With these smart initiatives, IT solves complex challenges and can handle exponential data growth by automating the entire process of operating IT in hybrid environments and creating an accurate inventory, allowing machines to independently correlate data points.

That way, they can apply it to ML to identify patterns in four key areas: event noise reduction, predictive alerting, probable cause analysis, and capability analysis.

Event noise reduction and predictive alerts

IT teams often struggle to manage the amount of false events and alerts emanating from the various monitoring tools installed in their environment. This is one of the main challenges they face. While alerts can sometimes be useful, more often than not they overwhelm inboxes and are ultimately just false alerts.

AIOps reduces the noise of these events in an environment by learning how it behaves day after day. This knowledge is then used to determine the nature and relevance of a specific alert. IT teams are only alerted if environment behavior reveals degradation of an application, service, or system shutdown. This allows them to set priorities and improve efficiency.

This also applies to predictive alerts, where AIOps automatically identifies seemingly innocuous events for further evaluation. This proactive approach allows, in particular, to analyze the data, identify the problem in a few minutes, even a few seconds, instead of several hours, and therefore reduces the risk of service interruptions.

Behavioral learning and advanced analytics also enable AIOps to help manage capacity and identify when and what resources are being used. They also determine which ones are needed to support the most requested applications and services by customers. Planning for future needs is therefore enabled and provides IT teams with the information they need to adapt resources, which helps to reduce costs and ensure optimal application operation. Thus, AIOps reduces the time spent by teams on these tasks in favor of initiatives with greater added value.

Adoption and integration into DevOps frameworks

AIOps is increasingly integrated into DevOps frameworks, especially for ingesting and analyzing logs and identifying risks in code. Going forward, its use in DevOps will no longer be focused on pre-production, but on metrics like user engagement, quality, and business relevance. All of this supports the idea that DevOps teams using AIOps platforms to monitor and support applications accelerate and streamline development.

Digital transformation translates into a shift from centralized IT to applications and developers, a faster pace of innovation and deployment, and the acquisition of new digital users. But these new technologies and new users are pushing traditional performance and service management strategies and tools to the breaking point.

Therefore, AIOps is the best strategy the IT operations team has to manage these digital transformation issues. The platform transforms IT operations so that automated AI-powered analytics can be applied to a wide range of ingested data on a modern, open observability platform. Thus, the teams focus on the pursuit of operational excellence and help the company evolve towards the Autonomous Digital Enterprise (ADE) model.

With these automation capabilities and the resulting time savings, AIOps enables ITOps to intelligently orchestrate infrastructure, applications, and services across hybrid cloud ecosystems to match the business and meet customer needs, on demand. Business leaders must realize that the transformation of the IT environment must be holistic to ensure the establishment of a smart enterprise capable of meeting the needs of a rapidly evolving digital marketplace.

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