Today’s businesses are thriving in the digital economy, adopting new technologies and working towards excelling in the rapidly evolving technology landscape. On the other hand, there is an increase in hybrid and multi-cloud infrastructure and modern application architecture leading to additional complexities.
The legacy IT strategies and infrastructure are certainly unable to resolve issues that occur with digital transformation. Furthermore, the massive volumes of data generated make it challenging for teams to leverage it and generate actionable insights. Site Reliability Engineers (SREs), who support the DevOps and IT operations teams by ensuring the underlying IT Infrastructure and the computing systems are working efficiently, have a tough task at hand.
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The shortage of skilled professionals is further adding to the challenges. As a relief, leveraging AI and machine learning or AIOps (Artificial Intelligence for IT Operations) will add efficiency and reduce dependency on the manual workforce and provide a much-needed solution.
AIOps: A snapshot
According to Gartner, AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.
AIOps is applying machine learning algorithms to the massive amounts of collected data to provide insights while ensuring a higher level of automation is made possible
AIOps is applying machine learning algorithms to the massive amounts of collected data to provide insights while ensuring a higher level of automation is made possible. This technology can power network performance monitoring (NPM) and application performance management (APM) in addition to infrastructure monitoring and incident response. AIOps helps to identify errors and remediate them.
AIOps: Key use cases
To understand how these AIOps tools can be leveraged, here are key use cases captured in the following lines.
Identifying issues based on anomalies
With the constantly growing number of apps and services, monitoring or tracking every single metric is cumbersome. Instead of focusing on several metrics at a time and tracking them in real-time, AIOps enables anomaly detection when a few of them are deviating from normal behavior. This can also proactively ensure issues do not occur going forward.
Preventing outages and downtime
Traditional IT monitoring systems are manually run and reactive in nature. But businesses cannot afford to wait till the IT incident occurs to address it as the delay can lead to losses on all fronts. AIOps, which has predictive analytics capabilities, can envision future incidents before operations get interrupted and prevent critical outages, thereby reducing maintenance costs too.
Correlating events to eliminate noise
Over time, organizations have built and accumulated numerous observability and monitoring tools to evaluate the performance of the infrastructure, applications, and services. But these tools create noise in the IT environments which are already complex. All teams are flooded with event alerts, and it is difficult to detect which of them are critical and has to be attended to immediately. Teams often fail to share information on incidents too. AIOps delivers event correlation capabilities that automatically triage an incident thereby enabling quicker resolution.
Data-driven automation
AIOps provides deep insights and help in identifying use cases for automation. Automation is based on the event and remedial measures to be taken. It will significantly reduce manual intervention and deliver faster outcomes. This can also ensure there are consistent service levels across distributed environments. Furthermore, the team has the free time to work on higher-value strategic tasks, while the automated ones are done more efficiently.
Enables data integrations
IT Ops, SRE, and DevOps teams are constantly faced with fragmented datasets across numerous tools and sources drastically slowing down the incident response and resolution time. AIOps helps to unify all the tools and datasets and connect them, giving businesses the option to replace traditional monitoring tools.
Enables root-cause analysis
It is true that with the complexities in modern IT systems and the large volumes of data and alerts it is rather time-consuming and tedious to detect the root cause of anomalies. Root cause analysis can be automated with AIOps, where events are collected and correlated. Then machine learning inference models are used to identify the root cause of the underlying issues. All relevant logs can be analyzed to establish the root cause of any incident, trigger the appropriate actions and fix issues with the reliance on manpower reduced. With this, the SREs can respond quickly and resolve problems in an efficient manner.
Ongoing improvement
The developer is aware that to establish the quality of the software it has to process the operational data used by the end user. By leveraging AIOps with operational data, there will be continuous improvement in the software development life cycle.
The experience of the past, current usage, and feedback of the user will help prevent issues similar to the historic ones thus driving continual or ongoing improvement. AIOps also get trained on the new knowledge to improve, get smarter, and deliver customized insights and recommendations.
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AIOps empowers teams to improve user experience with predictive and valuable insights in addition to holistic orchestration. As manual tasks are eliminated, streamlining of workflows is made possible thereby enhancing operational efficiencies, among other business benefits.
Today, when the board at every organization is putting pressure on IT heads to cut costs, AIOps could provide the right solution, driving forward-thinking businesses to adopt it. Agree, AIOps is the future of ITOps.
Guest contributor Rajarshi Bhattacharyya is the Co-founder, Chairman, & Managing Director of ProcessIT Global, a company that helps organizations achieve high standards of cybersecurity, AIOPs, and automation. Any opinions expressed in this article are strictly that of the author.
