By Arvind Raman, Global Head – Service Management, Infosys
Today, IT operations lie at the heart of any organisation as businesses increasingly depend on technology to stay competitive. Therefore, unresolved IT operation challenges impact the organisation negatively. For instance, an unexpected outage can result in steep downtime costs. Unless we can map the health of the IT systems to the relevant business metrics, it can result in unactionable alerts that can increase the time to repair incidents. Indirectly, these can lead to poor customer experience.
To elaborate, an organisation will want to observe several dimensions of its operations, such as, are customers able to log in to applications, are the pages available, are the databases up, is the firewall effective, how healthy are the web servers, is someone monitoring incidents and keeping an eye on the cloud environment. Organisations want to understand and measure their end-user experience and application performance. They want to keep track of their security, ITSM processes, and cloud systems.
Organisations appoint different teams to measure and monitor the various parts of the IT stack. For example, the DevOps team focuses on time lags or crashes; the site calamity engineers look after network issues, while the head of ITSM keeps a close watch on application metrics. There is a need to correlate these various elements to give a holistic picture of the health of IT operations and their impact on the business.
The cloud is the perfect tool to bring together the different capabilities required for managing IT operations. It offers a highly agile and cost-effective approach to implementing new frameworks and principles such as Artificial Intelligence for IT Operations (AIOps) and full stack observability. AIOps uses AI and machine learning to automate IT operations tasks. On the other hand, full stack observability refers to the visibility into the overall health of systems and infrastructure that can be tied back to business KPIs. Predictive analytics is another key technology that is transforming IT operations.
Greater visibility and problem-solving with AIOps
AI and ML are the key components of AIOps that help in the proactive diagnosis of the IT estate. It can alert the IT operations teams about incidents and point out the root cause, thus, reducing the mean time to identify (MTTI) and mean time to repair (MTTR). Assignment of issues can be automated using predictive intelligence.
AIOps offer end-to-end visibility for site reliability engineering (SRE). SRE refers to a set of principles that can guide engineers to apply aspects of software engineering to IT infrastructure and operations. This approach encourages a deep analysis of the systems till the code level. For instance, in the case of an application failure, engineers try to identify the causal link to understand why the failure occurred rather than just debugging the app. With AIOps, operations engineers can identify the business service affected by an incident.
AIOps and predictive analytics can help organisations to identify and resolve issues before they escalate. AI in AIOps reduces the ‘alert noise’ by proactively detecting anomalies and managing incidents, resulting in the elimination of most incidents. For example, one of our clients, a large food and beverage company used predictive AIOps to get alerts on any anomalies during the delivery of their cans of beverages and correlated them with insights from telemetric and logs of application and infrastructure components. This helped them with faster triaging, allowing them to predict when the issues will come up next and preventing them. Troubleshooting a problem becomes easier with AIOps.
AIOps can help increase the effectiveness of IT operations by addressing all areas including:
Observation: This includes recording metrics, logs, and traces, observing historic, real-time data, and log data, and tracking service availability
Organisation: Putting in place service maps and identifying resource dependency and discovery of infrastructure, cloud, and application to enable simpler resolution of challenges.
Analysis: Alert correlation, root cause analysis, event management, log analytics, dynamics threshold, and anomaly detection can help narrow down any issues.
Management: AIOps enable simpler IT service management, dashboards, orchestration, automation and self-healing, and cloud management
Collaboration: This includes incident response, on-call management, and faster response time.
Even the most complex IT challenges can be solved effectively by an AIOps solution because it combines observability, performance management, service management, and automation into a single cycle. It offers security, reliability, and user experience, integrating and optimising enterprise management and making a positive impact on business profitability.
In the future, we can expect generative AI to shape the ITOps landscape by unleashing the power of large language models to complement AIOps. It will facilitate a greater degree of workflow automation, bring productivity gains, eliminate mundane tasks, and help cut costs. Mature enterprises are increasingly pivoting towards AIOps and paving the way for the new era of IT operations management.
