Embracing Change: Essential for IT Operations
IT Operations professionals are known for their cautious approach. The unspoken rule in this field is “if it ain’t broke, don’t touch it.” And there’s a good reason for that. No one wants to face management and explain why a website is down, especially if it’s due to upgrading or refactoring something that was working perfectly fine before.
However, this caution can sometimes hinder IT Operations from recognizing and adapting to change in a timely manner.
While there is a lot of focus on deployment, hand-offs from development teams, and automating related tasks, the majority of IT Operations work revolves around maintaining existing services. These services, along with the underlying infrastructure, are fully instrumented (or at least they should be). However, the data generated by this instrumentation presents its own challenges. Monitoring a few servers and applications is one thing, but when monitoring expands to thousands of systems, operators can easily become overwhelmed with a flood of alerts.
The Evolution from ITOA to AIOps
Enter IT Operations Analytics (ITOA) – a category of techniques used to uncover complex patterns in large volumes of IT system availability and performance data. Initially, this approach worked well. However, the analytics methods used were relatively rigid and struggled to adapt to changes in the IT infrastructure.
With the introduction of virtualization, self-service provisioning, cloud computing, and agile software development methodologies, the rate of change in IT surpassed the ability of traditional ITOA approaches. A new approach was needed.
In a dynamic world where the pace of change outstrips the capabilities of traditional models, and IT teams face an ever-increasing stream of events, a more dynamic and adaptive analysis approach is required. This need gave rise to Algorithmic IT Operations, commonly referred to as AIOps.
AIOps sits at the intersection of monitoring, service desk operations, and automation. This approach involves gathering inputs from various monitoring tools, applying algorithmic techniques to analyze and sift through the data, and finally delivering valuable insights to IT Operations. The aim is to reduce the number of low-quality tickets in the service desk and provide early warnings of potential issues instead of documenting failures that have already occurred. Moreover, these insights can be integrated with orchestration or run-book automation tools, facilitating rapid incident resolution once issues are identified.
The key to AIOps lies in the use of dynamic, real-time algorithmic techniques instead of static models for analysis. This enables IT Operations to adapt and respond to changes in their environment without the need for constant manual updates.
Four Key Features of AIOps
Alert De-Duplication
One common challenge in IT Operations is dealing with an excessive number of alerts, whether they are recurring alerts within a single channel or similar alerts across multiple channels. In the worst-case scenarios, this can lead to an overwhelming alert storm. AIOps aims to identify and eliminate these duplicates, without having to define them in advance or discarding valuable information.
Event Correlation
Even after sifting through the monitoring data and removing unnecessary alerts, there is still a risk of wasted effort if each event is considered in isolation. Traditional approaches to identifying relationships require pre-configured knowledge of the infrastructure and applications, which becomes impractical in dynamic environments where infrastructure changes frequently. AIOps leverages algorithms to automatically identify correlations based solely on the event stream itself, avoiding duplicate efforts by disconnected teams and saving valuable time during incident detection and analysis.
Situation Workflow & Remediation
Detecting and analyzing incidents is only half the battle; IT Operations need to resolve the identified problems as well. AIOps enables different teams to work together effectively by algorithmically correlating events from each functional area. This collaboration prevents unnecessary reassignments and escalations, enabling seamless communication and faster incident resolution.
Knowledge Sharing
Once an incident has been resolved, it’s crucial to capture the lessons learned for future reference. Traditional approaches involve documenting incidents in a knowledge base or FAQ system, but this is often time-consuming and limited to major incidents. For lower-severity incidents, valuable knowledge remains scattered across individuals’ minds or in their inboxes, forming part of the hidden “dark matter” of organizational knowledge. AIOps proposes capturing the collaboration process itself and automatically making this valuable knowledge available for future incidents, without the need for manual knowledge base articles.
Advantages of AIOps in IT Operations
Implementing AIOps brings several improvements to key IT Operations metrics.
Firstly, it enables faster detection and diagnosis of issues, often uncovering problems before end users even become aware of them or before the impact becomes widespread.
Reducing Mean Time To Detect (MTTD) significantly shortens the overall incident duration. This reduction can be further amplified by accelerating Mean Time To Resolve (MTTR) through improved collaboration between different teams and eliminating wasted effort.
Ultimately, implementing AIOps leads to a decrease in the overall number and duration of incidents, resulting in a significantly improved experience for all users who rely on IT Operations.
In conclusion, while ITOA was designed for a static IT environment with limited changes, AIOps is the answer to the dynamic and ever-evolving IT landscape of today and the future. To keep up with the demands placed on IT by businesses undergoing rapid change, static models are no longer sufficient; algorithms are the key to success.
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