Identifying and Removing IT Redundancies with Automation

AIOps, which uses AI-powered insights to automate IT operations processes, has unleashed a plethora of new opportunities for streamlining software delivery and management processes.

In fact, AIOps can be used to do so many different things that it can be hard to know where to apply it first—which is a good problem to have but is nonetheless a problem that must be addressed in order to take advantage of AIOps.

The fact is that you can't use AIOps to automate every process in your software delivery pipeline from "day one." You need to make strategic decisions about where to deploy AIOps tools initially, in order to get the greatest return on your investment, and then later work to expand your AIOps deployments to address other types of pain points.

With that reality in mind, this article offers guidance on where to start on the path to AIOps-powered automation nirvana, and how to scale up from there.

Where to Start with AIOps Automation: Root-Cause Analysis

As a general guideline, the best place to start taking advantage of AIOps within your software delivery and management processes is to analyze the root causes of incidents that are reported in production.

There are two reasons for this: the first (and most basic) is that production-level incidents are serious problems, so it makes sense in that respect to use AIOps to speed their resolution.

The second reason is that root-cause analysis is something that AIOps is particularly well-suited to perform quickly and accurately, as compared to manual root-cause analysis. For a human engineer to receive an alert, comb through complex log files and service mappings from multiple sources, and make a determination about the underlying cause of an incident requires considerable time. As a result, it could take anywhere from several minutes (for a simple incident) to several days (for a truly complex one) for engineers to determine what the root cause of a problem is, and then devise a plan to address it.

It's worth noting, too, that manual root-cause analysis is complicated by the fact that different engineers may have different opinions about what the root cause is. Those disagreements may delay response even more.

AIOps avoids these pitfalls by analyzing as much relevant data as you have available regarding an incident, then making a decision about its root cause, almost instantaneously, at any time of day or night. And as long as the data source and the AIOps tool you use are consistent, the analysis will always be the same—there will be no difference of opinion that you have to sort through before taking action.

Next Steps in AIOps: Find and Fix Your Incident Response Pain Points

AIOps can do much more than just analyze root causes. It can also automate the actions that are taken in response to various types of incidents.

To get the most value from AIOps, you should identify the largest pain points in your current incident response processes, then deploy AIOps solutions to address them. The greatest pain points will vary from team to team, but a simple way to find them is to analyze your ticketing data to determine which types of tickets take the longest to resolve and/or require the greatest amount of manual effort on the part of your staff.

You can then calculate how much time could be saved by using AIOps to automate different types of responses and deploy solutions accordingly.

Additional Efficiency: Ending Alert Fatigue with AIOps

As a further step, consider using AIOps to reduce the number of tickets that your monitoring tools generate in the first place. Cutting down on tickets will help your IT staff know which incidents to prioritize, as well as save them from becoming so overwhelmed with alerts that they struggle to identify and fix the most pressing issues.

Traditionally, IT monitoring systems were often configured to fire alerts based on static thresholds. For example, a server that failed to respond to a request in a given period, or a CPU utilization that surpassed a certain level, would trigger an alert, and in turn, lead to a ticket that would have to be resolved.

This approach helped give IT teams early warnings of issues that might escalate into larger problems. But it also generated false positives because, depending on the specifics of a situation, surpassing a preset alert threshold might not actually indicate a real issue. Sometimes, CPU usage just spikes for benign reasons, for instance, not because a server is about to crash.

AIOps can reduce false positives—and, by extension, the number of overall alerts—by providing deeper, more nuanced analyses of potential incidents. Instead of relying on thresholds alone to trigger alerts, monitoring systems powered by AIOps can rely on sophisticated analysis of a range of data points—such as the history of an application environment, and whether similar behavior was experienced in the past without leading to a legitimate incident—in order to determine whether an alert is merited or not.

The Holy Grail: NoOps

The end-goal of AIOps is to achieve what has been called NoOps—a software delivery pipeline that is so completely automated that it does not require manual oversight or maintenance by IT engineers at all.

For most organizations at present, that goal is out of reach. But moving toward it requires just incremental improvements upon the way teams use AIOps. By starting small and applying AIOps to the most obvious weak points in their software delivery and management operations, they can expand gradually until AIOps is delivering the insights they need to automate their entire operation.