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The Role of AIOps in Causal Analysis

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The Role of AIOps in Causal Analysis

With AIOps, development teams can assess complex relationships automatically, which means less effort for your engineers and quicker resolution of issues for your users.

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In modern software environments, tracing uptime or performance issues to their root cause is not as simple as it once was. Other types of causal analysis can be very difficult to perform as well.

Fortunately, by combining data with advanced machine learning, a new solution for causal analysis is possible: AIOps. AIOps enables IT teams to sort through complex sets of variables and interrelated issues in order to map causal relationships in ways that would simply not be possible using manual analysis.

What Is Causal Analysis?

As the term implies, causal analysis is the process of determining what caused a given event on your infrastructure or in your software environment.

Root-cause analysis, which involves tracing a problem to its underlying source (or sources, in the event that there are multiple interrelated factors at play), is the most common type of causal analysis.

However, root-cause analysis is not the only type of causal analysis use case. You may also want to investigate how a change in one component impacts another, or analyze what would happen if you removed a node or service.

Why Causal Analysis Is Difficult Today

Causal analysis was relatively simple in the days when your application ran on a single server, and your networking and storage systems mapped directly to the underlying hardware and you did not have to contend with the demands of continuous software delivery.

Things have changed. Today, your application probably runs in a distributed environment that depends on multiple host nodes. It may be deployed using a series of containers and microservices. It likely depends on software-defined networking and storage systems, which restrict visibility into the underlying hardware. And it needs to be updated continuously in order to keep pace with user expectations.

In a modern environment, sorting through all of the complexity in order to determine causal relationships can be very tricky. To take just a basic example, consider the following root-cause analysis scenario: You receive an alarm from your monitoring software about a data read-write error. In an environment that uses a software-defined storage system and multiple host servers, the problem could be caused by a number of different components. You might have a configuration problem with your software-defined storage system. There might be a hardware issue with a disk. One of your host servers might be experiencing a failure, although pinpointing which one is challenging. The problem could lie with the network as well.

It’s also possible that there is more than one issue that is contributing to the failure.

Investigating each of these possibilities manually would require a great deal of effort, and is unlikely to lead to a quick resolution.

Streamlining Causal Analysis With AIOps

With the help of AIOps, determining the cause of an issue is much faster and simpler. By constantly collecting and analyzing data about your environment, AIOps tools can map complex relationships between components in ways that your engineers just can’t do manually, no matter how experienced they are.

To go back to the example above, an AIOps tool would quickly be able to analyze data about the past performance of the various components that are involved in the scenario, while also comparing data about the issue to data collected about previous similar problems from your environment. Using this information, the tool would be able to make an informed decision about what the likely cause or causes of the problem are. It might even be able to take automatic action to resolve the issue, in order to avoid having to wait for a human to intervene.

Admittedly, AIOps-driven causal analysis can involve a margin of error or uncertainty. Your AIOps tools may not always have enough data to make a determination with absolute confidence. In this case, they can recommend an action to your engineers, rather than take automated action themselves. Even in this type of situation, however, having AIOps on your side can lead to a much faster response than forcing your engineers to sort through complex data and test different variables manually.


As software environments and the infrastructure that hosts them grow more and more complex, manually analyzing causal relationships are becoming less and less feasible. AIOps makes it possible to assess complex relationships automatically, leading to less effort for your engineers and quicker resolution of issues for your users.

More Reading

Definitive Guide to AIOps

causal analysis ,ai ,aiops ,machine learning

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