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AIOps explained in 5 minutes

AIOps enables organizations to automate and accelerate IT tasks. Done properly, AIOps increases IT's flexibility and versatility, minimizes errors and faults, and frees IT staff to handle strategic tasks for the business.

AIOps tools vary, but generally speaking, they ingest data from IT systems and analyze the information to discover patterns and anomalies related to how those systems perform. Watch the explainer video to understand how these tools gain insights, what machine learning does and the benefits and limitations users experience with these tools.

Editor's note: Not an auditory learner, or want to add information from the video into your notes? Check out the transcript at the bottom of this page to get all the information.

The problems AIOps solves

AIOps, a combination of automation, orchestration, machine learning and analytics, should be tuned to support the often complex and confusing modern IT infrastructure deployments.

Digitally enabled businesses cannot run on just a few servers in a data center anymore. Enterprises often use heterogeneous infrastructure, which spans local data centers, edge locations and multiple cloud platforms. In addition, they mix in-house applications with products from SaaS providers and offer access to mobile devices and workstations. It's difficult for an IT team to find and manage all of the resources and services under its watch -- and that list can change day to day and even hour to hour -- without the aid of an AIOps tool.

Analysis and reporting couldn't be more important in enterprise IT shops, but manual approaches are equally as complex as modern infrastructure. IT admins must address anomalies and problems as immediately as possible, if not prevent them from happening altogether, and they turn to AIOps tools to support this work.

IT admins need a wide array of logs and reporting data for long periods of time. A human IT staff cannot predict impending issues or respond in real time to complex events, such as system failures. IT staff find it time-consuming to simply locate a specific log entry for a specific device, let alone correlate multiple log reports to an event.

The drive toward self-service IT -- wherein users consume virtual environments, or gain access to a program, via a service portal -- complicates matters further, as business groups and divisions build their own IT tools and services. With ease of access to cloud, low-code and other worker-friendly IT offerings, the principal IT staff might not even know about some installed or SaaS applications or cloud services in parts of the business. AI-enabled IT management and helpdesk tools can streamline interactions between IT's consumers and the staff that support services for them.

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Transcript - AIOps explained in 5 minutes

IT operations professionals need to manage and maintain a bewildering array of systems, provision and track resources, deploy and support complex workloads, and accommodate vast numbers of users. All of this needs to happen quickly and accurately, while keeping the IT environment secure and compliant.

One of the most exciting technologies to help IT professionals meet modern demands is called "AIOps."

I'm Steve Bigelow, senior technology editor with TechTarget, and I'm here to talk about AIOps, its role, and its potential in enterprise IT.

So what is AIOps? AIOps is shorthand for artificial intelligence for IT operations. Ideally, it combines technologies including artificial intelligence and machine learning, automation, orchestration, systems management and monitoring.

AIOps is a series of layers that all work together to make the right IT decisions, and then execute them with little (if any) human intervention.

Let's break down the layers of how these tools work.

An AIOps platform will require vast amounts of data in order to learn. All of the data must be ingested -- taken in -- from numerous sources, such as log files, status messaging, alerts and more.

Discovery tells the AIOps platform the hardware, resources, software and services that are actually running in the enterprise environment. Discovery also works well with change management and enforcement, allowing an AIOps platform to determine when something changes and the impact of that change on the environment.

Correlation identifies the connections between resources and services to find cause-and-effect relationships across the infrastructure. This kind of insight is critical in AIOps because the platform has to know how its decisions will impact the environment.

Visualization is the way that correlations are represented -- for example, in network topology maps and IT- or business-focused application performance dashboards.

Automation and orchestration give AIOps its autonomy -- a means of responding to events and carrying out its decisions within the guidelines of an established workflow or process. For example, if a workload's traffic exceeds a "normal" threshold by a certain percentage, the AIOps platform could add resources to the workload or migrate it to another system much like a human admin would.

The heart of AIOps is AI -- usually a form of machine learning. An AIOps platform will ingest a huge volume of data about the IT environment, and then process that data through a machine learning algorithm. This way, the platform can learn what hardware and software is available, see resources, understand when and how those resources are typically deployed or changed, and come to distinguish errors, faults and anomalies from normal operation. The platform is always training, learning, tweaking and optimizing the AI model over time so that it can make better decisions.

If you take away the AI from AIOps, all you have left is "Ops." Automation, log analytics, discovery, visualization and other underlying elements are hardly new.

An AIOps platform provides the benefits of insight and speed.

Machine learning analyzes all correlations and actions, so an AIOps platform can spot cause-and-effect relationships that might be invisible to a systems admin. For example, an AIOps platform is well suited to troubleshooting and root cause analysis.

And an AIOps platform can work quickly, alerting administrators or making autonomous optimizations and fixes.

Since an AIOps platform deals with the entire enterprise IT environment -- all of the hardware and software -- it overcomes IT silos that can slow coordination and response times.

The work performed by the AIOps platform can also free time for IT staff to evaluate and experiment with new IT technologies that can help the business.

But there are some limitations to AIOps technology. First, an AIOps platform is not automatic. It has to be trained and managed. This takes a serious investment in time and effort. While the payoff can be impressive, AIOps really only makes sense for larger enterprises with many services and a complex infrastructure.

AI is only as good as the data it ingests. Partial, incomplete or short-term data limits the learning potential for AIOps platforms, at least in the early stages of AIOps adoption.

In addition, automation relies on consistency, so businesses that allow many exceptions or employ different practices across groups, teams or divisions might have problems adopting an AIOps platform.

So that's the short story on AIOps and what it can do for the enterprise. I'm Steve Bigelow, senior technology editor with TechTarget -- thanks for watching.

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