Artificial intelligence for IT operations, better known as AIOps, leverages the strengths of AI to streamline IT monitoring and remediate issues. Machine learning algorithms collect, reconcile and analyze large volumes of data collected from various tools, providing IT teams with a unified view across the IT environment.

More importantly, AIOps can identify and prioritize issues so that IT teams can proactively address them before they become major incidents. AIOps can even predict and resolve problems to prevent incidents from occurring.

Allied Market Research predicts that the global AIOps market will register a compound annual growth rate of 37.9 percent through 2030. Ever-increasing IT complexity, continued cloud adoption and growing volumes of operational data are driving AIOps adoption. However, capitalizing on the value of AIOps requires professionals with the expertise to handle its implementation and operation.

The Evolution of AIOps

It’s somewhat hard to believe that the AIOps concept dates to 2016. That’s when Gartner coined the term to refer to the use of AI and machine learning to automate IT operational processes. At the time, AIOps stood for algorithmic IT operations, applying big data and machine-assisted analytics to IT-specific use cases.

AIOps arose as IT teams were struggling to manage skyrocketing numbers of users, devices, systems and platforms. Applications had to meet increasing performance demands without the slightest disruption. IT teams were overwhelmed with data from disparate sources, and lacked the tools they needed to analyze performance data and assess the health of the IT environment.

AIOps evolved from IT operations analytics (ITOA), which also involves monitoring systems and collecting and analyzing operational data from multiple sources. However, ITOA focuses on historical data, while AIOps allows IT teams to visualize and analyze network conditions in real time. They can pinpoint problems quickly, automatically address many issues and gain continuous insight into both business processes and IT operations.

AIOps Components

AIOps platforms consist of five key components, blending big data, analytics and artificial intelligence capabilities:

  • Data aggregation. AIOps platforms ingest and aggregate telemetry data from across the IT environment, providing the visibility that’s critical to maintaining high performance.
  • Machine learning. Machine learning techniques reduce the “noise” created by data aggregation and provide real-time insight so issues can be proactively addressed. Machine learning tools correlate events and use predictive analysis to anticipate potential issues.
  • Automation features. AIOps automation tools go beyond traditional scripts by rapidly analyzing data for real-time insights. Automation features also streamline routine tasks such as monitoring and incident detection and response.
  • Data visualization tools. Dashboards, graphics and reports present complex data in an easy-to-digest format so that IT teams can rapidly make decisions requiring human analysis and insight.

The overall goal of AIOps is to predict issues before they occur and automate resolution as much as possible to minimize the impact on users. AIOps can also be used for anomaly detection and root cause analysis.

Overcoming AIOps Challenges

Although AIOps can deliver tremendous benefits, it can be difficult to implement and use. Many IT teams struggle to integrate data from diverse sources, particularly legacy systems, to ensure the continuous flow of quality data in sufficient volume to support the AIOps platform.

Technologent’s team of AI, DevOps and automation experts can help you select and deploy the right AIOps solutions, knock down data silos and implement a scalable data platform. We also help IT teams standardize and document processes to facilitate automation.

As IT complexity and performance demands continue to increase, more organizations are using AIOps solutions to manage and optimize IT business operations. IT teams can also leverage AIOps to identify the cause of incidents that impact the delivery of services, and then address these issues before they cause costly disruptions. Let Technologent help you utilize AIOps to streamline IT monitoring and problem resolution.

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Lee Yates
Technologent
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