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What is AIOps?

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Overview

AIOps is the common, shortform name for artificial intelligence for IT Operations. Other names for AIOps are IT operations analytics (ITOA), advanced operational analytics, AI for ITOM, IT data analytics, and Cognitive Operations.

AIOps is the multi-layered application of big data analytics, AI, and machine learning to IT operations data. The goal is to automate IT operations, intelligently identify patterns, augment common processes and tasks, and resolve IT issues.

By bringing together service management, performance management and automation, AIOps helps organizations realize continuous insights and improvement. It can monitor and manage the performance and reliability of applications and hardware systems, detect anomalous problems, adapt to changes in load, handle failures, and proactively adjust with minimal disruption.

AIOps

Defining AIOps

AIOps stands for Artificial Intelligence for IT Operations. It is advanced analytics including machine learning and AI to monitor and manage the performance and reliability of applications and hardware systems, detect anomalous problems, adapt to changes in load, handle failures, and proactively or rapidly adjust with no or minimal disruption of services. Other names for AIOps are IT operations analytics (ITOA), advanced operational analytics, AI for ITOM, IT data analytics, and Cognitive Operations.

AIOps is the multi-layered use of big data analytics and machine learning applied to IT operations data. The goal is to automate IT operations, intelligently identify patterns, augment common processes and tasks and resolve IT issues. AIOps brings together service management, performance management, event management, and automation to realize continuous insights and improvement.

Industry analysts have defined a set of capabilities that an AIOps platform should provide. These include:

  • Collecting and aggregating data from many sources such as: networks, applications, databases, tools, and cloud as well as in a variety of forms including metrics, events, incidents, changes, topology, log files, configuration data, KPIs, streaming and unstructured data like social media posts, and documents (natural language processing).
  • Managing the data, storing the data in a single place accessible for analysis and reporting, also including functions like indexing and expiration.
  • Analyzing data through machine learning including pattern detection, anomaly detection, and predictive analytics.
  • Separate significant alerts from ‘noise’.
  • Correlate and contextualize data together with real-time processing for problem identification.
  • Acting as a strategic overlay that aggregates multiple monitoring tools and other investments.
  • Codify knowledge into automation and orchestration of response and remediation.
  • Continuous learning to improve handling and resolution of problems in the future.

Why is AIOps needed?

Many organizations have transitioned from the static, disparate on-site systems to a more dynamic mix of on-premises, public cloud, private cloud and managed cloud environments where resources are scaled and reconfigured constantly.

More devices (most notably Internet of Things, or IoT), systems and applications are providing a tsunami of data that IT needs to monitor. For example, if you have 10,000 servers or VMs and are monitoring 100 metrics per minute you have 60 million data points per hour. 

No human can process the explosion of data IT Operations is expected to handle. IT teams cannot prioritize different issues for resolution in a timely fashion. They are inundated with a large volume of alerts, many of which are redundant. This can cause alert fatigue, where important alerts may be ignored due to all the noise of unimportant alerts. This negatively impacts user and customer experience.

Traditional IT management solutions cannot keep up with this volume. They cannot intelligently sift through metrics and events from the sea of information. They cannot correlate data across interdependent but separate environments. They cannot deliver the predictive analysis and real-time insights IT operations needs to respond to issues quickly enough.

To identify, resolve, and prevent high-impact outages and other IT operations problems faster, organizations are turning to AIOps. AIOps enables IT operations teams to respond quickly and proactively to outages and slowdowns while expending much less effort. It bridges the gap between a dynamic, diverse, and difficult IT landscape on the one hand and user expectations for minimal or no interruption in system availability and performance on the other.

What are the benefits of AIOps?

The benefits of AIOps to IT operations include:

  • More efficient use of infrastructure and capacity.
  • Better correlation between change and performance and other improved change management efficiencies.
  • Prevent problems before customers are impacted via anomaly detection.
  • Pinpoint the problem or reduce the number of items operators must look at when incidents occur to a small set with faster root cause analysis (RCA).
  • Achieve faster mean time to detect problems (MTTD) and Mean Time to Resolve (MTTR) problems in essential IT systems.
  • Unify the view of the IT environment.
  • Get insights into what workloads drive costs.
  • Reduce costly disruptions.
  • Support for traditional infrastructure, public cloud, private cloud, and hybrid cloud.
  • Faster time to deliver new IT services.

Benefits to employee and customer experience include:

AIOps generally improves the quality of IT service due to optimization of networks, it modernizes IT operations and the IT operations team, going beyond solving problems to improvements that make IT systems and operations better over time.

All of which will improve the quality of service, customer satisfaction, and reduce churn, while saving costs significantly over more manual methods of IT operations management.

Three stages of AIOps

Detect IT incidents

Identify and report IT incidents when they happen or have happened.

  • Historical analysis
  • Performance analysis
  • Find bottlenecks
  • Show which devices are overloaded
  • Find service faults
  • Correlate and contextualize various events, logs, and metrics

Predict IT incidents

Identify potential IT incidents and report on them before they impact users.

  • Anomaly detection
  • Change impact analysis
  • Predicting faults, overloads, or other failure conditions before they impact users
  • Capacity management

Mitigate IT incidents

Automatically fix IT incidents or send reports to humans to make it simpler for them to fix the problem.

  • Root cause analysis
  • Automated or assisted predictive maintenance
  • Automated or assisted network optimization
  • Augmented tech support

How to get started with AIOps

When you decide to move forward with AIOps there are two main paths:

There are pros and cons to each, but they are roughly equivalent to buying a great engine to build your own car or buying a fast car. Consider which you would like to do.

Build your own AIOps solution

Reasons you might build your own with a fast, embedded AIOps engine are:

  • You have a unique IT environment or atypical requirements.
  • You want to incorporate AIOps into a broader company AI project.
  • You have an extensive skilled IT, data science, and software engineering department.
  • You wish to build and sell an AIOps solution to other companies focused on an industry, such as telecommunications.

OpenText™ Vertica™ Data Platform is a powerful data analytics engine inside many AIOps solutions companies that sell AIOps solutions, often customized to a particular industry or geography.

Examples include:

Buy an out-of-the-box AIOps solution

Reasons to buy a prepackaged out-of-the-box AIOps solution:

  • You want to leverage the expertise of the vendor.
  • You want to ramp up faster, that is you don’t have time to build your own.
  • You want to focus your experts on the core competencies of your company, not on IT operations.
  • You don’t want to provide ongoing support for the software.

OpenText™ Operations Bridge is an enterprise event and performance management software that automatically monitors and analyzes the health and performance of multi-cloud and on-premises resources for any device, operating system, database, application, or service across all data types.

Explore how AIOps technology offers the speed and scale to stay ahead of service reliability problems that impact the customer experience, and ultimately revenues.

AIOps success stories

AIOps platform offers AI-based correlation to reduce noise

AIOps helps NOS, the biggest communications and entertainment group in Portugal, to distinguish noise from fact by using AI-based automatic event correlation (AEC) using machine learning and algorithms to analyze patterns in the event stream and using these patterns to group events together, which, with a high probability, originate from the same problem. This grouping enables focused event processing for an operator. It shows all related events grouped together by AEC, making it easier to identify and work on the root cause.

AIOps powers automated IT monitoring solution

French IT service provider NXO France uses AIOps to build and deploy an innovative automated IT monitoring solution to gain a complete and accurate view of the dynamic and complex networks NXO customers work with and automates remedial action tasks with thousands of out-of-the-box operations.

Proactive issue resolution, improved service quality, and better decision-making processes with AIOps

Türk Telekom is Türkiye’s leading information and communication technologies company. They use AIOps to provide instant impact analysis and the ability to automatically run algorithms to detect the root cause of an issue, with the results monitored in real-time. “We partner with OpenText in other areas of our business and felt the AIOps suite of solutions would benefit this project.”

Related products

Our AIOps solution includes multi-cloud, SaaS, application, infrastructure, and network monitoring; automation and configuration management.

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OpenText™ Universal Discovery and CMDB

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