What is AIOps? AIOps Explained

What is AIOps? Simply put, AIOps uses big data, analytics and machine learning to automate and improve IT operations (ITOps). AI is particularly important in ITOps functions such as anomaly detection and event correlation, as it has the ability to analyze large volumes of network and machine data to find patterns, identify the cause of existing problems and find ways to forecast and prevent future issues.

The complexity of modern data environments, which includes microservices, multicloud or hybrid cloud architectures and containers, along with the proliferation of distributed systems have resulted in massive and unwieldy volumes of log and performance data that can quickly overwhelm IT analysts and impede visibility into the health and safety of the network. AIOps solutions help resolve these IT issues by effectively monitoring assets and expanding visibility into dependencies, both internally as well as outside of IT systems — and all without human intervention.

In the following article, we’ll articulate how AIOps work, its myriad use cases and many benefits, and how you can get started effectively implementing AIOps in your organization. 

AIOps Basics & History

In 2016, Gartner coined the term "AIOps" as a shortened version of "Algorithmic IT Operations". It was intended to be the next iteration of IT Operations Analytics (ITOA). Within a year or so, Gartner shifted the phrase to "Artificial Intelligence for IT Operations" - a subtle but powerful change in the marketing of the concept.

AIOps is designed to bring the speed and accuracy of AI to IT operations. IT operations management has become increasingly challenging as networks have become larger and more complex. Traditional operations management tools and practices struggle to keep up with the ever-growing volumes of data from many sources within complex and varied network environments. To combat these challenges, AIOps tools:

  • Bring together data from multiple sources: Conventional approaches, tools and solutions weren’t designed in anticipation of the volume, variety and velocity generated by today’s complex and connected IT environments. Instead, they consolidate and aggregate data and roll them up into averages, compromising data fidelity. A fundamental tenet of an AIOps platform is its ability to capture large data sets of any type across the environment, while maintaining data fidelity for comprehensive analysis.
  • Simplify data analysis: One of the big differentiators for AIOps platforms is their ability to collect all formats of big data in varying velocity and volume. The platform then applies automated advanced analytics on that data to predict and prevent future issues and identify the cause of existing issues that enable better decision making.

aiops orbit diagram

Using machine learning and big data, an AI platform helps IT operations deliver greater business value.

AIOps Platforms

According to Gartner, an “AIOps platform combines big data and machine learning to support IT operations through the scalable ingestion and analysis of data generated. The platform enables the concurrent use of multiple data sources, data collection methods, and analytical and presentation technologies.”

Among other things, an AIOps platform needs to be able to both analyze stored data and provide real-time analytics at the point of ingestion. The central functions of an AIOps platform, as defined by Gartner, include:

  • Ingesting data from multiple sources agnostic to source or vendor
  • Performing real-time analysis at the point of ingestion
  • Performing historical analysis of stored data
  • Leveraging machine learning
  • Initiating an action or next step based on insights and analytics

AIOps platforms address rapidly escalating challenges around managing complex data ecosystems. In the 2022 Gartner Market Guide for AIOps Platforms, Gartner notes that "data management costs and complexity are becoming a concern for many enterprises that have adopted AIOps platforms as they expand their use,” further noting that “AIOps platform adoption is growing rapidly across enterprises."

Given this, it’s likely that AIOps platforms will continue to be an attractive solution for organizations looking to make their cloud computing and data environment more efficient, cost effective and manageable.

Key AIOps Use Cases

According to Gartner, there are five primary use cases for AIOps:

  1. Big data management (volume, variety, variability and velocity)
  2. Performance monitoring and analysis
  3. Anomaly detection
  4. Event correlation and analysis
  5. IT service management

application for aiops diagram

According to Gartner, the five primary use cases of AIOps include big data management, performance analysis, anomaly detection, event correlation and IT service management.

  1. Performance analysis: AIOps is a key use case for application performance analysis, using AI and machine learning to rapidly gather and analyze vast amounts of event data to identify the root cause of an issue. A key IT function, performance analysis has become more complex as the volume and types of data have increased. It’s become increasingly difficult for IT professionals to analyze their data using traditional IT methods, even as those methods have incorporated machine learning technology. AIOps helps solve the problem of increasing volume and complexity of data by applying more sophisticated AI techniques to analyze bigger data sets. It can predict likely issues and quickly perform root-cause analysis, often preventing problems before they happen.
  2. Anomaly detection: Anomaly detection in IT (also "outlier detection") is the identification of data outliers — that is, events and activities in a data set that stand out enough from historical data to suggest a potential problem. These outliers are called anomalous events.

    Anomaly detection relies on algorithms. A trending algorithm monitors a single KPI by comparing its current behavior to its past. If the score grows anomalously large, the algorithm raises an alert. A cohesive algorithm looks at a group of KPIs expected to behave similarly and raises alerts if the behavior of one or more changes.

    AIOps makes anomaly detection faster and more effective. Once a behavior has been identified, AIOps can monitor the difference between the actual value of the KPI versus what the machine learning model predicts, and watch for significant deviations.
  3. Event correlation and analysis: Event correlation and analysis is the ability to see through an “event storm” of multiple, related warnings to the underlying cause of events and make a determination on how to fix it. The problem with traditional IT tools, however, is that they don’t provide insights into the problem, just the storm of warnings.

    AIOps uses AI algorithms to automatically group notable events based on their similarity. This reduces the burden on IT teams to manage events continuously and reduces unnecessary (and annoying) event traffic and noise. AIOps then performs rule-based actions, such as consolidating duplicate events, suppressing alerts or closing notable events when they are received.
  4. IT service managementIT service management (ITSM) is a general term for everything involved in designing, building, delivering, supporting and managing IT services within an organization. ITSM encompasses the policies, processes and procedures of delivering IT services to end users within an organization.

    AIOps provides benefits to ITSM by applying AI to data to identify issues and help fix them quickly, thereby helping IT departments be more efficient and effective. AIOps for ITSM can be applied to data, from monitoring the IT service desk to managing devices.

    AIOps for ITSM can help IT departments to:
    • Manage infrastructure performance in a multicloud environment
    • Make more accurate predictions for capacity planning
    • Maximize storage resources by automatically adjusting capacity
    • Improve resource utilization based on historical data and predictions
    • Identify, predict and prevent IT service issues
    • Manage connected devices across a network
  5. Automation: Legacy monitoring tools often require manually cobbling information together from multiple sources before it’s possible to understand, troubleshoot and resolve incidents. AIOps provides a significant advantage with its ability to automatically collect and correlate data from multiple sources, greatly increasing speed and accuracy. The AIOps approach automates these functions across an organization’s IT operations, including:
    • Servers, OS and networks: Collect all logs, metrics, configurations, messages and traps to search, correlate, alert and report across multiple servers.
    • Containers: Collect, search and correlate container data with other infrastructure data for better service context, monitoring and reporting.
    • Cloud monitoring: Monitor performance, usage and availability of cloud infrastructure.
    • Virtualization monitoring: Gain visibility across the virtual stack, make faster event correlations, and search transactions spanning virtual and physical components.
    • Storage monitoring: Understand storage systems in context with corresponding app performance, server response times and virtualization overhead.

Business Benefits of Implementing AIOps

By automating IT operations functions and using AI to enhance and improve system performance, AIOps can provide significant business benefits to an organization. For example:

key business benefits of AIOps

AIOps provides countless benefits to organizations, including avoiding downtime, correlating data, accelerating root cause analysis, discovering and fixing errors — all of which give leadership more time to collaborate.

By improving performance of both cloud computing and on-premises IT infrastructure and applications, AIOps elevates KPIs that define business success.

  • Avoiding downtime improves customer satisfaction.
  • Bringing together data sources that had previously been siloed allows more complete analysis and insight.
  • Accelerating root-cause analysis and remediation saves time, money and resources.
  • Increasing response time and consistency of response improves service delivery.
  • Finding and fixing errors that would be tedious and time-consuming for people to address increases job satisfaction and lets IT teams focus on higher-value analysis and optimization.
  • Giving IT leadership more time to collaborate with business peers demonstrates the strategic value of the IT organization.

Many of the challenges that AIOps can help IT operations resolve are common across all industries. There are, however, issues that are more prevalent or more threatening in particular industries, including healthcare, manufacturing and financial services. By automating IT operations and using AI to enhance and improve system performance, AIOps can provide significant business benefits to an organization. For example:

AIOps can be used in healthcare IT (HIT):

  • Keeping electronic personal healthcare information (ePHI) safe in compliance with the Health Insurance Portability and Accountability Act (HIPAA).
  • Reducing the hazards of mobile networking and bring-your-own-device (BYOD) practices by medical professionals.
  • Preventing ransomware attacks, which disproportionately target healthcare organizations.
  • Making big data, both internal and external, available for research and diagnostic use.

AIOps can be used in IT for manufacturing:

  • Automating the collection and analysis of disparate data sources created by the integration of supply chain, plant operations and product and service life-cycle management.
  • Using real-time monitoring to track every machine on the factory floor, bringing together such data as manufacturing cycle times, quality yields by machine and production run, capacity utilization and supplier quality levels.
  • Preventing production slowdowns and troubleshooting using historical data combined with AI-driven predictive analytics, thereby protecting revenue streams and increasing customer satisfaction.
  • Using machine data to enable predictive maintenance, fixing machines before they break.
  • Better utilizing data to create more efficient supply chain management systems.

AIOps can be used in IT for financial services:

  • Preventing increasingly sophisticated security breaches and cybercrime.
  • Making customer data available to drive marketing and growth opportunities.
  • Analyzing historical customer data to create more accurate revenue growth predictions.
  • Ensuring data security and regulatory compliance.
  • Providing a framework for integrating multiple, large data sets to allow emerging technologies like blockchain.
  • Keeping up with consumer expectations of mobile and digital banking experiences.
  • Improving network speed and performance.

The Future of AIOps

In recent years, AIOps platforms have gained significant popularity in the enterprise, as organizations across multiple industries have deemed AIOps a critical tool in managing their data environment and expanded its use across ITOM functions.  Consequently, the AIOps market is primed for significant growth without signs of a slowdown. According to Gartner, the value of the projected size of the AIOps market will be around $2.1 billion by 2025 with an annual growth rate (CAGR) of around 19%. Correspondingly, Future Market Insights anticipates that the AIOps platform market will likely reach $80.2 billion by 2032, at a CAGR of 25.4% between 2022 and 2032. 

With the explosive growth of Chat GPT, it’s likely that generative AI will play a role in the development and evolution of AIOps. A TechTarget report suggests that generative AI could be used in the development of application code, as well as some routine engineering tasks such as test generation. Observability functions and automation of resilience workflows, such as penetration testing, could also be affected by generative AI. It could also potentially be used to provide analysis on unstructured data sets that include audio and chat files.

Exactly how generative AI will impact these functions remains to be determined. But it’s likely that it will play an increasingly bigger and more significant role as organizations integrate AIOPs into their digital transformation journey.

Getting Started

The best way to get started with AIOps is an incremental approach. One best practice is to start small by reorganizing your IT domains by data source. Learn how to work with large, persistent data sets from a variety of sources. Let your IT operations team become familiar with the big data aspects of AIOps. Start with historical data, and gradually add new data sources as you improve your practice.

Focus on ingesting data first: Ingesting and analyzing all of the data effectively and quickly can be daunting. Instead start by accessing and analyzing raw historical machine and metric data to establish a base understanding, and use clustering algorithms and analytics to identify trends and patterns. Raw data is the best type if you truly want real-time detection. Then you can begin to analyze streaming data to see how it fits those patterns, applying AI powered by machine learning to introduce automation and, eventually, predictive analytics.

Ingest and analyze as many data types as you can: If you start by analyzing and understanding past states of your systems, you will be able to correlate what you learn with the present. To achieve this, organizations must ingest and provide access to a wide range of historical and streaming data types. The data type that you select — be it log, metric, text, wire or social media data — depends on the problem you’re solving. For example, you can use metric data from your infrastructure to monitor capacity, or application logs to ensure that you are providing an outstanding experience to your customers. Ultimately, enterprises should select those platforms that are capable of ingesting and analyzing data from multiple sources.

Don’t try to do it all at once: Focus on finding the root cause of your highest priority problem. Then progress to monitoring data. Only after this has been accomplished should AI be approached. Even then, take it step-by-step:

  • Start by implementing an AIOps platform that gives you an effective foundation for organizing large volumes of data, and that make it easy to take action and monitoring capabilities that reveal patterns.
  • Next, explore the degree to which those patterns enable you to predict incidents and have a more proactive IT approach that allows you to decrease not only your MTTR but also the number of business-impacting incidents.
  • Finally, work with machine-learning-powered root-cause analysis to get to a predictive state in which you can determine the incident and its impact before it even affects your key business services and customer experience.

The Bottom Line: Making an investment in AIOps will help improve your business

If you’re an IT and networking professional, you’ve been told repeatedly that data is your company’s most important asset, and that it will transform your world forever. AI is a revolution and it’s here to stay — and AIOps provides a concrete way to turn the hype about AI and big data into reality for your business initiatives. From improving security to streamlining operations to increasing productivity, AIOps is a practical, readily available way to help you grow and scale your IT operations to meet future challenges, solidifying IT’s role as a strategic enabler of business growth.

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Stephen Watts
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Stephen Watts

Stephen Watts works in growth marketing at Splunk. Stephen holds a degree in Philosophy from Auburn University and is an MSIS candidate at UC Denver. He contributes to a variety of publications including CIO.com, Search Engine Journal, ITSM.Tools, IT Chronicles, DZone, and CompTIA.