What is AIOps?

Discover how AIOps leverages data and machine learning to enhance and automate IT service management

Coined by Gartner, AIOps—i.e. artificial intelligence for IT operations—is the application of artificial intelligence (AI) capabilities, such as natural language processing and machine learning models, to automate and streamline operational workflows.

Specifically, AIOps uses big data, analytics, and machine learning capabilities to do the following:

  • Collect and aggregate the huge and ever-increasing volumes of data generated by multiple IT infrastructure components, application demands, and performance-monitoring tools, and service ticketing systems
  • Intelligently shift ‘signals’ out of the ‘noise’ to identify significant events and patterns related to application performance and availability issues.
  • Diagnose root causes and report them to IT and DevOps for rapid response and remediation —or, in some cases, automatically resolve these issues without human intervention. 
AIOps
What is AIOps? 4

By integrating multiple separate, manual IT operations tools with into a single, intelligent, and automated IT operations platform, AIOps enables IT operations teams to respond more quickly—even proactively—to slowdowns and outages, with end-to-end visibility and context.

Coined by Gartner, AIOps (Artificial Intelligence for IT Operations) applies AI capabilities, including natural language processing and machine learning, to automate and streamline operational workflows. AIOps utilizes big data, analytics, and machine learning to:

  1. Collect and aggregate vast data volumes from various IT infrastructure components, application demands, performance-monitoring tools, and service ticketing systems.
  2. Intelligently discern meaningful events and patterns related to application performance, separating them from the noise.
  3. Diagnose root causes, promptly reporting them to IT and DevOps for rapid response or automatically resolving issues without human intervention.

By integrating diverse manual IT operations tools into a unified, intelligent, and automated platform, AIOps empowers IT operations teams to respond swiftly and proactively to slowdowns and outages. It provides end-to-end visibility and context, bridging the gap between a complex IT landscape and siloed teams, and meeting user expectations for uninterrupted application performance. Widely seen as the future of IT operations management, the demand for AIOps continues to rise with the growing emphasis on digital transformation initiatives.

What is AIOps?

The journey towards AIOps varies for each organization. Once you assess your progress on this path, you can integrate tools that assist teams in observing, predicting, and swiftly addressing IT operational issues. When considering tools for enhancing AIOps, ensure they possess the following key features:

  1. Observability: This involves software tools and practices for ingesting, aggregating, and analyzing a continuous stream of performance data from a distributed application and its hardware. While providing a holistic view across applications, infrastructure, and networks, these tools do not take corrective action. They alert end users of potential issues, relying on IT service teams for remediation.
  2. Predictive Analytics: AIOps solutions analyze and correlate data to offer insights and automated actions, enabling IT teams to navigate complex environments and ensure application performance. Automatic anomaly detection, alerts, and solution recommendations reduce downtime, incidents, and tickets. Predictive analytics also facilitates dynamic resource optimization, ensuring application performance while lowering resource costs amid demand variability.
  3. Proactive Response: Some AIOps solutions proactively respond to unintended events, such as slowdowns and outages, in real-time. By feeding application performance metrics into predictive algorithms, they identify patterns preceding IT issues. AIOps tools can launch automated processes in response, improving mean time to detection and addressing issues swiftly.

This technology represents the future of IT operations management, enhancing both employee and customer experiences. AIOps systems ensure timely resolution of IT service issues and act as a safety net for operational teams, addressing issues that may be overlooked due to human factors like organizational silos and under-resourced teams.

Benefits of AIOps:

  • Timely resolution of IT service issues
  • Safety net for addressing issues overlooked by human factors
  • Improved mean time to detection (MTTD)
  • Enhanced employee and customer experiences
  • Intelligent automation for proactive response to IT problems

As organizations embrace AIOps, they position themselves to not only streamline IT operations but also elevate overall business performance and user satisfaction.

The primary advantage of AIOps lies in its ability to expedite the identification, resolution, and mitigation of slowdowns and outages, surpassing the efficiency of manual analysis through multiple IT operations tools. This results in several key benefits:

  1. Faster Mean Time to Resolution (MTTR): AIOps, by cutting through the noise of IT operations and correlating data from diverse environments, can pinpoint root causes and propose solutions at a pace and accuracy beyond human capabilities. This leads to significantly reduced MTTR, achieving goals previously deemed unattainable.
  • Example: Vivy’s IT infrastructure reduced MTTR for its app by 66%, from three days to one day or less.
  1. Lower Operational Costs: Automatic identification of operational issues and reprogrammed response scripts result in reduced operational costs, enabling more efficient resource allocation. This allows staff to focus on innovative and complex tasks, enhancing the overall employee experience.
  • Example: Providence saved over USD 2 million through optimization while ensuring app performance during peak periods.
  1. Enhanced Observability and Collaboration: AIOps monitoring tools with integrated features facilitate effective cross-team collaboration, fostering improved communication and transparency. This, in turn, enables quicker issue response and better decision-making.
  • Example: Dealerware enhanced observability in its container-based architecture, improving app performance during the pandemic and reducing delivery latency by 98%.
  1. Transition from Reactive to Proactive to Predictive Management: AIOps, with built-in predictive analytics, continuously learns to identify and prioritize urgent alerts, allowing IT teams to address potential problems before they result in slowdowns or outages.
  • Example: Electrolux accelerated IT issue resolution from 3 weeks to an hour, saved over 1,000 hours annually by automating repair tasks.

AIOps Use Cases:

AIOps integrates big data, advanced analytics, and machine learning for the following use cases:

  1. Root Cause Analysis: Identifying and resolving the root cause of problems to prevent future occurrences.
  2. Anomaly Detection: Recognizing atypical data points in large datasets, predicting problematic events such as data breaches.
  3. Performance Monitoring: Acting as a monitoring tool for cloud infrastructure, virtualization, and storage systems, providing insights into usage, availability, and response times.
  4. Cloud Adoption/Migration: Offering visibility into interdependencies, reducing operational risks during cloud migration and hybrid cloud approaches.
  5. DevOps Adoption: Providing visibility and automation to support DevOps without additional management effort.

How does AIOps work?

Understanding how AIOps operates involves examining the roles of its key components—big data, machine learning, and automation—in the overall process.

  1. Big Data Platform:
  • Aggregating Data: AIOps utilizes a big data platform to consolidate disparate IT operations data, teams, and tools into a unified repository.
  • Included Data Types:
    • Historical performance and event data
    • Streaming real-time operations events
    • System logs and metrics
    • Network data, including packet data
    • Incident-related data and ticketing
    • Application demand data
    • Infrastructure data
  1. Focused Analytics and Machine Learning:
  • Signal Separation: AIOps analyzes IT operations data to differentiate between significant event alerts (signals) and extraneous information (noise).
  • Root Cause Identification: Correlating abnormal events with other data, AIOps pinpoints the cause of outages or performance issues and proposes potential solutions.
  • Automated Responses: AIOps can automatically route alerts and suggested solutions to the relevant IT teams. It may also trigger real-time automated system responses based on machine learning results, resolving issues before users are aware of them.
  • Continuous Learning: AI models within AIOps continually learn and adapt to environmental changes, such as infrastructure modifications by DevOps teams, enhancing future problem handling.

In summary, AIOps employs big data to unify IT operations data, while machine learning and automation enhance the analysis, identification of root causes, and proactive resolution of IT issues. The continuous learning aspect ensures adaptability to changes in the environment, contributing to ongoing improvements in problem management.

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