Reasons We Need AIOps

in AIOps

There are many open source AIOps platforms that also offer this productivity

AIOps implementation makes life easier for the IT environment whereby it takes on the various challenges facing the existing system and provides us with all end to end report and visibility of our IT environment, and makes any IT team to be proactively respondent to any technical issue that arises.

    What Is AIOps?

    Coined by Gartner, AIOps meaning “Artificial Intelligence for IT Operations” or Algorithmic Operations refer to technology platforms that use Machine Learning (ML) and data science to solve IT operation problems.

    The increasing complexity in data management and demand for faster and more efficient data management needs a quick fix, birthing AIOps.

    AIOps combines Big Data, Machine Learning, and other advanced Data Analytics to improve and partially replace the main functions of IT operations, don’t confuse AIOps to MLOps.

    How Does AIOps work?

    AIOps with the help of Artificial Intelligence is predicated “breaks down data silos” - meaning it takes on the various challenges facing the existing system and provides us with all end to end report and visibility of our IT environment.

    There are many open source AIOps platforms that also offer this productivity. However, it does this with the following main elements;

    • Extensive and deep-level IT diagnostics; AIOps executes a profound correlation of data and analyzes and presents an understandable diagnostic of the IT environment.
    • Automation; With the need for human-interaction, AIOps helps to automate some major and time-consuming tasks so that IT operators can easily focus on other top-priority issues.
    • Predictive Analysis; AIOps’ machine learning allows it to understand data in real-time, analyze it, and predict probable future incidents.
    • Artificial Intelligence; When well taught, AIOps uses artificial intelligence to make IT operations management simple by analyzing present and future problems in combination with diagnostics.

    Why AIOps?

    Thanks to the technological advancement in Machine Learning and Artificial Intelligence, AIOps enables IT operations teams to respond more quickly and proactively to slowdowns and outages, with a lot less effort.

    Improves Monitoring and Analytics Challenges

    AIOps is equipped with the ability to analyze the rapidly growing data generated by IT infrastructures and applications practically and understandably. It monitors data like:

    • Infrastructure
    • Application performance
    • End-user
    • IT service management (ITSM) data, such as tickets, and change controls
    • Business Insights

    It’s not only difficult but inefficient, using various monitoring tools to collect multiple application data and performance metrics across the entire business application.

    This makes it nearly impossible to quickly arrive at results to solve surfacing IT problems before it causes costly downtime.

    It is recorded that 72% of IT organizations rely on up to 9 different tools for modern applications data monitoring.

    Data generated by IT continues to increase in volume, type, and generation speed. The AIOps platform (some are open source AIOps platforms) captures and analyzes these data and provides the results in a useful way.

    AIOps solves this problem by monitoring, managing, collecting, and correlating events data from dynamic multi-cloud sources and effectively analyzing them for superseding actions by the IT operation team.

    It eliminates the need for multiple monitoring tools by delivering analysis across all targeted services in a single and comprehensive pane of glass.

    Having these comprehensive insights from user behavior, infrastructure and performance will help to improve the general user experience.

    It Improves collaboration between IT teams and other business departments

    Comprehensive reports and data presentation facilitates collaboration within the IT team and between IT operators and other business units.

    It helps the IT team effectively communicate business-oriented metrics to improve their services or application.

    Also in situations where IT operators are geographically apart, AIOps facilitates remote collaboration with streamlined incident management.

    AIOps does the time-consuming tasks of ingesting, analyzing, and correlating alerts.

    The machine learning that powers AIOps can detect abnormal activities on the network, thereby automatically notifying the team on underlying issues for further diagnostics.

    AIOps make teamwork more productive with customized and intuitive data dashboards which makes team members understand tasks ahead and the process.

    Speeds up problem-solving and deployment

    Solving problems resulting from various IT activities has always been a time-consuming task. Owing to the unavailability of detailed data insights to trace problems to their roots.

    With the right insights and analytics provided by AIOps, it helps to identify root causes of problems and underlying issues, so that corresponding action can be taken on the discovery.

    AIOps helps to facilitate problem-solving and solution thinking processes.

    Reduces IT operation costs

    Expenditures for hardware acquisition and maintenance, software procurement, electricity, and other IT operational costs contribute to the high day to day bill of IT organizations.

    AIOps possess the ability to radically reduce IT operations costs.

    It eliminates the need for the purchase and maintenance of costly infrastructures and also optimizes the required number of IT personnel. Leading to a significant reduction in staffing, hardware, and other related operational costs with increased output, as major tasks are been automated


    It's a huge task to manually collect data and analyze it while also trying to fix IT-generated problems whose root-cause is not well understood.

    Integrating AIOps into IT operations helps to improve automation by triggering actions and workflows without human intervention.

    By leveraging on the data collected and analyzed by AIOps, it can predict possible future incidents which can help the IT operations team to easily and effectively get over it.

    AIOps also helps to schedule workloads, create data back-ups, restore systems after outages, this reduces the workload on Ops personnel and allows them to spend their time on other critical issues.

    Pattern Discovery

    AIOps pattern recognition technology can loop through historical data to find variations in data pattern - normal activities and anomalies.

    Pattern discovery can be used to quickly detect irregularities in the system operation aiding proactive incident management.

    It can be used to discover patterns in data to understand historical conditions and can make predictions for future planning.

    It also assists in finding the root causes of problems to find a proportional solution.

    Service management (Engage), performance management (Observe), and automation (Act)

    Reduces IT noise

    One of the most serious and overwhelming problems faced by IT Ops is the increasing IT operational noise. It generates several false-positives and makes it difficult to proactively solve problems by burying the critical root-cause.

    IT noise creates serious problems for the business causing higher maintenance costs, performance, and availability issues.

    AIOps with the power of Artificial intelligence can help businesses to eliminate IT noise by collecting data insights that are otherwise difficult for humans to uncover, correlating multiple monitoring, observability, change, and topology data into the well understandable incident.

    It also helps in avoiding distraction and enables IT personnel to focus on more service-impacting issues.

    Get similar stories in your inbox weekly, for free

    Share this story:
    The Chief I/O

    The team behind this website. We help IT leaders, decision-makers and IT professionals understand topics like Distributed Computing, AIOps & Cloud Native


    Latest stories

    The Problems 40,000+ Developers Have in Common

    A summarized detail on the State of the Octoverse report released by GitHub. It is …

    How Communities Can Impact Developers Positively

    Building a sustainable and welcoming community is a step closer to improving the developer experience, …

    Coding Efficiency Improvement; What You Need to Know

    GitHub released a report of analysis done on over 4 million repositories about the problems …

    Kubernetes Tools Digest (Nov 2021): Represents All K8s Objects in a Graph

    These 5 Kubernetes tools are not as popular as Helm, Prometheus, or Istio, but they …

    Blue-Green Deployment Explained

    This article discusses what the blue-green deployment strategy is, its pros and cons, and its …