What is AIOps?

A Quick Guide to Artificial Intelligence for IT Operations (AIOps)

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In this day and age, an organization’s fate is often determined by its ability to wield technology. Whether it be market growth, increased profitability or some other strategic objective, business units rely on the IT department to equip them with the tight rools, services and knowledge to succeed in a rapidly changing, digital world.

As IT becomes the heart of the business, organizations must ensure that IT operations run as smoothly and efficiently as possible, much like the arteries that feed into our beating, human hearts. 

However, while the pressure on the IT department to deliver transformational results grows, so do the challenges. Increasing IT complexity, talent shortages, budget reductions, evolving cybersecurity threats, budget cuts and so much more make effectively managing the modern IT ecosystem harder than ever. 

In order to increase the speed and effectiveness at which they detect, diagnose, and resolve incidents, organizations are embracing AIOps. 

 

What is AIOps?

AIOps stands for “artificial intelligence for IT operations.” As the name suggests, AIOps refers to the application of AI to automate and optimize IT processes. Leveraging both machine learning and advanced analytics techniques, AIOps proactively identifies, isolates and resolves IT issues. 

The ultimate goal of AIOps? To build autonomous IT operations. In fact, the key difference between AIOps and traditional IT analytics tools is the automation component. 

AIOps platforms work by, first, ingesting, consolidating and analyzing all IT data into one, centralized platform. This means aggregating both historical and real-time data from dozens of sources including helpdesk systems, multi-cloud environments, containerized applications, storage, databases, events and logs, APIs and SDKs, APM, monitoring, and data streams. The system then applies a series of analytics to this data, ranging from statistical and probabilistic analysis, to automated pattern discovery and prediction, unsupervised learning for anomaly detection and topological analysis to any combination of these techniques. 

 

*Image sourced from https://www.techtarget.com/searchitoperations/definition/AIOps 


Benefits of AIOps

By increasing the velocity, agility and effectiveness of IT operations, AIOps can deliver significant business value especially when it comes to enabling digital transformation. In fact, according to a report by BCC Research, the global market for AIOps is estimated to more than triple from $3.0 billion in 2021 to  $9.4 billion by 2026, at a CAGR of 26.1%. Gartner even predicts that 40% of companies will be using AIOps for application and infrastructure monitoring by 2023. 


Enterprise Management Associates surveyed more than 200 global IT professionals and found the top business benefits of AIOps to be “improved alignment between IT and the business, higher-quality IT and business services, improved experiences for customers and employees, improved business process efficiency, and accelerated digital transformation.”

More specifically, AIOps can:

  • Observe causal relationships over multiple systems, services and resources, clustering and correlating disparate data sources in real time. This allows AIOps to not only identify indicidents, but perform root cause analysis as well.
  • Significantly reduce alert noise. The system even learns over time which types of alerts should be sent to which teams.
    Automatically process and fulfill an IT helpdesk user requests. AIOps solutions can even handle routine tasks like backups, server restarts, and low-risk maintenance activities
  • Recognize cybersecurity threats, such as malware attacks, much earlier and more accurately than humans can
  • Centralize all IT data into one dashboard enabling real-time monitoring of complex IT landscapes and enhanced, data-driven decision making
  • Proactively tackle performance issue and other events before they occur using predictive analytics
  • Re-route network traffic to reduce congestion and free up bandwidth, spin up additional cloud instances and concurrently expand the SD-WAN fabric, and re-distribute containerized workload, for optimum resource utilization and cost-savings
  • Provide assessments based on a calculated quality of experience (QoE)
  • Significantly reduce the administrative burden placed on IT admins, allowing them to focus their attention on high-value activities
  • Enable developers to model and test the performance requirements of new applications and features prior to production, resulting in accelerated development cycles and improved time-to-market

 

Drawbacks of AIOps


Though AIOps has the potential to deliver transformational results, the technology is not without its downsides.

First and foremost, AIOps platforms can be expensive and difficult to implement. Like with any new technology, before you implement it, it’s important to have a clear idea of how the technology will solve problems and deliver business value. In addition, most organizations opt to deploy the technology incrementally to reduce upfront costs and integration issues. 


Secondly, AIOps does not work on siloed data. If it only has access to 50% of an organization’s data, it will only be 50% effective. Likewise, if the system is ingesting low quality data, it will be unable to deliver desired results. At a bare minimum, AIOps success requires foundational data integrity, organizational alignment, and a holistic enterprise data strategy. 

 

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