What is AIOps?


Advancements and complexity in technology have meant that organizations can no longer rely on humans to manage several essential processes. In particular, the challenges that IT operations face have become increasingly complex and overwhelming. 

For instance, decades ago, a single human was perfectly capable of handling and monitoring organization data centers. Nowadays, with the advent of cloud computing, the demand to quickly derive insight from vast amounts of data makes it impossible for organizations to rely solely on humans. Instead, the responsibility falls on machines that can automate tasks, improve, and extract insights from data.

On the other hand, the demands from real-time streaming data are overtaking data analytics; humans can no longer keep up with it. To do that, organizations need machine learning algorithms.

But how can you set up your organization to take full advantage of the new shift in operations? One way to achieve this is through AIOps. According to Gartner, “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” 

To help you better understand AIOps, we’ll describe what it is, how to get started with AIOps tools, and how it can benefit your business.

What is AIOps

The dynamic and ever-changing nature of the IT environment requires modern solutions, tools, workflows, and processes. In the same way, the management process must be equally dynamic as IT infrastructures evolve from relatively static environments to software-defined resources capable of changing and repositioning on demand. That has meant several Ops paradigms being introduced to address these issues.

AIOps or its longer form “artificial intelligence for IT operations” refers to the use of big data, machine learning, and several other advanced analytics to garner dynamic, proactive, personal insight that enhances IT operations. Today's hybrid, scalable, and distributed IT environments heavily rely on a central monitoring and management system. 

You might wonder, how does AIOps actually work? Through an AIOps platform, you can load heterogeneous data, including network and application traffic, infrastructure, cloud storage, and cloud instance statistics. Following this, it automatically removes noise and duplicates data by using entropy algorithms. By doing this, Ops teams are no longer overburdened with alerts, as many redundant tickets no longer have to be routed to various teams.

With AIOps, ITOps and DevOps teams can identify and rectify issues in their infrastructure much earlier, rather than wait till they negatively affect business operations or customers. AIOps analyzes IT data algorithmically to help teams work faster and effectively. With AIOps, Operations teams can manage the complexity and massive amounts of data generated by modern IT environments, reducing downtime, improving uptime, and satisfying SLAs.

Human Limitations and Machine Learning

The simple truth is, it is almost impossible for a human to process the massive amounts of data IT operations have to deal with daily. Similarly, it is also a challenge for the IT team to choose what issues to prioritize and remediate. The ITOps team is typically overwhelmed with a vast amount of issues, most of which are redundant. They are unable to intelligently and effectively manage the huge data available to them. 

To resolve this, organizations turn to machine-learning algorithms to correlate data from several interconnected, but separate environments to provide predictive analysis and gain the insight necessary to offer solutions. With AIOps, organizations can quickly identify, locate, and fix IT operations problems. 

Is AIOps The Future of DevOps?

As DevOps drives change and innovation within organizations, AIOps also represents a data-driven shift in identifying and uncovering valuable insight from diverse data points to drive decisions. The DevOps philosophy also encourages teams to update their processes often and in small batches to continuously improve their products. That desire to improve is something both AIOps and DevOps share.

AIOps and DevOps require a culture change and how organizations regard systems in an integrated way. The two are in agreement on the importance of automating processes and monitoring the entire application ecosystem. Neither is limited to products, technologies, or architectural layers, and automation is a big part of both of their effort too.

Despite this, AIOps and DevOps have a few unique differences. In contrast to DevOps, AIOps uses more data to assist operations beyond running automated processes and tests. AIOps does not just automate the implementation and deployment of software, it also uses data to predict and optimize regular operations. It also adapts automatically to changes in the production environment.

Additionally, whereas DevOps requires manual intervention to identify root causes, AIOps can identify performance issues and suggest optimizations, and it can even go so far as to pinpoint the cause. The AIOps approach picks up where DevOps left off. By utilizing even more data, it provides more automation.

AIOps Benefits For Businesses

With AIOps, organizations can access a wide range of opportunities and benefits. They include:

  • Enables digital transformation: AIOps provides you with more business value and saves you time and effort in the process as you scale, grow, and innovate. 
  • Better DevOps and automation process: With AIOps, you can take advantage of smart automation processes that can help monitor the health and performance of your application, and quickly roll back, fix, or suggest solutions.
  • Faster application deployment: The AIOps approach lets you automate business logic actions for known events.
  • Enhanced visibility: Through AIOps, you can get a clearer view of your operations infrastructure, network, and data centers.
  • Data-driven recommendation: AIOps will provide you with real-time insight, and historical recommendations to assist you in making an informed decision.
  • Real-time analytics and notifications: AIOps provides you with real-time analytics by applying the necessary algorithms to reduce operational noise and prioritize alerts according to their importance.
  • Faster MTTD and Reduced MTTR: AIOps drastically reduces the mean time to detect--the average time taken for an incident to be detected-- and reduces the mean time to repair.

AIOps Platforms: What They Are and How They Work

AIOps platforms aim to empower IT professionals and organizations with the data needed to make an informed and faster decision, prevent downtime, resolve issues as soon as possible, and even uncover insights. It allows the integration of data from multiple sources into a central location, thus eliminating data silos. In this way, AI/Ops platforms provide engagement and context for users through monitoring, observability, and automation.

  • Datadog: A cloud-based platform for managing and monitoring infrastructure. It allows you to create automated AIOps workflows.
  • Dynatrace: An AI-powered application performance monitoring (APM) solution.
  • Cisco AppDynamics: A cloud and on-premises APM and operation analytics solution.
  • Splunk Enterprise: A powerful platform for monitoring services using analytics and automation.
  • PagerDuty: A SaaS platform for incident response and allows you to collect telemetry data and get alerts with machine learning and automation.
  • LogicMonitor: A cloud-based platform for monitoring APM, AIOps, and networks.

The Future of Ops

While AIOps introduces significant changes for IT operations, it does not represent a radical shift in how analytics and machine learning are applied. As organizations look towards building better applications, they must turn to the workflows, content applications, processes, and technologies that facilitate them. With DevOps and automation tools, application development becomes more manageable. 

The fact is, eventually, AIOps will become a bit redundant as AI will become infused into the entire DevOps operation. However, this still may not suffice for content-driven applications. As a result, the DevContentOps approach offers a modern solution to developing a content-rich application by leveraging the best tools and platforms such as headless CMS, sophisticated Git-based repositories for content as well as code, CI/CD tool and process integration, and ultimately a means to enhance digital product quality and innovation through AIOps.

Topics: Leadership

Tags: Gartner, AI/ML