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According to Gartner’s definition, Artificial Intelligence for IT Operations, also known by the acronym AIOps, is a collection of multi-layered applications with resources aimed at enhancing and automating core IT Operations tasks. Structurally, these solutions bring together Machine Learning, Big Data and Analytics, observability, and automation systems with the goal of automating and accelerating the identification and resolution of IT issues.
Currently, systems within organizations generate massive volumes of data, easily reaching millions of relevant events per day. Considering this scale, any effective manual analysis of events becomes completely unfeasible, let alone executing fixes for identified failure events. This creates the need for automation, machine learning, and problem prediction capabilities.
In essence, AIOps solutions offer functionality similar to existing event management solutions but add the features necessary for modern, complex environments.
As a result, applying AIOps solutions allows companies to identify and react to IT problems faster, using fully predictive and automated analysis. Consequently, they ensure greater agility and assertiveness in keeping systems running, avoiding the interruption of corporate activities for long periods.
The goal of these platforms, therefore, is to eliminate the difficulty that IT Operations leaders and professionals have faced in recent years managing their infrastructures, especially regarding the analysis of threats and failures. This is a particularly important challenge today, given the increased division of data loads spread across Cloud environments, third-party services, Software as a Service (SaaS) integrations, mobile devices, etc.
Another highlight is the reduction in time required to apply fixes and adjustments to the system. By adopting intelligent AIOps platforms, companies improve their processes, identify the root causes of possible failures more agilely, and discover how to correct these errors quickly and efficiently.
AIOps works by connecting data from various sources, gathering and consolidating them. We can consider relevant items for aggregation and analysis to be performance data, data collected by observability tools, logs, alerts, incident records, among many others.
Next, the system separates the most relevant data for the proper functioning of applications and the business, creating a pertinent data set for analysis and automation. This automated process identifies the cause of incidents, makes predictions based on history regarding problems that may occur in the near future, and proposes solutions.
Once the main problems and possible solutions are formatted and available for viewing, automation tools are triggered — usually through webhooks — to execute actions for correcting an application or environment, as well as notifying the teams involved in case of process failure or failure to fix the incident’s root cause. In this way, it is possible to consolidate the resolution of common problems into just a few tools, gaining scale and speed in IT management.
As previously seen, there is a need for integration between various tools to perform multiple types of analysis, organize data, and automate, among other tasks. Thus, it is necessary to develop all integrations between tools, creating a mesh capable of providing the solutions needed for speed in identifying and correcting problems.
A simple example would be fixing a problem in a fully automated way. However, before this correction, it is necessary to identify an event, validate its correlation with other sets of problems or metrics, decide on the best solution based on previous data, and only then execute the fix, resulting in either a record of successful execution or the summoning of a human in cases of failure.
This complexity of collecting, aggregating, analyzing, and acting is only possible by integrating several tools specialized in each of these requirements. It is about improving IT understanding and management, understanding its complexities per system, and then integrating data collectors, Data Lakes, AI, and automation and notification tools.
With the experience gained by Edge UOL over time, we realized that it is not enough to simply have access to intelligence, automation, and observability tools and solutions. It is necessary to ensure that systems meet some basic requirements to start an automated operation project.
We list here some requirements considered as the baseline:
These four items enable a journey toward automated operations, allowing for the development and customization of tools that will ensure the best availability and experience for the end user.
AIOps will help automate and streamline the analysis of data related to IT environments — previously done manually — making monitoring much more practical and functional. Modern systems include continuous analysis of dependencies between components, active monitoring of all data sources, automatic topology checks, anomaly detection, and predictive event evaluation, among other features.
Artificial Intelligence and automation are ready to radically change the game in operations. More than that, these innovations can apply intelligence across the entire digital IT value chain, from software development to service delivery and customer interactions. As today’s corporate systems increase in size, the benefits of relying on software that adds Artificial Intelligence will ensure that IT Operations are prepared for the challenges of speed, scale, and complexity of digital transformation.
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