AIOps stands for AI for IT Operations and refers to the way data and information from the development environment is managed by IT teams, in this case using AI. AIOps platforms leverage big data, machine learning, and analytics to improve IT operations through monitoring, automation, and service desk capabilities with proactive and personal insights, and the use of multiple data sources and data collection methods. make it possible. In theory, AIOps can resolve outages and other performance issues faster and reduce the costs associated with IT challenges.
The benefits of AIOps are driving enterprise adoption. With 87% of respondents to a recent OpsRamp survey agreeing that AIOps tools improve data-driven collaboration, Gartner predicts AIOps service usage will grow from 5% in 2018 to 30% by 2023. We expect it to increase. However, companies that do not have a clear picture of potential blockers can face challenges when deploying AIOps solutions. So it’s important that he understands AIOps holistically before strategizing.
What is AIOps?
The AIOps platform collects data from various IT operational tools and automatically detects problems while providing historical analysis. These typically consist of his two components, big data and machine learning, and the need to break away from siled IT data to aggregate observational data along with engagement data in ticket, incident and event records. there is. Seth Paskin, operations manager at BMC Software, writes:
“The outcomes an IT professional expects from his AIOps can be broadly categorized into automation and predictive. The first expectation of AIOps is to automate tasks that are currently done manually and improve the speed at which those tasks are performed. Here’s a specific example I’ve heard:
Correlate customer profile information with financial processing application and infrastructure data to identify outliers in transaction duration and highlight factors that impact performance. Evaluate unstructured data in service tickets to identify problematic automation candidates. Categorize workloads for optimal infrastructure placement. Correlate incidents with changes, work logs, and app development activity to measure the impact of infrastructure and application changes on your production environment. ”
An AIOps platform canvasses data on logs, performance alerts, tickets, and other items using an auto-discovery process that automatically collects data across infrastructure and application domains. The process identifies infrastructure devices, running apps, and business transactions and correlates all the data in a contextual form. Automatic dependency mapping determines the relationships between elements such as the physical and virtual connections at the networking layer by mapping app flows to the supporting infrastructure and between the business transactions and the apps.
AIOps’ automated dependency mapping has another benefit:
helping to track relationships between hybrid infrastructure entities. AIOps platforms can create service and app topology maps across technology domains and environments, allowing IT teams to accelerate incident response and quantify the business impact of outages.
To identify patterns and predict future events, like service outages, AIOps employs supervised learning, unsupervised learning, and anomaly detection based on expected behaviors and thresholds. Particularly useful is unsupervised machine learning, which enables AIOps platforms to learn to recognize expected behavior and set thresholds across data and performance metrics. The platforms can analyze event patterns in real time and compare those to expected behavior, alerting IT teams when a sequence of events (or groups of events) demonstrates activity that indicates anomalies are present.
The insights from AIOps platforms can be turned into a range of intelligent actions performed automatically, from expediting service desk requests to end-to-end provisioning to deployment of network, compute, cloud, and applications. In sum, AIOps brings together data from both IT operations management and IT service management, allowing security teams to observe, engage, and act on issues more efficiently than before. Theme
Not all AIOps deployments go as smoothly as planned. Challenges such as poor data quality and mistakes made by the IT team can get in the way. An employee may struggle to learn how to use his AIOps tools. Also, handing over control to an autonomous system can raise concerns among senior management. Additionally, deploying a new His AIOps solution can take time. The majority of OpsRamp survey respondents said it would take them 3-6 months to implement an AIOps solution, and 25% said it would take them 6 months or more to implement.
Data science challenges may hinder the success of his AIOps strategy, as AIOps platforms rely heavily on machine learning. For example, accessing high-quality data to train machine learning systems is challenging. According to his Rackspace Technology survey in 2021, poor data quality was the top reason for machine learning R&D failures for his 34% of respondents. 31% said they lack production-ready data.
Beyond data challenges, skills shortages are also a barrier to AIOps adoption, with a 2021 Juniper report finding that a majority of respondents expect their organizations to scale their workforces to integrate with AI systems. said they are struggling with Complaints about AI talent shortages have become common in the private sector. In O’Reilly’s 2021 Survey of AI Adoption in Enterprises, the shortage of skilled workers and the difficulty of recruiting topped his list of AI challenges, with 19% of his respondents citing this. increase. as a “significant” impediment.
Unrealistic expectations of senior management are also a major reason machine learning projects fail. A survey of executives found that 9 out of 10 of his respondents called AI “the next technological revolution,” but Edelman said the lack of buy-in from executives contributed to his slow adoption of AI. Algorithmia has found that it is one of the causes. advantage
The success of AIOps deployments is not taken for granted, but many organizations find the benefits worth the challenges. AIOps systems reduce the flood of alerts that overwhelm IT teams, learning over time which types of alerts to send to which teams to reduce redundancy. They can be used to handle routine tasks such as backups, server reboots, and low-risk maintenance activities. It can also predict events before they occur, such as when network bandwidth is maxed out.
Ultimately, as Accenture explains in a recent white paper, AIOps can improve the capabilities of his IT organization and become an effective partner for the business. “An IT operations platform with built-in AIOps capabilities can help IT departments proactively identify and resolve potential problems with the services and technology they provide to the enterprise before they become problems,” said the consultancy firm. writing. “This is the value of a single data model that services and operations management applications can seamlessly share.”