AIOps stands for Artificial Intelligence for IT operations. It refers to a multi-level technology platform that automates and improves IT operations through analytics and machine learning (ML). The AIOps platform uses big data and aggregates a wide range of data from various IT operating tools and devices to automatically detect and respond to real-time issues while providing traditional historical analysis.
What is AIOps?
AIOps combines several principles of advanced analytics, machine learning (ML), and AI in order to build a solution that goes beyond just detecting and responding to issues with existing IT operations tools, but has the capability to help IT operations pros make major strides in developing self-service digital experiences.
As the conversation surrounding AI and ML gets louder, we’re seeing machine learning systems being utilized in a variety of different scenarios.
Companies like Sony and Philips have developed products that predict the best-selling part of a product before it’s even shipped. These predictions can provide insight that speeds up product and customer ordering and helps organizations increase revenues.
How does AIOps work?
AIOPS works by using machine learning to understand what’s going on in the data. AIOps platforms pull information from multiple sources including, but not limited to, IT operations tools such as ServiceNow, Microsoft System Center, VMware vRealize Operations, TeamCity, and CA ServiceNow, as well as business data sources such as email, SMS, end-user experience (EUX) software and email marketing software.
The platforms use machine learning to understand how each of these multiple sources affects the overall performance of the IT systems and automatically makes adjustments to help the systems perform better.
How does AIOps benefit IT and DevOps? By utilizing AIOPS, IT organizations are able to run the business more efficiently and effectively.
Benefits of AIOps
Analytics AI and ML models are used to uncover anomalies and create recommendations to reduce costs and improve efficiency. This helps companies better predict and respond to issues that are prone to surprise and disaster or things that most people consider surprises.
It also keeps abreast of expected problems that can be mitigated before they occur. The AIOPS platform provides real-time, pre-emptive, and self-healing capabilities for everything from alert generation and remediation, to proactive and predictive insights that help companies respond effectively to issues and business requirements.
Analytics Organizations can predict and create models to fix the issue before it becomes an issue.
AIOps in action
Not all AIOps products are created equal. To get the maximum benefit, organizations should implement it as an independent platform that collects data from all IT monitoring sources and acts as a central engagement system.
Such a platform must be supported by five types of algorithms that fully automate and simplify the five main dimensions of monitoring IT operations:
- Data selection
Take the huge amount of highly redundant and noisy IT data generated by today’s IT environment and select the data elements that show the problem, which often means filtering out up to 99% of that data. - Pattern Discovery
Correlation and discovery of relationships between selected and relevant data elements and their groupings for further analysis. - Inference
Identify the root cause of problems and recurring issues so you can take action publicly. - Collaboration
Informing the right operators and teams and facilitating collaboration between them, especially when people are geographically dispersed, and storing incident data can speed up the diagnosis of the problem in the future. - Automation
Automate response and distance as far as possible to make decisions more accurately and faster.
Conclusion
The ultimate goal of AIOps is to enable IT transformation as well as smarter and more predictable operations. With AIOps tools, IT organizations receive unified information about events, reduce noise in IT data and eliminate work, reduce IT ticket volume, resolve IT issues faster, predict/prevent disruptions before the effects of customers automate root cause analysis, accelerate the resolution of accidents or problems, improve IT performance and lower the total cost of ownership.