AIOps is a multi-layered platform using artificial intelligence (AI) and machine learning (ML) technologies to manage complex IT operations and services at scale. A successful AI implementation anchors on the application of AI to improve systems, customer experience, and software development lifecycle.
First coined by Gartner in 2016, AIOps requires and is the main component of digital transformation.
AIOps deals with big data—collecting, aggregating, and analyzing data from various sources, like devices, applications, and other IT operations tools. It is also critical for cloud migration processes and the adoption of DevOps (development and operations).
Why does a business need AIOps?
The shift from traditional IT infrastructure to cloud-based environments and solutions makes AIOps necessary and invaluable.
Aside from dealing with big data, AIOps platforms enable organizations to gain intelligence and insights across operations, automate business processes, and track performances and engagements. It brings a robust IT environment that enhances performance and increases the efficiency of business processes, operations, and services at scale.
How does AIOps work?
AIOps consists of the following elements:
- Data collects diverse data to detect anomalies, identify patterns, and track changes;
- Analytics uses machine learning in aggregating and analyzing data for actionable insights;
- Correlation contextualizes data, processes, and various business metrics to stream IT operations events in real time;
- Documentation creates and secures documentation of all processes for audit and regulatory compliance; and
- Automation automates workflows, documentation, and systems to speed up problem resolution and simplify complex processes.
AIOps helps process a growing volume of operations data, isolate significant trends and patterns from the noise, and diagnose root causes for immediate response.
What are the benefits of AIOps?
Embracing AIOps means taking advantage of the opportunities that come with AI implementation, such as growth, increased efficiency, agility, and innovation. It speeds up detection of root causes and problem resolution, promotes predictive management, and modernizes IT operations.
With AIOps, a company improves agility and adaptability as it constantly deals with a rapidly changing business environment.
What are some examples of AIOps platforms?
Since Gartner introduced AIOps to the public in 2016, major industry players accelerated the race for comprehensive AI solutions for IT operations. As the competition intensifies, a few platforms emerged to become industry leaders.
IBM’s Watson AIOps delivers AI to enhance an organization’s IT operations management. And then there’s Cisco coming out with AppDynamics and later Moogsoft. Other notable names are Dynatrace, Datadog, Splunk, LogicMonitor, and BigPanda.