Last year, Australia’s two largest supermarket chains suffered nationwide technical issues, forcing the companies to close stores until the issues were fixed. The result: Lost revenue and frustrated customers.
The reality is that IT teams are dealing with increasing amounts of data and a variety of tools to monitor that data, which can mean significant delays in identifying and solving issues.
IT operations is challenged by the rapid growth in data volumes generated by IT infrastructure and applications that must be captured, analyzed and acted on. Coupled with the reality that IT operations teams often work in disconnected silos, this makes it challenging to ensure that the most urgent incident at any given time is being addressed.
To prevent, identify and resolve high-severity outages and other IT operations problems more quickly, businesses are turning to artificial intelligence (AI) for IT operations (AIOps).
What is AIOps?
Put simply, AIOps is the application of machine learning (ML) and data science to IT operations problems. AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operations functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation.
AIOps platforms consume and analyze the ever-increasing volume, variety and velocity of data generated by IT and present it in a useful way.
Gartner predicts that large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. The long-term impact of AIOps on IT operations will be transformative.
IT leaders are enthusiastic about the promise of applying AI to IT operations, but as with moving a large object, it will be necessary to overcome inertia to build velocity. The good news is that AI capabilities are advancing, and more real solutions are becoming available every day.
How to launch an AIOps initiative
Padraig Byrne, senior director analyst at Gartner authored this article, which can also be found here.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.