AIOps Use Cases
AIOps platforms combine machine learning, analytics and data at scale to deliver a superior digital experience. Read about challenges AIOps addresses and six common use cases for AIOps platforms.
Key Challenges AIOps addresses
The monitoring gap is growing
Traditional monitoring solutions can’t keep up with the number and volume of data sources from rapidly expanding app and cloud environments. Enterprises are missing full the picture and lack visibility to connect the dots.
Too many tools, not enough answers
The number of different products in the monitoring stack is obstructing IT’s ability to understand the full impact of an issue. The consequence? Time kills and ‘Not my problem’ is the problem.
Humans are drowning in data and noise
It’s hard to find good talent, keep the lights on, let alone support rapid deployment of new technologies and environments. Manual analysis alone can’t stay on top of the volume of data and noise to make timely decisions.