Help predict the infrastructure resources needs and gaps by considering future events and historical patterns.
Using detailed baseline data and correlation, resource allocation can be optimized more effectively than traditional approaches.
With time-shift analysis and leading indicators information, outages can be prevented by predicting problems before they become service impacting.
Allows changing the values of operation metrics to see the impact on success criteria. This will help in developing plans to mitigate those scenarios.
Event correlation and ability to drill down to specific rules causing the negative outcomes within the context of application makes it easy to pinpoint the root cause.
Provides an extensible framework for data scientists to use their own algorithm and verify the performance before deploying.