Proactive Response
Instead of constantly fighting fires, ITOps teams can be more proactive when it comes to incident response. Incidents can be prevented before they occur through more accurate alerting. In addition, early warning can be provided by applying machine learning to historical data to better forecast and alert on potential issues.
In order to quickly and easily debug problems in microservices, developers need a simple and cost-effective way to send all the data about their applications into one place. After a problem is detected, they need a solution that can quickly guide them to the root cause of the issue and give them all the information they require to resolve it.
And finally, to create consistent, optimal user experiences, your engineering teams first need a good way to measure and understand them. Doing so requires detailed front-end metrics that go beyond basic availability. It requires the ability to measure performance proactively through synthetic tests, and a way to validate the actual experience of each and every user. After an issue is identified, you need a simple way to isolate which of the dozens or hundreds of components that impact the user experience actually caused the issue.
The use cases provided in this proactive response section can help with all these observability business problems and goals.
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Splunk On-Call
Splunk On-Call can also help your teams be more proactive. Splunk On-Call helps you improve business outcomes with automated incident response, optimize application delivery, respond quickly, stay ahead with robust reporting, and simplify on-call scheduling. Use the following links to learn some best practices for using Splunk On-Call.