Measuring the business outcomes of AIOps
In the previous sections, we discussed shift left and saw how we can define DevOps as a service. Next, we learned how to create total visibility of all assets in the enterprise as a starting point to implement AI-driven processes that will help improve development, deployment, and operations and with that, accelerate a shift left in IT. How will AI help with that?
- AI is about analyzing data. AIOps is no different: it analyzes operational data and is able to give recommendations on improving systems in terms of performance and efficiency.
- To get valid data and recommendations, AIOps has to reduce noise. Noise is a very common problem in operations and specifically in monitoring systems and CMDBs, as we learned in the previous section. What is really an issue and what is a false alert? AIOps is capable of analyzing these alerts and, with the help of algorithms that group alerts, can identify and prioritize these. The outcome is that...
