Up until this point, we have extensively explained the value of detecting anomalies across metrics and logs separately. This is extremely valuable, of course. In some cases, however, the knowledge that a particular metric or log file has gone awry may not tell the whole story of what is going on. It may, for example, be pointing at a symptom and not the cause of the problem. To have a better understanding of the full scope of an emerging problem, it is often helpful to look holistically at many aspects of a system or situation. This involves smartly analyzing multiple kinds of related datasets together.

In this chapter, we will cover the following topics:

• Understanding the roles and importance of key performance indicators (KPIs) and other supporting metrics
• Learning methods to organize, filter, and enrich the data to supply context
• Exploiting that contextual information via ML jobs that employ data splitting and statistical influencers
• Combining the anomalies that are created by ML into one common view that enables better triage, collaboration, and resolution