Abstract: Techniques of contextual data analysis in distributed computing systems are disclosed herein. In one example, upon receiving operational data and attribute data representing attributes of multiple entities in the distributed computing system, a decision tree is generated based on the received operational and attribute data via machine learning. The decision tree has a root and multiple branches each representing a set of the attributes in the attribute data and a corresponding probability value representing a likelihood that one of the multiple components with the set of the attributes would be associated with an event in the distributed computing system. The multiple branches can then be parsed to identify a common subset of the attributes of the multiple components as most closely related to an occurrence of the event in the distributed computing system.