Visual analytics (VA) has become a standard tool to process and analyze data visually to generate novel insights. Unfortunately, each component can introduce uncertainty in the visual analytics process. These uncertainty events can originate from many effects and need to be differentiated. In this work, we propose a taxonomy of potential uncertainty events in the visual analytics cycle. Here, we structure the taxonomy along the components included in the visual analytics cycle. Based on this taxonomy, we provide a list of dependencies between these events. At last, we show how to use our taxonomy by providing a real-world example.