  It is well-established that the provenance of a scientific result is
important, sometimes more important than the actual result. For computational
analyses that involve visualization, this provenance information may contain
the steps involved in generating visualizations from raw data. Specifically,
data provenance tracks the lineage of data and process provenance tracks the
steps executed. In this paper, we argue that the utility of computational
provenance may not be as clear-cut as we might like. One common use case for
provenance is that the information can be used to reproduce the original
result. However, in visualization, the goal is often to communicate results to
a user or viewer, and thus the insights obtained are ultimately most important.
Viewers can miss important changes or react to unimportant ones. Here,
interaction provenance, which tracks a user's actions with a visualization, or
insight provenance, which tracks the decision-making process, can help capture
what happened but don't remove the issues. In this paper, we present scenarios
where provenance impacts reproducibility in different ways. We also explore how
provenance and visualizations can be better related.
