In this position paper we discuss the use of information visualization techniques as a mean to find and characterize inconsistencies in music datasets. This idea is supported by empirical findings in a previous work dedicated to the visualization of a large dataset of music metadata called WASABI. During the development process of a visualization technique for exploring the multitude of multimedia attributes in the WASABI dataset (which includes lyrics, chords, audio, graphics describing sound analysis, etc.), we found visual patterns suggesting data inconsistencies (ex. ambiguities, inaccuracies, missing data, conflicts, etc.), which might have occurred during the integration from diverse sources. Traditionally, information visualization techniques are used to understand the data corpus and identify causal relationships, trends, patterns of data concentrations. Nevertheless, our findings suggest that information visualization techniques can be used to inspect data quality and highlight the parts of the datasets that need to be corrected/improved. Furthermore, we suggest that information visualization could be used as an entry point for repairing the dataset. More specifically, our aim is to use information visualization techniques to: communicate data quality issues to users, compare the outcomes of methods (such as crowdsourcing, matrix vectorization, graph reasoning, among other) used to fix the dataset, and observe the evolution of problem solving during the maintenance of the dataset.