Assessing and improving the quality of data are fundamental challenges in Big-Data applications. These challenges have given rise to numerous solutions targeting transformation, integration, and cleaning of data. However, while schema design, data cleaning, and data migration are nowadays reasonably well understood in isolation, not much attention has been given to the interplay between standalone tools in these areas. In this article, we focus on the problem of determining whether the available data-transforming procedures can be used together to bring about the desired quality characteristics of the data in business or analytics processes. For example, to help an organization avoid building a data-quality solution from scratch when facing a new analytics task, we ask whether the data quality can be improved by reusing the tools that are already available, and if so, which tools to apply, and in which order, all without presuming knowledge of the internals of the tools, which may be external or proprietary.