The barriers to entry for high-performance scalable data management and computing continue to fall, and “big data” is rapidly moving into the mainstream. So it’s easy to become so focused on the anticipated business benefits of large-scale data analytics that we lose sight of the intricacy associated with data acquisition, preparation and quality assurance. In some ways, the clamoring demand for large-scale analysis only heightens the need for data governance and data quality assurance. And while there are some emerging challenges associated with managing big data quality, reviewing good data management practices will help to maximize data usability. In this paper we examine some of the challenges presented by managing the quality and governance of big data, and how those can be balanced with the need to deliver usable analytical results. We explore the dimensions of data quality for big data, and examine the reasons for practical approaches to proactive monitoring, managing reference data and metadata, and sharing knowledge about interpreting and using data sets. By examining some examples, we can identify ways to balance governance with usability and come up with a strategic plan for data quality, including tactical steps for taking advantage of the power of the cluster to drive more meaning and value out of the data. Finally, we consider a checklist of characteristics to look for when evaluating information management tools for big data. Understanding Big Data Quality for Maximum Information Usability