Understanding Big Data Quality for Maximum Information 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.

Sponsor: SAS