Data Product Quality Best Practices : a white paper from the observatory best practices/lessons learned series.

Abstract

Data Product Quality is broadly defined based on the fitness for use of data in a particular application. In this way the needs of the user dictate whether data can be considered of sufficient quality. With the increase in digital data and the separation between data generators and data users, it is important for observatories and aggregators to be clear about what their data represent and how the data have been processed. By clearly articulating these steps and utilizing community standards, data repositories can increase the trustworthiness of themselves as a resource and of their data. In this paper, we focus on this concept of trustworthiness, reliability, and user support. Specifically, this white paper examines the current trends and drivers for data quality by focusing on four key best practice topic areas: Data Quality Control Practices, Data Support Services, Metadata, and Interoperability.