With the rise of data analytics use in government, government organizations are starting to explore the possibilities of using business data to create further public value. This process, however, is far from straightforward: key questions that governments need to address relate to the quality of this external data and the value it brings. In the domain of global trade, customs administrations are responsible on the one hand to control trade for safety and security and duty collection and on the other hand they need to facilitate trade and not hinder economic activities. With the increased trade volumes, also due to growth in eCommerce, customs administrations have turned their attention to the use of data analytics to support their risk management processes. Beyond the internal customs data sources, customs is starting to explore the value of business data provided by business infrastructures and platforms. While these external data sources seem to hold valuable information for customs, data quality of the external data sources, as well as the value they bring to customs need to be well understood. Building on a case study conducted in the context of the PROFILE research project, this contribution reports the findings on data quality and data linking of ENS customs data with external data (BigDataMari) and other customs (import declaration) data and we discuss specific lessons learned and recommendations for practice. In addition, we also develop a data quality and data value evaluation framework applied to customs as high-level framework to help data users to evaluate potential value of external data sources. From a theoretical perspective this paper further extends earlier research on value of data analytics for government supervision, by zooming on data quality.