Using Business Data in Customs Risk Management

Using Business Data in Customs Risk Management: Data Quality and Data Value Perspective

Hofman, Wout (TNO)
Migeotte, Jonathan (Belgian Administration of Customs & Excises)
Labare, Mathieu L.M. (Belgian Administration of Customs & Excises)
Rukanova, B.D. (TU Delft Information and Communication Technology)
Tan, Y. (TU Delft Information and Communication Technology)

Scholl, Hans Jochen (editor)
Gil-Garcia, J. Ramon (editor)
Janssen, Marijn (editor)
Kalampokis, Evangelos (editor)
Kalampokis, Evangelos (editor)
Lindgren, Ida (editor)
Rodríguez Bolívar, Manuel Pedro (editor)

2021

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.

Data quality
Data analytics
Value
Government supervision
Customs
Risk analysis

http://resolver.tudelft.nl/uuid:0355634e-1c06-40ff-b783-073618dd3033

https://doi.org/10.1007/978-3-030-84789-0_20

Springer Nature

2022-03-03

9783030847883

Electronic Government - 20th IFIP WG 8.5 International Conference, EGOV 2021, Proceedings: Electronic Government

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12850 LNCS

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2021 Wout Hofman, Jonathan Migeotte, Mathieu L.M. Labare, B.D. Rukanova, Y. Tan