TY - GEN
T1 - Future government data strategies
T2 - 21st Annual International Conference on Digital Government Research: Intelligent Government in the Intelligent Information Society, DGO 2020
AU - Van Donge, W.
AU - Bharosa, N.
AU - Janssen, M. F.W.H.A.
N1 - 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.
PY - 2020
Y1 - 2020
N2 - Comparable to the concept of a data(-driven) enterprise, the concept of a ggovernment as data (-driven) enterprise' is gaining popularity as a data strategy. However, what it implies is unclear. The objective of this paper is to clarify the concept of the government as data (-driven) enterprise, and identify the challenges and drivers that shape future data strategies. Drawing on literature review and expert interviews, this paper provides a rich understanding of the challenges for developing sound future government data strategies. Our analysis shows that two contrary data strategies dominate the debate. On the one hand is the data-driven enterprise strategy that focusses on collecting and using data to improve or enrich government processes and services (internal orientation). On the other hand, respondents point to the urgent need for governments to take on data stewardship, so other parties can use data to develop value for society (external orientation). Since these data strategies are not mutually exclusive, some government agencies will attempt to combine them, which is very difficult to pull off. Nonetheless, both strategies demand a more data minded culture. Moreover, the successful implementation of either strategy requires mature data governance - something most organisations still need to master. This research contributes by providing more depth to these strategies. The main challenge for policy makers is to decide on which strategy best fits their agency's roles and responsibilities and develop a shared roadmap with the external actors while at the same time mature on data governance.
AB - Comparable to the concept of a data(-driven) enterprise, the concept of a ggovernment as data (-driven) enterprise' is gaining popularity as a data strategy. However, what it implies is unclear. The objective of this paper is to clarify the concept of the government as data (-driven) enterprise, and identify the challenges and drivers that shape future data strategies. Drawing on literature review and expert interviews, this paper provides a rich understanding of the challenges for developing sound future government data strategies. Our analysis shows that two contrary data strategies dominate the debate. On the one hand is the data-driven enterprise strategy that focusses on collecting and using data to improve or enrich government processes and services (internal orientation). On the other hand, respondents point to the urgent need for governments to take on data stewardship, so other parties can use data to develop value for society (external orientation). Since these data strategies are not mutually exclusive, some government agencies will attempt to combine them, which is very difficult to pull off. Nonetheless, both strategies demand a more data minded culture. Moreover, the successful implementation of either strategy requires mature data governance - something most organisations still need to master. This research contributes by providing more depth to these strategies. The main challenge for policy makers is to decide on which strategy best fits their agency's roles and responsibilities and develop a shared roadmap with the external actors while at the same time mature on data governance.
KW - Data enterprise
KW - Data governance
KW - Data stewardship
KW - Data-driven government
KW - E-government
UR - http://www.scopus.com/inward/record.url?scp=85086902726&partnerID=8YFLogxK
U2 - 10.1145/3396956.3396975
DO - 10.1145/3396956.3396975
M3 - Conference contribution
AN - SCOPUS:85086902726
T3 - ACM International Conference Proceeding Series
SP - 196
EP - 204
BT - Proceedings of the 21st Annual International Conference on Digital Government Research
A2 - Eom, Seok-Jin
A2 - Lee, Jooho
PB - Association for Computing Machinery (ACM)
Y2 - 15 June 2020 through 19 June 2020
ER -