How does Enterprise Architecture support the Design and Realization of Data-Driven Business Models? An Empirical Study
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Innovation Through Information Systems - Volume III: A Collection of Latest Research on Management Issues. Hrsg. / Frederik Ahlemann; Reinhard Schütte; Stefan Stieglitz. Cham: Springer Nature Switzerland AG, 2021. S. 662-677 (Lecture Notes in Information Systems and Organisation; Band 48 LNISO).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - How does Enterprise Architecture support the Design and Realization of Data-Driven Business Models?
T2 - 16th International Conference on Business Information Systems Engineering - WI 2021
AU - Rashed, Faisal
AU - Drews, Paul
N1 - Conference code: 16
PY - 2021/1/1
Y1 - 2021/1/1
N2 - As part of the data evolution, data-driven business models (DDBMs) have emerged as a phenomenon in great demand for academia and practice. Latest technological advancements such as cloud, internet of things, big data, and machine learning have contributed to the rise of DDBM, along with novel opportunities to monetize data. While enterprise architecture (EA) management and modeling have proven its value for IT-related projects, the support of EA for DDBM is a rather new and unexplored field. Building upon a grounded theory research approach, we shed light on the support of EA for DDBM in practice. We derived four approaches for DDBM design and realization and relate them to the support of EA modeling and management. Our study draws on 16 semi-structured interviews with experts from consulting and industry firms. Our results contribute to a still sparsely researched area with empirical findings and new research avenues. Practitioners gain insights into reference cases and find opportunities to apply EA artifacts in DDBM projects.
AB - As part of the data evolution, data-driven business models (DDBMs) have emerged as a phenomenon in great demand for academia and practice. Latest technological advancements such as cloud, internet of things, big data, and machine learning have contributed to the rise of DDBM, along with novel opportunities to monetize data. While enterprise architecture (EA) management and modeling have proven its value for IT-related projects, the support of EA for DDBM is a rather new and unexplored field. Building upon a grounded theory research approach, we shed light on the support of EA for DDBM in practice. We derived four approaches for DDBM design and realization and relate them to the support of EA modeling and management. Our study draws on 16 semi-structured interviews with experts from consulting and industry firms. Our results contribute to a still sparsely researched area with empirical findings and new research avenues. Practitioners gain insights into reference cases and find opportunities to apply EA artifacts in DDBM projects.
KW - Business informatics
KW - Data-driven
KW - Business model
KW - enterprise architecture
KW - Informatics
UR - https://aisel.aisnet.org/wi2021/EManagementofdigitalprocesses20/Track20/3/
UR - http://www.scopus.com/inward/record.url?scp=85119336159&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/e3e584de-d1bc-33d3-9841-a6fe691cab59/
U2 - 10.1007/978-3-030-86800-0_45
DO - 10.1007/978-3-030-86800-0_45
M3 - Article in conference proceedings
SN - 978-3-030-86799-7
T3 - Lecture Notes in Information Systems and Organisation
SP - 662
EP - 677
BT - Innovation Through Information Systems - Volume III
A2 - Ahlemann, Frederik
A2 - Schütte, Reinhard
A2 - Stieglitz, Stefan
PB - Springer Nature Switzerland AG
CY - Cham
Y2 - 9 March 2021 through 11 March 2021
ER -