TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Kehrer, Stefan
A1  - Jugel, Dierk
A1  - Zimmermann, Alfred
ED  - Dijkman, Remco
T1  - Categorizing requirements for enterprise architecture management in big data literature
T2  - 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), EDOCW 2016, 5. - 9. September 2016, Vienna, Austria
N2  - Organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Sociotechnical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle complex business and IT architectures. The transformation of an organization’s EA is influenced by big data transformation processes and their data-driven approach on all layers. In this paper, we review big data literature to analyze which requirements for the EA management discipline are proposed. Based on a systematic literature identification, conceptual categories of requirements for EA management are elicited utilizing an inductive category formation. These conceptual categories of requirements constitute a category system that facilitates a new perspective on EA management and fosters the innovation-driven evolution of the EA management.
discipline.
KW  - enterprise architecture management
KW  - big data transformation
KW  - conceptual categories of requirements
Y1  - 2016
SN  - 978-1-4673-9933-3
SB  - 978-1-4673-9933-3
U6  - https://doi.org/10.1109/EDOCW.2016.7584352
DO  - https://doi.org/10.1109/EDOCW.2016.7584352
SP  - 98
EP  - 105
S1  - 8
PB  - IEEE
CY  - Piscataway, NJ
ER  -