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Abstract
The rise of Deep Learning and Distributed Ledger Technology has led to an increase of intricacy in the transfer of solutions to business problems. Due to the diversity of such technologies the bundeled design of solutions is complex. Thus, the selection of the right technology variant to a problem misses guidance which is relevant to fulfill business requirements. The goal of this paper is the definition of a conceptual model that provides a comprehensive view of the concepts and relationships between data, governance, processing, analytics and storage by abstracting from the technologies of Deep Learning and Distributed Ledger Technology. Based on a constructed case in healthcare and expert interviews, the methodology of conceptual modelling following a Design Science Research approach is used to build the corresponding data-driven solution. Conclusively, the resulting transparency decreases the intricacy and complexity of the technology environment and guides the technology integration of the given case.
Recommended Citation
Burkhardt, Daniel and Lasi, Heiner, "A Conceptual Model of Data-Driven Solutions" (2020). AMCIS 2020 Proceedings. 7.
https://aisel.aisnet.org/amcis2020/systems_analysis_design/systems_analysis_design/7
A Conceptual Model of Data-Driven Solutions
The rise of Deep Learning and Distributed Ledger Technology has led to an increase of intricacy in the transfer of solutions to business problems. Due to the diversity of such technologies the bundeled design of solutions is complex. Thus, the selection of the right technology variant to a problem misses guidance which is relevant to fulfill business requirements. The goal of this paper is the definition of a conceptual model that provides a comprehensive view of the concepts and relationships between data, governance, processing, analytics and storage by abstracting from the technologies of Deep Learning and Distributed Ledger Technology. Based on a constructed case in healthcare and expert interviews, the methodology of conceptual modelling following a Design Science Research approach is used to build the corresponding data-driven solution. Conclusively, the resulting transparency decreases the intricacy and complexity of the technology environment and guides the technology integration of the given case.
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