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Paper Type
Complete
Description
In a world where the amount of generated and stored data is steadily increasing, Data Science (DS) has emerged as an integral discipline for organizations to extract knowledge and value from these resources. Due to the high failure rate in these undertakings, new approaches to support the project management are urgently needed. The use of appropriate technologies constitutes a success factor in DS and its selection process is regarded as a strategic project management activity. As technology selection is not described sufficiently in current DS process models, a structured literature review is performed in this article that concentrates on the addressed technology layers, the applied decision procedures, and the specific technology instances of the identified approaches. The study shows that holistic technology selection approaches that consider all activities of the DS lifecycle and the entire required tool stack are severely lacking and should, therefore, be further researched in the future.
Paper Number
1524
Recommended Citation
Haertel, Christian; Pohl, Matthias; Nahhas, Abdulrahman; Staegemann, Daniel; and Turowski, Klaus, "A Survey of Technology Selection Approaches in Data Science Projects" (2023). AMCIS 2023 Proceedings. 2.
https://aisel.aisnet.org/amcis2023/sig_itpm/sig_itpm/2
A Survey of Technology Selection Approaches in Data Science Projects
In a world where the amount of generated and stored data is steadily increasing, Data Science (DS) has emerged as an integral discipline for organizations to extract knowledge and value from these resources. Due to the high failure rate in these undertakings, new approaches to support the project management are urgently needed. The use of appropriate technologies constitutes a success factor in DS and its selection process is regarded as a strategic project management activity. As technology selection is not described sufficiently in current DS process models, a structured literature review is performed in this article that concentrates on the addressed technology layers, the applied decision procedures, and the specific technology instances of the identified approaches. The study shows that holistic technology selection approaches that consider all activities of the DS lifecycle and the entire required tool stack are severely lacking and should, therefore, be further researched in the future.
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