What Drives Success in Data Science Projects: A Taxonomy of Antecedents

Date

2023-01-01

Author

Gökay, Gonca Tokdemir
Nazlıel, Kerem
Şener, Umut
GÖKALP AYDIN, EBRU
Gökalp, Mert Onuralp
Gençal, Nergiz
Dağdaş, Gizemnur
Eren, Pekin Erhan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Organizations have been trying to reshape their business processes and transform them into a smart environment to attain sustainable competitive advantage in their markets. Data science enables organizations to define interconnected and self-controlled business processes by analyzing the massive amount of unstandardized and unstructured high-speed data produced by heterogeneous Internet of Things devices. However, according to the latest research, the success rate of data science projects is lower than other software projects, and the literature review conducted reveals a fundamental need for determining success drivers for data science projects. To address these research gaps, this study investigates the determinants of success and the taxonomy of antecedents of success in data science projects. We reviewed the literature systematically and conducted an expert panel by following a Delphi method to explore the main success drivers of data science projects. The main contributions of the study are twofold: (1) establishing a common base for determinants of success in data science projects (2) guiding organizations to increase the success of their data science projects.

Subject Keywords

Critical success factors, Data science, Project management, Project success

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85151051777&origin=inward
https://hdl.handle.net/11511/102963

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Graduate School of Informatics, Conference / Seminar