Data Science Technology Selection: Development of a Decision-Making Approach



Data Science Roadmapping: Towards an Architectural Framework
KAYABAY, KEREM; Gökalp, Mert Onuralp; Gökalp, Ebru; Eren, Pekin Erhan; Koçyiğit, Altan (2020-11-24)
The availability of big data and related technologies enables businesses to exploit data for competitive advantage. Still, many industries face obstacles while leveraging data science to overcome business problems. This paper explores the development of a roadmapping approach to address data science challenges. Towards this goal, we customize technology roadmapping by synthesizing roadmapping, big data, data science, and data-driven organization literature. The resulting data science roadmapping approach li...
Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization
Kayabay, Kerem; Gökalp, Mert Onuralp; Eren, Pekin Erhan; Koçyiğit, Altan (2022-01-01)
Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, as there is no comprehensive approach that allows strategic planning for organization-wide data science efforts and data assets. Accordingly, this study explores the Data Science Roadmapping (DSR) to guide organizations in aligning their business strategies with data-related, technological,...
Data Science Capability Maturity Model
Gökalp, Mert Onuralp; Koçyiğit, Altan; Department of Information Systems (2021-11-09)
Today, data science presents immense opportunities in attaining competitive advantage, generating business value, and driving revenue streams for organizations. Data science has also significantly changed our understanding of how businesses should operate. In order to survive, it is now indispensable for a contemporary organization to adopt data science as part of its business processes. However, organizations face difficulties in managing their data science endeavors for reaping these potential benefits....
Data Management in Astrobiology: Challenges and Opportunities for an Interdisciplinary Community
Aydınoğlu, Arsev Umur; Malone, Jim (2014-06-01)
Data management and sharing are growing concerns for scientists and funding organizations throughout the world. Funding organizations are implementing requirements for data management plans, while scientists are establishing new infrastructures for data sharing. One of the difficulties is sharing data among a diverse set of research disciplines. Astrobiology is a unique community of researchers, containing over 110 different disciplines. The current study reports the results of a survey of data management p...
Data-driven manufacturing: An assessment model for data science maturity
Gökalp, Mert Onuralp; KAYABAY, KEREM; Koçyiğit, Altan; Eren, Pekin Erhan (2021-07-01)
© 2021 The Society of Manufacturing EngineersToday, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide organizations by providing an extensive roadmap for improvement in...
Citation Formats
K. Nazlıel, “Data Science Technology Selection: Development of a Decision-Making Approach,” presented at the 2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE), İzmir, Türkiye, 2022, Accessed: 00, 2022. [Online]. Available: