Data science technology selection: development of a decision-making approach

Nazlıel, Kerem
Developments in IT, Cloud, Analytics, and related fields have created an abundance of Data Science technologies for practitioners, developers, and organizations to use. This abundance and variety complicate the Data Science technology selection and management processes for the analytics teams. When teams select and use improper tools and technologies, they encounter problems and inefficiencies, also known as technical debt. As a remedy, this thesis proposes a systematic technology selection method considering the analytics technology selection literature and tests it on a case study. This method consists of a survey with open-ended questions to determine the requirements of a given Data Science Workflow, linkage grids to map technologies to these requirements, and multi-criteria-decision-making to rank the technologies according to practitioners’ needs and preferences. This method enables decision-makers to compare the technology alternatives and select the most suitable Data Science Technology Stack. While the existing studies in this domain consider the technology selection problem in isolation and investigate a subset of technologies, the proposed method encapsulates the end-to-end Data Science Process and the entire analytics technology landscape considering the key principles for developing industrially relevant strategic technology management toolkits.


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 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 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...
Image sequence analysis for emerging interactive multimedia services - The European COST 211 framework
Alatan, Abdullah Aydın; WOLLBORN, MİCHAEL; MECH, RONALD; TUNCEL, ERTEM; Sikora, T (1998-11-01)
Flexibility and efficiency of coding, content extraction, and content-based search are key research topics in the field of interactive multimedia. Ongoing ISO MPEG-4 and MPEG-7 activities are targeting standardization to facilitate such services. European COST Telecommunications activities provide a framework for research collaboration. COST 211(bis) and COST 211(ter) activities have been instrumental in the definition and development of the ITU-T H.261 and H.263 standards for video-conferencing over ISDN a...
Gökalp, Mert Onuralp; Kayabay, Kerem; Eren, Pekin Erhan; Koçyiğit, Altan (2016-12-17)
Exponential growth in data volume originating from Internet of Things sources and information services drives the industry to develop new models and distributed tools to handle big data. In order to achieve strategic advantages, effective use of these tools and integrating results to their business processes are critical for enterprises. While there is an abundance of tools available in the market, they are underutilized by organizations due to their complexities. Deployment and usage of big data analysis t...
Citation Formats
K. Nazlıel, “Data science technology selection: development of a decision-making approach,” M.S. - Master of Science, Middle East Technical University, 2022.