Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization

Date

2022-01-01

Author

Kayabay, Kerem
Gökalp, Mert Onuralp
Eren, Pekin Erhan
Koçyiğit, Altan

Metadata

Show full item record

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

Item Usage Stats

320
views

0
downloads

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, and organizational resources. The proposed approach is built on the widely adopted technology roadmapping framework and customizes its context, architecture, and process by synthesizing data science, big data, and data-driven organization literature. Based on industry collaborations, the framework provides a hybrid and agile methodology comprising the recommended steps. We applied DSR with a research group with sector experience to create a comprehensive data science roadmap to validate and refine the framework. The results indicate that the framework facilitates DSR initiatives by creating a comprehensive roadmap capturing strategy, data, technology, and organizational perspectives. The contemporary literature illustrates prebuilt roadmaps to help businesses become data-driven. However, becoming data-driven also necessitates significant social change toward openness and trust. The DSR initiative can facilitate this social change by opening communication channels, aligning perspectives, and generating consensus among stakeholders.

Subject Keywords

Technology roadmapping, Technology management, Data science, Digital transformation, Data-driven organization, Big data, TECHNOLOGY ROADMAP, BIG DATA, CHALLENGES, SECTOR

URI

https://hdl.handle.net/11511/94720

Journal

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE

DOI

https://doi.org/10.1016/j.techfore.2021.121264

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

Data science technology selection: development of a decision-making approach Nazlıel, Kerem; Eren, Pekin Erhan; Kayabay, Kerem; Department of Information Systems (2022-12-29) 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 consideri...
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 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....
BIG DATA FOR INDUSTRY 4.0: A CONCEPTUAL FRAMEWORK 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...
Assessment of process capabilities in transition to a data-driven organisation: A multidisciplinary approach Gökalp, Mert Onuralp; Gökalp, Ebru; Koçyiğit, Altan; Eren, Pekin Erhan (2021-06-01) The ability to leverage data science can generate valuable insights and actions in organisations by enhancing data-driven decision-making to find optimal solutions based on complex business parameters and data. However, only a small percentage of the organisations can successfully obtain a business value from their investments due to a lack of organisational management, alignment, and culture. Becoming a data-driven organisation requires an organisational change that should be managed and fostered from a ho...

Citation Formats

K. Kayabay, M. O. Gökalp, P. E. Eren, and A. Koçyiğit, “Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization,” TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, vol. 174, pp. 0–0, 2022, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94720.