Data Science Roadmapping: Towards an Architectural Framework

Date

2020-11-24

Author

KAYABAY, KEREM
Gökalp, Mert Onuralp
Gökalp, Ebru
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

248
views

0
downloads

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 links business strategy with data-related, technological, and organizational resources. It also enables communication, stakeholder buy-in, and project prioritization. While most of the existing studies illustrate prebuilt roadmaps, this study focuses on the process of roadmapping. The application of the roadmapping process rather than a particular roadmap provides the benefits above.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103741925&origin=inward
https://hdl.handle.net/11511/91457

DOI

https://doi.org/10.1109/ictmod49425.2020.9380617

Conference Name

2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

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,...
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...
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...
Open-Source Big Data Analytics Architecture for Businesses Gökalp, Mert Onuralp; Koçyiğit, Altan; Eren, Pekin Erhan (null; 2020-01-23) Unaware of existing big data technologies, organizations fail to develop a big data capability despite its disruptive impact on today's competitive business environment. To determine the shortcomings and strengths of developing a big data architecture with open-source tools from technical and managerial perspectives, this study (1) systematically reviews the available open-source big data technologies to present a comprehensive picture, and (2) proposes an open-source architecture for businesses to take as ...
Big data maturity models for the public sector: a review of state and organizational level models OKUYUCU, ARAS; Yavuz, Nilay (Emerald, 2020-07-01) Purpose Despite several big data maturity models developed for businesses, assessment of big data maturity in the public sector is an under-explored yet important area. Accordingly, the purpose of this study is to identify the big data maturity models developed specifically for the public sector and evaluate two major big data maturity models in that respect: one at the state level and the other at the organizational level. Design/methodology/approach A literature search is conducted using Web of Science an...

Citation Formats

K. KAYABAY, M. O. Gökalp, E. Gökalp, P. E. Eren, and A. Koçyiğit, “Data Science Roadmapping: Towards an Architectural Framework,” presented at the 2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020, Marrakech, Fas, 2020, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103741925&origin=inward.