DEVELOPING AN ARCHITECTURAL FRAMEWORK FOR FACILITATING TRANSFORMATION TOWARDS DATA-DRIVEN ORGANIZATIONS

Download

10454356.pdf

Date

2022-2-11

Author

Kayabay, Kerem

Metadata

Show full item record

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

Item Usage Stats

525
views

230
downloads

Paradigm shifts such as digital transformation and Industry 4.0 produce complex data, also called big data. Businesses increasingly focus on exploiting big data for competitive advantage, leveraging data science. However, many industries cannot effectively leverage data science since no comprehensive approach allows strategic planning for organization-wide data science projects and data assets. After recognizing the industry`s need, this thesis explores the Data Science Roadmapping Framework`s (DSR) development to help businesses align their business strategy with data-related, technological, and organizational resources. First, it utilizes a systematic approach to identify factors related to data science usage in organizations and challenges that data- driven transforming organizations face. In the proposed DSR framework, the resulting knowledge is synthesized with well-established technology roadmapping (TRM) literature, customizing TRM according to context, architecture, and process. Lastly, this study adopts the action research design to validate and refine the proposed framework in multiple iterations. The results indicate that the framework can help businesses initiate data science roadmapping initiatives, taking a step towards becoming data-driven. The DSR initiative also facilitates communication among business functions and generates consensus between stakeholders, including data owners, domain experts, and IT experts. While contemporary studies in the literature illustrate prebuilt roadmaps to help businesses get data-driven, this study focuses on the process of roadmapping to generate a tailored roadmap, providing the benefits above.

Subject Keywords

technology roadmapping, data science, digital transformation, data-driven organization, architectural framework

URI

https://hdl.handle.net/11511/96723

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

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...
Evaluating the convergence of high-performance computing with big data, artificial intelligence and cloud computing technologies Dildar Korkmaz, Yeşim; Eren, Pekin Erhan; Kayabay, Kerem; Department of Information Systems (2023-1-24) The advancements in High-Performance Computing (HPC), Big Data, Artificial Intelligence (AI), and Cloud Computing technologies have led to a convergence of these fields, resulting in the emergence of significant improvements for a wide range of fields. Identifying the state of development of technology convergence and forecasting promising technology convergence is critical for both academia and industry. That's why technology assessment and forecasting for HPC-Big Data-AI-Cloud Computing convergence is nee...
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,...
INVESTIGATION OF NEW ARCHITECTURAL FEATURES TO SUPPORT PERFORMANCE IMPROVEMENT IN EMBEDDED PROCESSORS Othman, Ahmad; Fahrioğlu, Murat; Yemişcioğlu, Gürtaç; Electrical and Electronics Engineering (2022-8) Recent advances in process automation, wireless sensor networks, and machine-to-machine (M2M) interfaces have caused embedded systems to be a blooming computing segment, with significant research focus on performance and energy efficiency. The embedded systems market witnessed enormous growth over the past decades and is foreknown to be boosted in the upcoming years. It has become harder to scale CMOS technologies compared to past and get performance and energy benefits through technology and circuits. Ther...
Using data analytics for collaboration patterns in distributed software team simulations Dafoulas, Georgios A.; Serce, Fatma C.; SWİGGER, Kathleen; BRAZİLE, Robert; Alpaslan, Ferda Nur; Alpaslan, Ferda Nur; Milewski, Allen (2016-08-05) This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of variou...

Citation Formats

K. Kayabay, “DEVELOPING AN ARCHITECTURAL FRAMEWORK FOR FACILITATING TRANSFORMATION TOWARDS DATA-DRIVEN ORGANIZATIONS,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.