THE CONCEPTUAL DESIGN AND IMPLEMENTATION OF A KNOWLEDGE MANAGEMENT SYSTEM FOR COLLABORATIVE DATA SCIENCE

Date

2024-6-6

Author

Civelek, Utku

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The new industrial revolution heavily depends on digital transformation. Companies strive to ameliorate or redesign their business processes with the help of digital technologies to preserve or attain competitive advantage. The most interactive field of digital transformation is data science, as it entails a longtime active collaboration among multiple partners. On the one hand, data scientists seek domain expertise to understand the structure and environment of the data to be analyzed. On the other hand, business users and managers take pains with data science concepts and sample use cases to exploit analytical capabilities and opportunities in their organizations. To this end, the extent of collaboration and knowledge share affects the success of data science projects. Nevertheless, the existing literature is dramatically limited in proposing a comprehensive solution to assist organizations in this scope. This thesis presents the conceptual design and implementation of CoDS (Collaborative Data Science Framework), a knowledge management system for consolidating data science activities in an enterprise, to address these important challenges. The CoDS presents a platform on which business details, data-related knowledge, modeling procedures, and deployment steps are shared for (i) mediating and scaling ongoing projects, (ii) enriching knowledge transfer among stakeholders, (iii) facilitating ideation of new products, and (iv) supporting the onboarding of new employees and developers. A case study is formed to evaluate its ease of creation, usefulness, attraction, and comprehensibility. Accordingly, this study proposes a novel structure and a roadmap for creating a data science knowledge management system for the collaboration of all stakeholders.

Subject Keywords

Digital Transformation, DataOps, Data Analytics, Data-driven Enterprise, Machine Learning

URI

https://hdl.handle.net/11511/109878

Collections

Graduate School of Informatics, Thesis