Increasing the collaboration of data science stakeholders with a knowledge management system

Purpose This paper presents the design and implementation of collaborative data science framework (CoDS), a knowledge management system for consolidating data science activities in an enterprise. Design/methodology/approach The development of the CoDS framework is grounded on the design science research methodology for information systems research. In our case study, we first designed the initial framework for CoDS based on a systematic literature review. Then, we collected the expert opinions of eight data scientists to validate the need for generic content for such a knowledge management system. In the second iteration, a portfolio prototype is developed by the same data scientists as a part of our technical action research. Finally, a survey is conducted with 57 data analyst candidates in the last iteration. Findings Using the CoDS portfolio strengthened the communication among data scientists and stakeholders to improve development and scaling activities. It eased the reuse or modification of existing analytical solutions in other company processes. Practical implications The CoDS presents a platform on which business details, data-related knowledge, modeling procedures and deployment steps are shared for (1) mediating and scaling ongoing projects, (2) enriching knowledge transfer among stakeholders, (3) facilitating ideation of new products and (4) supporting the onboarding of new employees and developers. Originality/value This study proposes a novel structure and a roadmap for creating a data science knowledge management system for the collaboration of all stakeholders in an enterprise.