Project management in social data science : integrating lessons from research practice and software engineering

Abstract

Online platforms, transaction processing systems, mobile sensors and other novel sources of data have shaped many areas of social research. The emerging discipline of social data science is subject to questions of epistemology, politics, ethics and responsibility, while the practice of doing social data science raises significant project management issues that include logistics, team communication, software system integration and stakeholder engagement. Keeping track of such a multitude of individual concerns while maintaining an overview of a social data science project as a whole is not trivial. This calls for provision of appropriate guidance for holistic project management.

The project management issues in social data science are strikingly similar to those arising in software engineering. In this thesis, I adapt a particular software engineering project management tool – the SEMAT Essence model (Jacobson et al., 2013) – to the needs of social data science. This model offers a holistic management approach by addressing key project aspects, including the often overlooked yet crucially important ones such as maintaining stakeholder engagement and establishing the ways of working. The SEMAT Essence is a progress tracking model and does not assume any specific work process, which is valuable given the great diversity of social data science projects.

To achieve this goal, I study the practice of doing social data science through participant observation of social data science projects and by providing ethnographic accounts for those. Using the ethnographic findings and the basic content and structure of the SEMAT model, I develop the Social Science Scorecard Deck – an agile project management tool for social data science. To assess the Scorecard Deck, I use the tool in management of a social data science project and then subject the tool to external validation by interviewing experts in social data science.