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.
Type
Thesis, PhD Doctor of Philosophy
Collections
Description of related resources
Project management in social data science (digital outputs: Social Data Science Scorecard Deck) (dataset). Lvov, I., University of St Andrews, 2019 DOI: https://doi.org/10.17630/880c2780-120c-48f8-9ccb-aff048397d57Related resources
https://doi.org/10.17630/880c2780-120c-48f8-9ccb-aff048397d57Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.