  Data science has employed great research efforts in developing advanced
analytics, improving data models and cultivating new algorithms. However, not
many authors have come across the organizational and socio-technical challenges
that arise when executing a data science project: lack of vision and clear
objectives, a biased emphasis on technical issues, a low level of maturity for
ad-hoc projects and the ambiguity of roles in data science are among these
challenges. Few methodologies have been proposed on the literature that tackle
these type of challenges, some of them date back to the mid-1990, and
consequently they are not updated to the current paradigm and the latest
developments in big data and machine learning technologies. In addition, fewer
methodologies offer a complete guideline across team, project and data &
information management. In this article we would like to explore the necessity
of developing a more holistic approach for carrying out data science projects.
We first review methodologies that have been presented on the literature to
work on data science projects and classify them according to the their focus:
project, team, data and information management. Finally, we propose a
conceptual framework containing general characteristics that a methodology for
managing data science projects with a holistic point of view should have. This
framework can be used by other researchers as a roadmap for the design of new
data science methodologies or the updating of existing ones.
