  In recent years, the data science community has pursued excellence and made
significant research efforts to develop advanced analytics, focusing on solving
technical problems at the expense of organizational and socio-technical
challenges. According to previous surveys on the state of data science project
management, there is a significant gap between technical and organizational
processes. In this article we present new empirical data from a survey to 237
data science professionals on the use of project management methodologies for
data science. We provide additional profiling of the survey respondents' roles
and their priorities when executing data science projects. Based on this survey
study, the main findings are: (1) Agile data science lifecycle is the most
widely used framework, but only 25% of the survey participants state to follow
a data science project methodology. (2) The most important success factors are
precisely describing stakeholders' needs, communicating the results to
end-users, and team collaboration and coordination. (3) Professionals who
adhere to a project methodology place greater emphasis on the project's
potential risks and pitfalls, version control, the deployment pipeline to
production, and data security and privacy.
