| Summary: | The increasing popularity of data projects has influenced many development initiatives aimed at improving business performance and decision-making. However, Data Science projects carry in their essence a set of specific risks and uncertainties. Good risk management is one of the most crucial components of a project. Its effective conduct increases the probability of project success, however, it is necessary to understand the environment and the components surrounding risks. In this context, this investigation was conducted to create a base list of the risks of Data Science projects and their surrounding factors. This research was guided by the Design Science Research approach and the data collection process was conducted through the Delphi technique, where it was possible to identify and analyze the risks, their factors, the failure scenarios of the projects, and to understand the contribution of the development methodologies in these projects. The study enabled the creation of an artifact, consisting of a list of specific data management-related risks and best practice recommendations. However, it was found that more than half of the risks at the top of the rankings are similar to the risks of other types of IT projects. This research contributes a consolidated list of 25 risks of Data Science projects intending to help decrease the failures of projects in this area.
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