Data science initiatives frequently exhibit high failure rates, driven by technical constraints, organizational limitations and insufficient risk management practices. Challenges such as low data maturity, lack of governance, misalignment between technical and business teams, and the absence of structured mechanisms to address ethical and sociotechnical risks have been widely identified in the literature. In this context, the purpose of this study is to conduct a comparative analysis of the main risk management methodologies applied to data science projects, aiming to identify, classify, and synthesize their similarities, differences and existing gaps. An integrative literature review was performed using indexed databases and a structured protocol for selection and content analysis. The study examines widely adopted risk management standards ISO 31000, PMBOK Risk Management and NIST RMF, as well as frameworks specific to data science workflows, such as CRISP DM and the recently proposed DS EthiCo RMF, which incorporates ethical and sociotechnical dimensions into the project life cycle. The findings reveal that traditional approaches provide limited coverage of emerging risks, whereas contemporary models propose multidimensional structures capable of integrating ethical oversight, governance and continuous monitoring. As a contribution, this work offers theoretical support for the development of hybrid frameworks that balance technical efficiency, organizational alignment and responsible data practices, while highlighting research gaps that can guide future investigations.