Risk Assessment of Data Science Projects: A Literature Review on Risk Identification

The effective implementation of data science projects (DSP) relies heavily on risk management, where understanding and managing potential risks are important factors. When implementing DSP, understanding and evaluating current and future risks significantly boosts the chances of success. Current research lacks a comprehensive analysis of potential risks to prevent early failure. This paper aims to fill this gap by conducting a systematic literature review, identifying the top ten risks in DSP and mapping them to the Risk Breakdown Structure (RBS) of the Project Management Body of Knowledge (PMBOK) Guide. The literature process follows the guidelines of Webster and Watson and is documented according to the recommendations of vom Brocke et al. As a result, 248 risks were identified and categorized in the RBS to determine the sources of risk.