The Risk Management Process for Data Science: Gaps in Current Practices

Abstract

Data science projects have unique risks, such as potential bias in predictive models, that can negatively impact the organization deploying the models as well as the people using the deployed models. With the increasing use of data science across a range of domains, the need to understand and manage data science project risk is increasing. Hence, this research leverages qualitative research to help understand the current practices concerning the risk management processes organizations currently use to identify and mitigate data science project risk. Specifically, this research reports on 16 semi-structured interviews, which were conducted across a diverse set of public and private organizations. The interviews identified a gap in current risk management processes, in that most organizations do not fully understand, nor manage, data science project risk. Furthermore, this research notes the need for a risk management framework that specifically addresses data science project risks.