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

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Title:The Risk Management Process for Data Science: Gaps in Current Practices
Authors:Lahiri, Sucheta
Saltz, Jeffrey
Keywords:Big Data and Analytics: Pathways to Maturity
big data
data science
erm
risk management
Date Issued:04 Jan 2022
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 with respect to 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 to a risk management framework that specifically addresses data science project risks.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/79479
ISBN:978-0-9981331-5-7
DOI:10.24251/HICSS.2022.147
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data and Analytics: Pathways to Maturity


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