The Need for an Enterprise Risk Management Framework for Big Data Science Projects

Jeffrey Saltz; Sucheta Lahiri

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Paper

The Need for an Enterprise Risk Management Framework for Big Data Science Projects

Topics: Data Governance; Data Management for Analytics; Modeling and Managing Large Data Systems

In Proceedings of the 9th International Conference on Data Science, Technology and Applications DATA - Volume 1, 268-274, 2020

Authors: Jeffrey Saltz and Sucheta Lahiri

Affiliation: Syracuse University, Syracuse, NY, U.S.A.

Keyword(s): Risk Management Framework (RMF), Big Data, Data Science, Enterprise Risk Management (ERM).

Abstract: This position paper explores the need for, and benefits of, a Big Data Science Enterprise Risk Management Framework (RMF). The paper highlights the need for an RMF for Big Data Science projects, as well as the gaps and deficiencies of current risk management frameworks in addressing Big Data Science project risks. Furthermore, via a systematic literature review, the paper notes a dearth of research which looks at risk management frameworks for Big Data Science projects. The paper also reviews other emerging technology domains, and notes the creation of enhanced risk management frameworks to address the new risks introduced due to that emerging technology. Finally, this paper charts a possible path forward to define a risk management framework for Big Data Science projects.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Saltz, J. and Lahiri, S. (2020). The Need for an Enterprise Risk Management Framework for Big Data Science Projects. In Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-440-4; ISSN 2184-285X, SciTePress, pages 268-274. DOI: 10.5220/0009874502680274

@conference{data20,
author={Jeffrey Saltz. and Sucheta Lahiri.},
title={The Need for an Enterprise Risk Management Framework for Big Data Science Projects},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA},
year={2020},
pages={268-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009874502680274},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA
TI - The Need for an Enterprise Risk Management Framework for Big Data Science Projects
SN - 978-989-758-440-4
IS - 2184-285X
AU - Saltz, J.
AU - Lahiri, S.
PY - 2020
SP - 268
EP - 274
DO - 10.5220/0009874502680274
PB - SciTePress