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Cited by 37Data Quality and ManagementBig Data and Business IntelligenceBig Data Technologies and Applications

Data Governance as Success Factor for Data Science

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Brous, Paul, Janssen, Marijn, Krans, Rutger·2020·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-44999-5_36PMC PMC7134294
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  • It remains unclear how data governance contributes to the development and maintenance of trust in data science for decision-making.

    “However, it remains unclear how data governance contributes to the development and maintenance of trust in data science for decision-making, leading to calls for more research in this area (Al-Ruithe et al. 2019; Brous et al. 2020).”

    From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Brous et al. 2020

    The Introduction (page not provided) says it remains unclear how data governance contributes to successful data science outcomes and notes calls for further research. It also links trust in data science products to managers’ willingness to use them for decisions. It does not specifically say that governance’s role in developing and maintaining that trust remains unclear. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Previous research has suggested that data governance can be viewed as a boundary condition for data science.

    “Previous research (Brous et al. 2020; Janssen et al. 2020) has suggested that data governance can be viewed as a boundary condition for data science.”

    From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Brous et al. 2020

    The Discussion (Proposition 3; page unavailable) describes compliance with relevant legislation as a necessary condition for data science, and the Conclusions call data governance an important success factor. The paper does not say that data governance as a whole is a required boundary condition; that stronger characterization extends its specific claim about compliance. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Data governance is a success factor for BDAS.

    “Data governance is a success factor for BDAS (Brous, Janssen, & Krans, 2020)”

    From Data governance: Organizing data for trustworthy Artificial Intelligence (2020) · cites this paper as Brous, Janssen, & Krans, 2020

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Cited by

Sorted by citations

Authors
Brous, P., Janssen, M., & Krans, R.
Affiliations
  • Delft University of Technology (NL)
  • Rijkswaterstaat (NL)
Corresponding author
Brous, Paul
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2020
Volume
LNCS-12066
Issue
Part I
Pages
431-442
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-44999-5_36
PMC
PMC7134294
OpenAlex
W3015446482
Cited by
37
References
3
Citations by year
2026: 3 · 2025: 9 · 2024: 10 · 2023: 5 · 2022: 5 · 2021: 3 · 2020: 2
FWCI
5.09
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • PubMed Central (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • Lirias (submittedVersion)
  • Oskar-Bordeaux (Universite de Bordeaux) (submittedVersion, cc-by)
  • Research Repository (Delft University of Technology) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Quality and Management
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Keywords
data governance, data lakes, asset management, predictive maintenance, anomaly detection, data quality, data ownership, regulatory compliance, infrastructure monitoring

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