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Cited by 154Research Data Management PracticesData Quality and ManagementData Analysis and Archiving

Data governance, data literacy and the management of data quality

Open access
Koltay, Tibor·2016·IFLA Journal
DOI 10.1177/0340035216672238
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Abstract

Data governance and data literacy are two important building blocks in the knowledge base of information professionals involved in supporting data-intensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data.

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Crossref · 10.1177/0340035216672238

Cites

47 of 47 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The choice of the data governance approach is critical for successful data science outcomes.

    “the choice of the data governance approach (Koltay 2016) are considered critical.”

    From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Koltay 2016

    The Conclusion (page not provided) calls data governance indispensable for effective data management that supports research data services. The paper does not specifically say that choosing a governance approach is critical to successful data science outcomes. (LLM, conf=0.84)

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  • The choice of the data governance approach is critical, albeit this choice is not always clear or even explicit.

    “Given such challenges, the choice of the data governance approach is critical, albeit this choice is not always clear or even explicit (Koltay, 2016).”

    From Data governance: Organizing data for trustworthy Artificial Intelligence (2020) · cites this paper as Koltay, 2016

    In “Data governance in detail” (page unavailable), Koltay says data governance should not be optional and that cross-functional perspectives on it vary. This supports its importance and suggests some ambiguity, but the passages do not say that choosing a governance approach is not always clear or explicit. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cites

Flagged first, then by citations

Research works printed but not linked: 12

  1. [5]5. Andretta S, Pope A and Walton G (2008) Information Literacy Education in the UK. Communications in Information Literacy 2(1): 36-51.
  2. [6]6. ANZIIL (2004) Australian and New Zealand Information Literacy Framework. 2nd ed., Bundy A (ed.). Adelaide: Australian and New Zealand Institute for Information Literacy.
  3. [7]7. Badke, W. (2010) Information overload? Maybe not. Online, 34(5): 52-54.
  4. [10]10. Boyd D and Crawford K (2012) Critical Questions for Big Data. Information, Communication and Society 15(5): 662-669.
  5. [27]27. Hughes H and Bruce CS (2012) Snapshots of informed learning: LIS and beyond. Education for Information 29(3-4): 253-269.
  6. [34]34. Kim J (2015) Competency-based Curriculum: An Effective Approach to Digital Curation Education. Journal of Education for Library and Information Science 56(4): 283-297.
  7. [36]36. Kowalczyk ST (2014) Where Does All the Data Go: Quantifying the Final Disposition of Research Data. Proceedings of the American Society for Information Science and Technology 51(1): 1-10.
  8. [45]45. Nicholas D, Watkinson A, Volentine R et al. (2014) Trust and Authority in Scholarly Communications in the Light of the Digital Transition Learned Publishing 27(2): 121-134.
  9. [46]46. NMC (2014) NMC Horizon Report: 2014 Library Edition. Austin, TX: The New Media Consortium, Available at: http://redarchive.nmc.org/publications/2014- horizon-report-library (accessed 2 April 2015).
  10. [49]49. Qin J and D'Ignazio J (2010) Lessons Learned from a Two-year Experience in Science Data Literacy Education. In Proceedings of the 31st Annual IATUL Conference Available at: http://docs.lib.purdue.edu/iatul2010/conf/day2/5 (accessed 2 May2016).
  11. [60]60. Smith AM (2007) Data Governance Best Practices -the Beginning. EIMInsight (1)1, Available at: http://www.eiminstitute.org/library/eimi-archives/volume-1- issue-1-march-2007-edition/data-governance-best-practices-2013-the- beginning (accessed 2 May2016).
  12. [66]66. Vitae (2011) Researcher Development Framework. Cambridge: Careers Research and Advisory Centre.

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Authors
Koltay, T.
Affiliations
Eszterhazy Karoly Catholic University (HU)
Corresponding author
Koltay, Tibor
ORCID
Koltay, Tibor
Venue
IFLA Journal
Publisher
SAGE Publications
Published
2016-11-30
Volume
42
Issue
4
Pages
303-312
ISSN
  • 0340-0352 (ISSN-L)
  • 1745-2651
DOI
10.1177/0340035216672238
OpenAlex
W2542768710
Cited by
154
References
47
Citations by year
2026: 1 · 2025: 16 · 2024: 21 · 2023: 24 · 2022: 17 · 2021: 36 · 2020: 15 · 2019: 10 · 2018: 10 · 2017: 4
FWCI
17.86
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • IFLA Journal (publishedVersion)
  • Acta Biologica Plantarum Agriensis (Eszterházy Károly University, Hungary) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
  • Social Sciences › Sociology and Political Science › Data Analysis and Archiving
Keywords
data governance, data literacy, data quality management, research data management, research data services, information professionals, librarians, big data, accountability, decision rights

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