Notes
A Practitioner's Guide to Data Governance
ISBN: 978-1-78973-570-3, eISBN: 978-1-78973-567-3
Publication date: 8 July 2020
Citation
Gupta, U. and Cannon, S. (2020), "Notes", A Practitioner's Guide to Data Governance, Emerald Publishing Limited, Leeds, pp. 227-229. https://doi.org/10.1108/978-1-78973-567-320201014
Publisher
:Emerald Publishing Limited
Copyright © 2020 Emerald Publishing Limited
Cannon and Gupta (2020) explore this issue in greater detail.
Oxford English dictionary.
The Merriam Webster dictionary.
Point of clarification: For this discussion, a business glossary captures the definition of the concept being defined. This is not to be confused with a data dictionary which we will define as the technical definition of a particular instance of concept. The glossary describes the concept; the dictionary describes the actual data element that captures that concept. See http://tdan.com/data-dictionary-vs-business-glossary/24652.
In research, the ability to appropriately describe and even cite a compendium of data is very important. Organizations with a research function would be well served to ensure that any definition of collective nouns for data elements works for both administrative and research functions. See https://www.asis.org/asist2010/proceedings/proceedings/ASIST_AM10/submissions/240_Final_Submission.pdf.
Dallemule, L., & Davenport, T. H. (2017). What's your data strategy? Harvard Business Review, May–June.
National Science Foundation recommendations for meeting data management plan requirement for funding. https://www.nsf.gov/pubs/policydocs/pappguide/nsf15001/gpg_2.jsp#IIC2j.
Accuracy is just one dimension of data quality. See Chapter 6 for further discussion.
Valerie Logan. (2018, September 21). Information as a Second Language: Enabling Data Literacy for Digital Society. ID: G00365697.
See Sebastian-Coleman (2012), Appendix B for a review.
https://cdn.ymaws.com/edmcouncil.org/resource/resmgr/featured_documents/BP_DQ_Dimensions_Oct17.pdf. Accessed July 29, 2019.
For example, see Babbie (2004).
- Prelims
- 1 Foundations of Data Governance
- 2 Impact of Organizational Culture and the Need for Change Management
- 3 Communication: Key to Success
- 4 Data Strategy
- 5 Data Governance Frameworks
- 6 Data Governance Components: Data Quality, Literacy, and Ethics
- 7 Data Governance Maturity Models
- 8 Summary Case Studies
- 9 Detailed Case Study
- 10 Execution Roadmap
- Notes
- Resources and References
- Index