Outline

Gliederung

Empowering Data Consumers to Work with Data: Data Documentation for the Enterprise Context
Clément Labadie, Markus Eurich, Christine Legner

Bibtex

Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Empowering Data Consumers to Work with Data: Data Documentation for the Enterprise Context", 
							Author= "Clément Labadie, Markus Eurich, Christine Legner", 
							Doi= "https://doi.org/10.30844/wi_2020_m7-labadie", 
							 Abstract= "Enterprises that are engaging in digital transformation need to empower an increasing number of data consumers (sometimes referred to as “data citizens”) to work with data. A prerequisite is data documentation – data assets should be inventoried and well-described to facilitate data selection by non-data experts, who need to both find and understand them. This research paper proposes a reference model for data documentation in the enterprise context. It was developed in collaboration with 25 large enterprises, following a Design Science Research process. Compared to existing metadata standards that contain flat lists of metadata attributes, the reference model organizes metadata objects in logical and physical layers and features views dedicated to usage and governance contexts. It thereby improves maintenance and consistency in data documentation, when dealing with hundreds of interdependent data resources, and allows to express inherent relationships between metadata attributes.", 
							 Keywords= "metadata, data documentation, reference model, design science research.", 
							}
					
Clément Labadie, Markus Eurich, Christine Legner: Empowering Data Consumers to Work with Data: Data Documentation for the Enterprise Context. Online: https://doi.org/10.30844/wi_2020_m7-labadie (Abgerufen 23.11.24)
https://doi.org/10.30844/wi_2020_m7-labadie

Open Access

Abstract

Abstract

Enterprises that are engaging in digital transformation need to empower an increasing number of data consumers (sometimes referred to as “data citizens”) to work with data. A prerequisite is data documentation – data assets should be inventoried and well-described to facilitate data selection by non-data experts, who need to both find and understand them. This research paper proposes a reference model for data documentation in the enterprise context. It was developed in collaboration with 25 large enterprises, following a Design Science Research process. Compared to existing metadata standards that contain flat lists of metadata attributes, the reference model organizes metadata objects in logical and physical layers and features views dedicated to usage and governance contexts. It thereby improves maintenance and consistency in data documentation, when dealing with hundreds of interdependent data resources, and allows to express inherent relationships between metadata attributes.

Keywords

Schlüsselwörter

metadata, data documentation, reference model, design science research.

References

Referenzen

1. Goetz, M., Leganza, G., Hoberman, E., Vale, J.: STIR Your Data For Context. Analyst Report, Forrester Research (2018)
2. Bharadwaj, A., El Sawy, O.A., Pavlou, P.A., Venkatraman, N.: Digital Business Strategy: Toward a Next Generation of Insights. MIS Quart. 37, 471–482 (2013)
3. Matt, C., Hess, T., Benlian, A.: Digital Transformation Strategies. Bus. Inf. Syst. Eng. 57, 339–343 (2015)
4. Pentek, T., Legner, C., Otto, B.: Towards a Reference Model for Data Management in the Digital Economy. In: Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology (DESRIST), Karlsruhe (2017)
5. Mohr, N., Hürtgen, H.: Achieving Business Impact with Data. Analyst Report, Digital McKinsey (2018)
6. Belissent, J., Leganza, G., Vale, J.: Determine Your Data’s Worth: Data Plus Use Equals Value. Analyst Report, Forrester Research (2019)
7. Schüritz, R., Seebacher, S., Dorner, R.: Capturing Value from Data: Revenue Models for Data-Driven Services. In: Proceedings of the 50th Hawaii International Conference on System Sciences, pp. 5348–5357, Waikoloa Village (2017)
8. Brownlow, J., Zaki, M., Neely, A., Urmetzer, F.: Data-Driven Business Models: A Blueprint for Innovation. Research Report, Cambridge Service Alliance (2015)
9. Wilkinson, M.D., Dumontier, M., Aalbersberg, Ij.J., et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016)
10. Roszkiewicz, R.: Enterprise metadata management: How consolidation simplifies control. J. Digit. Asset Manag. 6, 291–297 (2010)
11. Inmon, W.H., O’Neil, B., Fryman, L.: Business Metadata: Capturing Enterprise Knowledge. Morgan Kaufmann, Burlington (2010)
12. Dublin Core Metadata Initiative: DCMI: DCMI Metadata Terms, https://www.dublincore.org/specifications/dublin-core/dcmi-terms/ (Accessed:
25.08.2019)
13. World Wide Web Consortium (W3C): Data Catalog Vocabulary (DCAT), https://www.w3.org/TR/vocab-dcat/ (Accessed: 25.08.2019)
14. Kerhervé, B., Gerbé, O.: Models for Metadata or Metamodels for Data? In: Proceedings of the 2nd IEEE Metadata Conference, Silver Spring (1997)
15. Marco, D.: Building and Managing the Meta Data Repository – A Full Lifecycle Guide. John Wiley & Sons, Hoboken (2000)
16. Hillmann, D.I., Marker, R., Brady, C.: Metadata Standards and Applications. Ser. Libr. 54, 7–21 (2008)
17. Sen, A.: Metadata Management: Past, Present and Future. Decis. Sup. Syst. 37, 151–173 (2004)
18. Burnett, K., Ng, K.B., Park, S.: A Comparison of the Two Traditions of Metadata Development. J. Am. Soc. Inf. Sci. 50, 1209–1217 (1999)
19. Chen, S., Alderete, K.A., Ball, A.: RDA Metadata Standards Directory, https://rdalliance. github.io/metadata-directory/standards/ (Accessed: 19.11.2019)
20. Ferguson, S., Hebels, R.: Access to information resources. In: Ferguson, S. and Hebels, R. (eds.) Computers for Librarians (Third Edition), pp. 81–109. Centre for Information Studies, Wagga Wagga (2003)
21. Clobridge, A.: Metadata. In: Clobridge, A. (ed.) Building a Digital Repository Program with Limited Resources, pp. 85–109. Chandos Publishing, Oxford (2010)
22. Vetterli, T., Vaduva, A., Staudt, M.: Metadata Standards for Data Warehousing: Open Information Model vs. Common Warehouse Metadata. SIGMOD Rec. 29, 68–75 (2000)
23. Xiao, B., Zhang, C., Mao, Y., Qian, G.: Review and exploration of metadata management in data warehouse. In: 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), pp. 928–933 (2015)
24. Baca, M.: Introduction to Metadata. The Getty Research Institute, Los Angeles (2016)
25. Poole, J., Chang, D., Tolbert, D., Mellor, D.: Common Warehouse Metamodel. An Introduction to the Standard for Data Warehouse Integration. John Wiley & Sons, New York (2002)
26. International Organization for Standards / International Electrotechnical Commission (ISO/IEC): International Standard ISO/IEC 11179-3. Information Technology – Metadata Registries (MDR) – Part 3: Registry Metamodel and Basic Attributes (2013)
27. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A Design Science Research Methodology for Information Systems Research. J. Manag. Inf. Syst. 24, 45–77 (2007)
28. Martin, J.: Computer Data-base Organization. Prentice Hall, Engelwood Cliffs (1977)
29. El Kharbili, M.: Business Process Regulatory Compliance Management Solution Frameworks: A Comparative Evaluation. In: Proceedings of the Eighth Asia-Pacific Conference on Conceptual Modelling, vol. 130, pp. 23–32. Australian Computer Society, Darlinghurst (2012)

Most viewed articles

Meist angesehene Beiträge

GITO events | library.gito