Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308633 
Year of Publication: 
2023
Citation: 
[Journal:] Electronic Markets [ISSN:] 1422-8890 [Volume:] 33 [Article No.:] 52 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
As organizations accumulate vast amounts of data for analysis, a significant challenge remains in fully understanding these datasets to extract accurate information and generate real-world impact. Particularly, the high dimensionality of datasets and the lack of sufficient documentation, specifically the provision of metadata, often limit the potential to exploit the full value of data via analytical methods. To address these issues, this study proposes a hybrid approach to metadata generation, that leverages both the in-depth knowledge of domain experts and the scalability of automated processes. The approach centers on two key design principles—semanticization and contextualization—to facilitate the understanding of high-dimensional datasets. A real-world case study conducted at a leading pharmaceutical company validates the effectiveness of this approach, demonstrating improved collaboration and knowledge sharing among users. By addressing the challenges in metadata generation, this research contributes significantly toward empowering organizations to make more effective, data-driven decisions.
Subjects: 
Data understanding
Data governance
Metadata generation
JEL: 
M15
L6
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.