Design Principles for Quality Scoring—Coping with Information Asymmetry of Data Products

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dc.contributor.authorGuggenberger, Tobias Moritz
dc.contributor.authorAltendeitering, Marcel
dc.contributor.authorSchlueter Langdon, Chris
dc.date.accessioned2023-12-26T18:44:14Z
dc.date.available2023-12-26T18:44:14Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.544
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherbaa5b22d-64d0-4f00-b3f9-95970ae2b9ef
dc.identifier.urihttps://hdl.handle.net/10125/106928
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFederated Industrial Platform Ecosystems: Technologies, Business Models, and Data-Driven Artifacts
dc.titleDesign Principles for Quality Scoring—Coping with Information Asymmetry of Data Products
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractData products are a hot topic within companies since more and more organizations are implementing data meshes and data product management. Product management is closely related to product quality. The concept of data quality is long established, and there are several notions to measure it. However, these approaches are less practical when data is shared between different domains and organizations with undefined contexts. Such as physical products, data products need a clear definition of what is inside and of what quality they are. With this article, we summarize existing approaches to data quality regarding data products, discuss how a lack of information provision restrains efficient data markets, and provide prescriptive knowledge. To do so, we identify meta-requirements from literature and derive design principles for data scoring systems that cope with information asymmetry for data products to enable efficient data markets.
dcterms.extent10 pages
prism.startingpage4526

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Federated Industrial Platform Ecosystems: Technologies, Business Models, and Data-Driven Artifacts