Requirements for Data Valuation Methods

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dc.contributor.authorStein, Hannah
dc.contributor.authorMaass, Wolfgang
dc.date.accessioned2021-12-24T18:15:55Z
dc.date.available2021-12-24T18:15:55Z
dc.date.issued2022-01-04
dc.description.abstractData is considered the most significant intangible asset for the 21st century enterprise. Serving as key asset for ever-increasing digital transformation and entrepreneurship, they ensure economic success through empowering new technologies, services and business models. Despite their high relevance, there exist neither consistent valuation methods nor specific requirements for developing such methods. Data valuation is crucial in order to better understand their value and, for example, incorporating them into financial statements. Existing literature indicates relationship between data value and quality. Thereupon, we conducted semi-structured expert interviews to gain insights on data valuation methods in connection with data quality. This results in 11 requirements for data valuation methods and seven value-driving quality criteria. Furthermore, several challenges for future data valuation are derived from the empirical results.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.746
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/80086
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th 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.subjectBusiness Intelligence, Business Analytics and Big Data: Innovation, Deployment, and Management
dc.subjectdata management
dc.subjectdata quality
dc.subjectdata valuation
dc.subjectrequirements
dc.subjectvaluation challenges
dc.titleRequirements for Data Valuation Methods
dc.type.dcmitext

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