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Title:A Pricing Model for Data Markets
Author(s):Heckman, Judd Randolph; Boehmer, Erin Laurel; Peters, Elizabeth Hope; Davaloo, Milad; Kurup, Nikhil Gopinath
Subject(s):data science including digital curation and big data
information policy and open access
quantitative analyses including statistics
Abstract:As companies and organizations explore the booming frontier of data, they operate in data markets that are largely unregulated. One of the foremost challenges within these emerging markets is establishing an accepted methodology for assessing the value of datasets. Current data pricing strategies are often driven by the seller, with little visibility into the cost of collection, cleansing and packaging to the buyer. This asymmetry of information results in a lack of pricing transparency; hurting the seller, who is then unable to price optimally in the market, and hurting the buyer, who then cannot strategically assess pricing options across data service providers. A more structured data market with a standardized pricing model would improve the transaction experience for all parties. In this paper, we describe a potential dataset valuation model and the impact such a model would have on data markets. We also explore how the model would assist with adding proprietary datasets as assets on corporate balance sheets,and with the formation of a futures market for data.
Issue Date:2015-03-15
Publisher:iSchools
Series/Report:iConference 2015 Proceedings
Genre:Conference Paper/Presentation
Type:Text
Language:English
URI:http://hdl.handle.net/2142/73449
Peer Reviewed:yes
Rights Information:Copyright 2015 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2015-03-23


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