A Pricing Model for Data Markets

Heckman, Judd Randolph; Boehmer, Erin Laurel; Peters, Elizabeth Hope; Davaloo, Milad; Kurup, Nikhil Gopinath

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https://hdl.handle.net/2142/73449 Copy

Description

Title

A Pricing Model for Data Markets

Author(s)

• Heckman, Judd Randolph
• Boehmer, Erin Laurel
• Peters, Elizabeth Hope
• Davaloo, Milad
• Kurup, Nikhil Gopinath

Issue Date

2015-03-15

Keyword(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.

Publisher

iSchools

Series/Report Name or Number

iConference 2015 Proceedings

Type of Resource

text

Language

English

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http://hdl.handle.net/2142/73449

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Copyright 2015 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

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