An Approximation Scheme for Data Monetization

23 Pages Posted: 22 Mar 2021 Last revised: 24 Mar 2021

See all articles by Sameer Mehta

Sameer Mehta

University of Illinois at Urbana-Champaign - College of Business

Milind Dawande

University of Texas at Dallas - Department of Information Systems & Operations Management

Ganesh Janakiraman

University of Texas at Dallas - Naveen Jindal School of Management

Vijay Mookerjee

University of Texas at Dallas - Naveen Jindal School of Management

Date Written: March 21, 2021

Abstract

The unprecedented rate at which data is being generated has led to the growth of data markets where valuable datasets are bought and sold. A salient feature of this market is that a data-buyer (agent) is endowed with multi-dimensional private information, namely her ``ideal'' record that she values the most and how her valuation for a given record changes as its distance from her ideal record changes. Consequently, the revenue-maximization problem faced by a data-seller (principal), who serves multiple buyers, is a multi-dimensional mechanism-design problem, which is well-recognized as being difficult to solve. Our main result in this paper is an approximation scheme that guarantees a revenue within as close a positive amount from the optimal revenue as desired. The scheme generates a posted-price menu consisting of a set of item-price pairs -- each entry in the menu consists of an item, i.e., a set of records from the dataset, and the price corresponding to that item. As a tradeoff, the length of the menu resulting from the scheme increases as the desired guarantee gets closer to zero. For convenience in practice, data-sellers may want the ability to limit the length of the menu used by the scheme. To facilitate this, we extend our analysis to obtain a general approximation guarantee corresponding to a menu of any given length. We also demonstrate how the seller can exploit buyers' preferences to generate intuitive and useful rules of thumb for an effective practical implementation of the scheme.

Keywords: Data Monetization, Multi-Dimensional Mechanism Design, Approximation Scheme

JEL Classification: C61, D47, D82

Suggested Citation

Mehta, Sameer and Dawande, Milind and Janakiraman, Ganesh and Mookerjee, Vijay, An Approximation Scheme for Data Monetization (March 21, 2021). Available at SSRN: https://ssrn.com/abstract=3808875 or http://dx.doi.org/10.2139/ssrn.3808875

Sameer Mehta (Contact Author)

University of Illinois at Urbana-Champaign - College of Business ( email )

Champaign, IL 61820
United States

Milind Dawande

University of Texas at Dallas - Department of Information Systems & Operations Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Ganesh Janakiraman

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Vijay Mookerjee

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

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