An Approximation Scheme for Data Monetization

Mehta, Sameer; Dawande, Milind; Janakiraman, Ganesh; Mookerjee, Vijay

doi:10.1111/poms.13676

Cited by 6 publications

(2 citation statements)

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“…(2021) and Mehta et al. (2022) argue that the context of data monetization offers a richer setting compared to traditional information goods. In these papers, the authors model data as structured information that can be arranged in a row–column format and analyze the performance of popular pricing mechanisms.…”

Section: Related Literaturementioning

confidence: 99%

“…Data are an information good and there is a rich stream of literature that studies a variety of issues related to the monetization of information goods such as bundling (Bakos & Brynjolfsson, 1999), pricing (Choudhary, 2010;Sundararajan, 2004), and differentiation (Bhargava & Choudhary, 2001. More recently, Mehta, Dawande, Janakiraman, et al (2021) and Mehta et al (2022) argue that the context of data monetization offers a richer setting compared to traditional information goods. In these papers, the authors model data as structured information that can be arranged in a row-column format and analyze the performance of popular pricing mechanisms.…”

Section: Related Literaturementioning

confidence: 99%

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Robin Hood to the Rescue: Sustainable Revenue‐Allocation Schemes for Data Cooperatives

Dawande

Mehta

2023

Production and Operations Management

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The promise of consumer data along with advances in information technology has spurred innovation not only in the way firms conduct their business operations but also in the manner in which data are collected. A prominent institutional structure that has recently emerged is a data cooperative—an organization that collects data from its members, and processes and monetizes the pooled data. A characteristic of consumer data is the externality it generates: Data shared by an individual reveal information about other similar individuals; thus, the marginal value of pooled data increases in both the quantity and quality of the data. A key challenge faced by a data cooperative is the design of a revenue‐allocation scheme for sharing revenue with its members. An effective scheme generates a beneficial cycle: It incentivizes members to share high‐quality data, which in turn results in high‐quality pooled data—this increases the attractiveness of the data for buyers and hence the cooperative's revenue, ultimately resulting in improved compensation for the members. While the cooperative naturally wishes to maximize its total surplus, two other important desirable properties of an allocation scheme are individual rationality and coalitional stability. We first examine a natural proportional allocation scheme—which pays members based on their individual contribution—and show that it simultaneously achieves individual rationality, the first‐best outcome, and coalitional stability, when members' privacy costs are homogeneous. Under heterogeneity in privacy costs, we analyze a novel hybrid allocation scheme and show that it achieves both individual rationality and the first‐best outcome, but may not satisfy coalitional stability. Finally, our RobinHood allocation scheme—which uses a fraction of the revenue to ensure coalitional stability and allocates the remaining based on the hybrid scheme—achieves all the desirable properties.

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Section: Related Literaturementioning

confidence: 99%