Data pricing strategy based on data quality. (October 2017)

• Record Type: Journal Article
• Authors: Yu, Haifei; Zhang, Mengxiao
• Is Part Of: Computers & industrial engineering. Volume 112(2017)
• Page Start: 1
• Page End: 10
• Publication Date: 2017-10
• Subjects: Big data -- Data marketplace -- Data pricing -- Production management -- Bi-level programming model; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.08.008
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A rigorous and reasonable data-pricing model is proposed. Multiple dimensions of data quality are taken into consideration. An integrated cost function and an integrated WTP function are developed. The data-pricing model helps data providers and customers to make decisions. Abstract: This paper presents a bi-level mathematical programming model for the data-pricing problem that considers both data quality and data versioning strategies. Data products and data-related services differ from information products or services in terms of quality assessment methods. For this problem, we consider two aspects of data quality: (1) its multidimensionality and (2) the interaction between the dimensions. We designed a multi-version data strategy and propose a data-pricing bi-level programming model based on the data quality to maximize the profit by the owner of the data platform and the utility to consumers. A genetic algorithm was used to solve the model. The numerical solutions for the data-pricing model indicate that the multi-version strategy achieves a better market segmentation and is more profitable and feasible when the multiple dimensions of data quality are considered. These results also provide managerial guidance on data provision and data pricing for platform owners.