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Developing a data pricing framework for data exchange

Author

Listed:
  • Rupsa Majumdar

    (Indian Institute of Technology Kanpur (IIT Kanpur))

  • Anjula Gurtoo

    (Indian Institute of Science (IISc))

  • Minnu Maileckal

    (Indian Institute of Science (IISc))

Abstract

Despite emergence of data markets such as Windows Azure Marketplace and India Urban Data Exchange (IUDX), comprehensive frameworks to determine data pricing and/or determine parameters for profit maximization remain a gap. Data valuation often gets guided by the sellers, ignoring the interests of the buyers. The information asymmetry results in lopsided pricing. The data sellers fail to price optimally, and the buyers are unable to optimize their purchasing decisions, thus, reinforcing the need for a structured data pricing framework. The paper reviews literature and applies the stages as reported by Ritchie and Spencer (in: Bryman, Burgess (eds) Analysing qualitative data, Routledge, London, 1994) for applied policy research to determine the main approaches of data pricing and develop a comprehensive pricing framework. Literature selection on pricing attributes and content analysis classifies data pricing models into five broad but distinct themes, based on the data pricing method, namely data characteristics-based pricing, quality-based pricing, query-based pricing, privacy-based pricing, and organizational value-based pricing. Application of the Ritchie and Spencer stages identifies eight factors, namely customer need, customer assigned value, market maturity, market structure, usable data, data quality, seller reputation and seller objectives as defining and intersecting with the five pricing models. A framework is hence developed to guide data pricing. Thereby, the paper creates a platform for prescribing data pricing formulas.

Suggested Citation

  • Rupsa Majumdar & Anjula Gurtoo & Minnu Maileckal, 2025. "Developing a data pricing framework for data exchange," Future Business Journal, Springer, vol. 11(1), pages 1-18, December.
    • Handle: RePEc:spr:futbus:v:11:y:2025:i:1:d:10.1186_s43093-025-00422-z
    DOI: 10.1186/s43093-025-00422-z
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    References listed on IDEAS

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    1. Dirk Bergemann & Alessandro Bonatti & Tan Gan, 2022. "The economics of social data," RAND Journal of Economics, RAND Corporation, vol. 53(2), pages 263-296, June.
    2. Jian Pei, 2020. "A Survey on Data Pricing: from Economics to Data Science," Papers 2009.04462, arXiv.org, revised Nov 2020.
    3. Yeh, Chih-Liang, 2018. "Pursuing consumer empowerment in the age of big data: A comprehensive regulatory framework for data brokers," Telecommunications Policy, Elsevier, vol. 42(4), pages 282-292.
    4. Axun Xing & Haiyan Wang, 2024. "Pricing and sample set strategies of data providers under quality information asymmetry," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 75(2), pages 278-296, February.
    5. Jian Yang & Chongchong Zhao & Chunxiao Xing, 2019. "Big Data Market Optimization Pricing Model Based on Data Quality," Complexity, Hindawi, vol. 2019, pages 1-10, April.
    6. Alex Smolin, 2023. "Disclosure and pricing of attributes," RAND Journal of Economics, RAND Corporation, vol. 54(4), pages 570-597, December.
    7. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asu Ozdaglar, 2022. "Too Much Data: Prices and Inefficiencies in Data Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 218-256, November.
    8. Kopalle, Praveen K. & Pauwels, Koen & Akella, Laxminarayana Yashaswy & Gangwar, Manish, 2023. "Dynamic pricing: Definition, implications for managers, and future research directions," Journal of Retailing, Elsevier, vol. 99(4), pages 580-593.
    9. Huseyin Gurkan & Francis de Véricourt, 2022. "Contracting, Pricing, and Data Collection Under the AI Flywheel Effect," Management Science, INFORMS, vol. 68(12), pages 8791-8808, December.
    10. Lin, Kyle Y., 2006. "Dynamic pricing with real-time demand learning," European Journal of Operational Research, Elsevier, vol. 174(1), pages 522-538, October.
    11. Ritala, Paavo & Keränen, Joona & Fishburn, Jessica & Ruokonen, Mika, 2024. "Selling and monetizing data in B2B markets: Four data-driven value propositions," Technovation, Elsevier, vol. 130(C).
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