Developing a data pricing framework for data exchange
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 intere...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Future Business Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43093-025-00422-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585698048212992 |
|---|---|
| author | Rupsa Majumdar Anjula Gurtoo Minnu Maileckal |
| author_facet | Rupsa Majumdar Anjula Gurtoo Minnu Maileckal |
| author_sort | Rupsa Majumdar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36a21048a09d40b4bb978dacc3eb9001 |
| institution | Kabale University |
| issn | 2314-7210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Future Business Journal |
| spelling | doaj-art-36a21048a09d40b4bb978dacc3eb90012025-01-26T12:35:43ZengSpringerOpenFuture Business Journal2314-72102025-01-0111111810.1186/s43093-025-00422-zDeveloping a data pricing framework for data exchangeRupsa Majumdar0Anjula Gurtoo1Minnu Maileckal2Department of Economic Sciences, Indian Institute of Technology Kanpur (IIT Kanpur)Center for Society and Policy, Indian Institute of Science (IISc)Center for Society and Policy, 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.https://doi.org/10.1186/s43093-025-00422-zData pricing frameworkData attributesData exchange/tradingData pricing |
| spellingShingle | Rupsa Majumdar Anjula Gurtoo Minnu Maileckal Developing a data pricing framework for data exchange Future Business Journal Data pricing framework Data attributes Data exchange/trading Data pricing |
| title | Developing a data pricing framework for data exchange |
| title_full | Developing a data pricing framework for data exchange |
| title_fullStr | Developing a data pricing framework for data exchange |
| title_full_unstemmed | Developing a data pricing framework for data exchange |
| title_short | Developing a data pricing framework for data exchange |
| title_sort | developing a data pricing framework for data exchange |
| topic | Data pricing framework Data attributes Data exchange/trading Data pricing |
| url | https://doi.org/10.1186/s43093-025-00422-z |
| work_keys_str_mv | AT rupsamajumdar developingadatapricingframeworkfordataexchange AT anjulagurtoo developingadatapricingframeworkfordataexchange AT minnumaileckal developingadatapricingframeworkfordataexchange |