Developing a data pricing framework for data exchange

Abstract

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.

Keywords

• Data pricing framework
• Data attributes
• Data exchange/trading
• Data pricing