Abstract:
The rapid expansion of digital platforms and data-driven economies has increased the necessity for effective strategies in data pricing, particularly for Data Exchange Platforms (DEPs). As these platforms expand, identifying appropriate pricing methods becomes more challenging due to the diverse characteristics of data, differing needs of consumers, and varying market conditions. This thesis tackles a significant void in existing literature by focusing on the absence of detailed mathematical frameworks for data pricing models. Through an extensive literature review, various parameters affecting data pricing were identified and fifteen different pricing models were reviewed. Each formulation is progressively deduced in a stepwise approach, incorporating key economic principles such as cost structures and data quality along with game theoretic aspects such as data quality, fluctuations in demand, market dynamics, and utility theory. A comparative analysis illustrates the advantages, drawbacks, and appropriateness of each model for different types of data and market scenarios. Closing discussion on the analysis presents a decision-making framework for organizations in selecting appropriate pricing strategies. Future work involves a working research paper on the same topic where these models are applied on real-world datasets to study their applicability and gather expert feedback to enhance their effectiveness. It also proposes implementation of machine learning techniques to conduct advanced operations and obtain progressive results. The research aims to provide actionable insights for data providers, intermediaries, and policymakers, supporting the development of equitable and efficient frameworks for data pricing.
Description:
This thesis explores the mathematical formulation and comparative analysis of fifteen data pricing models relevant to Data Exchange Platforms (DEPs). Motivated by the growing importance of data in digital economies and the lack of standardized, formal pricing structures, the research identifies key pricing parameters from both literature and industry insights. Each model is formulated through a structured, stepwise approach integrating economic and game-theoretic principles. A comparative framework highlights their relative strengths and contextual suitability. The work aims to support data providers, platform designers, and policymakers in adopting fair and efficient pricing strategies. Future directions include empirical validation and machine learning-based extensions.
