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<p>In the era of digital transformation, data is a critical asset driving innovation and competitive advantage for businesses. Data marketplaces have emerged as a key solution for data sharing, yet they face significant challenges, including competitive concerns, matchmaking between data providers and consumers, and a lack of appropriate market mechanisms. This study introduces a data asset value quantification and selection mechanism (DQSM) as an innovative feature for data marketplaces to address these concerns. The DQSM uses Machine Learning and Explainable AI methods to assess the value of data assets, aiding consumers in making informed purchasing decisions. This mechanism addresses the inherent complexities of data asset valuation and selection, thereby increasing marketplace efficiency. Using a design science research approach, the study identifies design principles for the development of the DQSM as a feature of data marketplaces, which are validated through technical experiments with industry and public datasets, as well as interviews with experts in this field. The findings highlight the potential of the DQSM to optimize the discovery and implementation of viable data sharing use cases and to incentivize the adoption of data marketplaces, thereby contributing to more viable and sustainable data ecosystems.</p>

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Selecting Data Assets in Data Marketplaces

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

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  • Dominik Martin,
  • Daniel Heinz,
  • Moritz Glauner,
  • Niklas Kühl

摘要

In the era of digital transformation, data is a critical asset driving innovation and competitive advantage for businesses. Data marketplaces have emerged as a key solution for data sharing, yet they face significant challenges, including competitive concerns, matchmaking between data providers and consumers, and a lack of appropriate market mechanisms. This study introduces a data asset value quantification and selection mechanism (DQSM) as an innovative feature for data marketplaces to address these concerns. The DQSM uses Machine Learning and Explainable AI methods to assess the value of data assets, aiding consumers in making informed purchasing decisions. This mechanism addresses the inherent complexities of data asset valuation and selection, thereby increasing marketplace efficiency. Using a design science research approach, the study identifies design principles for the development of the DQSM as a feature of data marketplaces, which are validated through technical experiments with industry and public datasets, as well as interviews with experts in this field. The findings highlight the potential of the DQSM to optimize the discovery and implementation of viable data sharing use cases and to incentivize the adoption of data marketplaces, thereby contributing to more viable and sustainable data ecosystems.