The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data. To acquire data for training algorithms and eventually empowering the digital transformation, data marketplaces are often required to support and coordinate cross-organizational data transactions. However, the prior industry practices have suggested that the transaction costs in the data marketplaces are severely high, and the supporting infrastructure is far from mature. This paper proposes a data attributes-affected data exchange (DADE) conceptual model to understand the challenges and directions for developing data marketplaces. Specifically, our model framework is built upon two dimensions, data lifecycle maturity and data asset specificity. Based on the DADE model, we propose four approaches for developing data marketplaces and discuss future research directions with an overview of computational methods as potential technical solutions.
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