In the age of artificial intelligence,firms' internal data are increasingly valuable when merged with each other for inter-firm analysis and predictions.However,the inter-firm data transactions represent a novel challenge on pricing due to the complex nature of data,such as quality information asymmetry,lack of pricing standards,and the negligible marginal cost.This paper conducts a case study at Shanghai Data Exchange to explore the factors that can facilitate the data transactions between buyers and providers.We use interview transcripts from 18 participating firms to construct our three theoretical dimensions:increasing the perceived value,mitigating the cost,and improving the market design.We then browse through 18 factors to assess their value for further improvements.The managerial implications are also discussed.