論數據交易類型化與規範準則

The Categorization and Normative Criteria of Data Transactions

數據交易是平等主體之間為實現數據產品等價交換而開展的商事交易，目的在於透過市場調節機制實現數據資源的最佳配置與利用。為了解決數據交易中的不完全契約問題，需要借助類型化和準則化的思維路徑有針對性地尋找法律規制方案。從參與主體視角，數據交易可以分為供需雙方直接交易，第三方平台交易和多邊封閉交易；從權利變動的視角，可以分為財產轉讓、使用許可和共同控制三種交易模式。同時，透過提取共性問題可以形成適用於所有數據交易的規範準則。其中，合規性準則確保交易的穩定有效，不損害國家、社會或他人的合法權益；標準化準則要求實現數據產品格式的通用性，以減少交易成本、提高流轉效率；透明性準則透過交易資訊披露緩解資訊不對稱，真正實現契約自由和交易公平，助力數據交易市場的蓬勃發展和數據經濟的騰飛。

Data transactions are commercial transactions between equal-footed subjects for the fair exchange of data products, with the purpose of realizing the optimal allocation and utilization of data resources through market adjustment mechanism. In order to solve the problem of incomplete contracts in data transactions, it is necessary to build a well-directed legal regulation scheme with the help of thinking in the way of categorization and criterion. From the perspective of participants, data transactions can be categorized into direct transactions between suppliers and demanders, third-party platform transactions and multilateral closed transactions. From the perspective of change of rights, it can be divided into three trading modes: property transfer, usage license and joint control. Meanwhile, another important task of constructing legal norms is to extract common problems and normative criteria applicable to all data transactions. Among them, the compliance criterion ensures that the transaction is stable and effective, and does not harm the legitimate rights and interests of the state, society or others; the standardization criterion requires the universality of data product formats to reduce transaction costs and improve circulation efficiency; the transparency criterion alleviates information asymmetry through disclosure of transaction information, and truly realizes freedom of contract and fairness of transaction. The above norms are conducive to the implementation and improvement of the data transaction management system, and help the vigorous development of the data transaction market and the soaring of the data economy.