Core Viewpoint - The article emphasizes the importance of establishing a payment mechanism for high-quality data as a fundamental requirement for the marketization and valuation of data, which is essential for fostering a robust data market and enhancing the digital economy's growth potential [1][2][4]. Group 1: Importance of Paying for Quality Data - Paying for quality data is essential for recognizing its intrinsic value as a new production factor and is a key measure to overcome current data circulation challenges [1][2]. - The payment mechanism serves as a cost compensation and innovation incentive for data "factorization," ensuring that the value of data is recovered and distributed effectively [2][5]. - The market demand for quality data is rapidly increasing, with over 4,500 data products listed on the Shanghai Data Exchange in 2024, leading to a transaction volume exceeding 4 billion RMB, reflecting a diverse supply and rising value [3][4]. Group 2: Mechanisms and Challenges - The payment mechanism acts as a benchmark for data "valuation," helping to reflect market demand for different types and qualities of data, thus guiding data flow to areas where it can create the most value [4][5]. - There are significant challenges in recognizing the value of quality data, including a lack of stable payment expectations and insufficient capabilities of data circulation service institutions [7][8]. - The establishment of a payment mechanism is crucial for building a fair and efficient data market system, addressing issues like information asymmetry and enhancing transaction efficiency [5][6]. Group 3: Market Awareness and Cultural Shift - Cultivating a market culture that values payment for quality data is necessary for deepening market-oriented reforms and stimulating the internal growth of the digital economy [8][9]. - Government departments are encouraged to take the lead in establishing data transaction-related systems and promoting a consensus on the value of data [8][9]. - Enterprises should recognize the strategic value of paying for quality data as a means to enhance competitiveness and innovation capabilities, viewing it as a critical investment for future growth [9][10]. Group 4: Future Directions - There is a need to accelerate the development of a data factor market and enhance the positive incentives for paying for quality data, which will help eliminate low-quality data and ensure reasonable returns for quality providers [10][11]. - Establishing a unified data quality governance system and developing third-party evaluation and certification mechanisms are essential for improving market trust and reducing risks associated with information asymmetry [10][11]. - Supporting various data transaction models and financial products, such as data asset pledge financing and data insurance, will facilitate the flexible allocation of data across different scenarios [10][11].

Core Viewpoint - The establishment of a scientific data payment mechanism is essential for building a sustainable data factor market, which will ensure the legitimate rights of data providers and promote efficient and compliant data circulation, thereby unleashing the value of data factors and empowering the development of the digital economy [5] Group 1: Data Market Development - The "14th Five-Year Plan" emphasizes the need to improve the foundational system for data factors and build an open, shared, and secure national integrated data market, which is crucial for deepening the development and utilization of data resources [1] - The National Data Bureau has called for increased investment in the data sector and the cultivation of a market awareness of "paying for data," which serves as a top-level guide for the market-oriented allocation of data factors [1][2] Group 2: Challenges in Data Quality and Pricing - The core obstacle in the development of the data factor market is the lack of a payment mechanism that accurately identifies and rewards data value, leading to market failure and stifling innovation in the AI industry [3] - The current market faces fundamental issues such as "bidirectional value uncertainty" and pricing failure, which hinder the circulation of high-quality data [3][4] - Insufficient incentives for high-quality data production result from the absence of a payment mechanism, leading to a severe shortage of specialized data sets necessary for deep AI development [4] Group 3: Proposed Solutions for Data Payment Mechanism - A technology-driven and mechanism-supported data payment system is essential to overcome existing challenges, focusing on quantifying value and ensuring fair distribution [6][7] - The transition from "fuzzy pricing" to "precise quantification" is necessary to address data pricing challenges, utilizing advanced technologies to measure the marginal contribution of specific data to model performance [7] - The establishment of a "data billing" system is crucial, which should balance cost compensation and fair profit sharing, ensuring that contributions determine rewards [8] Group 4: Strategic Importance of Data Payment Mechanism - Establishing a scientific payment mechanism for quality data is a strategic and long-term investment for the future of digital economy development and the healthy growth of the AI industry [9] - A fair and effective data evaluation and pricing system is fundamental to resolving the "lemon market" effect and "bidirectional value uncertainty" issues, providing a clear technical solution to implement the "data twenty articles" [9]