In 2025, the provincial government report presented a new breakthrough in the digital economy. (c) maintaining calculus, data, applications, industry linkages and high-quality areas for digital economic development innovation. From the creation of national high-calculations to attract head enterprises, to the development of the data industry, to the extraction of data values and the acceleration of digital transformation, guizhou has made steady progress in the digital economy. To realize the full potential of data, it is necessary first to address the issue of how to accurately value data, facilitate the pricing, trading and circulation of data assets, and activate and improve the market for data elements。
Release data values and promote healthy development of the data industry. The establishment and protection of data intellectual property rights are fundamental to digital economic development, while valuation of data intellectual property rights is key to the assetization and marketing of data, which are closely interlinked and mutually reinforcing. An in-depth analysis of multiple applications, such as digital media, live telecasts, medical care, finance, etc., provides a clear picture of the need for and complexity of assessing the value of data intellectual property rights in the real context, as well as of the importance of dynamic and diverse assessment methods, standardized assessment indicator systems and assessment models, improved legal protection and policy environments in applied practice. Data intellectual property value assessments provide authoritative value references for data asset transactions, remove information asymmetries between trading parties and facilitate efficient data transactions. For example, in the data-trading market, sellers and sellers often disagree on the value of data, and the objective value ranges given by the assessment of the value of intellectual property in professional data can significantly increase the success rate of transactions. At the same time, value assessments can also contribute to the precision of risk assessment and management by enterprises and investors, which, when investing in data assets, can anticipate risks in advance and plan rationally through value assessments. Accurate assessment of the value of the data can stimulate the enthusiasm of market agents to develop and use the data, facilitate the integration of data elements across industries and sectors and achieve industrialization applications。
Customized assessments, adapted to multiple applications. A customized assessment programme should be provided to address the large differences in the valuation of the intellectual property rights of the same data due to different application scenarios and different assessment methods. In terms of assessment methods, there is a fine combination of qualitative and quantitative multiple tools, such as multilinear regression analysis, system dynamics models, artificial intelligence-aided assessments, etc. In the case of the booming telecom industry, for example, the live platform accumulates large numbers of viewing, interacting and buying behavioral data from users. When used for precision marketing, these data are assessed in terms of value by focusing on indicators such as the conversion and repurchase rates of users, using a profit-based approach that combines large data analysis models to predict the potential benefits of the data for future marketing activities; and, when used for market trend analysis, more attention is given to the comprehensiveness and timeliness of data, using system dynamics models to assess the value of the data to capture market dynamics, taking into account industry trends. This customized assessment can more accurately reflect the true value of the data in different scenarios。
Deep-seated key issues and clear paths. First, data quality is closely linked to value assessment. The accuracy, completeness, consistency, timeliness and credibility of data can be significantly enhanced by the establishment of rigorous data quality management systems. Secondly, building systems and models of indicators for assessing the value of scientific data on intellectual property is key to improving transparency and systematization of assessments. Drawing on the idea of assessing the amount of quotations in patent information in relation to patent value, in the area of data intellectual property rights, the high value and impact of a data set is illustrated by its frequent references and references. At the same time, the “total-sub-spectrum” architecture model in the programming has been introduced, decomposing the overall assessment of the value of intellectual property rights in data into a detailed analysis of data sources, data-processing methods, data application effects, etc., and consolidating a comprehensive assessment perspective to ensure that the assessment process is retroactive and improves the quality of the assessment. Furthermore, in the face of uncertainty in the value assessment process, multiple analytical methods and tools are used for effective management. In the emerging field of artificial intelligence data training, for example, the value of data is influenced by a variety of uncertainties such as technological developments and changing market demand. In-depth identification and analysis of sources and types of uncertainty, development of risk response strategies to ensure consistency and reliability of assessment results, including through probability analysis, sensitivity analysis, etc。
Four-wheel drive, build a combination strategy. Moving forward with the reform of key aspects of valuation of intellectual property rights for data requires a concerted effort from the legal, policy, technical and application dimensions to build a comprehensive mix of practices. At the legal level, legislative reforms are being actively promoted to clarify key issues such as the attribution of rights, the scope of protection and legal liability to intellectual property rights for data; and the establishment of data-sharing platforms and mechanisms to facilitate the legal flow and sharing of data, while safeguarding data security. At the policy level, increase policy support for data valuation of intellectual property rights. Establish specific enabling funds to encourage technical innovation and business outreach by evaluation agencies; strengthen guidance to local functional units, organize training in digital literacy, and upgrade their cognitive and managerial capacity for assessing the value of data intellectual property rights; and promote cross-sectoral collaboration to jointly develop national standards or industry norms for value assessment and improve the professionalism and effectiveness of evaluation. At the technical level, research and development inputs are continuously scaled up and advanced data assessment tools and algorithms are continuously developed. The use of machine learning techniques for the in-depth excavation and analysis of big data to improve the accuracy of value assessment; and the use of block chain technology to ensure the authenticity, non-molecularity and traceability of data to enhance the credibility of value assessment results. At the application level, market demand-driven, promotes standardization and marketization of data assets. Supporting financial institutions in conducting data asset listings and financing lending operations to provide businesses with more diversified access to finance。
Yuan hua, guizhou business school
Plan production, chen sheng
Editor, xu gio, qin wen qin, wang qi
Second trial, yang chun-ling, wei-l-si, wang-li
Third instance
