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Development Background of Data Assetization in the AI Era (1) The foundation laid by informatization and digitalization Informatisation and digitalization are the solid foundation for data assetization.
The development of the internet has led to the widespread application of technologies such as cloud computing, the Internet of Things, and big data, providing support for the generation, storage, transmission, and processing of data.
China's digital economy is at the forefront in terms of scale and application.
The proliferation of the internet, the rise of mobile internet, and the development of technologies such as cloud computing and the Internet of Things have enhanced the level of informatization and digitalization.
The development of informatization and digitalization is not only reflected at the technological level but also at the policy and institutional level.
The Chinese government has introduced policies and regulations to provide legal protection for the generation, storage, transmission, and processing of data, ensuring the security and compliance of data during its generation and use.
In addition, the development of informatization and digitalization has been widely applied in various industries such as manufacturing, finance, and healthcare, not only promoting the digital transformation and upgrading of industries but also providing a wealth of data resources for data assetization.
(2) The ongoing reform of data elements Data, as a key factor of production in the new era, has been playing an important role in the digital economy and social development.
With the acceleration of the digitalization process, the value of data is becoming increasingly prominent, and the reform of data elements is gradually becoming an important part of the national strategy.
In recent years, the Chinese government has introduced a series of policies to promote the market-oriented allocation of data elements and to facilitate the development and utilization of data elements.
In 2019, the Fourth Plenary Session of the 19th Central Committee of the Communist Party of China listed data as a factor of production for the first time, marking the official launch of data element reform.
In April 2020, the Central Committee of the Communist Party of China and the State Council issued the "Opinions on Building a More Improved System and Mechanism for the Market-oriented Allocation of Factors," which clarified the direction of factor market system construction and key reform tasks, proposing to guide and cultivate the big data trading market, carry out data transactions in accordance with laws and regulations, and promote the opening and sharing of government data.
Specific measures of data element reform include the establishment of data property rights systems, the cultivation of data trading markets, and the strengthening of data security governance.
In December 2022, the Central Committee of the Communist Party of China and the State Council issued the "Opinions on Building a Data Basic System to Better Utilize the Role of Data Elements," proposing to establish a data property rights system, clarify the ownership and usage rights of data, and ensure the legality and compliance of data transactions.
Data assetization is one of the important directions of data element reform.
In August 2023, the Ministry of Finance issued the "Interim Provisions on Accounting Treatment of Enterprise Data Resources," standardizing the accounting treatment of enterprise data resources, strengthening related accounting information disclosure, promoting and regulating the implementation of accounting standards by data-related enterprises, and accurately reflecting the business and economic essence of data-related operations.
(3) Artificial Intelligence Empowering Various Industries In recent years, artificial intelligence (AI) technology has developed rapidly and has become an important force in promoting scientific and technological progress and economic and social development.
The autonomous learning and adaptation capabilities of AI, its powerful data processing capabilities, and its wide application in various fields make it an important driving force for economic and social development.
The enabling effect of AI on various industries is particularly significant.
By introducing AI technology, industries can achieve automation and intelligence in the production process, improving production efficiency and service quality.
For example, in the manufacturing industry, artificial intelligence can optimize production processes and achieve intelligent manufacturing; in the medical field, AI technology enhances the quality and efficiency of medical services through assisted diagnosis and personalized healthcare.
China has attached great importance to AI policies, and since the Ministry of Industry and Information Technology issued the "Three-Year Action Plan for Promoting the Development of a New Generation of Artificial Intelligence Industry (2018-2020)" in 2017, central and local governments have introduced a series of policy documents to promote the rapid development of artificial intelligence.
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The Importance of Data Assetization in the AI Era In the era of artificial intelligence, data, as a new type of factor of production, is becoming a key resource for promoting high-quality economic and social development.
The core of data assetization lies in the confirmation of data resources, circulation and trading, and providing data support for the training of AI large models, promoting the intelligent transformation and technological innovation of various industries.
(1) A key measure to achieve high-quality economic and social development Data assetization has become an important engine for achieving high-quality economic and social development by supporting the intelligent transformation of traditional industries, aiding digital economy innovation, and improving the efficiency of social operations.
It provides a solid data foundation for artificial intelligence technology, enabling it to play a greater role in various industries, promoting the optimization of the economic structure and the improvement of efficiency.
First, data assetization provides support for the intelligent upgrade of traditional industries, enabling enterprises to transform production data into high-quality datasets, promoting the intelligentization of production processes.
Second, data assetization promotes the innovation of digital business models and the formation of new business forms by providing core data support for emerging digital economies.
Third, data assetization comprehensively improves the efficiency of social operations by promoting the widespread application of artificial intelligence in various industries.
(2) Support for cultivating and developing new productive forces Data assetization enhances scientific and technological innovation capabilities, optimizes the allocation of factors of production, and helps form high-tech industries and innovative ecosystems, becoming an important support force for cultivating new productive forces.
First, data assetization plays a crucial role in scientific and technological innovation.
Data assetization enables research institutions and enterprises to obtain the key data they need through the compliant circulation of data, promoting rapid breakthroughs in artificial intelligence in cutting-edge fields such as quantum computing and life sciences.
Second, data assetization effectively improves the level of productivity by promoting innovative allocation of factors of production.
Third, data assetization also promotes the cultivation of new productive forces by driving the formation of high-tech and emerging industries.
Fourth, data assetization also promotes the construction of an innovation and development ecosystem.
Through the capitalization and marketization of data, it attracts a large amount of venture capital and policy support, further promoting the deep integration of the innovation chain.
(3) Building a fusion hub for a unified national data and technology market Data assetization builds a fusion hub for a unified national data and technology market by promoting data circulation, standardized management, and market-oriented transactions.
First, data assetization accelerates the construction of a unified national data market by promoting the market-oriented circulation of data elements.
Data assetization breaks down industry and regional barriers through data confirmation, evaluation, standardized processing, and compliant circulation, achieving efficient allocation and maximum value of data resources.
Second, data assetization promotes the deep integration of the technology market and the data market, forming an innovative ecosystem where data and technology support each other.
The development of artificial intelligence technology is highly dependent on high-quality datasets, and data assetization ensures the acquisition, circulation, and legal use of high-quality data.
Third, data assetization provides support for unified market standards and regulatory systems, ensuring the healthy operation of data and technology markets, such as the "Data Security Management Measures" issued by the Ministry of Industry and Information Technology in 2022, which clarify the compliance and security requirements for data in circulation.
Fourth, data assetization also plays an important role in promoting industrial upgrading and integrated innovation in the process of building a unified national market.
Data assetization connects the data element market and the technology market, forming a fusion hub that spans multiple industries, regions, and technical fields.
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Core Pathways of Data Assetization in the AI Era In the era of artificial intelligence, data is not only a collection of information but also an important resource for promoting economic development and an important support for cultivating new productive forces.
Data assetization refers to the process of transforming data from its original state into an asset with economic value, tradable, and manageable, with the goal of training models, the core of compliant circulation, and the key step of standardization and specification.
(1) Training models is the goal Training artificial intelligence large models is the ultimate goal of data assetization.
Data assetization is the process of transforming data resources into assets with clear economic value.
After data aggregation, circulation, and systematic, standardized processing, high-quality datasets are formed.
High-quality datasets are a set of interrelated data collections applied to train generative artificial intelligence large models, guide production and business activities, and play an important value.
Relying on high-quality datasets resources from the government, regions, and industries, national, regional, and industry-specific artificial intelligence large models are trained to empower the digital transformation and intelligent development of industries.
(2) Compliant circulation is the core Data circulation and utilization become the core way to realize the value of data assetization.
Under the background of data elementization, data circulation and utilization are the necessary path to form high-quality datasets and the inevitable path to promote the market-oriented use of data and release the dividends of data elements.
However, data circulation faces a series of security and compliance issues, such as unclear boundaries of data ownership, multi-subject participation in data production, processing, and application, ownership attribution involving multiple boundaries such as data collectors, data processors, and individual users; compliance with cross-border data flow poses higher requirements for corporate globalization.
Only when data can circulate, trade, aggregate, and share safely and compliantly within the framework of laws and regulations can high-quality datasets be formed at the industry, regional, and industrial levels, allowing data to fully release value and promote the digital transformation of industries and enterprises.
(3) Standardization and specification are key Standardization and specification are the key and guarantee for achieving data assetization.
First, in terms of data quality, data standards ensure the consistency and accuracy of data by unifying data formats, data structures, data types, data naming, data cleaning, and annotation, ensuring standardization in the production, processing, and application of data, reducing data errors and redundancy, improving data quality, and increasing data credibility.
Second, in terms of data circulation, unified data standards and interface definitions can effectively promote data sharing and integration between different applications, eliminate data silos, accelerate efficient data circulation and transactions, reduce data management costs, and enhance data maintainability and scalability to maximize data value.
Third, in terms of data sharing, unified terminology and basic data standards can ensure that various departments and teams within an organization maintain consistent understanding and processing throughout the entire lifecycle of data management, avoiding problems caused by independent actions.
Fourth, in terms of data infrastructure, standardization and specification for data assetization provide guidelines for the planning and construction of data infrastructure, ensuring that the architecture for data storage, processing, and transmission meets business needs and industry standards, providing institutional arrangements for the financial capital market to participate in data assetization.
Fifth, in terms of data privacy protection, data security governance standards and systems are important measures to ensure data security and personal privacy.
By establishing data security protection standards and risk management mechanisms, the security protection capabilities of data at various stages such as collection, storage, transmission, and use are strengthened, personal privacy protection is enhanced, and the occurrence of data leaks and misuse incidents is prevented.当然可以，不过您还没有提供需要翻译的内容。请提供您想要翻译的文本，我会帮您翻译成英文。
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