The National Data Administration (NDA) is building a formalized vocabulary for China’s emerging data governance regime, an action that may prove more consequential for China’s digital future than many of the high-profile AI headlines that dominate Western coverage. The goal is straightforward: define a glossary of “common and authoritative” technical and policy language that can be applied across government, industry, and society as China advances its national digital strategy, Digital China.

On December 30, 2024, the NDA Expert Group for Drafting Definitions of Terms in the Data Domain (数据领域名词解释起草专家组) published the first batch of official definitions. This is the opening installment in what will become a multi-stage glossary of core concepts essential to China’s digital transformation, ranging from data elements and data resources to industrial digitalization and new governance mechanisms.

Much outside commentary frames China’s digital trajectory primarily through the lens of China–U.S. competition in artificial intelligence. But the NDA’s terminology project is a reminder that Digital China’s strategic horizon is much broader. AI is an important tool, but the central mission is to build nationwide data intelligence to support economic development, social governance, and ultimately the long-term goal of Smart Society (智慧社会). Standardized terminology is the governance scaffolding required for a country-wide digital transformation of this scale.

The NDA prefaced the release of Batch One with a statement underscoring both ambition and openness:

To build broad consensus, and with strong support from all sectors of society, we have diligently researched and compiled the first batch of Commonly Used Terms in the Data Domain. We will continue to iterate and refine these terms based on practical application and development needs, and welcome continued attention from all sectors of society.

The resulting list is more than a set of definitions; it is an early blueprint for how the Party-state intends to structure China’s data domain over the next decade. Follow-on batches, which will also be available on this site’s Specialized Digital Terminology page are gradually filling in the contrasts and contours of this governance landscape.

Most important Batch One concept for Digital China’s future:

Whole-of-City (All Domain) Digitalized Transformation (城市全域数字化转型)

This concept stands as a strategy milestone that captures a critical shift in Digital China’s trajectory: the move from sector-by-sector digitalization toward systems-level integration at the urban scale. The transition was first articulated in a National Development and Reform Commission-led guiding opinion issued in May 2024, which framed cities, not individual industries or technologies, as the primary units of digital transformation. The document opens with a simple but consequential assertion: “Cities are the comprehensive carriers for advancing the construction of Digital China.”

In this formulation, the city becomes a fully digitalized, data-driven environment rather than a collection of digitalized projects. Whole-of-City Digitalized Transformation thus serves as the conceptual bridge between today’s informatized infrastructure and the Party’s envisioned Smart Society end state. It also underscores a core principle of Beijing’s approach: digital transformation is not only a sectoral undertaking, but a territorial and governance project as well.

Most underestimated Batch One concept by Western experts:

Industrial Internet (both the narrow 产业互联网 and broad 工业互联网 versions of the term)

This concept describes the digitalized re-architecture of China’s manufacturing base and supply-chain system, arguably one of the most geopolitically consequential pillars of Digital China. In Beijing’s formulation, the Industrial Internet is not simply an industrial technology domain, but the foundational infrastructure for intelligent manufacturing, real-time industrial data circulation, and long-term supply-chain resilience. It provides the connective tissue linking production equipment, enterprises, regions, and data platforms into an integrated industrial system.

Western analysis often treats the Industrial Internet as a discrete subfield of industrial technology. Beijing, by contrast, approaches it as a strategic national capability, essential to New Type Industrialization and to the systemic integration of the real and digital economies.

The full list of terms from Batch One is provided below and will continue to be integrated into the consolidated glossary found on this site’s Specialized Digital Terminology page. As Digital China accelerates, these definitions will serve as an increasingly important guide to understanding how China conceptualizes its digital transformation and how it intends to govern it.

Glossary of Commonly Used Terms in the Data Domain (Batch One)

数据领域常用名词解释 (第一批)

Source: National Data Administration (国家数据局 ), Expert Group for Drafting Definitions of Terms in the Data Domain (数据领域名词解释起草专家组)

Publication Date: December 30, 2024

  1. Data (数据): Data is a record of information, whether electronic or otherwise. Data is referred to variously as raw data, derived data, data resources, data products and services, data assets, or data elements. | 数据，是指任何以电子或其他方式对信息的记录。数据在不同视角下被称为原始数据、衍生数据、数据资源、数据产品和服务、数据资产、数据要素等。
  2. Raw Data (原始数据): Raw data is data that is initially generated or collected at the source and has not been processed. | 原始数据是指初次产生或源头收集的、未经加工处理的数据。
  3. Data Resource (数据资源): Data resources are data with the potential to create value, typically referring to data sets recorded and stored electronically, machine-readable, and available for social reuse. | 数据资源是指具有价值创造潜力的数据的总称，通常指以电子化形式记录和保存、可机器读取、可供社会化再利用的数据集合。
  4. Data Element (数据要素): Data elements refer to data resources that are invested in production and business activities and participate in value creation. | 数据要素是指投入到生产经营活动、参与价值创造的数据 资源。
  5. Data Products and Services (数据产品和服务): Data products and services refer to processed data outputs and data services, formed through data processing, that can meet specific needs. | 数据产品和服务是指基于数据加工形成的，可满足特定需求的数据加工品和数据服务。
  6. Data Asset (数据资产): Data assets refer to data resources lawfully owned or controlled by a specific entity, which can be monetarily measured and are capable of generating economic or social benefits. | 数据资产是指特定主体合法拥有或者控制的，能进行货币计量的，且能带来经济利益或社会效益的数据资源。
  7. Market-Based Allocation of Data Elements (数据要素市场化配置): Market-based allocation of data elements refers to the allocation of data, a new production factor, through market mechanisms. It aims to establish a more open, secure, and efficient data circulation environment and continuously unlock the value of data elements. | 数据要素市场化配置是指通过市场机制来配置数据这一新型生产要素，旨在建立一个更加开放、安全和高效的数据流通环境，不断释放数据要素价值。
  8. Data processing (数据处理): Data processing includes the collection, storage, use, processing, transmission, provision, and disclosure of data. | 数据处理包括数据的收集、存储、使用、加工、传输、提供、公开等。
  9. Data Processor (数据处理者): A data processor is an individual or organization that independently determines the purpose and method of data processing. | 数据处理者是指在数据处理活动中自主决定处理目的和处理方式的个人或者组织。
  10. Trusted Data Processor (受托数据处理者): A trusted data processor is an individual or organization that processes data on behalf of another person. | 受托数据处理者是指接受他人委托处理数据的个人或者组织。
  11. Data Circulation (数据流通): Data circulation refers to the process by which data flows between different entities, including data openness, sharing, trading, and exchange. | 数据流通是指数据在不同主体之间流动的过程，包括数据开放、共享、交易、交换等。
  12. Data Trading (数据交易): Data trading refers to a trading activity between a data supplier and a data demander (recipient), in which specific forms of data serve as the subject matter and currency or other equivalents are used as consideration (payment). | 数据交易是指数据供方和需方之间进行的，以特定形态数据为标的，以货币或者其他等价物作为对价的交易行为。
  13. Data Governance (数据治理): Data governance refers to the process of improving data quality, security, and compliance, and promoting its effective use. It encompasses organizational data governance, industry data governance, and social data governance. | 数据治理是指提升数据的质量、安全、合规性，推动数据有效利用的过程，包含组织数据治理、行业数据治理、社会数据治理等。
  14. Data Security (数据安全): Data security refers to taking necessary measures to ensure that data is effectively protected and legally used, as well as the ability to maintain a continuous state of security. | 数据安全是指通过采取必要措施，确保数据处于有效保护和合法利用的状态，以及具备保障持续安全状态的能力。
  15. Public Data (公共数据): Public data refers to data generated by Party and government organs, enterprises, and institutions at all levels in the course of performing their duties or providing public services in accordance with the law. | 公共数据是指各级党政机关、企事业单位依法履职或提供公共服务过程中产生的数据。
  16. Digital Industrialization (数字产业化): Digital industrialization refers to the process of transforming digital technologies such as mobile communications and artificial intelligence into digital products and services, and transforming data into resources and factors, thereby forming new digital industries, new business forms, and new models. | 数字产业化是指移动通信、人工智能等数字技术向数字产品、数字服务转化，数据向资源、要素转化，形成数字新产业、新业态、新模式的过程。
  17. Industrial Digitalization (产业数字化): Industrial digitalization refers to the process by which traditional industries, such as agriculture, industry, and services, improve operational efficiency and reduce production and operating costs by applying digital technologies, collecting and integrating data, and mining the value of data resources. This process, in turn, reshapes thinking and cognition, holistically reshapes organizational management models, systemically transforms production and operational processes, and continuously improves total factor productivity. | 产业数字化是指传统的农业、工业、服务业等产业通过应用数字技术、采集融合数据、挖掘数据资源价值，提升业务运行效率，降低生产经营成本，进而重构思维认知，整体性重塑组织管理模式，系统性变革生产运营流程，不断提升全要素生产率的过程。
  18. High Quality Development of the Digital Economy (数字经济高质量发展): High Quality Development of the Digital Economy refers to a new stage of digital economic development aimed at strengthening, optimizing, and expanding the digital economy, centered around accelerating the cultivation of New Quality Productive Forces, with marketized reform in the allocation of data elements as the primary focus. This will be achieved through the coordinated improvement of basic systems for data and digital infrastructure, the comprehensive promotion of the deep fusion of digital technology and the real economy, and the continuous improvement of digital economic governance capabilities and the level of international cooperation. | 数字经济高质量发展是指围绕加快培育新质生产力，以数据要素市场化配置改革为主线，通过协同完善数据基础制度和数字基础设施、全面推进数字技术和实体经济深度融合、持续提升数字经济治理能力和国际合作水平，实现做强做优做大目标的数字经济发展新阶段。
  19. Digital Consumption (数字消费): Digital consumption refers to consumer activities and consumption patterns enabled by digital technologies and applications. It encompasses not only the consumption of intelligent digital technologies, products, and services, but also the digitalization and intelligentization of consumption content, channels, and environments, as well as new consumption models that deeply fuse online and offline activities. | 数字消费是指数字技术、应用支撑形成的消费活动和消费方式，既包括对数智化技术、产品和服务的消费，也包括消费内容、消费渠道、消费环境的数字化与智能化，还包括线上线下深度融合的消费新模式。
  20. Industrial Internet (产业互联网): Industrial Internet refers to the use of digital technologies and data elements to promote data integration and interconnection across entire industrial chains, enabling the digitalized, networkized, and intelligentized development of industries. It drives the reorganization and transformation of business processes, organizational structures, and modes of production, facilitates coordinated transformation of upstream and downstream industrial chains, and fosters fused online–offline development.  Entire industries will achieve cost reduction, efficiency improvement, and High Quality Development, thereby forming a new system for industrial collaboration, resource allocation, and value creation. | 产业互联网是指利用数字技术、数据要素推动全产业链数据融通，赋能产业数字化、网络化、智能化发展，推动业务流程、组织架构、生产方式等重组变革，实现产业链上下游协同转型、线上线下融合发展、全产业降本增效与高质量发展，进而形成新的产业协作、资源配置和价值创造体系。
  21. Whole-of-City (All Domain) Digitalized Transformation (城市全域数字化转型): Whole-of-City Digitalized Transformation refers to a new model of urban High Quality Development in which cities, guided by the overarching goal of comprehensively deepening data integration, connectivity, and utilization, make combined use of digital technologies and institutional innovation tools to reshape their technological architecture, transform urban management processes, and achieve deep fusion of industry and city. This model promotes efficiency gains across all domains of digital transformation, comprehensively enhances supporting capabilities, and optimizes the entire transformation ecosystem. | 城市全域数字化转型，是指城市以全面深化数据融通和开发利用为主线，综合利用数字技术和制度创新工具，实现技术架构重塑、城市管理流程变革和产城深度融合，促进数字化转型全领域增效、支撑能力全方位增强、转型生态全过程优化的城市高质量发展新模式。
  22. Eastern Data, Western Computing Project (东数西算工程): The Eastern Data, Western Computing project is a major project that brings data and demand generated by economic activities in the eastern region to western regions for computation and processing. It coordinates and plans the layout, networking, power, energy consumption, computing power, and data management of data centers. For example, business scenarios like AI model training and inference, and machine learning, can be migrated from eastern regions to western regions rich in wind, solar, and hydropower through the “Eastern Data, Western Computing” approach, achieving coordinated development between the east and west. Accelerating the development of the “Eastern Data, Western Computing” project will effectively stimulate innovation in data elements, accelerate the processes of digital industrialization and industrial digitalization, and foster the emergence of new technologies, new industries, new business formats, and new models, in support of High Quality Development of the economy. | 东数西算工程是把东部地区经济活动产生的数据和需求放到西部地区计算和处理，对数据中心在布局、网络、电力、能耗、算力、数据等方面进行统筹规划的重大工程，比如人工智能模型训练推理、机器学习等业务场景，可以通过“东数西算”的方式让东部业务向西部风光水电丰富的区域迁移，实现东西部协同发展。加快推动“东数西算”工程建设，将有效激发数据要素创新活力，加速数字产业化和产业数字化进程，催生新技术、新产业、新业态、新模式，支撑经济高质量发展。
  23. High Speed Data Network (高速数据网): High-speed data networks are designed for data circulation and utilization scenarios, leveraging technologies such as network virtualization and software-defined networking (SDN) to provide data transmission services with flexible bandwidth, security, reliability, and high transmission efficiency. | 高速数据网是指面向数据流通利用场景，依托网络虚拟化、软件定义网络（SDN）等技术，提供弹性带宽、安全可靠、传输高效的数据传输服务。
  24. National Unified Computing Power Network (全国一体化算力网): The National Unified Computing Power Network refers to a digital infrastructure that uses information network technology to enable highly integrated, large-scale scheduling and operation of various computing power resources nationwide. As the 2.0 version of the “Eastern Data, Western Computing” project, it features four key characteristics: intensification, integration, coordination, and value creation. | 全国一体化算力网是指以信息网络技术为载体，促进全国范围内各类算力资源高比例、大规模一体化调度运营的数字基础设施。作为“东数西算”工程的2.0 版本，具有集约化、一体化、协同化、价值化四个典型特征。
  25. Metadata (元数据): Metadata is data that defines and describes specific data. It provides information about the structure, characteristics, and relationships of the data, facilitating its organization, retrieval, understanding, and management. | 元数据是定义和描述特定数据的数据，它提供了关于数据的结构、特征和关系的信息，有助于组织、查找、理解、管理数据。
  26. Structured Data (结构化数据): Structured data refers to a form of data representation in which each record, composed of data elements, has the same structure, and can be effectively described using a relational model. | 结构化数据是指一种数据表示形式，按此种形式，由数据元素汇集而成的每个记录的结构都是一致的，并且可以使用关系模型予以有效描述。
  27. Semi-Structured Data (半结构化数据): Semi-structured data refers to a type of data model structure that does not conform to the tabular format of a relational database or other data tables, but contains relevant markers used to separate semantic elements and to organize records and fields into hierarchical levels. | 半结构化数据是指不符合关系型数据库或其他数据表的形式关联起来的数据模型结构，但包含相关标记，用来分隔语义元素以及对记录和字段进行分层的一种数据化结构形式。
  28. Unstructured Data (非结构化数据): Unstructured data refers to data that does not have a predefined model or is not organized in a predefined way. | 非结构化数据是指不具有预定义模型或未以预定义方式组织的数据。
  29. Data Analysis (数据分析): Data analysis refers to the process of organizing, studying, reasoning, and summarizing data using specific techniques and methods to extract useful information, discover patterns, and form conclusions. | 数据分析是指通过特定的技术和方法，对数据进行整理、研究、推理和概括总结，从数据中提取有用信息、发现规律、形成结论的过程。
  30. Data Mining (数据挖掘): Data mining is a method of data analysis. It is the process of extracting information or value hidden in data through techniques such as statistical analysis, machine learning, pattern recognition, and expert systems. | 数据挖掘是数据分析的一种手段，是通过统计分析、机器学习、模式识别、专家系统等技术，挖掘出隐藏在数据中的信息或者价值的过程。
  31. Data Visualization (数据可视化): Data visualization refers to the use of graphical tools such as statistical charts, graphs, and maps to clearly and effectively convey the useful information contained in data, allowing data users to better understand and analyze the data. | 数据可视化是指通过统计图表、图形、地图等图形化手段，将数据中包含的有用信息清晰有效地传达出来，以便于数据使用者更好地理解和分析数据。
  32. Data Warehouse (数据仓库): A data warehouse is a database used to permanently store data after data preparation. | 数据仓库是指在数据准备之后用于永久性存储数据的数 据库。
  33. Data Lake (数据湖): A data lake is a highly scalable data storage architecture designed to store large amounts of raw and derived data from a variety of sources in various formats, including structured, semi-structured, and unstructured data. | 数据湖是指一种高度可扩展的数据存储架构，它专门用于存储大量原始数据和衍生数据，这些数据可以来自各种来源并以不同的格式存在，包括结构化、半结构化和非结构化数据。
  34. Lake Warehouse Integration (湖仓一体): Lake warehouse integration refers to a new, open storage architecture that connects data warehouses and data lakes, combining the high performance and management capabilities of data warehouses with the flexibility of data lakes. The underlying layer supports the coexistence of multiple data types, enabling data sharing. The upper layer can access data through a unified, encapsulated interface, supporting real-time query and analysis. | 湖仓一体是指一种新型的开放式的存储架构，打通了数据仓库和数据湖，将数据仓库的高性能及管理能力与数据湖的灵活性融合起来，底层支持多种数据类型并存，能实现数据间的相互共享，上层可以通过统一封装的接口进行访问，可同时支持实时查询和分析。
  35. Privacy Preserving Computing (隐私保护计算): Privacy preserving computing refers to a type of information technology that analyzes and computes data without disclosing the original data to the data provider. This ensures that data remains “available but invisible” throughout the entire data flow process, including generation, storage, computation, application, and destruction. Common technical solutions for privacy-preserving computing include secure multi-party computation, federated learning, trusted execution environments, and secret computing. Common underlying technologies include obfuscated circuits, oblivious transfer, secret sharing, and homomorphic encryption. | 隐私保护计算是指在保证数据提供方不泄露原始数据的前提下，对数据进行分析计算的一类信息技术，保障数据在产生、存储、计算、应用、销毁等数据流转全过程的各个环节中“可用不可见”。隐私保护计算的常用技术方案有安全多方计算、联邦学习、可信执行环境、密态计算等。常用的底层技术有混淆电路、不经意传输、秘密分享、同态加密等。
  36. Secure Multiparty Computation (安全多方计算): Secure multi-party computation (SMC) involves multiple participants in a distributed network, each holding secret data. Each participant wishes to jointly compute a function using this data as input. Each participant is required to be unable to access any input information from other participants, except for the computational results and any information that is intended to be publicly available. This research primarily addresses the problem of secure multi-party collaborative computation without a trusted third party. | 安全多方计算是指在一个分布式网络中，多个参与实体各自持有秘密数据，各方希望以这些数据为输入共同完成对某函数的计算，而要求每个参与实体除计算结果、预期可公开的信息外均不能得到其他参与实体的任何输入信息。主要研究针对无可信第三方情况下，安全地进行多方协同的计算问题。
  37. Federated Learning (联邦学习): Federated learning refers to a model in which multiple participants collaborate to complete a machine learning task by exchanging intermediate computational results in a privacy-protected manner, while ensuring that their original private data remains within a trusted domain defined by the data owner. | 联邦学习是指一种多个参与方在保证各自原始私有数据不出数据方定义的可信域的前提下，以保护隐私数据的方式交换中间计算结果，从而协作完成某项机器学习任务的模式。
  38. Trusted Execution Environment (可信执行环境): A Trusted Execution Environment (TEE) is a software runtime environment built on hardware-level isolation and secure boot mechanisms to ensure confidentiality, integrity, authenticity, and non-repudiation of data and code related to security-sensitive applications. | 可信执行环境是指基于硬件级隔离及安全启动机制，为确保安全敏感应用相关数据和代码的机密性、完整性、真实性和不可否认性目标构建的一种软件运行环境。
  39. Confidential Computing (密态计算): Confidential computing refers to the use of cryptography, trusted hardware, and system security technologies to ensure that computing data is available but invisible, while maintaining confidentiality of computational results. This supports the construction of complex combined computations, provides full-chain computing security, and prevents data leakage and misuse. | 密态计算是指通过综合利用密码学、可信硬件和系统安全相关技术，实现计算过程数据可用不可见，计算结果能够保持密态化，以支持构建复杂组合计算，实现计算全链路保障，防止数据泄漏和滥用。
  40. Blockchain (区块链): Blockchain is a new type of database software that integrates distributed networks, encryption, smart contracts, and other technologies. It features decentralization, trusted consensus, immutability, and traceability. It is primarily used to address trust and security issues in data circulation. | 区块链是分布式网络、加密技术、智能合约等多种技术集成的新型数据库软件，具有多中心化、共识可信、不可篡改、可追溯等特性，主要用于解决数据流通过程中的信任和安全问题。