Data Assets

Name: Data Assets
ISBN: 9787115571403

朱扬勇 / 叶雅珍Posts & Telecom Press · ChinaOct 2021

3.9

Word count: 134K words
Chapters: 184
ISBN: 9787115571403

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Summary

It is now widely agreed that data is a key factor in the digital economy, and the conversion of data into assets is a prerequisite for building the data factor market. Data differs from conventional tangible assets and intangible intellectual property assets: data assets constitute a new asset class.

When useful data accumulates to a certain scale, it forms data resources; once data resources meet basic conditions such as clear data ownership, reliably measurable cost or value, and readable data, they can become data assets.

This book analyzes and discusses the asset nature of data, provides a clear definition of data assets based on the attributes of data, and unifies information assets, digital assets, and data assets under the single category of data assets. On this basis, the book systematically introduces the asset eligibility of data, the framework for converting data into assets, the valuation and pricing of data assets, the forms and circulation models of data products, and the data factor market.

The book is intended primarily for researchers and practitioners of data assets, including professionals in the securities investment industry, the asset management industry, enterprises and public institutions whose work relates to data assets. It can also serve as a reference for teaching in fields such as accounting, economics, data science, and big data technology.

Editor's review

At a time when the digital economy is sweeping the globe, Professor Zhu Yangyong's Data Assets arrives at just the right moment, offering a systematic, profound, and highly practical framework for thinking. The book's value lies first in the clarity of its theoretical construction: it successfully lifts "data" out of a vague technical concept or business buzzword and elevates it into an "asset" with clear property-rights attributes, valuation methods, and operational management logic. Drawing on deep academic grounding and rich industry insight, the author not only explains why data can become an asset but, more importantly, parses in detail the full process of recognition, measurement, management, and application that data must undergo to become one.

Unlike the many books on the market that speak of data value only in general terms, this book stands out for its professionalism and systematicity. It explores in depth core challenges such as defining ownership of data assets, cost aggregation, value assessment models (including the applicability and limitations of the income, market, and cost approaches), accounting treatment dilemmas, and asset-based management processes. For enterprises and institutions attempting to put data resources "on the balance sheet," or to use data assets for financing or trading, this content offers direct operational reference value. At the same time, the book does not stop at the micro-technical level; it extends its perspective to macro governance of data assets, the legal and regulatory environment, and future development trends, displaying a broad vision.

For readers, reading this book is more like a reshaping of thinking. It asks readers to step outside the traditional IT mindset that treats data as a "by-product" or "raw material" and instead examine the long-term, sustainable economic value embedded in data through the eyes of an asset manager and strategic decision-maker. The case analyses and logical reasoning woven throughout help readers build a complete knowledge map of data assetization. Although some content involving assessment models and accounting standards poses a certain reading threshold, the author's explanations strive for clarity and are sufficient to guide motivated readers into the subject and deepen their understanding. Overall, this is a foundational work on "confirming rights and pricing" the core production factor of the digital economy era, and it deserves in-depth reading by all professionals concerned with enterprise digital transformation, the building of the data factor market, and related policy-making.

Who it's for

A good fit for

Leaders of enterprise digital transformation, chief data officers (CDOs), and data strategy planners
Professionals in finance and investment focused on data asset valuation and trading
Faculty and students at universities and research institutions studying the digital economy, information management, and accounting
Personnel in relevant government departments involved in designing the data factor market and formulating data governance policies

Not a good fit for

General leisure readers seeking only accessible popular-science introductions to data concepts, with no professional background
Beginners expecting specific, standalone guides to programming techniques or data analysis tools

Reading tips

It is recommended to first read the table of contents to grasp the core logic of "from resource to asset," then proceed to close reading of each chapter
For chapters involving financial assessment and accounting, readers may selectively focus on them according to their industry background or consult relevant professional materials to aid understanding
While reading, reflect on the current state of data in one's own organization or research field and try applying the book's framework for analysis, to deepen understanding

About the publisher

Posts & Telecom Press China · Founded in 1953

Posts & Telecom Press is known for its strengths in science and technology, computing, and information and communications titles, and has extended its publishing into photography, art, software learning, and inspirational growth, giving it a fairly diverse range. Its catalog includes computing and communications works such as Refactoring and Patterns (Revised Edition) and Huawei Switch Learning Guide (Second Edition), alongside bilingual collector's editions such as The Little Prince (Chinese-English-French illustrated collector's edition), The Great Gatsby, and Three Days to See. Beijing Ten Years: 1980-1990 and Ghost Blade: WLOP Personal Illustration Collection reflect its presence in photography and art. Its authors include Antoine de Saint-Exupéry, Helen Keller, Joshua Kerievsky, Wang Da, Zhang Zhaozeng, and WLOP.

版权信息
内容提要
前言
第1章绪论
1.1 界定数据
1.1.1 数据的含义
1.1.2 数据的属性
1.1.3 数据与物质
1.1.4 大数据
1.2 数据资源
1.2.1 信息化的意外馈赠
1.2.2 各领域产生的数据
1.2.3 数据资源开发利用
1.3 数据资产
1.3.1 什么是资产？
1.3.2 数据的资产性
1.3.3 数据资产化
1.4 数据要素
1.4.1 数据生产与再生产
1.4.2 数据产业
1.4.3 数据成为生产要素
1.5 小结
参考文献
第2章数据资产的相关概念
2.1 相关概念状况
2.2 信息技术发展和广泛渗透
2.2.1 信息资产术语发展
2.2.2 相关术语发展概要
2.3 “数字”被广泛认识和使用
2.3.1 数字资产术语发展
2.3.2 相关术语发展概要
2.4 大数据时代的到来和兴起
2.4.1 数据资产术语发展
2.4.2 相关术语发展概要
2.5 相关概念分析
2.5.1 概念的发展没有次序关系
2.5.2 概念的内涵是网络空间的内容
2.6 小结
参考文献
第3章数据资产是新的资产类别
3.1 数据资源的资产性
3.1.1 资产的特征和类别
3.1.2 数据资源具备资产的基本条件
3.2 定义数据资产
3.2.1 与数据资产相关的定义
3.2.2 数据资产定义的统一
3.2.3 基于数据属性定义数据资产
3.3 数据资产的属性和类别
3.3.1 数据资产的属性
3.3.2 数据资产的有形性和无形性
3.3.3 数据资产的流动性和长期性
3.4 数据资产的六大类难题
3.5 小结
参考文献
第4章数据的资产满足性
4.1 数据资产的必要条件及其可满足性
4.1.1 数据权属
4.1.2 数据计量
4.1.3 数据价值
4.1.4 数据读取
4.2 数据资产的附加条件及其可满足性
4.2.1 良好的数据质量
4.2.2 合理的货币计价与评估
4.2.3 折旧和增值规则
4.3 数据资源的资产化
4.3.1 可资产化的数据资源
4.3.2 可管理的数据资产
4.4 小结
参考文献
第5章数据资产化框架
5.1 数据资产化工作内容
5.2 数据资产化框架设计
5.2.1 数据资产流程设计
5.2.2 有效性分析
5.3 数据资产化探索与实践
5.3.1 数据资源确权
5.3.2 数据价值确认与质量管控
5.3.3 数据装盒入库
5.3.4 货币计价与评估
5.3.5 数据资产折旧和增值的管理
5.4 小结
参考文献
第6章数据资产估值
6.1 数据资产的价值体现
6.1.1 数据是否有用
6.1.2 数据是否够用
6.1.3 数据是否可用
6.1.4 数据是否好用
6.2 数据资产评价体系
6.2.1 数据真实性评价
6.2.2 数据使用性评价
6.2.3 数据质量评估
6.2.4 数据价值确认
6.3 数据质量评估方法
6.3.1 定性评估方法
6.3.2 定量评估方法
6.3.3 综合评估方法
6.4 数据资产价值评估
6.4.1 市场法
6.4.2 成本法
6.4.3 收益法
6.4.4 组合方法
6.5 小结
参考文献
第7章数据资产管理
7.1 数据资产目录
7.1.1 目录的要求
7.1.2 目录编制
7.1.3 目录管理
7.2 数据资产入库
7.2.1 数据中心/云存储
7.2.2 资产管理软件
7.2.3 数据安全管理
7.2.4 持续入库
7.3 数据资产折旧
7.3.1 时效性数据
7.3.2 时效性使用
7.3.3 资产管理成本累积
7.4 数据资产增值
7.4.1 时间推移带来的数据完整性提升
7.4.2 时间推移带来的数据新用途
7.4.3 存储技术发展带来的单位存储成本下降
7.5 小结
参考文献
第8章数据资产定价
8.1 定价因素
8.1.1 成本因素
8.1.2 市场结构因素
8.1.3 需求因素
8.1.4 竞争因素
8.1.5 其他因素
8.2 定价策略
8.2.1 基本定价策略
8.2.2 基于价值的定价策略
8.2.3 基于独特性的定价策略
8.3 市场定价分析
8.3.1 协议定价
8.3.2 明码标价
8.4 数据资产量化定价框架
8.4.1 背景和意义
8.4.2 基于测度的定价模型
8.4.3 基于测度空间的量化定价
8.5 小结
参考文献
第9章数据资产运营
9.1 数据产品流通
9.1.1 数据产品及其特性
9.1.2 数据产品流通存在的问题
9.2 典型行业的数据产品运营
9.2.1 典型行业的数据产品
9.2.2 典型行业数据产品运营体系
9.3 数据产品运营模式设计
9.3.1 数据产品形态
9.3.2 两阶段授权模式
9.3.3 运营平台体系结构
9.4 数据自治技术
9.4.1 数据盒
9.4.2 数据站
9.4.3 权益保护措施
9.4.4 运营模式
9.5 小结
参考文献
第10章数据要素市场
10.1 数据要素市场培育
10.1.1 构建数据法律体系
10.1.2 推进数据开放共享
10.1.3 提升数据资源价值
10.1.4 加强数据安全管理
10.2 数据开放
10.2.1 开放数据运动
10.2.2 政府数据开放
10.3 数据共享
10.3.1 科学数据共享
10.3.2 数据共享联盟
10.4 数据交易
10.4.1 数据交易现状
10.4.2 数据的交易权
10.4.3 数据商品交易
10.5 数据出版
10.5.1 内涵与目的
10.5.2 数据出版条件
10.5.3 数据出版方法
10.6 小结
参考文献

FAQ

How theoretically deep is this book? Is it suitable for readers without a finance or legal background?

The book's theoretical system is complete and somewhat deep; its core chapters involve asset valuation and accounting treatment, requiring readers to have a certain capacity for logical thinking and patience in learning. Although readers without a finance or accounting background may encounter challenges in some details, the author does an excellent job of explaining basic principles and business logic. As long as readers have a genuine interest in data practice, they can fully grasp the core ideas through reading. Professional details can serve as an index for further study.

Does the book provide actionable methods or tools for data asset management?

Yes. This is not a purely theoretical work; it systematically introduces the process framework for asset-based data management, including methodologies for key steps such as inventory, rights confirmation, valuation, and operations. The book discusses the applicable scenarios and pros and cons of multiple value assessment models and touches on practical issues such as management organizational structure and institutional design, providing enterprises with a clear roadmap and thinking tools for building a data asset management system.

How closely is the book's content related to current hot topics such as the national push for "data factorization" and "putting data assets on the balance sheet"?

Extremely closely. The book's core topic is precisely how data, as a production factor, is recognized as an asset and realizes its value, which aligns fully with the policy and practice frontier of cultivating the data factor market and "putting data assets on the balance sheet" (recording them in the balance sheet). The book's theoretical discussion and practical analysis provide an essential knowledge base and analytical framework for understanding and participating in these major trends.

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About the author

朱扬勇 Author

Zhu Yangyong is an author who writes on data-related topics. Public information about him is limited, and his nationality, dates of birth and death, and detailed career history cannot currently be confirmed. Four of his works are included in the collection: Data in All Its Beauty: Understanding Big Data in 100 Minutes, Data Assets, Big Data Technology, and Experimental Datasets for Similar Point Set Mining. Judging from the titles and the common categories, his writing spans both computer science and inspirational self-development: the former focuses on professional issues such as big data technology, data assets, and data mining, while the latter tends to guide readers in a popular way to understand data and its value. Overall, Zhu Yangyong's works combine technical depth with accessibility, discussing data methods and experimental datasets for professional readers while also trying to lower the threshold for reading about big data in a concise format. They are suitable for readers who wish to learn about the data field step by step, from introductory to applied levels.

叶雅珍 Author · Chinese

Ye Yazhen is a Chinese author about whom limited public information is available, and few personal details can currently be confirmed. Judging from the holdings in our library, her work Data Assets is classified under general computer science, suggesting that her writing focuses on the intersection of data elements and digital assets. The book discusses the value, management and application of data as an asset, addressing readers who wish to understand the basic issues of turning data into assets. Its style emphasizes conceptual clarification combined with a practical perspective rather than advanced technical derivation. As information about the author's background and experience has not appeared in public sources, the book may be regarded as a stage result on this subject, suitable as an introductory read on data assets and leaving room for further observation of her subsequent writing.