数据资产入表

数据资产登记是我国数据要素市场体系建设的重要组成部分。近几年我国个别地区和机构开始了数据资产登记工作的探索。然而，我国目前存在着数据资产登记概念不清晰、登记制度和服务体系缺失等问题。文章在总结我国现有部分典型资产登记制度的基础上，提出了数据资产登记的概念及其“七统一”的原则。基于数据要素流通的视角，建立了数据要素的价值链，并提出了数据资源性资产和经营性资产的概念，分析了数据资产登记的功能及其意义。文章提出了构建全国统一的数据资产登记体系的3点建议：构建以数据资产登记相关法律法规为核心制度的登记依据、创建全国一体化组织模式的登记机构、建设集成的登记平台系统作为登记载体。

随着数据资产交易规模的不断扩大，企业需要准确把握数据资产的特点，不断优化数据资产的会计核算工作，进一步提高企业财务的适明度，强化企业风险管理，并为企业的经营发展提供可靠的数据支持。文章通过文献分析总结数据资产的定义及类型，阐述数据资产的核心特点，再从数据资产的确认、计量、摊销及信息披露等三个方面提出企业数据资产的会计核算策略。

数字经济增长测算和数据生产要素统计核算问题均是数字化转型背景下，国民经济核算和政府统计领域面临的重要研究课题。文章梳理了国际上关于数字经济的概念、范围和分类，阐述了数字经济增长测算方法，进一步探讨了数据概念、特征与分类以及数据资产的概念和价值测度等问题，并针对数字经济增长测算及数据生产要素统计核算面临的挑战提出了相关建议，以期为推动数字经济增长测算，完善数据生产要素统计核算提供参考。

数据交易服务化表明,财产型交易模式无法为数据交易双方提供稳定的收益,市场主体自发地把数据“藏了起来”,将数据与数字技术结合在特定的应用场景为用户提供数据服务。那么,以确权为核心的数据资产登记便陷入了:数据要素公共性与财产权客体私有性之间的矛盾;数据要素共享性与登记确权排他性之间的矛盾。为此,应“扬弃”登记确权的初始逻辑,转而以确认数据资产合规安全为核心,重塑数据资产登记的制度功能。在此基础上,应确立数据资源和数据产品作为登记标的物,采用“物的编制主义”“基本信息、应用场景、数据来源、数据结构、更新频次和技术规则”“合规安全描述性事项”为基础的形式要件,形成以合规登记、安全登记与合同备案登记为内容的登记类型,并赋予登记以相应的法律效力,以此构成数据资产登记制度的核心架构。最后,根据数据资产登记逻辑转变与核心架构,建构符合现状的数据资产登记“1+(M+N) +Xx”组织体系与模式,以及“一体多级多面”规范支撑体系。

【目的】 在数字经济时代，数据已经成为赋能经济发展的关键生产要素。研究表明数据要素质变升级的发展路径尚不明确，数据要素的价值释放面临诸多困难。 【方法】 本文系统研究了国内外相关文献及国家政策，总结分析了数据要素价值释放的研究现状，基于数据要素的价值提升，对我国数据要素发展路径展开相关研究。 【结果】 本文明确了从原始数据到数据资本的各类数据形态，提出了数据要素价值演进路径理论模型和数据要素发展路径。 【结论】 本研究厘清了数据要素“六状态”和价值演进 “五阶段”，对于规范数据要素发展具有政策参考和实践意义，为促进数据要素价值的有效释放提供有力支持。

… Additionally, our results reveal several challenges in data asset reporting (eg, data property rights and data valuation). This highlights the areas calling for more research from economics…

… personal data as an asset. While it might sound as though treating data as an asset is a … businesses—don’t have to account for their personal data holdings on their balance sheets; …

Digital personal data is increasingly framed as the basis of contemporary economies, representing an important new asset class. Control over these data assets seems to explain the emergence and dominance of so-called “Big Tech” firms, consisting of Apple, Microsoft, Amazon, Google/Alphabet, and Facebook. These US-based firms are some of the largest in the world by market capitalization, a position that they retain despite growing policy and public condemnation—or “techlash”—of their market power based on their monopolistic control of personal data. We analyse the transformation of personal data into an asset in order to explore how personal data is accounted for, governed, and valued by Big Tech firms and other political-economic actors (e.g., investors). However, our findings show that Big Tech firms turn “users” and “user engagement” into assets through the performative measurement, governance, and valuation of user metrics (e.g., user numbers, user engagement), rather than extending ownership and control rights over personal data per se. We conceptualize this strategy as a form of “techcraft” to center attention on the means and mechanisms that Big Tech firms deploy to make users and user data measurable and legible as future revenue streams.

The production of goods and services is central to understanding economies. The textbook description of a firm, typically in agriculture or manufacturing, focuses on its physical “tangible” capital (machines), labor (workers), and the state of “know-how. ” Yet real-world firms, such as Apple, Microsoft, and Google, have almost no physical capital. Instead, their main capital assets are “intangible”: software, data, design, reputation, supply-chain expertise, and R&D. We discuss investment in these knowledge-based types of capital: How to measure it; how it affects macroeconomic data on investment, rates of return, and GDP; and how it relates to growth theory and practical growth accounting. We present estimates of productivity in the US and European economies in recent decades including intangibles and discuss why, despite relatively rapid growth in intangible capital and what seems to be a modern technological revolution, productivity growth has slowed since the global financial crisis.

Current accounting practice expenses many investments in intangible assets to the income statement, confusing earnings from current revenues with investments to gain future revenues. This has led to increasing calls to book those investments to the balance sheet. Drawing on relevant research, we evaluate solutions for intangible asset accounting that contrast with balance sheet recognition, and we compare these with current practice under IFRS. Key is acknowledging that an accounting solution comes from a double-entry system, which produces both an income statement and a balance sheet, and which has features that both enable and limit the information that can be conveyed about intangible asset value. In this system, asset recognition in the balance sheet must consider the effect on measurement in the income statement, for the income statement conveys value added to investment on the balance sheet. A determining feature is uncertainty about investment outcome and how that affects the income statement, so our solutions centre on accounting under uncertainty. Two other accounting features are added: there has to be an investment expenditure for balance sheet recognition, and that expenditure must be separately identifiable from transactions. These features, rather than the tangible-intangible asset dichotomy, lead to the prescribed solutions.

Internet-based companies such as Amazon, Facebook, and Tencent hold an enormous amount of consumer data that are utilized to create business value via big data analytics. Although some companies use big data to provide professional services, such as targeted advertising and product recommendations, according to the current CAS, IFRS, and U.S. GAAP accounting standards, these assets are not recognized as assets since they are generated internally. This paper starts with a discussion of how Amazon, Facebook, Tencent, and Walmart use big data to create value for their businesses and then argues why it makes sense to recognize big data as intangible assets. Possible methods of data asset evaluation and their implications for business managers are also explored.

… a growing recognition of the paramount importance of data … By examining how data governance techniques impact these … data asset value, thereby advancing data governance …

To promote the theoretical and empirical research on the impact of data assets on the operation and development of enterprises, this paper reviews the relevant research on the impact of data assets on the operation and development of enterprises. At present, scholars' research on the impact of data assets on the operation and development of enterprises mainly focuses on the impact of data assets on the micro-operation efficiency of enterprises (including: impact mechanism, impact law, impact core), the impact of data assets on the high-quality development of enterprises (including: theoretical logic, impact mechanism), and the impact of data asset information disclosure on the enterprise value (including: impact mechanism, analyst attention). In general, scholars' research on the impact of data assets on the operational risk of enterprises is relatively weak; in addition, scholars have rarely studied the impact mechanism of data assets on the credit risk and debt financing of enterprises. In the context of the digital economy, scholars' theoretical and empirical research on the impact of data assets on the operation and development of enterprises needs to be further deepened.

: With the development of the digital economy, data has become a resource as important as traditional production factors. This paper distinguishes the concepts of data, data resources

In the twenty-first century, the most valuable firms in the world are valued primarily for their data. This article describes a set of tools to measure and value data and highlights …

This article aims to explore the impact of the recognition of data assets in the balance sheets of the computer industry on the market. After the formal implementation of the Interim Provisions on the Accounting Treatment of Enterprise Data Assets issued by the Ministry of Finance of China, a large number of studies have selected the entire industry as the research object, lacking in - depth analysis of a single industry and ignoring the influence of industry characteristics on the research conclusions. Through the research in this article, we found that the recognition of data assets in the balance sheets of the computer industry in the third quarter showed a negative market reaction, which was contrary to the research conclusions of the semi - annual report data. Combining with industry characteristics, the research suggests that in the subsequent work of recognizing data assets in the balance sheets, the supervision of the quality of data assets shou·ld be strengthened, the work of enterprises in recognizing data assets in the balance sheets should be continuously promoted, and the relevant accounting treatment standards should be improved in a timely manner.

As digital transformation redefines business models, enterprise value increasingly depends on intangible assets, especially data, rather than traditional physical assets like buildings and equipment. Traditional accounting has long focused on valuing physical assets based on their anticipated future economic benefits, distinguishing between operating and capital expenditures. However, intangible assets, such as data, are more complex to evaluate due to their dependence on business context, lifecycle, and specific uses. This literature review examines data valuation as an intangible asset for accurate enterprise valuation, relevant in investments, mergers, acquisitions, and understanding enterprise worth. The article highlights multiple emerging valuation approaches, including customer transactions, lifetime value, shareholder value, and customer equity, which provide a more nuanced view of data's worth. Advanced techniques like cooperative game theory, Shapley Values, machine learning, and meta‐learning frameworks are also explored as tools to quantify data value more precisely. Data quality is emphasized as a critical component of data valuation, with ongoing challenges due to regulatory uncertainties and inconsistent reporting practices. These complexities in data valuation signal a significant research opportunity to refine valuation methods as data continues to shape enterprise value across industries.

… Our approach values public or private data, data about one or many assets, and data … data types, of course, have different valuations, heterogeneous investors also value the same data …

… data value. Against this background, we propose the following research question: What is a data resource … , and data as a relational resource; 2) Proposing a data value quantification …

… Improved resource allocation occurs when accurate data valuation allows farmers to capture fair compensation for their information contributions, providing financial resources to invest …

… data from firm balance sheets to measure intangible assets, where they consider both data … The availability of data on intangibles from firm balance sheets varies across countries and …

The impact of corporate intangibles on a company’s market value has been a widely debated topic. A large body of literature has separately examined the industry’s effect- or firm-specific attributes, such as industry type, company size, company age, or indebtedness and profitability, on the motivation to disclose information on intangible assets, but without considering a comprehensive view. This paper examines the role intangible assets play in a firm’s market valuation besides other firm-specific characteristics. The reducted dataset we use in this study comprises 250 publicly traded companies operating in four different business sectors in France, Germany, and Switzerland for the ten years from 2009 to 2018. Based on the panel data regression models, the study provides an extension of previous knowledge about the effect intangible assets may have on the investors’ view of a company’s value, where the value added of this paper is the empirical evidence of a possible link between the intangible assets’ disclosure and the market value of German, French, and Swiss enterprises. The importance of our contribution lies in a comparative analysis carried out to reveal substantial differences in the impact of intangible assets and innovation activity on the market value firms in three European countries and across four industry sectors. Although the results show the positive impact of intangible assets on the companies’ market value, we suggest that investors still assess companies based on their profitability rather than considering the information on intangible assets the enterprises disclose in their financial statements.

The valuation of intangible assets has become increasingly critical in the digital economy, where assets such as intellectual property, brand reputation, and customer data drive business value. This study proposes a conceptual advancement in financial analysis models to effectively value intangible assets, addressing the complexities and challenges presented by the digital transformation of business environments. Traditional valuation methods, often based on tangible asset assessments, fail to capture the full economic potential of intangible assets, which are non-physical but contribute significantly to a company's competitive advantage and market position. The study introduces an integrated framework combining financial modeling techniques with digital economy-specific metrics, such as data analytics, brand equity, and intellectual capital. This framework incorporates emerging methodologies, including machine learning and big data analysis, to quantify intangible asset value and predict its future contributions. The research emphasizes the importance of incorporating both financial and non-financial data to create more comprehensive valuation models, accounting for the dynamic nature of digital assets and the rapidly changing market conditions. Key components of the proposed model include a multidimensional approach to assessing intellectual property rights, customer relationships, and digital platforms. By utilizing advanced statistical methods and data-driven insights, businesses can better estimate the value of intangible assets, enabling more informed decision-making in mergers, acquisitions, and financial reporting. Furthermore, the model accounts for the role of regulatory frameworks, such as digital rights management, and integrates sustainability considerations in valuing digital assets. The findings underscore the need for updated financial analysis practices that reflect the growing importance of intangible assets in the digital economy. This study contributes to bridging the gap between traditional financial models and the evolving digital landscape, offering a forward-looking approach to valuing intangibles and ensuring accurate financial assessments.

PurposeIn 2000, Cañibano et al. published a literature review entitled “Accounting for Intangibles: A Literature Review”. This paper revisits the conclusions drawn in that paper. We also discuss the intervening developments in scholarly research, standard setting and practice over the past 20+ years to outline the future challenges for research into accounting for intangibles.Design/methodology/approachWe conducted a literature review to identify past developments and link the findings to current accounting standard-setting developments to inform our view of the future.FindingsCurrent intangibles accounting practices are conservative and unlikely to change. Accounting standard setters are more interested in how companies report and disclose the value of intangibles rather than changing how they are determined. Standard setters are also interested in accounting for new forms of digital assets and reporting economic, social, governance and sustainability issues and how these link to financial outcomes. The IFRS has released complementary sustainability accounting standards for disclosing value creation in response to the latter. Therefore, the topic of intangibles stretches beyond merely how intangibles create value but how they are also part of a firm’s overall risk and value creation profile.Practical implicationsThere is much room academically, practically, and from a social perspective to influence the future of accounting for intangibles. Accounting standard setters and alternative standards, such as the Global Reporting Initiative (GRI) and European Union non-financial and sustainability reporting directives, are competing complementary initiatives.Originality/valueOur results reveal a window of opportunity for accounting scholars to research and influence how intangibles and other non-financial and sustainability accounting will progress based on current developments.