扫码加入数字化转型网数据要素X专题研习社：
摘要：
在数字化时代，数据已成为关键的生产要素，对企业运营和经济增长具有重要影响。数据资产入表，即将数据资源作为企业资产的一部分正式纳入财务报表，是企业数字化转型的重要里程碑。本文将深入探讨数据资产入表的核心问题，为大家提供一个基本的了解。
Q1：数据要素化是什么意思？
数据要素化是一个动词。所谓“要素化”，指的是使其对象成为生产要素。也就是说，数据要素化指的就是指要将数据变成一种新型生产要素，使其满足生产要素的判断条件，成为驱动企业生产经营活动的一种重要输入。 数字化转型网（www.szhzxw.cn）
Q2：为什么数据可以作为一种生产要素？
数据作为一种生产要素，主要是因为它在现代经济中扮演着越来越重要的角色。以下是一些原因，解释了为什么数据可以被视为一种生产要素：
1. 价值创造：数据可以用来创造新的产品、服务和业务模式，为企业和个人提供价值。
2. 决策支持：数据提供了洞察力，帮助企业和个人做出更明智的决策，优化资源分配和运营效率。
3. 效率提升：通过分析数据，可以发现流程中的瓶颈和低效环节，从而提高生产效率和降低成本。
4. 个性化服务：数据使企业能够提供定制化的产品和服务，满足消费者的个性化需求。
5. 市场洞察：数据可以帮助企业更好地理解市场趋势和消费者行为，从而制定有效的市场策略。
6. 风险管理：数据可以用来预测和评估风险，帮助企业和个人做出更安全的投资和经营决策。
7. 创新驱动：数据是创新的源泉，通过数据分析可以发现新的商业机会和创新点。
8. 竞争优势：拥有和有效利用数据的企业可以在市场中获得竞争优势，因为它们能够更快地适应变化并抓住机会。 数字化转型网（www.szhzxw.cn）
9. 资源优化：数据可以帮助企业更有效地利用资源，例如通过预测分析来优化库存管理。
数据作为一种生产要素，其价值在于它能够转化为知识和行动，从而推动经济增长和社会进步。随着技术的发展，数据的收集、存储、处理和分析能力不断提高，数据在经济和社会中的作用也越来越重要。
Q3：哪些数据资源可以入表？
个人数据、企业数据和公共数据都有可能被开发成数据产品，并以数据资产的形式入表，但它们需要满足一定的条件和要求。
1、个人数据：个人数据通常包含敏感信息，需要在合规的框架下进行处理和使用。如果个人数据经过匿名化处理，并且企业拥有合法的使用权，它们可以被集成到数据产品中。然而，个人数据需要确保符合《中华人民共和国个人信息保护法》等相关法律法规的要求，获取数据主体的明确同意，并确保数据安全和隐私保护 。
2、企业数据：企业自身生成或合法获取的数据，如交易记录、用户行为数据等，如果能够为企业带来经济利益，并且成本或价值能够可靠计量，可以作为数据资产入表。企业数据产品化后，可以作为无形资产或存货入表，具体取决于其预期用途和业务模式 。
3、公共数据：公共数据是指政府或其他公共机构发布的数据，通常可以公开获取。企业可以利用公共数据开发数据产品，如果这些产品能够为企业带来经济利益，并且满足会计准则的资产确认条件，它们也可以作为数据资产入表。 数字化转型网（www.szhzxw.cn）
数据资源能否入表作为数据资产，主要取决于它们是否满足以下条件：
1）企业必须合法拥有或控制数据资源。
2）数据资源预期会给企业带来经济利益。
3）数据资源的成本或价值能够可靠地计量。
数据资产入表后，企业需要在财务报表中对其进行适当的列报和披露，包括数据资产的初始计量、后续计量、处置和报废等信息。同时，企业还需要根据《企业数据资源相关会计处理暂行规定》进行详细的信息披露，如数据资源的账面原值、累计摊销、减值准备和账面价值等。
Q4：数据如何确权？
数据确权主要分为四步：
（1）权益主体的确认。
主体包括数据来源者和数据处理者。数据来源者指在数据产生过程中提供或创造数据的个人或组织， 享有获取或复制转移由其促成产生的数据的权益。数据处理者是 指对数据的收集、存储、使用、加工、传输等数据处理活动中自 主决定处理目的、处理方式的组织或个人。主体的权益一般包括 数据资源持有权、数据加工使用权、数据产品经营权等。
（2）数据来源合规审核。
确保登记主体数据来源的合法合规， 且有权进行数据资产的登记。“数据二十条” 区分了公共数据、 企业数据和个人数据这三类数据的授权运营机制，根据数据资产 所涉及数据的不同类型，按照不同的数据来源执行相应的合规审 核标准。 数字化转型网（www.szhzxw.cn）
（3）登记主体的身份认证。
个人实名认证可采用人脸识别、 短信验证、银行卡验证等方式；机构法人可通过法人身份验证、 对公银行账号汇款等方式。
（4）数据资产确权存证。
对数据的来源、数据来源者的权益、 数据的使用场景、适用条件及使用细节约定、禁用范围， 以及数据处理者的权益、数据加工授权协议，实质性加工和创新性劳动相应的证明材料等相关信息进行确认和存证， 以锁定证据和提供合法性参考和背书。
Q5：数据质量如何评估？
数据质量评估是指对数据的质量进行系统 性检查和评价的过程，通常围绕规范性、完整性、准确性、一致性、时效性、可访问性开展评估。通过质量评估，可以识别数据 中的问题和不足，从而采取相应的措施提升数据质量，保障数据 适合其预期的用途。根据《信息技术 数据质量评价指标》国家标准，数据质量评 价指标体系按照以下6 个维度进行分析定义：
①规范性：数据符合数据标准、数据模型、元数据、业务规 则、权威参考数据或安全规范的度量；
②完整性：包括数据元素的完整性和数据记录的完整性；
③准确性：数据准确性的评价维度包括数据内容的正确性、 数据格式的合规性、数据重复率、数据唯一性和脏数据出现率； 数字化转型网（www.szhzxw.cn）
④一致性：包括相同数据一致性和关联数据一致性；
⑤时效性：包括基于时间段的正确性、基于时间点的及时性和时序性；
⑥可访问性：数据在需要时可以获取，在设定的有效生存周期内可以使用。
数据质量评估的实施流程参考如下：
（1）构建质量管理组织。
通常由数据分析师、IT 专家、业务 代表以及管理层组成，主要负责制定数据质量标准、监督数据质 量评估流程的实施，并确保所有相关方都遵循既定的流程。
（2）建立数据规范。
数据规范是一套明确的规则和标准，用 于指导数据的收集、存储和使用。数据规范应涵盖数据的定义、 类型、格式、结构、处理流程以及安全要求等方面。
（3）确定评价指标。
基于数据质量评价指标体系，根据实际的业务需求和数据使用场景，确定数据质量的评价指标与规则， 以确保它们能够全面反映数据的质量状况。
（4）实施质量评价。
根据数据质量的评价指标，对数据进行 质量评估，包括异常检测、数据交叉验证等方式，最终形成相应 的数据质量评估报告。 数字化转型网（www.szhzxw.cn）
（5）数据质量提升。
根据数据质量评估的结果，制定并实施 数据质量提升计划，包括改进数据收集流程、更新数据存储系统、 培训数据录入人员、优化数据处理算法等措施。数据质量提升是 一个持续的过程，需要定期评估和调整以应对不断变化的业务需 求数据环境。
（6）数据交付使用。
数据交付使用是数据质量评估流程的最 终目标，在确保数据满足既定的质量标准后，数据才能被交付给 最终用户使用。在数据交付使用前，还应确保用户了解数据的使 用方法和限制， 以充分发挥数据的价值。
数据质量的评估最终目的是达到数据使用者对数据使用的要求。
Q6：数据价值如何评估？
数据价值评估方法通常包括收益法、成本法和市场法三种基 本方法及其衍生方法。评估人员应当根据评估目的、评估对象、 价值类型、资料收集等情况，选择评估方法。数据价值评估方法 及步骤参考如下：
（1）收益法。
通过预测数据资产未来能够产生的收益，并将 其折现到评估基准日，以此作为数据资产的价值。评估步骤包括： 数字化转型网（www.szhzxw.cn）
①收益预测，分析数据资产的历史应用情况和未来应用前景，合理预测其未来能够带来的收益。包括直接收益预测、分成收益预测、超额收益预测和增量收益预测。
②风险分析，考虑数据资产 应用过程中可能遇到的风险，如管理风险、流通风险、数据安全 风险等，并据此估算适当的折现率。
③收益期限确定，根据数据 资产的法律有效期限、合同有效期、更新时间、时效性等因素， 合理确定收益期限。
④折现计算，将未来收益按照折现率折现到评估基准日，得出数据资产的现值。
收益法评估的基本计算模型为：
式中：P——评估值，F_t——数据资产未来第t个收益期的收益额，n——剩余收益期，t——未来第t年，i——折现率。
（2）成本法。
基于数据资产的重置成本，即重新创建类似数 据资产所需的成本，来评估现有数据资产的价值。评估步骤包括：
①成本识别，确定数据资产从产生到评估基准日所发生的全部成 本，包括前期费用、直接成本、 间接成本等。 数字化转型网（www.szhzxw.cn）
②成本核算，对识 别出的成本进行核算，确保所有成本因素都被合理考虑。
③贬值 调整，根据数据资产的使用情况和预期剩余经济寿命，确定贬值率，并进行贬值调整。④价值计算，从重置成本中扣除贬值，得出数据资产的评估价值。
对于成本法，数据资产的价值由该资产的重置成本扣减各项贬值确定。其基本计算公式为：P = TC×（1－δ）
其中：P——评估值，TC——重置成本，δ——贬值率。
或者：P = TC×(1+R)×U
其中：P——评估值，TC——数据资产总成本，R——数据资产成本投资回报率，U——数据效用。
U=α*β(1+l)(1-r)
其中：α—数据质量系数；β—数据流通系数；l—数据垄断系数；r—数据价值实现风险系数。
（3）市场法。
通过比较市场上类似数据资产的交易价格，调 整特定因素后得出待评估数据资产的价值。评估步骤包括：
①市场数据收集，搜集市场上类似数据资产的交易信息，包括交易价格、交易条件等。
②可比案例选择，选取与待评估数据资产相似 的可比案例，确保可比性。
③调整系数确定，根据质量、供求、 期 日、容量等因素确定调整系数。
④价值估算，将参照数据集的价值乘以相应的调整系数，得出待评估数据资产的价值
首先，将待评估数据资产分解成n个待评估数据集；其次，每个待评估数据集选取参照数据集进行对比调整；最后，将n个调整后结果加总得出待评估数据资产的价值。
市场法评估模型如下：
式中，P——待评估数据资产价值，
n——待评估数据资产所分解成的数据集的个数，Qi——参照数据集的价值，
Xi1——质量调整系数，Xi2——供求调整系数，Xi3——期日调整系数，Xi4——容量调整系数，Xi5——其他调整系数；
所使用的模型应满足各影响因素与数据资产价值存在线性关系。若不存在线性关系，则应根据实际情况对模型进行适当调整。 数字化转型网（www.szhzxw.cn）
在数据资产价值评估中，成本法、市场法和收益法是三种常用的基本方法，它们各有优缺点和适用场景。根据搜索结果，并没有明确指出哪一种方法是使用最多的，但每种方法都有其特定的应用场景和优势。
成本法：易于理解和实施，尤其在数据资产的直接成本容易量化的情况下。它为数据资产的初始价值提供基本参考，但可能无法准确反映数据资产的市场价值，尤其是当数据资产具有潜在的高市场价值时，它忽略了数据资产的潜在收益和未来价值。
市场法：适用于在市场上有明确交易记录和可比性的数据资产，如公开交易的数据集或数据服务。市场法依赖于市场上可比较交易的可用性和信息的透明度，是一种实际和市场导向的方法。
收益法：适用于能够直接或间接产生经济收益的数据资产。收益法较真实、准确地反映了数据资产本金化的价值，更容易被交易各方所接受。然而，预测未来收益存在不确定性，可能导致评估结果的偏差，对于没有明确收益模式的数据资产，收益法可能难以应用。
在实际评估过程中，为了提高评估的准确性和全面性，企业可能会结合这三种方法进行综合评估，同时引入专家知识和行业经验，对数据资产的价值进行更全面的评估 。此外，随着数据资产市场的不断发展和成熟，各种评估方法的应用频率和偏好可能会随之变化。
Q7：数据合规登记如何做？
数据资产合规登记是在数据资产权属信息确认的基础上，对数据资产的权利进行登记的行为。目前数据登记的形式主要有三种： 数字化转型网（www.szhzxw.cn）
●数据交易平台颁发的数据资产登记证书
●数据产品登记证书
●数据知识产权平台颁发的数据知识产权登记证书
以广州数据交易所登记为例， 数据资产登记的流程为：
①登记主体应当通过数据资产登记平台 填写相应的登记申请表，并向登记机构提交有关材料；
②由数据 交易所进行初审工作， 出具初审意见；
③针对涉及特殊类型数据 的产品，初审通过后， 由数据交易所提请广东数据资产登记合规 委员会进行复审，并出具合规性审核意见；
④复审通过的，通过 数据资产登记平台向社会公示；
⑤公示期满无异议的， 由广东省 政务服务和数据管理局颁发《数据资产登记凭证》。
Q8：数据产品一定是数据资产？
数据资产：则是指企业拥有或控制的、能够为企业带来未来经济利益的数据资源。数据资产强调的是数据的经济价值和对企业财务的贡献。
数据产品：指的是以数据为核心价值，通过加工、分析、整合等方式形成的，可以为用户或客户提供信息、知识或决策支持的数字化产品。数据产品可以是软件、服务、应用或其他形式，它们通常具有特定的功能和用途。 数字化转型网（www.szhzxw.cn）
因此，数据产品不一定是数据资产。虽然数据产品有潜力成为数据资产，但只有当它们能够为企业带来实际或预期的经济利益时，才能被视为数据资产。数据资产是更为严格的条件下的数据产品。
Q9：数据资产入表的条件？
数据资产入表指的是企业将数据资源按照会计准则确认为资产并反映在财务报表上的过程。根据《企业数据资源相关会计处理暂行规定》，数据资产入表需要满足以下条件：
1. 合法拥有或控制：企业必须合法拥有或控制数据资源。
2. 预期经济利益：数据资源预期会给企业带来经济利益。
3. 成本或价值可靠计量：数据资源的成本或价值能够被可靠地计量。
4. 符合无形资产或存货的定义和确认条件：数据资源需要符合《企业会计准则第6号——无形资产》或《企业会计准则第1号——存货》的定义和确认条件。
Q10：数据资产入表后什么情况？
数据资产入表后，可以被作为无形资产或者存货，那么相关条件是：
无形资产定义：企业拥有或者控制的没有实物形态的可辨认非货币性资产。资产满足下列条件之一的，符合无形资产定义中的可辨认性标准：
（1）能够从企业中分离或者划分出来，并能单独或者与相关合同、资产或负债一起，用于出售、转移、授予许可、租赁或者交换；
（2）源自合同性权利或其他法定权利，无论这些权利是否可以从企业或其他权利和义务中转移或者分离。
存货定义：企业在日常活动中持有以备出售的产成品或商品、处在生产过程中的在产品、在生产过程或提供劳务过程中耗用的材料和物料等。可以确认为存货的数据资源需要同时满足下列条件：
（1）与该存货有关的经济利益很可能流入企业； 数字化转型网（www.szhzxw.cn）
（2）该存货的成本能够可靠地计量
无形资产或者存货主要区别在于它们的持有目的和业务模式：
无形资产的数据资源是企业使用来提供服务或用于内部生产过程中的，它们通常不直接出售，而是通过提供服务或产品间接产生经济利益。
存货的数据资源则是企业为了出售而持有的，它们是企业日常经营活动中的一部分，目的是通过出售实现经济利益。
以上是数据资产入表的核心十问，希望对于了解数据资源成为资产并入表有一个基本的了解。
四、数字化转型网数据要素X专题包含哪些内容？
数字化转型网数据要素X专题将关注数据治理、数据质量管理、数据架构、主数据管理、数据仓库、元数据管理、数据备份、数据挖掘、数据分析、数据安全、大数据、数据合规、等数据相关全产业链相关环节。
数字化转型网数据要素X专题包含： 数字化转型网（www.szhzxw.cn）
1、数据相关外脑支持：100+数据相关专家、100+数据实践者、1000+相关资料
2、数据要素X研习社：与全球数据相关专家、实践者共同探讨相关问题，推动产业发展！
3、国际认证培训：目前已引进DAMA国际认证CDMP，其他国内外认证也在逐步引进中
4、典型案例参考：与数字化转型网数据要素X研习社社员一起学习典型案例，共探企业数据落地应用
翻译：
Data assets into the table core ten questions
Abstract:
In the digital age, data has become a key factor of production and has a significant impact on business operations and economic growth. Data asset entry, that is, the formal inclusion of data resources as part of enterprise assets in financial statements, is an important milestone in the digital transformation of enterprises. This article will delve into the core issues of data asset entry to provide a basic understanding.
Q1: What does data facturing mean?
Data factualization is a verb. The so-called “factualization” refers to making its object a factor of production. In other words, data factionalization refers to turning data into a new type of production factor, making it meet the judgment conditions of production factors, and becoming an important input to drive the production and operation activities of enterprises.
Q2: Why can data be used as a factor of production?
Data as a factor of production, mainly because it plays an increasingly important role in the modern economy. Here are some reasons why data can be considered a factor of production:
1. Value creation: Data can be used to create new products, services and business models that provide value to businesses and individuals. 数字化转型网（www.szhzxw.cn）
2. Decision support: Data provides insights that help businesses and individuals make more informed decisions, optimize resource allocation and operational efficiency.
3. Efficiency improvement: Through data analysis, bottlenecks and inefficient links in the process can be found, thereby improving production efficiency and reducing costs.
4. Personalized services: Data enables enterprises to provide customized products and services to meet the individual needs of consumers.
5. Market insights: Data can help companies better understand market trends and consumer behavior, so as to develop effective market strategies.
6. Risk management: Data can be used to predict and assess risks to help businesses and individuals make safer investment and business decisions.
7. Innovation-driven: Data is the source of innovation, and new business opportunities and innovation points can be found through data analysis. 数字化转型网（www.szhzxw.cn）
8. Competitive Advantage: Businesses that own and use data effectively can gain a competitive advantage in the marketplace because they are able to adapt to change faster and seize opportunities.
9. Resource optimization: Data can help companies use resources more efficiently, such as through predictive analytics to optimize inventory management.
The value of data as a factor of production lies in its ability to be translated into knowledge and action that drives economic growth and social progress. With the development of technology, the collection, storage, processing and analysis of data continue to improve, and the role of data in the economy and society is becoming more and more important.
Q3: What data resources can be entered into the table?
Personal data, corporate data and public data can all be developed into data products and tabulated as data assets, but they are subject to certain conditions and requirements.
1. Personal Data: Personal data usually contains sensitive information and needs to be processed and used within a framework of compliance. If personal data is anonymized and companies have legitimate rights to use it, they can be integrated into data products. However, personal data needs to ensure compliance with the requirements of relevant laws and regulations such as the Personal Information Protection Law of the People’s Republic of China, obtaining the explicit consent of the data subject, and ensuring data security and privacy protection.
2. Enterprise data: data generated or legally obtained by the enterprise itself, such as transaction records, user behavior data, etc., can be entered as data assets if it can bring economic benefits to the enterprise and the cost or value can be measured reliably. Once productized, enterprise data can be tabulated as an intangible asset or inventory, depending on its intended use and business model.
Public data: Public data refers to data released by governments or other public bodies and is generally publicly available. Enterprises can use public data to develop data products, and if these products can bring economic benefits to the enterprise and meet the asset recognition conditions of accounting standards, they can also be listed as data assets.
Whether data resources can be listed as data assets depends on whether they meet the following conditions: 数字化转型网（www.szhzxw.cn）
1) The enterprise must legally own or control data resources.
2) Data resources are expected to bring economic benefits to enterprises.
3) The cost or value of data resources can be reliably measured.
After the data asset is entered into the statement, the enterprise needs to properly present and disclose it in the financial statements, including the initial measurement, subsequent measurement, disposal and retirement of the data asset. At the same time, enterprises also need to disclose detailed information in accordance with the Interim Provisions on Accounting Treatment of Enterprise Data Resources, such as the original book value, cumulative amortization, impairment reserve and book value of data resources.
Q4: How can data be validated?
Data confirmation is mainly divided into four steps:
(1) Confirmation of the interest subject.
The subject includes the data source and the data processor. Data source refers to the person or organization that provides or creates data in the process of data generation and enjoys the right to obtain or copy and transfer the data generated by it. Data processor refers to the organization or individual who independently decides the purpose and method of processing in data processing activities such as data collection, storage, use, processing and transmission. The rights and interests of the subject generally include the right to hold data resources, the right to use data processing, and the right to operate data products. 数字化转型网（www.szhzxw.cn）
(2) Data source compliance review.
Ensure that the data source of the registration subject is legal and compliant, and has the right to register data assets. The “Data Article 20” distinguishes the authorized operation mechanism of public data, enterprise data and personal data, and executes the corresponding compliance audit standards according to different types of data involved in data assets and according to different data sources.
(3) Identity authentication of the registration subject.
Personal real name authentication can adopt face recognition, SMS verification, bank card verification and other ways; Institutions and legal persons can verify the identity of legal persons, remittances to public bank accounts, etc.
(4) Data asset ownership certificate.
Confirm and document the data source, the rights and interests of the data source, the use scenario, applicable conditions and use details of the data, and the prohibited scope, as well as the rights and interests of the data processor, the data processing authorization agreement, the certification materials corresponding to substantive processing and innovative labor, so as to lock the evidence and provide legal reference and endorsement. 数字化转型网（www.szhzxw.cn）
Q5: How is data quality assessed?
Data quality evaluation refers to the process of systematically checking and evaluating the quality of data, which usually focuses on normalization, completeness, accuracy, consistency, timeliness and accessibility. Through quality assessment, problems and deficiencies in the data can be identified, so that appropriate measures can be taken to improve data quality and ensure that the data is suitable for its intended use. According to the national standard “Information Technology Data Quality Evaluation Index”, the data quality evaluation index system is analyzed and defined according to the following six dimensions:
① Normative: data conforms to data standards, data models, metadata, business rules, authoritative reference data or security specifications;
Integrity: including the integrity of data elements and the integrity of data records;
(3) Accuracy: The evaluation dimensions of data accuracy include the correctness of data content, compliance of data format, data repetition rate, data uniqueness and dirty data occurrence rate;
Consistency: including the same data consistency and associated data consistency;
(5) Timeliness: including time-based correctness, timeliness and timing based on time points;
Accessibility: Data can be obtained when needed and can be used within the set effective lifetime.
The implementation process of data quality assessment is as follows:
(1) Establish a quality management organization. 数字化转型网（www.szhzxw.cn）
Typically composed of data analysts, IT specialists, business representatives, and management, they are responsible for setting data quality standards, overseeing the implementation of data quality assessment processes, and ensuring that established processes are followed by all interested parties.
(2) Establish data specifications.
A data specification is a set of clear rules and standards that guide the collection, storage and use of data. Data specifications should cover the definition, type, format, structure, processing flow and security requirements of data.
(3) Determine evaluation indicators.
Based on the data quality evaluation index system, the data quality evaluation indexes and rules are determined according to the actual business needs and data usage scenarios to ensure that they can fully reflect the data quality status. 数字化转型网（www.szhzxw.cn）
(4) Implementation of quality evaluation.
According to the evaluation index of data quality, the data quality evaluation is carried out, including anomaly detection, data cross-validation, etc., and the corresponding data quality evaluation report is finally formed.
(5) Data quality improvement.
Based on the results of data quality assessment, develop and implement data quality improvement plans, including improving data collection process, updating data storage system, training data entry personnel, optimizing data processing algorithms, etc. Data quality improvement is an ongoing process that requires regular assessment and adjustment to respond to the changing data environment of business requirements.
(6) Data delivery.
Data delivery is the ultimate goal of the data quality assessment process, ensuring that the data meets the established quality standards before it can be delivered to the end user. Before the data is delivered, users should also ensure that they understand the use methods and limitations of the data to maximize the value of the data. 数字化转型网（www.szhzxw.cn）
The ultimate goal of data quality evaluation is to meet the requirements of data users for data use.
Q6: How to evaluate the value of data?
Data value evaluation methods usually include income method, cost method and market method three basic methods and their derivative methods. The evaluator shall choose the evaluation method according to the evaluation purpose, evaluation object, value type, data collection, etc. Data value evaluation methods and steps refer to the following:
(1) Income method.
The value of the data asset is calculated by predicting the future income that the data asset will generate and discounting it to the base date of evaluation. The assessment steps include:
(1) Income forecast: analyze the historical application and future application prospects of data assets, and reasonably predict the future income it can bring. It includes direct income forecast, share income forecast, excess income forecast and incremental income forecast.
(2) Risk analysis, considering the risks that may be encountered in the application of data assets, such as management risks, circulation risks, data security risks, etc., and estimating the appropriate discount rate accordingly. 数字化转型网（www.szhzxw.cn）
(3) The income period shall be determined reasonably according to the legal validity period, contract validity period, renewal time, timeliness and other factors of the data asset.
④ Discount calculation, discount the future income according to the discount rate to the base date of evaluation, to obtain the present value of the data asset.
The basic calculation model of income method evaluation is as follows:
Where: P — evaluation value, F_t — income of the data asset in the t future income period, n — remaining income period, t — future year t, i — discount rate.
(2) Cost method.
Evaluate the value of an existing data asset based on its replacement cost, i.e. the cost of recreating a similar data asset. The assessment steps include:
① Cost identification: Determine the total cost of the data asset from the generation to the base date of evaluation, including upfront costs, direct costs, indirect costs, etc.
(2) Cost accounting, accounting of identified costs to ensure that all cost factors are reasonably considered. 数字化转型网（www.szhzxw.cn）
(3) Depreciation adjustment, according to the use of data assets and the expected remaining economic life, determine the depreciation rate, and depreciation adjustment. ④ Value calculation, deducting depreciation from replacement cost to obtain the assessed value of data asset.
For the cost method, the value of a data asset is determined by the replacement cost of the asset less depreciation. Its basic calculation formula is: P = TC× (1-δ)
Where: P – estimated value, TC – replacement cost, δ – depreciation rate.
Or: P = TC x (1+R) x U
Where: P – evaluation value, TC – total cost of data asset, R – return on investment of data asset cost, U – data utility.
U = alpha beta (1 + l) * (1 – r)
Where: α – data quality coefficient; β – data flow coefficient; l – Data monopoly coefficient; r – Data value realization risk factor.
(3) Market law.
By comparing the trading prices of similar data assets in the market, the value of the data assets to be evaluated is obtained after adjusting certain factors. The assessment steps include:
(1) Market data collection, collecting trading information of similar data assets in the market, including trading prices, trading conditions, etc.
Select comparable cases that are similar to the data assets to be evaluated to ensure comparability.
The adjustment coefficient is determined according to the quality, supply and demand, date, capacity and other factors. 数字化转型网（www.szhzxw.cn）
(4) Value estimation: Multiply the value of the reference data set by the corresponding adjustment coefficient to obtain the value of the data asset to be evaluated
Firstly, the data assets to be evaluated are decomposed into n data sets to be evaluated. Secondly, each data set to be evaluated is selected for comparison and adjustment with reference data set. Finally, the value of the data asset to be evaluated is obtained by adding n adjusted results.
The market method evaluation model is as follows:
Where, P — the value of the data asset to be evaluated,
n — the number of datasets decomposed into the data asset to be evaluated, Qi — the value of the data set,
Xi1 – Quality adjustment coefficient, Xi2 – supply and demand adjustment coefficient, Xi3 – day adjustment coefficient, Xi4 – Capacity adjustment coefficient, Xi5 – other adjustment coefficient;
The model used should satisfy the linear relationship between the influencing factors and the value of the data asset. If there is no linear relationship, the model should be adjusted appropriately according to the actual situation. 数字化转型网（www.szhzxw.cn）
Cost method, market method and income method are three basic methods in data asset value evaluation, each of which has its own advantages and disadvantages and applicable scenarios. According to the search results, it is not clear which method is the most used, but each method has its own specific application scenarios and advantages.
Cost method: Easy to understand and implement, especially when the direct cost of the data asset is easily quantified. It provides a basic reference for the initial value of the data asset, but may not accurately reflect the market value of the data asset, especially when the data asset has a potentially high market value, it ignores the potential income and future value of the data asset.
Market approach: applies to data assets with a clear trading record and comparability in the market, such as publicly traded datasets or data services. The market approach relies on the availability of comparable transactions and transparency of information in the market and is a practical and market-oriented approach. 数字化转型网（www.szhzxw.cn）
Income method: applies to data assets that can directly or indirectly generate economic benefits. The income method reflects the principal value of the data asset more truly and accurately, and is more easily accepted by the transaction parties. However, there are uncertainties in predicting future returns, which may lead to bias in the evaluation results, and income method may be difficult to apply for data assets without a clear return model.
In the actual evaluation process, in order to improve the accuracy and comprehensiveness of the evaluation, enterprises may combine these three methods for comprehensive evaluation, while introducing expert knowledge and industry experience to conduct a more comprehensive evaluation of the value of data assets. In addition, as the data asset market continues to evolve and mature, the frequency of application and preferences of various evaluation methods may change accordingly.
Q7: How to do data compliance registration?
Data asset compliance registration is an act of registering the right of data asset on the basis of confirming the ownership information of data asset. At present, there are three main forms of data registration: 数字化转型网（www.szhzxw.cn）
● Data asset registration certificate issued by the data trading platform
● Data product registration certificate
● Data intellectual property registration certificate issued by data intellectual property platform
Taking Guangzhou Data Exchange registration as an example, the process of data asset registration is as follows:
① The registration entity shall fill in the corresponding registration application form through the data asset registration platform and submit the relevant materials to the registration authority;
② The data exchange shall conduct the preliminary review and issue the preliminary review opinions;
(3) For products involving special types of data, after the initial examination is passed, the data exchange shall request the Guangdong Data Asset Registration Compliance Committee to review and issue compliance review opinions; 数字化转型网（www.szhzxw.cn）
④ If the review is passed, it shall be publicized to the public through the data asset registration platform;
⑤ If there is no objection after the expiration of the publicity period, the “Data Asset Registration Certificate” shall be issued by the Guangdong Provincial Government Services and Data Administration Bureau.
Q8: Data products must be data assets?
Data assets: Data resources owned or controlled by the enterprise that can bring future economic benefits to the enterprise. Data assets emphasize the economic value of data and its contribution to business finances. 数字化转型网（www.szhzxw.cn）
Data products: refers to the digital products that take data as the core value and are formed through processing, analysis, integration, etc., and can provide users or customers with information, knowledge or decision support. Data products can be software, services, applications, or other forms, and they usually have specific functions and purposes.
Therefore, data products are not necessarily data assets. While data products have the potential to be data assets, they can only be considered data assets if they deliver actual or expected economic benefits to the business. Data assets are data products under more stringent conditions.
Q9: What are the conditions for data assets to enter the table?
The statement of data assets refers to the process that an enterprise recognizes data resources as assets in accordance with accounting standards and reflects them in financial statements. According to the Interim Provisions on Accounting Treatment Related to Enterprise Data Resources, data assets must meet the following conditions when entering the statement:
1. Legal ownership or control: The enterprise must legally own or control data resources.
2. Expected economic benefits: Data resources are expected to bring economic benefits to enterprises.
3. Reliable measurement of cost or value: The cost or value of data resources can be reliably measured.
4. Meet the definition and recognition conditions of intangible assets or inventories: Data resources need to meet the definition and recognition conditions of Accounting Standards for Enterprises No. 6 – Intangible Assets or Accounting Standards for Enterprises No. 1 – Inventories.
Q10: What happens after the data asset is entered into the table?
After the data asset is entered into the statement, it can be regarded as intangible assets or inventories, then the relevant conditions are:
Definition of intangible assets: identifiable non-monetary assets without physical form owned or controlled by an enterprise. Assets that meet one of the following conditions meet the identifiable criteria in the definition of intangible assets: 数字化转型网（www.szhzxw.cn）
(1) can be separated or divided from the enterprise and can be sold, transferred, licensed, leased or exchanged, either alone or in conjunction with related contracts, assets or liabilities;
(2) arising from contractual rights or other statutory rights, whether or not these rights can be transferred or separated from business or other rights and obligations.
Inventory definition: the finished products or commodities held by enterprises in daily activities for sale, the products in the production process, the materials and materials consumed in the production process or the provision of services. Data resources that can be recognized as inventories must meet the following conditions:
(1) The economic benefits associated with the inventory are likely to flow to the enterprise;
(2) The cost of the inventory can be measured reliably
Intangible assets or inventories differ primarily in their purpose of ownership and business model:
Data resources of intangible assets are used by enterprises to provide services or for internal production processes, and they are usually not sold directly, but indirectly generate economic benefits through the provision of services or products. 数字化转型网（www.szhzxw.cn）
Inventory data resources are held by enterprises for sale, they are part of the daily business activities of enterprises, and the purpose is to realize economic benefits through sale.
The above are the core ten questions of data asset entry, and I hope to have a basic understanding of how to understand data resources as assets incorporation tables.
What does the Digital Transformation Network Data Elements X project contain?
The Digital Transformation Network Data Elements X topic will focus on data governance, data quality management, data architecture, master data management, data warehouse, metadata management, data backup, data mining, data analysis, data security, big data, data compliance, and other relevant links of the data-related whole industry chain.
Digital Transformation Network Data Elements X topics include:
1. Data related external brain support: 100+ data related experts, 100+ data practitioners, 1000+ related materials
2. Data Elements X Workshop: Discuss related issues with global data-related experts and practitioners to promote industrial development! 数字化转型网（www.szhzxw.cn）
3, international certification training: At present, DAMA international certification CDMP has been introduced, and other domestic and foreign certifications are gradually introduced
4. Reference of typical cases: Learn typical cases together with members of Digital Transformation Network Data Elements X Study Club to explore the application of enterprise data
本文由数字化转型网（www.szhzxw.cn）转载而成，来源于ruby的数据漫谈 ，作者ruby；编辑/翻译：数字化转型网宁檬树。
