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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.
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.
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:
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.
(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.
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.
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.
(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.
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.
(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.
(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.
(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.
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.
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:
● 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;
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.
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:
(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.
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.
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