Zhifengyun:On January 11, 2024, the Ministry of Finance issued the Guiding Opinions on Strengthening the Management of Data Assets, which stipulates the management of data assets in accordance with laws and regulations, the strengthening of the management of the use of data assets, and the steady promotion of the development and utilization of data assets.
Author |Zhiben consulted Hu Jing, State-owned Enterprise Industry Research Institute.
Editor-in-charge|billionsEdit|Ling.
On the same day, Hunan Province completed the first unsecured financing of data assets, and a technology company obtained a credit line of 5 million yuan from the bank. On January 26, the Beijing Data Asset Circulation Innovation Center landed in Shijingshan. Data assets, an emerging asset type, are becoming increasingly active in economic operations.
In such a digital information age, whether it is wind information, which has become a necessary for market investment due to its focus on a huge investment project library, or CNKI, which is increasingly moving towards more users due to the gathering of academic literature, has embarked on the road of gathering sand into a tower of data assets.
Data assets have gradually become an important strategic resource for enterprises, and what data assets are, how to manage them, how to help them improve business performance, and how to "monetize" have become one of the important topics that must be studied.
The following will combine the previous theoretical research on data assets and the current practical exploration of enterprises to talk to you about these issues.
Data assets are defined in the Data Asset Management Practice of the China Academy of Information and Communications Technology (CAICT) as: "Data legally owned or controlled by organizations (** institutions, enterprises and institutions, etc.), recorded electronically or otherwise, such as text, images, voices, web pages, databases, sensor signals and other structured or unstructured data, which can be measured or traded, and can directly or indirectly bring economic and social benefits." ”
From this definition, we can conclude that there are two key points that data must have in order for data to become a data asset:
One is"Possession" means that the ownership of the data assets has been obtained, and "control" means that the data assets can be possessed and used to serve the enterprise, which is also a prerequisite for data assetization.
Figure: The basic idea of confirming the ownership of data assets.
Data**: Collation of public information.
The second isThese data need to be able to generate an inflow of economic benefits. Data assetization requires that data can realize the value creation process, so that economic benefits flow into the enterprise.
Figure: Data assetization process.
The third isFrom the perspective of financial statement, only data resources owned or controlled by the accounting entity and can bring economic benefits to the accounting entity can be recognized as data assets.
After clarifying the definition of data assets, let's take a look at the data assets of the enterprise.
Combined with the situation of the enterprise, the data assets can be sorted out from five modules: internal data assets, external data assets, operational data assets, customer data assets and intellectual property data assets, and the accumulation of data assets can be carried out, as follows:
Data asset lifecycle management is a long-term task for enterprises. It is the only way to realize data assetization by doing a good job in the whole life cycle management of data assets and forming a closed-loop data ecology of the whole process from data planning, creation, use, and archiving.
Drawing on the practice of China Southern Power Grid Corporation, it is believed that the management functions of the three modules of data governance, data operation and data circulation can be mapped with these eight links from the eight business links of data planning, creation, transmission, storage, processing, publishing, use and archiving, and the management functions of the three modules of data governance, data operation and data circulation can be mapped to form a matrix management model for data assets.
Figure: Mapping of the whole life cycle and management functions of data assets of China Southern Power Grid.
Data**: China Southern Power Grid Data Asset Management System***
The management functions of the data asset management system of China Southern Power Grid include six modules: data strategy, data governance, data operation, data circulation, organizational support, and technical support, with a total of 36 management functions. At the same time, the company has subdivided and defined both modules and functions:
Table: Contents of some modules of China Southern Power Grid Data Asset Management.
It is believed that there are two paths to the "monetization" of data assets, "external" and "internal".
Figure: Application of data assets.
Data**: Collation of public information.
Let's first take a look at "external realization", that is, to let the data assets of enterprises be commercialized externally, such as using data assets for financing, carrying out direct transactions of data assets, etc., to achieve economic value and benefits in the upstream and downstream of the industry, financial institutions and even the world.
In order to "monetize externally", it is necessary to price assets first, but the valuation and measurement of data assets, as an important basis for determining the amount of data assets recorded, is also the most important foundation for realizing the realization of external market monetization, which still needs to be further consolidated.
Although there are already guiding policies in relevant aspects, such as the "Asset Valuation Expert Guidelines No. 9 - Data Asset Valuation" and "Guiding Opinions on Data Asset Valuation", the current data asset value evaluation standard system has not yet been established, and there is still a lack of system.
1. Scientific and effective evaluation methods to accurately measure data assets.
The main difficulties include: on the one hand, the immature development of the data element market, the lack of rich data application scenarios, and the unclear path to value realization lead to great uncertainty in the value of data assets, and it is difficult to use traditional asset valuation standards and methods for evaluation;
On the other hand, from the perspective of data transactions, due to the lack of reference transaction cases, and the differences between the two parties in terms of their own resources, capabilities, business and market, there are different value expectations for the same data asset.
In this situation, for the valuation and measurement of data assets at this stage, enterprises need to establish a flexible, dynamic, both current and long-term valuation framework, and use different methods to evaluate the value of data assets under different purposes from different dimensions.
At present, the practice of enterprises in this area is still mainly exploration.
Taking China Southern Power Grid as an example, in view of the fact that there is no authoritative and unified method for the pricing of data assets, China Southern Power Grid adopts the strategy of "market-oriented, taking into account cost and efficiency, phased pricing, and personalized pricing".
As a data provider, the cost method will be more in line with the characteristics of the enterprise, but the market method and the income method consider the income of assets, so at this stage, China Southern Power Grid adopts a combination of market method and cost method to price digital assets.
At the same time, China Southern Power Grid also considers other factors in the transaction, and gives a certain degree of discount on the basis of digital assets according to the importance of the customer, so as to facilitate the completion and re-trading of the transaction.
Table: List of data asset value of China Southern Power Grid.
Table: China Southern Power Grid Data Asset Pricing Methodology.
In addition to determining the pricing of data assets, China Southern Power Grid has also formulated a first-class system to provide guidance for the classification and pricing of data assets. At the same time, it has also established a review and management mechanism for data assets to strengthen the management of data assets.
Once you've determined the value of your organization's data assets, the next step is to find ways to commercialize them externally.
Due to the sensitivity of data and other reasons, large state-owned enterprises are more cautious at present, and in terms of market practice, more small and medium-sized enterprises use data assets to carry out credit financing.
Taking Jiangsu Province as an example, in 2023, a number of projects will be implemented through credit financing of intellectual property data assets.
Figure: 2023 index property rights data credit financing project in Jiangsu Province.
However, foreign companies mainly realize data assets at the time of asset acquisition to achieve value appreciation, some of which are as follows:
Let's take a look at the internal "monetization" of data assets, that is, the use of data assets to achieve value creation in the internal operation and management of enterprises.
For enterprises, it is the most direct and convenient way to realize the economic value of data assets at this stage by using these data assets to analyze internal efficiency, improve operational efficiency, and provide more efficient data support for decision-makers, so as to improve production and operation efficiency.
Specifically, enterprises can gradually accumulate data assets to form financial chains and business chains through the establishment of the company's "index chain system", which can be used to serve their own business decisions and business processes, so as to improve the company's profitability. (For the construction of the index chain, please refer to the article "How to Accurately Score in the Year-end Assessment of State-owned Enterprises?") Please use the "indicator chain" system).
In this regard, energy and power companies have a relatively rich practice.
For example, State Grid Ningxia Electric Power Company carried out big data monitoring for the "whole process of the project" and gradually formed operational data assets.Figure: Ningxia Electric Power Operation Performance Monitoring System.
Guangdong Power Grid Co., Ltd. relies on operational data assets to build a cross-business application scenario system, and uses big data analysis to conduct joint monitoring and analysis of cross-business areas such as reliability and customer complaints, accurately locate problems, and effectively support the high-quality development of the power grid.
Combined with the current policies and enterprise practices, it can be seen that although the top-level design of data assets in China is constantly improving, due to the particularity of data assets and the fact that the data element market is still in the early stage of development, the promotion of the "table" standard of data assets still faces a series of obstacles.
In addition, due to the data security problems caused by the particularity of the industry in which state-owned enterprises are located, there are few large enterprises in the actual data asset transactions, and mainly small and medium-sized enterprises carry out credit financing of intellectual property data assets.
To sum up, state-owned enterprises can work on data assets from the following aspects at this stage:
First, starting from management, we should consider focusing on data standards, data quality, and data security to strengthen management, continuously accumulate data, effectively improve the stock and value of data resources, and provide guarantee for the company's data assetization.
The second is to further strengthen the ability to analyze and mine data assets, starting from the operation and management of data assets, and promote the quality and efficiency of enterprise operation and management through the analysis of different scenarios and models, so as to realize the economic benefits of data assets internally;
The third is to start by following up policy changes and specific practices, continuously optimize the construction of the management system of data assets, try to build a measurement and valuation model suitable for its own data assets, and explore the market-oriented application of data assets.
Produced by Mixed Reform Fengyunxin**.
Unauthorized prohibitions**.
Welcome to share to Moments.
Submission, **Business Cooperation: wyf35729
Beijing Zhiben Entrepreneurship Management Consulting has the most core expert team, the most original management technology, and hundreds of large state-owned enterprise consulting service experience.