The national team mines the data gold mine.

As the end of 2025 approached, a series of data - related meetings were held intensively within the PipeChina National Oil and Gas Pipeline Network Group Co., Ltd. (hereinafter referred to as "PipeChina"). The focus of these meetings shifted from traditional pipeline pressure and gas transmission volume to how to make more secure and efficient use of over 10 billion core data accumulated by PipeChina in its five - year operation, and even "trade them out".

These meetings are paving the way for a possible data cooperation and development agreement to be signed soon. The national "unified oil and gas network" operated by PipeChina is not only a physical energy artery but also a "unified data network" that gathers a vast amount of information on production, transportation, trading, and equipment status. The current task is to figure out how to achieve "soft connectivity" of the data in these "hard facilities" to break market barriers and activate the industrial chain.

The sense of urgency for action stems from the implementation of a top - level design. On November 25, 2025, the National Data Administration and the State - owned Assets Supervision and Administration Commission of the State Council jointly launched the "Pilot Program for the Development and Utilization of State - owned Enterprise Data Resources". The pilot program designated 12 central enterprises, including PipeChina, China Mobile, Sinomach, China Automotive Technology and Research Center, and China Southern Power Grid, as the first batch of leading pilot units. They are required to form "cooperation camps" with private enterprises and research institutions to jointly explore the path for data to transform from resources to assets and then to capital.

According to Chen Ronghui, the deputy director of the National Data Administration, this is not an ordinary information technology upgrade but an "organic combination" of the "Action to Improve the Data Efficiency of State - owned Enterprises" and the new round of in - depth reform and improvement of state - owned enterprises, aiming to drive a "profound transformation" of corporate governance and business models.

A person from the SASAC said that the significance of the pilot program lies in the fact that it is the first systematic attempt to push the "national team" that occupies key areas of the national economy and possesses a vast amount of high - value data into the deep waters of the data factor market. The most challenging task is to address three core bottlenecks in the process of data factorization: difficult data rights confirmation, difficult data pricing, and difficult data circulation.

With the start of the pilot program, a "new exploration" driven by data has been launched simultaneously in multiple key industries such as energy and power, transportation and logistics, and equipment manufacturing.

Zhao Lingxing, a person from a central enterprise in the transportation infrastructure industry, believes that for state - owned enterprises, this means a transformation from "data managers" to "data operators". For the industry, it means that the collaborative efficiency of the industrial chain is expected to increase exponentially due to the smooth flow of data.

After the Starting Gun

On November 25, 2025, at the third press conference of "We Are Taking Action on Data Value Realization", Chen Ronghui, the deputy director of the National Data Administration, presented the schedule and roadmap for the "Pilot Program for the Development and Utilization of State - owned Enterprise Data Resources": focusing on four major aspects and deploying ten key tasks, with the goal of significantly improving the level of data development and utilization by 2027 and driving more than 100,000 small and medium - sized enterprises.

A single document quickly put the well - prepared central enterprises into a sprint state.

China Southern Power Grid has taken the lead in building a trusted data space for the southern energy industry, aiming to achieve the goal of "data being usable but invisible, controllable and measurable". According to Li Peng, the chief artificial intelligence officer of China Southern Power Grid, the pilot representatives can operate this "data square" more openly to attract partners for trading and co - creation. Currently, more than 370 ecological partners have applied to enter.

On Zhang Dong's schedule, the deputy general manager of China Mobile, the meetings with industrial chain partners are fully booked. The "Wutong Big Data Platform" they built has accumulated up to 2000PB (petabytes) of core data assets.

Zhang Dong said that after the start of the pilot program, the "Huaxin Community" open - source platform jointly built by China Mobile and 53 central enterprises will operate at an accelerated pace, opening more pre - trained models to form a closed - loop of "data sharing - joint R & D - scenario co - creation".

PipeChina is refining its data open - service list. Its goal is not only for internal use but also to safely serve upstream and downstream enterprises to optimize resource allocation, accurately connect with the market, and ultimately improve the supply security capacity and operational efficiency of the entire natural gas industrial chain.

The "state - owned enterprise data resources" highly valued by these giants come in various forms. For PipeChina, it is the real - time sensing data of pressure and flow of the national main pipelines, equipment maintenance records, and market trading information; for Sinomach, it is more than 1 billion pieces of operation data transmitted daily by more than 1.2 million agricultural machinery across farmlands; for China Southern Power Grid, it is the integrated data set of power grid load, photovoltaic power output, and user electricity consumption habits across five provinces and regions...

The development of state - owned enterprise data resources is generally understood in three levels: First, "resource - based", that is, collecting, cleaning, and integrating scattered data in different systems and formats to form high - quality and usable "data minerals"; second, "product/service - based", that is, processing data into data products (such as prediction models and analysis reports) or data services (such as credit portraits and supply - chain optimization solutions) that can directly empower businesses; third, "asset/capital - based", that is, through rights confirmation, evaluation, and registration, including data in the enterprise's balance sheet and exploring its financial attributes such as pledge financing and trust.

Different types of enterprises have different development paths. The data of central enterprises such as PipeChina and China Southern Power Grid are characterized by universality and scarcity. The key is to achieve "controllable sharing" under the premise of national security, playing the role of builders of data circulation infrastructure. The data of equipment manufacturers such as Sinomach are deeply bound to physical products, with the goal of using data to optimize the whole - life - cycle service and transform into "equipment + data service" providers. Telecommunication operators such as China Mobile rely on their extensive connection and processing capabilities and aim to become platform - type providers of data technology and services.

Regardless of the path, at this moment when the starting gun has been fired, they all need to take a substantial step towards the goal of "making data move, be used, and come alive".

Who Owns the Data?

The enterprises in the forefront first hit an invisible high wall, which is data rights confirmation.

A person in charge of data management at PipeChina raised a question: Who exactly owns the data flowing in PipeChina's pipelines? Is it the state, the enterprise, or the users (shippers)?

He said that the pipeline pressure data involves national energy security, and the definition of its ownership is complex. The historical gas consumption data of users is related to privacy. Due to the great value and unclear ownership of the data, it is difficult for enterprises to rightfully include it as an "asset" in the balance sheet.

He gave an example: "It's like a gold mine. You know it's valuable, but you don't know who the mined gold belongs to. How dare you start large - scale operations?"

This is not an isolated case. A person from a central enterprise in the transportation and logistics industry described data rights confirmation as "a mess that is hard to sort out". The data such as the trajectory, fuel consumption, and cargo status generated by a truck involve multiple parties such as the vehicle owner, driver, logistics company, and cargo owner. The historically accumulated data often has no ownership agreement, and it is necessary to "make up lessons" at the legal and contractual levels before development, which requires a huge amount of work.

The difficulty in rights confirmation directly leads to "difficult pricing" and "difficult circulation". Without clear ownership, it is impossible to conduct a fair market - based valuation.

The Economic Observer learned that previously, some operators tried to evaluate user data assets using the historical cost method, and the market value was far lower than the potential commercial value. Some medical groups also suffered from a damaged valuation during the process of attracting investment due to unclear data ownership.

Within some enterprises, business departments are worried that sharing data will weaken their own value, while data middle - platforms or strategic departments advocate integration and sharing to give full play to the aggregated value. A data governance project manager of an energy central enterprise described that coordinating such internal contradictions is "much more difficult than writing code".

From the legal and regulatory perspectives, does data fall under property law, intellectual property law, or does it require a brand - new data property law? When it comes to cross - border data flow, how should the boundaries between national sovereignty, enterprise rights, and individual privacy be defined?

Currently, a more practical approach is to "discuss each case on its own merits" in specific scenarios. For example, PipeChina and downstream city gas companies cooperate to develop a demand prediction model. Through a contract, it is agreed that the ownership of the original data remains unchanged, the intellectual property rights of the jointly developed model are shared, and the benefits are divided proportionally. However, this method is difficult to form a scale effect.

Rights confirmation has become the deepest gully in front of the data "gold mine". One of the important missions of the pilot program is to explore the way for national legislation and standard - setting through real - life enterprise cases.

Data Assets on the Balance Sheet

Facing core problems such as rights confirmation, the pilot enterprises have carried out a multi - dimensional practice. The long - term goal of this practice is to promote the development of data resources through the stages of "governance - sharing - empowerment - assetization" and ultimately enable data factors to participate in enterprise value creation.

Technology has become the primary tool to solve the problem. To achieve safe circulation in the context of unclear ownership, the "trusted data space" has become the choice of many central enterprises.

Li Peng gave an example. China Southern Power Grid integrated technologies such as blockchain, privacy computing, and data sandbox to build a "data square". Data providers can set rules such as "usable but invisible". Demanders can obtain results through algorithms within the "sandbox", and the whole process can be audited and measured, thus realizing the circulation of data value without transferring ownership. Currently, 45 cross - industry integrated application scenarios have been incubated in this space.

The mechanism is also being innovated. China Mobile's "Huaxin Community" has built a closed - loop ecosystem of "data sharing - joint R & D - scenario co - creation". Partners can enter with computing power and algorithms, train models using China Mobile's data within the compliance framework, jointly develop solutions, and share the benefits. This is equivalent to opening a data laboratory to solve the pain point of limited data application scenarios for a single enterprise.

The most notable solution points to "assetization". China Construction Third Engineering Bureau Information Technology Co., Ltd. has included the first three batches of data assets in its balance sheet and registered them at the Beijing International Big Data Exchange, with a total amount of more than 1.2 million yuan. The key steps are: transforming data resources into data assets with clear ownership and controllable quality through governance; introducing professional institutions to conduct valuations using multiple models; and recognizing them as intangible assets according to accounting standards. A financial person in charge involved in the work said that including data assets in the balance sheet makes the financial value of data intuitively visible, thus leveraging more resources and opening the door for capital operations such as data pledge financing.

Enterprises are also looking for breakthroughs in specific businesses. PipeChina uses global data to develop pipeline intelligent recognition and fault prediction models to improve safety and efficiency; Sinomach's "Agricultural Machinery Cloud" uses data services to help farmers optimize planting and scheduling, transforming into an agricultural comprehensive service provider.

However, solving the problem is accompanied by new games. The data asset evaluation standards vary. How can audits recognize them? How should the benefits of data products be distributed internally? How should the high - cost input and output of data security be matched for assessment? These questions are constantly debated in the meeting rooms of pilot enterprises, and the answers will have a profound impact on the future direction.

The Emerging Industrial Chain

The loosening of the "data veins" of central enterprises affects far more than themselves. An emerging industrial chain driven by data factors is emerging, and the market forces with a keen sense of smell have become runners on this new track.

New trends are emerging in the business of the Beijing International Big Data Exchange. After the start of the pilot program, the number of state - owned enterprises consulting about the registration, evaluation, and trading process of data assets has increased significantly. The exchange is designing trading rules more suitable for the characteristics of state - owned assets. The entry of high - quality data from central enterprises is expected to enhance the activity and credibility of the entire market.

Technology companies in the field of data security and privacy computing have also seized the opportunity. A private enterprise that provides the underlying technology for the trusted data space of China Southern Power Grid reported that in the past, it was very difficult to promote privacy - computing technology to enterprises. After the introduction of the pilot policy, many state - owned enterprises have actively approached them to build compliant data circulation infrastructure, and the market has entered an explosive period from the education period.

Small and medium - sized enterprises in the upstream and downstream of the industrial chain have become direct beneficiaries. China Southern Power Grid has built "credit portraits" based on enterprise electricity consumption data, which has helped more than 1000 small and medium - sized enterprises obtain more than 1.6 billion yuan in credit support. A person from the company said that the vast amount of operation data it holds is a "gold mine" for equipment manufacturers to optimize their designs. They are exploring to jointly analyze fault data with partners in the trusted space to achieve a win - win situation.

In the pilot - focused fields such as new energy vehicles, mineral resources, and medical health, new business models are in the making. The Economic Observer learned through interviews that some data technology companies have started to cooperate with automobile manufacturers and battery factories to develop services such as battery residual value assessment and insurance pricing using the whole - chain data.

This data change also poses new challenges to the supervision of state - owned assets. The above - mentioned SASAC person believes that when data becomes a measurable and tradable asset, the supervision dimension needs to be extended from the traditional "managing people, affairs, and assets" to "managing capital and data". How to evaluate the value of data assets, assess the development efficiency, and prevent asset loss has become a new challenge.

For private enterprises involved, this is both a huge market opportunity and a change in the game rules. The above - mentioned private enterprise person believes that in the future, the cooperation with state - owned enterprises may shift from project - based to a "data - ecosystem partnership" relationship. The key lies in whether they can create unique value with algorithms in the trusted space built by state - owned enterprises.

The new round of reform of state - owned assets and enterprises has added new connotations due to the injection of data. The entry of the "national team" of data factors is not only about digging their own gold mines but also about breaking through the barriers of the industrial chain through data circulation.

The starting gun has been fired. Whether it is the central enterprises leading the pilot program or the market forces waiting for opportunities, they have all started to run hard in this "new exploration" full of problems such as "rights confirmation, pricing, and circulation".

This article is from the WeChat official account "Economic Observer" . Author: Wang Yajie. Republished by 36Kr with permission.