Data Trust as a Solution for Data Element Circulation: Ecosystem Analysis, Policies, and Practices

Data Factor Ecosystem Analysis

In the digital economy, data is recognized as the fifth production factor alongside land, capital, labor, and technology. The article outlines five related concepts—data, data resources, data assets, data products/commodities, and digital assets—using a restaurant analogy to clarify their relationships.

The data‑element ecosystem consists of national policies, infrastructure, market mechanisms, government regulation, and various participants, all interacting to promote healthy development of the data market.

Policy Landscape

Key policies include the "Data Twenty Articles" (《数据二十条》) establishing a data‑basic‑system framework with three‑fold data rights (ownership, processing, product), and the Ministry of Finance’s interim measures allowing data resources to be recorded as assets (intangible assets, inventory, expenses) in corporate financial statements.

Enterprises seeking asset entry fall into three categories: startups (enhancing valuation), city‑investment firms (improving balance sheets), and state‑owned enterprises (responding to national directives).

Data Marketization Stages

Data flows through supply, circulation, and application, with marketization divided into four stages: resource‑based, asset‑based, commodity‑based, and capital‑based, reflecting a shift from technical to financial attributes.

Infrastructure and Participants

Data infrastructure, as described by the Director of the National Data Administration, integrates network, computing, and security services to provide unified data aggregation, processing, circulation, and operation.

Participants include data producers, holders, market builders, service providers, users, and regulators, with increasing involvement from legal and consulting firms.

Regulation and Challenges

Multiple government bodies oversee data security, rights registration, usage, circulation, and capitalisation, yet a complete end‑to‑end regulatory framework is still lacking.

Key challenges involve efficient multi‑source data aggregation, fair revenue distribution, security and privacy protection, and the development of supporting legal frameworks.

Data Trust Concept and Solutions

Data trust adapts the traditional trust model to data assets: a data owner (trustor) entrusts data rights to a trustee, who manages and disposes of the data in accordance with the trustor’s intent and legal requirements, aiming to generate and allocate benefits.

Core characteristics of trusts—independence, risk isolation, flexibility, professional investment, and security—are applied to data.

Participants in Data Trusts

Trustor: data producers, holders, or processors.

Beneficiary: can be the same as the trustor (self‑benefit) or a different party (third‑party benefit).

Trustee: typically a trust company that manages data sharing, compliance, and risk.

Data Exchange: platforms facilitating data matching and transactions.

Professional Service Providers: offer technical, legal, security, valuation, and IP registration services.

Data Users: access and use data under the trust’s terms, paying for value.

Service Content

Data trusts transform entrusted data into data capital, generate revenue, and distribute it to beneficiaries, offering services such as governance, valuation, compliance, and asset‑backed financing.

Types of Data Trusts

Enterprise Data Trust: common model linking data owners and users through a trust to enable governance, trading, and capitalisation.

Personal Data Trust: individuals delegate data rights to a trustee who oversees authorized use and shares resulting profits.

Public Data Trust: public sector data is managed under trust principles while protecting privacy and security.

Advantages for the Trust Industry

The trust sector’s expertise in asset management, risk isolation, and regulatory compliance provides a strong foundation for developing data‑trust services.

Domestic and International Exploration and Practice

Internationally, data‑trust initiatives focus on personal data (e.g., UK biobank, Korea MyData). Domestically, early pilots exist but are still nascent.

Case studies include:

Aggregating multi‑source charging‑station data via a data trust to unlock multiplicative value.

Financial transaction data governance and monetisation through a trust plan.

Personal data authorization platforms enabling individuals to manage consent and benefit sharing.

The article concludes that data trusts offer a promising mechanism for secure, efficient data circulation and equitable benefit distribution.