Unlocking Data Value: The Four Stages of Enterprise Data Asset Realization

Data has become a recognized production factor, yet only data that can be repeatedly used gains asset value; such data can be priced, turned into data products, and ultimately become assets.

01 Enterprise Data Value Realization Four Stages

Entering data assets into financial statements is a key step that improves transparency, management efficiency, and reflects economic value, supporting resource allocation and decision‑making.

1. Production Informatization Stage

Enterprises first complete comprehensive informatization, digitizing production processes and establishing mechanisms to accurately collect and store raw production data.

2. Data Resource Stage

After informatization, companies acquire high‑quality, trustworthy data through collection, cleaning, labeling, and analysis, laying a solid foundation for later assetization.

Data asset entry at this stage involves aggregating scattered data into standardized datasets.

3. Data Assetization Stage

Data is transferred to the market for trade, generating economic benefits. Clear ownership rights and pricing are essential, converting data from a resource to an economic asset.

4. Data Financialization Stage

Data becomes capital that can be exchanged and circulated, driving digital‑economy growth and deep integration with other production factors.

02 Data Asset Triple‑Entry Theory

The "Three‑Times‑Entry" theory proposes three accounting stages for data assets, helping enterprises identify their current entry phase and guide subsequent work.

1. First Entry: Base Asset

Enterprises record original data resources as intangible assets on the balance sheet according to interim accounting regulations, establishing a solid asset base.

2. Second Entry: Value‑Added Asset

After processing, the added value is reflected in financial statements in monetary form.

3. Third Entry: Financial Asset Conversion

When data assets are transformed into monetary‑measured financial assets through transactions, they are entered a third time.

03 Full Process of Triple Entry

The full workflow maps the evolution of data forms within an enterprise, outlining a step‑by‑step implementation route.

1. Raw Data Collection

(1) Informatization is the foundation – assess the enterprise's informatization level before data collection.

(2) Data Preparation – establish data demand management to ensure systematic, complete, and consistent raw data.

Data tracing: verify data sources for authority.

Source data assessment: evaluate anomalies, inconsistencies, and missing values.

Data model building: design storage models based on type, scale, timeliness, and scenarios.

Data standards: create unified dimensions and standardize management.

Data integration and flow: design processes for storage, computation, and presentation.

2. Forming Data Resources

Raw data is aggregated into reusable, accessible datasets, supported by a data capability and governance framework aligned with data‑driven business models.

Data integration optimizes models and establishes sharing mechanisms across systems and departments.

3. Identifying Reportable Data Resources

The interim regulations define criteria for data that can be entered into statements; registration documents serve as evidence.

4. First Entry

Using cost method, data is separated from information systems and recorded under intangible assets on the balance sheet.

Separate data from systems and reflect it in the asset‑liability statement.

Confirm data resources as corporate equity.

Perform accounting measurement and recording.

5. Data Product Development and Production

(1) Data product R&D – analyze customer data needs, select pilot customers, and organize development of data products and service terminals.

(2) Data product classification – a data product equals data resource + algorithm model + service terminal (App, website, API, SaaS, VPN, etc.).

Typical data product types include:

Data sets: database‑based products for model‑driven needs.

Data information services: information‑type services built on data resource libraries.

Data applications: application‑level products leveraging data resources and models.

6. Data Trading

Data products achieve external value through on‑platform (exchange) and off‑platform (direct negotiation) transactions, both requiring compliance with legal and security standards.

7. Second Entry

Data asset certificates record contracts, delivery, and settlement, providing evidence for recognizing data as intangible assets, inventory, or a distinct asset class.

8. Data Asset Evaluation

Professional assessors develop evaluation plans, produce reports, and disclose results, enabling data to be used for equity investment, data‑based financing, and other financial activities.

9. Data Finance

Evaluated data assets can serve as collateral for loans, participate in pledge financing, data trusts, data insurance, and other innovative financial services.

10. Third Entry

When data assets are converted into monetary‑measured financial assets, they are entered a third time, completing the full accounting cycle.