Why Data Asset Inclusion in Financial Statements Is the Next Competitive Edge for Enterprises

In 2024, a series of favorable policies for the data‑element industry have accelerated the top‑level design of data assets, making the inclusion of data assets in financial statements a strategic trend for enterprises seeking digital transformation and competitive advantage.

What Is Data Asset Inclusion

Data asset inclusion means recording a company’s data resources as assets on its balance sheet, enabling clearer measurement of scale, quality and value.

Key Concepts

Data Resource : Original, machine‑readable data collections that can be reused socially.

Data Asset : Structured or unstructured data (photos, videos, contracts, etc.) that can generate economic value.

Data Factor : Data treated as a production factor that creates economic benefits in the digital economy.

Data‑Factor Marketization : Trading and allocating data through market mechanisms to maximize its value.

Implementation Path of Data‑Elementization

Enterprises, especially commercial banks, typically pass through three stages to participate in the data‑element ecosystem.

Business Dataization

Transforming business activities into quantifiable, recordable data to improve management, decision‑making and risk control.

Data Assetization

Identifying, classifying and valuing data (customer, transaction, risk, market data) to enhance operational efficiency and generate new revenue streams.

Asset Factorization

Extracting actionable insights from active data assets to support decisions, innovation and external data‑trading opportunities.

Benefits of Data Asset Inclusion

For Enterprises : Improves data management efficiency, strengthens decision‑making, unlocks asset value, expands financing channels (e.g., data‑collateral loans, leasing) and enhances market valuation.

For the Economy : Promotes data circulation, breaks data silos and supports the growth of the digital economy.

For the Nation : Demonstrates digital‑economy strength, boosting international competitiveness.

Preparation: Data Governance

Data governance is the foundational engine for activating data‑element value, encompassing top‑level design, governance framework, services and insights.

Top‑Level Design

Define data strategy, align it with business goals, and categorize strategies as decision‑leading, operation‑leading or monetization‑leading.

Establish Data Governance Committee

Create a cross‑functional committee (decision, management, execution layers) to coordinate data governance activities.

Performance Evaluation

Set up a balanced scorecard covering governance personnel, data quality issues, standard compliance, strategy execution, technology delivery and business value realization.

Core Governance Modules

Metadata Management : Capture, store and control metadata as the DNA of enterprise data.

Master Data Management : Identify core data entities, assess maturity, plan and implement master data solutions.

Data Standard Management : Develop, publish, enforce and maintain data standards through a five‑step lifecycle.

Data Quality Management : Define, measure and improve data quality across completeness, consistency, accuracy, timeliness, uniqueness and accessibility.

Data Exchange & Sharing : Enable cross‑system data flow through five layers (external resources, aggregation, fusion, service management, portal).

Data Security Governance : Protect confidentiality, integrity, availability and traceability throughout the data lifecycle.

Data Lifecycle Management : Govern creation, usage, archiving and destruction of data.

Data Asset Inventory Process

The inventory consists of seven steps: define objectives, scope and content, confirm templates, conduct overall data sweep, collect metadata, generate the asset catalogue, and publish the catalogue on a dedicated platform.

Data Asset Inclusion Workflow

Assessment: Determine market, investment, usage and liquidation values using cost, income and market approaches.

Compliance & Confirmation: Verify legal source of data resources and transaction compliance of data products.

Registration: Record data resources and products to establish ownership.

Trading: Conduct on‑ and off‑exchange transactions, including registration, ordering, contract evaluation, delivery and settlement.

Cost Allocation: Allocate acquisition, processing, storage and management costs to inventory.

Reporting & Disclosure: Follow accounting standards for recognition, measurement and mandatory/voluntary disclosure of data assets.

Enterprise‑Wide Response

To fully realize data‑assetization, companies should build robust data‑governance systems, enhance data processing and analytics capabilities, innovate business models and organizational structures, and strengthen collaboration with external partners.

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