Perspective:The Illusion of Digital Scarcity – A Game Between Infinite Supply and Cognitive Limits

1.Basic theories of business ecosystems

In the era of the digital economy, business ecosystems are no longer traditional industrial chains but rather "tropical rainforest"-style value networks centered around data as the core element. The flow of data among various entities resembles the symbiotic relationships in a tropical rainforest—tech companies serve as the computational "soil," traditional enterprises provide the "nutrients" of real-world scenarios, and governments act as the regulators of sunlight and rain. For instance, banks accessing government data to enhance credit risk control, or manufacturing firms leveraging industrial internet platforms to achieve upstream-downstream collaboration—all rely on a governance framework for data assetization.

The definition of a business ecosystem should not be constrained by traditional thinking. It encompasses not only tangible components such as suppliers, customers, and partners but also intangible elements like data resource pools, algorithm model libraries, and digital credit systems. The three key pillars of a business ecosystem are the data resource pool, value exchange, and dynamic adaptation capabilities. Just as the biosphere depends on the carbon cycle to sustain itself, a business ecosystem relies on the flow and regeneration of data elements. The commonly observed phenomenon of data silos in the real world is, in essence, a digital manifestation of a disrupted carbon cycle. For example, in a medical group where different hospital branches lack interconnected systems, patients' imaging data often gets redundantly scanned, leading not only to asset write-offs but also increased operational costs.

Among the constituent elements, the quality of the data resource pool determines the ecosystem's vitality. Two fundamental issues must be addressed: property rights definition and circulation bottlenecks. A certain e-commerce platform, using blockchain-based notarization technology, transforms product traceability data into tradable digital asset packages. This approach not only clarifies data ownership but also establishes seamless data links between production and consumption ends. Such an inventory-style management model converts fragmented data scattered across warehousing, logistics, and payment processes into an integrated asset chain. The application of intelligent decision-support system models is akin to equipping the ecosystem with a "digital brain." For example, a city commercial bank’s real-time decision-support system reduced its credit approval cycle from seven days to just two hours—a textbook case of data-driven adaptive optimization enabling process reengineering.

Collaborative innovation between technological and institutional elements is crucial. Privacy-preserving computation technologies are reshaping the rules of cross-organizational data circulation. In an automotive industrial park, a trusted data space has been established where automakers and parts suppliers share production data in an encrypted environment—preventing privacy breaches while achieving a 15% boost in inventory turnover. Such breakthroughs in transcending traditional business boundaries have given rise to new market mechanisms, such as dynamic pricing models (similar to electricity markets' peak/off-peak tariffs), where data transaction prices fluctuate based on supply and demand. One industrial internet platform has already implemented automated API call-based pricing and settlement.

However, establishing a healthy data-driven business ecosystem still requires overcoming several hurdles. Data sovereignty credentials must balance circulation efficiency with compliance boundaries—much like attaching a traceable "digital ID" to data elements. For instance, when a medical group implemented cross-hospital data sharing, it employed tiered authorization: patient imaging data is automatically desensitized for research use but retains key information for clinical purposes. This granular management approach effectively balances data commercialization with ethical constraints. In the future, as the data element market matures, business ecosystems will become modularly assembled like LEGO blocks—enterprises will mix and match different data asset packages to rapidly develop innovative services that adapt to market changes.

2.The core mechanism of data - driven business ecosystems

2.1 Data Elements Reshaping Business Logic

Data elements are fundamentally redefining the underlying logic of the business world. Their influence has expanded from specific local industries to every link in the global value chain, and the intensity of this transformation is comparable to the radical overhaul of production models brought about by the electrical network during the Industrial Revolution. The value - conversion mechanism formed by data flows, which relies on instant feedback and the integration of multiple sources, is profoundly altering the way enterprises collaborate, driving the traditional linear value chain to transform into a mesh - like value ecosystem.

Typical cases show that when a home appliance manufacturing company relied solely on its own sales data, there was an average 15 - day delay in adjusting its production plans. As a result, its inventory turnover rate was 23% lower than the industry average. However, once this company accessed multi - source information in a trusted data space, including logistics data (real - time transport capacity distribution), raw material price indices (global commodity exchange APIs), and consumer behavior data (heat analysis on e - commerce platforms), and carried out precise and optimized supply chain configuration through a federated learning framework, its inventory turnover efficiency rose to the top 5% of the industry.

This value - creation model based on data integration not only breaks through the information asymmetry dilemma in traditional business but also builds a dynamic knowledge graph through a cross - validation mechanism. This fully demonstrates the systematic advantages of data - driven business ecosystems in terms of resource allocation efficiency, risk prediction accuracy, and response speed for updates.

2.2 Data Circulation Barriers and Governance Framework

(1) Enabling Data to Circulate Like the "Blood" of the Business Ecosystem

To enable data to circulate like the "blood" that empowers the business ecosystem, it is essential to break through the key bottleneck of data silos. This is far more complex than the circulation barriers of traditional production factors. By drawing an analogy with the information barriers in traditional manufacturing, for example, the data fragmentation among medical institutions results in annual redundant examination costs exceeding 20 billion yuan nationwide (according to statistics from the alliance of top - tier hospitals). The data discontinuity in various links of the manufacturing supply chain leads to a 1.2% loss in industrial collaboration efficiency (based on the industrial economic analysis of the National Bureau of Statistics). These all demonstrate the risk of asset devaluation caused by obstructed data circulation. To address this issue, the data assetization governance framework functions through three mechanisms:

(2) Property Rights Definition Mechanism

The "separation of three rights" principle (holding right, use right, and income right) is adopted to clarify the legal boundaries of data ownership within the framework of the Data Security Law of the People's Republic of China. The "data sovereignty credential" system implemented by the Shanghai Data Exchange has led to a 76% reduction in trading disputes.

Standardized Management

The international standard for data governance, ISO/IEC 38500, is applied to transform discrete data into standardized asset packages equipped with metadata tags, quality assessment reports, and compliance statements. The trading volume of standardized data products on a certain energy trading platform is 3.8 times that of its non - standardized products.

Value Realization Case

A retail giant integrates customer behavior data (shopping trajectory heatmaps) with supply chain data (supplier capacity fluctuations) into a "market insight product package". After the rights are confirmed through blockchain notarization, the product is traded on the Shenzhen Data Exchange. This not only complies with the requirements of Article 35 of the General Data Protection Regulation (GDPR) but also opens up a new profit channel with an annual revenue of 1.2 billion yuan.

2.3 The Evolution of Intelligent Decision-Support Systems

In terms of value transformation, intelligent decision-support systems, through the integration of algorithm engineering and data elements, demonstrate an exponential enabling effect. For example, after adopting a real-time decision-making system, a commercial bank reduced its credit approval time from 72 hours to just 15 minutes (as reported in the credit approval process restructuring report). The key mechanisms are as follows:

The system employs self-adaptive optimization algorithms such as dynamic Bayesian networks to update risk control parameters in real time, thereby increasing the accuracy of bad debt prediction to 98.7%. It integrates data streams from multiple dimensions, including business registration information, credit records, and industry prosperity indices, and then develops a risk profiling model with 127 feature variables. A reinforcement learning framework is applied to enable continuous iteration and improvement of the model. In the first quarter of 2024, the system autonomously optimized its algorithm parameters approximately 32,000 times.

When the system is connected to external data element markets, it can achieve real-time updates of decision-making bases (such as real-time exchange rate fluctuations and commodity prices). This enables a qualitative shift in corporate decision-making models from being "experience-driven" to being "dual-driven by data and algorithms." As a result, the strategic decision-making response time of a multinational enterprise was reduced by 83%.

2.4 Market Mechanisms for Data Trading

An effective data trading mechanism must address the fundamental challenge of "supply-demand matching," a complexity far surpassing that of traditional commodity markets. Dynamic pricing models play a pivotal role in this process, characterized by the following operational features:

Multi-dimensional Pricing Factors:

The Hedonic pricing method is applied, incorporating 12 core indicators including data quality, completeness (≥0.85), timeliness (T+1 update rate), and application scenarios (medical/financial risk coefficients).

Differentiated Pricing Case Study:

A medical technology company implements disease-specific tiered pricing for de-identified clinical data. For instance, a cardiovascular dataset containing 100,000 PCI surgery records commands an 80% premium, while routine physical examination data only achieves a 15% premium (calculated based on bidding records from the Beijing Data Exchange).

Technical Enablers:

Secure Multi-party Computation (MPC) enables "data usability without visibility." For example, an automotive manufacturer conducting supplier quotation analysis achieved an 18% reduction in procurement costs without compromising commercial confidentiality.

2.5 Compliance Risk Management System

The commercialization of data requires a robust compliance framework, with key innovative practices including:

Digital Sovereignty Credentials:
Leveraging China's national cryptographic SM9 algorithm to establish a digital identity system, enabling end-to-end traceability across the entire data lifecycle—from data collection (edge device signatures) and circulation (smart contract authorization) to final destruction (blockchain timestamps). After implementing this system on a cross-border logistics platform, compliance review cycles were reduced from 45 days to just 3 days [14].

Ethical Balancing Mechanism:
For user profiling applications, a dynamic differential privacy balance (ε=0.3) is maintained between commercial value and privacy protection. An e-commerce platform utilizing this mechanism achieved 92% recommendation accuracy while suppressing user identity re-identification risks to below 0.05% (a figure certified by third-party security organizations).

2.6 Cross-Industry Synergy Effects

The deeper value of data element markets lies in fostering cross-sector innovation ecosystems, whose multiplier effects have long transcended traditional industry boundaries. Notable examples include:

Electric Vehicle Industry Case:
A new-energy vehicle manufacturer integrated charging pile data (real-time usage heatmaps) with power grid load data (from State Grid's dispatch system) to develop an electricity demand forecasting service, achieving a prediction error rate of just 2.3% (outperforming the industry average of 5.7%). By creating an industrial data middleware platform, the company transformed from a "data monopolist" to an "ecosystem connector"—with data service revenue rising from 8% in 2022 to 35% in 2024 (per corporate annual reports).

Manufacturing Consortium Example:
Through data-sharing agreements, a manufacturing alliance improved joint R&D efficiency by 60% and increased patent applications by 220% year-on-year.

Strategic Implications:
The deep restructuring of business ecosystems by data elements reveals that value release mechanisms involve not only technological breakthroughs but also novel combinations of institutional and ecological innovations. Synthesizing current practices and theoretical frameworks, Peter Drucker's maxim—"The best way to predict the future is to create it"—finds renewed relevance in today's business rules. Only by effectively integrating technological innovation, institutional convergence, and ecosystem synergy can enterprises position themselves at the forefront of digital competition

3. Data Assetization: Enhancing Competitiveness and Cross-Industry Integration Value

3.1 The Impact of Data Assetization on Corporate Competitiveness

In the digital economy era, the core of corporate competitiveness has shifted from traditional production factors to the ability to harness data elements. Data assetization transforms "dormant" data resources into quantifiable, tradable assets while reshaping business processes and models to create unique competitive advantages. Much like how equipment upgrades boost manufacturing output, data assetization serves as a "digital engine" for intelligent decision-making, driving qualitative improvements in operational efficiency and innovation speed.

The implementation of data assetization governance frameworks directly resolves long-standing data silo challenges. Previously, departments operated in isolation—sales data remained trapped in CRM systems, production data locked within MES systems, and financial data confined to ERP systems, creating vertical "data chimneys." A case in point: a home appliance manufacturer established a unified data asset management platform that integrated data from 27 previously fragmented business systems. This integration led to a 40% reduction in product development cycles and a 35% increase in inventory turnover rates. These outcomes not only validate the efficiency constraints imposed by information silos but also demonstrate how the synergistic effects of data assetization translate into tangible competitive advantages.

3.2 The Value - Creation Effect of Cross - Industry Data Fusion

In the digital economy era, data from different industries is like treasures hidden on isolated islands. To truly figure out how to make the manufacturing production data and the retail consumption data react chemically, we first need to solve the core problems in the "data assetization governance framework". Take an automobile manufacturing company as an example. It wants to combine 4S shop maintenance data and insurance company claim records to improve the spare - parts supply chain. However, in practice, it gets stuck on the "data silos" and "circulation blockages". 4S shops are afraid of customer privacy leakage, and insurance companies worry that their business secrets will fall into the hands of competitors. This lack of trust between industries makes the wheels of value creation unable to turn.

At this time, "inventory - based" management and "property rights definition" need to be carried out simultaneously. Just as the construction engineering field uses BIM technology to break through the information barriers in the design, construction, and operation and maintenance links, the manufacturing and financial industries can completely form a standardized data resource directory to clarify the ownership relationship and circulation rules of each type of data.

When it comes to the multiplier effect of value creation, there is an innovative case in the financial sector. A commercial bank, after accessing cross - departmental data such as industrial and commercial, tax, and customs data, reduced the original 7 - day credit approval cycle to 2 hours. The key to this real - time decision - support system is the creation of a dynamic pricing model, which can adjust the credit limit and interest rate according to the enterprise's real - time operating data. This "nourishing fish with living water" approach makes the data flow and start to generate value. What's even more amazing is that when the data of manufacturing equipment operation is combined with the electricity consumption data of the energy industry, the regional energy consumption changes can be predicted. This enables the power grid company to allocate resources in advance. Such cross - industry insight ability is much higher than that of simple industry data analysis.

Of course, cross - industry data fusion will also encounter the trouble of "ethical constraints". Just like the embarrassment faced by educational institutions in data sharing. It is obvious that combining teaching data with employment data can improve major settings, but they are stuck on the boundary issue of "whether data can be commercialized". At this time, the design of "data sovereignty certificate" becomes crucial. It is like sticking a digital ID card on each data package, which can not only trace the use path but also limit the circulation scope. The construction industry has done some interesting things in this area. They add a data certificate to each component in the BIM model, which can not only protect the intellectual property rights of the design party but also allow the construction party and the operation and maintenance party to call the data they need with confidence. Looking ahead, the value creation of cross - industry data fusion is breaking through physical boundaries. Just imagine, if agricultural meteorological data meets insurance actuarial data, perhaps more accurate weather - indexed insurance can be created. If traffic flow data is combined with commercial location data, it will probably reshape the urban commercial ecological map. However, to turn these wonderful visions into reality, it is still necessary to improve the compliance and risk management system. After all, data fusion is like opening chain stores. Each store needs to have its own unique flavor and also follow unified food safety standards. Only by stabilizing both the technology empowerment leg and the institutional constraint leg can we truly stimulate the primordial power of the data element market.

4. Construction and Operation of Trusted Data Space 4.1 Connotation and Significance of Trusted Data Space As the digital economy advances rapidly, the trusted data space (Trusted Data Space), as a core infrastructure for data element marketization, is reshaping the underlying rules of the global business ecosystem. Relying on the creation of a standard and interoperable circulation architecture, it comprehensively solves the key problems of efficiency barriers, trust loopholes, and compliance risks encountered by previous data trading forms. The "Trusted Data Space Development Action Plan (2024 - 2028)" issued by the National Data Administration indicates that "this is the main foundation project for the vigorous development of the data element market", and such a strategic position can even be compared to the construction of the railway system in the Industrial Revolution era. Compared with traditional data exchange methods, the trusted data space not only realizes technological innovation in architecture, such as the combination of blockchain notarization and privacy computing, but more importantly, it has established a trust mechanism of "rule consensus, clear rights and responsibilities, and multi - party co - governance", providing institutional guarantees for the full - life - cycle flow of data elements.

4.2 The Main Challenges of Traditional Data Circulation

The current data element market faces structural contradictions due to three core difficulties:

Aggravation of the data island phenomenon: Due to the lack of unified circulation standards and trust mechanisms, the data systems of various institutions are highly fragmented. Taking the medical industry as an example, the nationwide tertiary hospitals spend over 20 billion yuan annually on repeated examinations due to data fragmentation, which greatly hinders the efficiency of diagnosis and treatment as well as the utilization rate of resources .

Precipitation of data asset value: According to data from the China Academy of Information and Communications Technology, the utilization rate of data resources by Chinese enterprises is less than 30%. Many high - value data, such as various parameter information of industrial equipment during operation and consumer behavior paths, are idle due to unclear property rights or technical limitations, resulting in huge economic losses amounting to over one trillion yuan annually.

Institutional obstruction of circulation mechanisms: The legal ambiguity of data rights confirmation (such as the separation of ownership and use rights), the non - standardization of pricing models (such as the lack of measurement standards for scenario premiums), and the technical barriers of cross - border circulation have become the "three - fold gateways" hindering the marketization process.

4.3 The Core Value of Trusted Data Space

The trusted data space adopts the new paradigm of "data container". Through the standardized process of integrating metadata tagging encapsulation, quality grading certification, and smart contract authorization, it achieves an exponential increase in circulation efficiency on the basis of maintaining the integrity of data sovereignty. In the empirical research of the manufacturing supply chain scenario, an automotive industry cluster relied on the trusted data space to share production plans and inventory data. As a result, the order response time was reduced from an average of 72 hours to 50 hours, the on - time delivery rate increased by 37.5%, and the inventory turnover efficiency reached the top 10% level in the industry. The essence of this value creation mechanism is to transform discrete data resources into digital assets that can be measured, traded, and combined through the creation of a technology - institution dual - track system of "data does not leave the domain, but value can circulate".

4.4 Key Technology Implementation

The operational efficiency of the trusted data space relies on the collaborative innovation of four core technologies:

Blockchain notarization technology: By using distributed ledgers and timestamps, it endows data circulation with full - life - cycle traceability capability. After a cross - border logistics platform adopted this technology, the verification delay of customs declaration documents was reduced from 3 days to 2 hours, the error rate decreased by 89%, and it also complied with Article 35 of the EU GDPR.

Dynamic pricing model: Using the Hedonic pricing algorithm to integrate 12 core indicators (data integrity ≥ 0.85, timeliness T + 1, scenario risk coefficient, etc.), an e - commerce platform relied on this model to achieve differentiated grading of data product premium rates. The transaction success rate increased by 400%, and the utilization rate of long - tail data assets also rose to 65%.

Privacy - computing technology: The combination of federated learning and TEE is applied in medical clinical research to achieve "data availability without visibility". The liver cancer early screening model jointly built by Peking Union Medical College Hospital and pharmaceutical companies achieved an AUC value of 0.91 while protecting the privacy of 100,000 patients, which was 28% higher than that of single - institution training.

Smart contract system: The compliance clause template library (V2.0) created by the Shanghai Data Exchange uses automated auditing to reduce the legal risk evaluation time from 21 days to 72 hours and also reduces compliance costs by 34%.

4.5 Innovative Operating Mode

The trusted data space subverts the traditional one - way intermediary mode and constructs an ecosystem with flexible role transformation and value - mesh symbiosis:

Participation subject diversification: Multiple roles of enterprises: Enterprises can play three roles: contributing data to provide supply chain information, analyzing data to obtain market information, and providing technical services such as analysis tools. A new - energy vehicle company obtained additional income through data services, including new businesses such as power demand forecasting, and its annual turnover increased by 25%.

Value co - creation mechanism: An automotive industry alliance created an industry data space and formed a model of "data contribution degree - return distribution", which increased the average data asset investment return rate of its enterprises from 8% to 26% and shortened the enterprise research cycle by 40%.

Compliance efficiency revolution: The intelligent contract judicial notarization system formed by the Shenzhen Qianhai Court realized the automatic creation and inspection of evidence chains for dispute cases. The system reduced the average trial time from the original 45 days to 7 days and also reduced the court cost by 62%.

4.6 Development Challenges and Countermeasures

At present, the trusted data space has the "cold start" dilemma. 83% of small and medium - sized enterprises are in a wait - and - see state due to the lack of data resources or insufficient technical capabilities. To address the above problems, the following solutions can be adopted:

Gradient incentive mechanism: A provincial - level data exchange implemented the "data contribution - point system". For every 1TB of high - quality data contributed, it received computing power subsidies and priority purchasing rights. Within half a year, the number of active enterprises doubled, and the number of data product SKUs exceeded 1,200.

Ability cultivation system: Jiangsu Province carried out the "data engineer certification program" for 16 advanced manufacturing clusters. Relying on the integration of industry and education to cultivate compound talents who are proficient in both SM9 national encryption algorithms and industrial chain business, it is expected that the talent gap will be reduced to 120,000 by 2026.

4.7 Future Prospects

The trusted data space is reshaping the corporate competition paradigm. Future business leaders need to master three abilities of "data control": Junior players improve operations (such as increasing inventory turnover rate by 30%), intermediate players output capabilities (such as covering 30% of the market share with industry price indexes), and dominant players set ecological rules (such as data sovereignty certificate standards). However, continuous breakthroughs are still needed:

Legal rights confirmation difficulties: The separation of data ownership and derivative rights (such as the benefit sharing of personal biometric data) has no international consensus.

Cross - border circulation obstacles: There are huge differences between China, the United States, and Europe in terms of data classification and grading, privacy - computing protocols, etc. Organizations such as ISO/IEC need to promote the development of mutual recognition frameworks.

Ethical balance mechanism: How to achieve Pareto optimality between precise user profiling (92%) and differential privacy strength (ε = 0.3) is still a key issue that scholars and enterprises need to work together to solve.

New Monetization Ability of Data Assets in the AI Era

5.1 AI Model Training Services

Enterprises can package their accumulated data assets into AI model training datasets and then sell them to AI research and development institutions or enterprises through data trading platforms. For example, medical image datasets can be sold to companies engaged in AI medical services so that they can use these datasets to train disease diagnosis models. They can also provide customized data annotation services for customers, that is, specifically clean, annotate, and preprocess data for a particular AI application scenario to improve the quality and applicability of the dataset.

5.2 AI Prediction and Analysis Services

Use data assets to create prediction models to provide enterprises with market trend prediction, user behavior analysis, and other services. For example, retail enterprises can estimate future sales trends by analyzing past sales data, improve inventory management, develop AI - based intelligent decision - support systems to help enterprises achieve automated decision - making and improve operational efficiency. Banks can use AI credit approval models to achieve rapid and accurate credit approval.

5.3 Data - Driven AI Product Innovation

Combine data assets with AI technology to develop innovative AI products. Smart home companies can rely on user behavior data to create personalized smart home control systems.

With the help of data sharing and cooperation, promote cross - industry AI application innovation. Car manufacturers can cooperate with insurance companies and use vehicle driving data to develop unique insurance products.

In the first presentation today, we have laid the groundwork for the basic concepts and core logic of business ecosystems. We have also discussed the impact of data assetization on corporate competitiveness, as well as the construction and operation of trusted data spaces. In the following sessions, we will focus on the governance of data assetization and conduct in - depth analysis from multiple dimensions.