国家数据局推能力提升工程 助产业互联网平台等升级发展

The article highlights China's National Data Administration's new guidelines to foster data circulation service providers, along with capability enhancement paths and practical cases for three types of platforms.

1. Policy & Action: The guidelines propose cultivating three types of data circulation service providers—industrial internet platforms, data infrastructure operators, and cloud service platforms—and implementing a "Capability Enhancement Project." Recent actions, such as the launch of Kun'an Large Model 2.0 and Beijing's data escrow platform, signal the entry into a scaled practice phase for data element circulation.

2. Industrial Internet Platform Case: Extending value chains through data governance. For example, a steel platform built an AI dataset system integrating billions of dialogue data points and millions of transaction records, deploying AI Agents into corporate WeChat to form a digital workforce and automate transactions. Kun'an Large Model upgrades platform data integration with a "use-driven governance" approach.

3. Data Infrastructure Operator Case: Emphasizing secure and trusted technology. Beijing's data escrow platform offers data hosting, desensitization, and fusion computing services; Xiamen's trusted data space integrates blockchain and privacy computing, adhering to "Three Unifications" standards to ensure secure data flow.

4. Cloud Service Platform Case: Optimizing storage and analysis with a lakehouse architecture. For instance, helping the automotive industry manage data growth through tiered storage services to reduce computing costs.

5. Future Collaboration: The three platform types must collaborate—industrial platforms provide scenarios, infrastructure ensures security, and cloud platforms offer technical support—to advance data from resource to asset.

While not directly addressing brand marketing, the circulation of data elements can indirectly influence consumer trend observation and user behavior analysis, offering strategic insights for brands.

1. Consumer Trend Insights: Industrial internet platforms accumulate vast transaction and conversational data (e.g., steel platform covering inquiries and transactions), enabling analysis of user behavior shifts to help brands identify emerging consumer demands.

2. Product Development启示: Data governance cases like Kun'an Large Model's "business-led" approach demonstrate turning data from records into assets, inspiring brands to leverage data for product design optimization, such as mining preferences from user dialogue data.

3. User Behavior Observation: Platforms embedding AI Agent systems into corporate ecosystems collect structured data for real-time user interaction insights, offering a model for brands to enhance channel strategies.

4. Case Study Reference: Examples like Kun'an Large Model and Beijing's data escrow platform show how data value release impacts market dynamics, providing lessons for brands navigating price competition.

The article interprets policy support, market opportunities, and collaboration models, offering growth insights and risk warnings for sellers.

1. Policy Interpretation: National guidelines encourage data circulation service providers, with support measures like capability enhancement projects, helping sellers access resources to tackle data marketization challenges.

2. Growth Markets & Opportunities: Data value release creates new demands, such as value chain extension services on industrial platforms. Sellers can explore partnerships to tap into incremental markets.

3. Risks & Mitigation: Data security compliance is a key risk. Cases like Beijing and Xiamen platforms emphasize "data usable but invisible" standards, urging sellers to adopt similar measures to avoid circulation risks.

4. Latest Business Models: Differentiated paths of the three platforms (e.g., cloud platforms' lakehouse architecture reducing computing costs) offer operational optimization lessons; collaboration models provide new partnership avenues.

The article reveals commercial opportunities for data in production and design, advancing digitalization insights for factories.

1. Production Needs: Industrial platform data governance cases (e.g., steel platform's AI datasets) inspire factories to use transaction data for optimizing production processes and efficiency.

2. Business Opportunities: Data value chain extensions, like Kun'an Large Model integrating with Jiwei IoT for digital logistics, offer new order opportunities for factories participating in such ecosystems.

3. Digitalization启示: Cloud platform technologies like lakehouse architecture for unified data management help industries like automotive optimize storage, providing a model for factories to advance e-commerce and reduce hardware costs.

4. Enterprise Reference: Cases like Kun'an Large Model's "agile data governance" demonstrate rapid data assetization, offering a replicable approach for factories.

The article focuses on industry trends, new technologies, and solutions to address client pain points.

1. Industry Trends: Data element circulation is entering a scaled practice phase, with guidelines driving the three-type institution development, presenting a growth direction for service providers.

2. New Technology Applications: Cutting-edge tech like blockchain and privacy computing are adopted by data infrastructure operators (e.g., Xiamen's trusted data space pioneering digital passes) for efficient circulation solutions.

3. Client Pain Points & Solutions: Addressing data security concerns, Beijing's platform offers escrow and desensitization services; for storage needs, cloud platforms' lakehouse architecture provides unified management—service providers can promote these solutions.

4. Case启示: Examples like Kun'an Large Model and industrial platform AI applications show how data governance solves efficiency issues, inspiring service providers to develop customized services.

The article details the latest platform practices, operational management, and collaboration needs.

1. Latest Practices: Industrial platforms deepen data insights (e.g., steel platform's AI Agents); data infrastructure operators build secure environments (e.g., Beijing platform's standardized services); cloud platforms enhance technical integration (e.g., lakehouse architecture for storage optimization).

2. Operational Management: Adherence to "Three Unifications" standards ensures compliance; Xiamen platform case highlights controllable data flow, urging platform operators to strengthen risk management.

3. Merchant Recruitment & Demand: Business needs like data security and innovation are met by cloud platforms' flexible solutions; collaboration models among the three platforms offer new ecosystem expansion opportunities.

4. Risk Avoidance: Policies like the guidelines require evaluation systems; platform operators must monitor data asset "on-balance-sheet" rules to avoid compliance risks.

The article explores industry movements, policy implications, and business model innovations.

1. Industry Trends: Data elements are shifting from infrastructure to value release, with three platform types collaborating to evolve data from resource to asset, as seen in SASAC's push for data asset on-balance-sheet treatment.

2. New Issues & Policy启示: Guidelines propose cultivating institutions and evaluation systems, suggesting researchers focus on data circulation mechanism design; policies like "Three Unifications" standards offer a regulatory framework for study.

3. Business Models: Differentiated paths of the three platforms—industrial platforms' value chain extension, infrastructure operators' ecosystem co-creation, cloud platforms' technical integration—provide fertile ground for analyzing model innovation.

4. Case Studies: Examples like Kun'an Large Model and trusted data spaces offer empirical research material, demonstrating practical applications for enhancing data element competitiveness.