亿邦智库：提高数据集质量提升数据资产价值有了实用标准

New standards like the "High-Quality Dataset Construction Guide" provide practical pathways to enhance data quality, addressing the challenge of "quantity over quality" in data.

1. Datasets serve as the "intelligent fuel" for AI, where quality directly determines the performance ceiling of AI models, impacting applications such as e-commerce recommendations or medical diagnostics.

2. The standards define a quality evaluation framework: knowledge depth is categorized for tailored approaches, avoiding one-size-fits-all solutions; static metrics include completeness, accuracy, consistency, and diversity; dynamic metrics validate effectiveness through model performance testing.

3. The "data + model" methodology enables efficient construction: automated data annotation and quality checks by models replace manual labor, reducing costs and improving efficiency.

4. Case studies highlight success through application orientation: for example, wind resource datasets address industry pain points and generate economic returns.

New standards emphasize the critical role of dataset quality in AI applications, indirectly supporting intelligent services like personalized recommendations in the brand sector.

1. Dataset quality determines AI model performance, which is crucial for brand-related precision recommendation systems, avoiding the "garbage in, garbage out" problem.

2. The standards provide quality specifications: static metrics such as diversity ensure coverage of various user scenarios, while dynamic metrics ensure model optimization, enhancing the reliability of brand services.

3. Scenario-driven principle: datasets must be tied to high-value applications, such as converting data directly into marketing insights in consumer trend analysis.

Policy interpretations indicate that the National Data Administration's standards pave the way for data assetization, creating growth markets and partnership opportunities.

1. Policy support: new standards clarify pathways for transforming data into assets, establishing evaluation and certification systems to reduce risks of ineffective investments.

2. Opportunity insights: businesses can shift to an application-oriented approach, following a "three-step" strategy (planning, construction, monitoring) to build core data assets, as seen in the wind resource dataset case which shortens the return period.

3. Risk mitigation: avoid blindly hoarding raw data; instead, use model testing to dynamically validate quality, improving return on investment.

New standards inform product design and digital transformation, focusing on optimizing production processes for dataset construction.

1. Dataset building is treated as a "data refining" process, similar to product design: automated annotation via the "data + model" method enhances production efficiency and consistency.

2. Business opportunities: high-barrier datasets (e.g., wind resource cases) become scarce assets, enabling factories to develop products with "three highs" characteristics (high value, high density, high technology).

3. Digital advancement: standards provide lifecycle management guidelines, helping factories systematically advance data governance to boost competitiveness.

Industry trends point to an AI boom, with new standards addressing client pain points and offering technical solutions.

1. Trend analysis: the data industry is shifting from quantity to quality, increasing demand for service providers to offer full-process data governance tools, such as annotation and model integration services.

2. Client pain points: issues like data inconsistency and inefficient labeling are mitigated by standards introducing automated model tools to reduce costs and improve efficiency.

3. Solutions: leveraging the "data + model" methodology, service providers can develop systems supporting dynamic quality checks to meet market certification needs.

Standards reveal platform construction needs and operational management methods, emphasizing high-quality data integration.

1. Platform requirements: data asset certification systems provide quality assurance for platform recruitment, attracting high-quality datasets.

2. Platform practices: case studies, such as digital intelligence service platforms, demonstrate "one-stop" smart infrastructure that supports functions like resource assessment through datasets.

3. Risk mitigation: dynamic metrics prevent theoretical exercises; platforms can use model validation to enhance data reliability and optimize operational efficiency.

Industry developments are reflected in the rollout of new standards, revealing policy implications and new research questions.

1. New trends: the data要素 market is entering a value extraction phase, with standards providing a policy framework, such as evaluation and accounting mechanisms.

2. Research questions: the effectiveness of dynamic metrics and rigorous testing methods for model performance warrant in-depth exploration.

3. Policy implications: standards suggest business model innovations, such as scenario-driven closed loops, providing an empirical basis for data regulations.