Deep thinking on the discipline of data as productive factor

ZHOU Aoying

doi:10.11959/j.issn.2096-0271.2026007

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Deep thinking on the discipline of data as productive factor

更新时间：2026-02-27

- Deep thinking on the discipline of data as productive factor
- BIG DATA RESEARCH Pages: 1-10(2026)
- 作者机构：
  
  华东师范大学数据科学与工程学院，上海 200062
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2026007
  CLC：
- Online First：25 February 2026，
- 稿件说明：
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周傲英.关于数据要素学科建设的深度思考[J].大数据,

ZHOU Aoying.Deep thinking on the discipline of data as productive factor[J].BIG DATA RESEARCH,
周傲英.关于数据要素学科建设的深度思考[J].大数据, DOI：10.11959/j.issn.2096-0271.2026007.

ZHOU Aoying.Deep thinking on the discipline of data as productive factor[J].BIG DATA RESEARCH, DOI：10.11959/j.issn.2096-0271.2026007.

摘要

数据作为第五生产要素是重大的理论突破。数据生产要素与土地、资本等传统生产要素相比，数据具有非稀缺性，数据越用越多、越用越有价值。当前的人工智能是数据智能，是联结主义的胜利，本质上是数据驱动的智能的自动化。数据智能热意味着整体论作为科学研究方法论的复兴，新的科学发现将不仅仅局限于还原论范式。数据是互联网的底层逻辑，党管互联网落实在操作层面就是党管数据。国家数据局成立两年来，在数据基础制度和数据基础设施等方面开展了全面的探索，迫切需要学术界和科技界提供理论和技术支持。开展数据要素学科自主知识体系构建的探索是当前我们义不容辞的责任。

Abstract

The recognition of data as the fifth factor of production represents a significant theoretical breakthrough. Unlike traditional factors of production such as land and capital

data is non-scarce; the more it is used

the more it accumulates and the greater its value becomes. Current artificial intelligence is essentially data intelligence

marking the triumph of connectionism

and is fundamentally the automation of intelligence powered by data. The fervor around data intelligence signals a revival of holism as a scientific research methodology

indicating that new scientific discoveries will no longer be confined to the paradigm of reductionism. Data underpins the logic of the internet

and the CPC's oversight of the internet

when implemented at the operational level

translates to the CPC's oversight of data. Over the two years since its establishment

the National Data Administration has conducted comprehensive explorations in areas such as fundamental data systems and data infrastructure

creating an urgent need for theoretical and technical support from academia and the scientific community. It is an unequivocal responsibility for us to develop a novel knowledge system for the study of data as a factor of production.

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references

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