The valorization of data as a factor of production faces widespread challenges

including insufficient supply

restricted circulation

and ineffective utilization. The core reason lies in the immaturity of data production modes

where highquality datasets still rely on workshopstyle production that fails to meet the largescale data demands of Artificial Intelligence (AI) large models. To address this problem

the concept of "Data Factory" is proposed and defined as a data infrastructure dedicated to the facilitybased

largescale

and standardized production of highquality datasets for AI large model applications. By tracing the evolution of infrastructure forms across industrial society

information society

and dataintelligent society

the theoretical logic of Data Factory as a fundamental building block of national data infrastructure is established. Based on characteristics such as physical distribution

organizational structure

and technological sophistication

Data Factories are classified into three types: centralized

semicentralized

and distributed. Five key features are identified: diversity

facilityorientation

scalability

standardization

and AIintegration. The study concludes that the development of Data Factories can effectively break through the data supply bottleneck in AI development

promote upstream and downstream collaboration in the data industry chain

and serve as a critical path to bridge the "last mile" gap between data and AI empowerment.