Exploration of statistical measurement methods for data elements
In the context of the digital economy,cutting-edge information technologies such as cloud computing,block-chain,and the Internet of Things are increasingly becoming key drivers of exponential growth in data volume.As an e-merging element in the contemporary economic system,data not only plays a crucial role in technology,but also gives rise to a new value transformation model in the economic field.The research purpose of this article is to explore effective statistical calculation methods for data elements and establish their circulation mechanism in the market as products.Cur-rent research mainly focuses on evaluating the overall scale of the data economy and the level of national economic devel-opment,while statistical calculation methods for data elements are still in the exploratory stage.In response to this re-search gap,the article proposes an innovative statistical measurement framework aimed at providing scientific and system-atic guidance for understanding and efficiently utilizing data elements.The core of the research is divided into three parts:the level of data element normalization,the level of data element structuring,and the exploration of data relationship pat-terns in data elements.The article delves into how data can be transformed from its original state into tangible assets that can circulate in the market,and constructs an evaluation index system that includes multiple dimensions of resource utili-zation,asset utilization,and capitalization.The global principal component analysis method is used to screen and redun-dancy test these indicators.In the study of data element structuring level,key factors such as heterogeneity of data fea-tures,heterogeneity of data objects,heterogeneity of data relationships,and timeliness of data were comprehensively con-sidered,and a quantitative model of data element structuring level was constructed based on these factors.By accurately modeling and quantifying the relationship between co frequency data and mixed frequency data,we can gain a deeper un-derstanding of the complex relationships between data,providing important theoretical support for effective management and value maximization of data elements.
data elementalizationdata element structuringdata element relationship model
万方数据
维普
