Data component: An innovative framework for information value metrics in the digital economy

The increasing dependence on data highlights the need for a detailed understanding of its behavior, encompassing the challenges involved in processing and evaluating it. However, current research lacks a comprehensive structure for measuring the worth of data elements, hindering effective navigation of the changing digital environment. This paper aims to fill this research gap by introducing the innovative concept of “data components.” It proposes a graph-theoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods. Additionally, the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value. The paper also assesses the value of information, suggesting a pricing mechanism based on its significance. In conclusion, this paper establishes a robust framework for understanding and quantifying the value of implicit information in data, laying the groundwork for future research and practical applications.