Optimal Selection Technology of Business Data Resources for Multi-Value Chain Data Space—Optimizing Future Data Management Methods

In the field of industrial big data, the key issue in discovering data value lies not in overcoming the bottlenecks formed by analysis methods and data mining algorithms but in the difficulty of providing data element resources that meet business analysis needs. Due to the surge in data volume and the increasing reliance of enterprises on data-driven decision-making, future data management strategies are constantly evolving to meet higher quality and efficiency requirements. Data metadata resources that meet business analysis needs require high-quality data integration, standardization, and metadata management. The key is to ensure the consistency and availability of data to support accurate analysis and decision-making. By leveraging automation and machine learning, organizations can more effectively integrate and manage data metadata resources, thereby improving data quality and analytical capabilities. The multi-value chain data space is a digital ecological platform for organizing and managing industrial big data. Research on optimizing the supply of its business data resources is a significant topic. This paper studies the evaluation index system of data quality and data utility, constructs an evaluation matrix of business data resources, and addresses the issues of data sparsity and cold start in evaluation calculations through a data quality-utility-based evaluation model of business data resources. It investigates a business data resource algorithm based on collaborative filtering, forming a recommendation set of similar data quality-utility data resources to provide to data analysis users. Finally, using actual production datasets, the paper validates the business data resource evaluation model, compares the performance and effectiveness of three business data resource recommendation algorithms based on collaborative filtering, empirically demonstrates the recommendation accuracy and stability performance of the combined improved data quality-utility collaborative filtering algorithm (CFA-DQU), and provides technical research recommendations for optimization of business data resources.