A Cost-Benefit Collaborative Optimization Method for High-Quality Data Preparation

Data governance is a fundamental basis for ensuring data quality and security, promoting efficient data circulation, and realizing data value. However, in practical machine learning (ML) model training scenarios, data preparation is still constrained by low resource utilization efficiency, uncontrollable costs, and insufficient optimization under budget constraints. To address these problems, a data preparation method oriented toward cost-benefit collaborative optimization was proposed. Mathematical programming was adopted to incorporate data preprocessing, cleaning, transformation, and other operations into a unified cost modeling and decision-making framework, and a cost-benefit analysis and planning method for the overall pipeline was designed. Experimental results showed that the proposed method achieved a better trade-off between cost and performance under limited budget conditions, thereby improving the overall performance and cost-effectiveness of the data preparation process. The results indicate that incorporating cost-benefit analysis and optimization into data preparation strategies facilitates efficient resource allocation and provides useful support for cost control and performance optimization in complex data processing scenarios.