A Study of Data Governance Methods Based on Non-Linear Evaluation of K-Domains

Abstract

The current traditional data governance methods mainly ensure the overall quality of data through data quality inspection, and the lack of data fusion processing leads to poor data governance results. In this regard, a data governance method based on nonlinear evaluation of K domain is proposed. By formulating data cleaning rules, data are processed such as weight reduction, gap filling and deletion, and data fusion operations are performed according to the fusion table attributes by configuring fusion rules. Finally, the data consistency as well as wholeness is checked, and the actual effect of data governance is determined by the data quality score results. In the experiments, the proposed method is validated for the governance effect. The analysis of the experimental results shows that the proposed method takes significantly shorter time for data fusion and has better data governance performance when the proposed method is used to govern multiple sources of data.