Application Research on the Master Data Tenant Management Model of State Grid Corporation Based on Data Governance and Decision Tree Algorithm Analysis

With the rapid development of State Grid's business and the deepening of digital transformation, master data management has become a key link in improving operational efficiency. Problems such as the dispersion of master data and the difficulty of management are becoming increasingly prominent,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE) S. 851 - 855
Hauptverfasser: Liu, Hu, Zheng, Zhonglong, Shu, Lu, Cui, Zhuang, Ruan, Xiuqiong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.03.2025
Schlagworte:
Analytical models
Companies
Data governance
Data integrity
Data models
Decision making
Decision Tree Algorithm
Decision trees
Digital transformation
Master Data Management
Resource management
Technological innovation
Tenant Management Model
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:With the rapid development of State Grid's business and the deepening of digital transformation, master data management has become a key link in improving operational efficiency. Problems such as the dispersion of master data and the difficulty of management are becoming increasingly prominent, and an efficient management model is urgently needed to optimize resource allocation and improve decision-making efficiency. This paper first introduces the current status and challenges of master data management in State Grid Corporation of China, and then elaborates on the construction process of the master data tenant management model based on data governance and decision tree algorithm, including the design of data governance framework, the application of decision tree algorithm and the implementation strategy of tenant management model. Then, through experimental investigations, this paper verifies the effectiveness of this model in improving data quality, optimizing resource allocation, and improving decision-making efficiency. Experimental results show that this model can significantly reduce data redundancy and improve data management efficiency. Its efficiency gradually increased from 0.76 to 1.00, shortening the decision-making cycle and providing strong support for the digital transformation of State Grid Corporation of China.
DOI:10.1109/EESPE63401.2025.10986912