Frontiers of Data and Computing ›› 2021, Vol. 3 ›› Issue (2): 68-76.doi: 10.11871/jfdc.issn.2096-742X.2021.02.008

• Special Issue: Management Decision and Intelligent Applications • Previous Articles     Next Articles

Research and Practice on ARP Data Governance System

SUN Jianying()   

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2021-03-01 Online:2021-04-20 Published:2021-05-18
  • Contact: SUN Jianying E-mail:jysun@cnic.cn

Abstract:

[Objective] The most valuable result of the informatization project is the massive data resources accumulated. Improving the quality of data, exploring research data resources, assisting management decisions and promoting data empowerment are the purpose of further development in informatization. [Context] ARP (Academia Resource Planning) is the working platform for daily scientific research management of units affiliated to the Chinese Academy of Sciences. More than ten years of management data is stored in the system but the quality of data restricts the accuracy of data analysis. Thus, how to improve data quality has become a hot topic. [Methods] Based on the ISO/IEC25024 data quality model, this paper focuses on data quality management standards and evaluation processes for information management applications. Combined with the characteristics of scientific research management informatization of the Chinese Academy of Sciences, this paper established an index ranking model by using grey relational degree and correlation coefficient. [Results] The results of data quality measurement and visualization of ARP system based on the proposed data quality assessment model show that the data after governance can basically satisfy the needs of intelligent decision support. Data quality has been greatly improved in terms of completeness, but more improvement is required in terms of data accuracy. [Conclusions] A scenario-based and systematic evaluation index system can improve data quality. Besides, the quantitative assessment of data governance could be more rational and effective if the evaluation model is built from the perspective of users.

Key words: ARP, data governance system, data quality, index correlation analysis, evaluation method