Research On Improving Data Quality By Data Mining Consulting

Lixingsen 1, Shiyong 2, Lijun 1 , Zhangpeng 1

1 College of Management, Graduate University of the Chinese Academy of Sciences, The CAS Research Center on Data Technology & Knowledge Economy, China

2 Graduate University of the Chinese Academy of Sciences, The CAS Research Center on Data Technology & Knowledge Economy, China

The reality demand of data mining in enterprise has been increased according to the development of information systems. However, data mining need high quality data while there are no good enough data in many enterprises for data mining to get a credible conclusion, which prevent its implement. Commonly data cleaning and Extraction-Transformation-Loading tools or tolerance algorithms have been used to mine low quality data. But those method only can improve the current data quality for mining, new created data from enterprise information system will make all data dirty again. By analyzing the reasons of low data quality on System Engineering Theory, A new method called data mining consulting has been established, which solves the data quality problem by metasynthesis method including software designing, management and data mining testing, etc. Its application in a web company shows that it has good practicality which can do data mining project in low quality data enterprises and increase the company’s decision levels.

Key Words: Data mining, Knowledge Management, Data Mining Consulting, Enterprise information system, Data quality

Author Description : Li, Xingsen, male, born on Jan. 1969, Senior Engineer, doctoral postgraduate in College of Management, Graduate University of the Chinese Academy of Sciences. His research interests are KM, Data Mining and enterprise operation systems. Email: vc6a@sohu.com.

Shi, Yong, male, born in 1956, tutor of doctoral students, Professor of Graduate University of the Chinese Academy of Sciences and the CAS Research Center on Data Technology & Knowledge Economy. He is a chair professor of "100 talent Program of Chinese Academy of Sciences". Email: yshi@gscas.ac.cn .