Assuring the Information Quality in Data Mining for a Finance Company

This paper describes a information quality assurance exercise undertaken for a finance company as part of a larger project in auto finance marketing. A methodology to estimate the effects of data accuracy, completeness and consistency on the data aggregate functions count, sum and average is presented. This methodology should be of specific interest to quality assurance practitioners for projects that harvest warehouse data for decision support to the management. The assessment comprised ten checks in three broad categories, to ensure the quality of information collected over 1103 attributes. The assessment discovered four critical gaps in the data that had to be corrected before the data could be transitioned to the analysis phase.