Paper Title
Data Quality of Data Warehouse: A Case Study

Data is knowledge in the present world and therefore need to have its high precision so as to achieve competitive goals. Data Quality (DQ) is always on priority for any institution, no matter big or small. Redman, T. (1998) says that the volume and complexity of Data Warehouse (DW) lead to errors in the data. According to Wixom and Watson (2001) DQ is a key success of any DW. There are many benefits of data having high quality. Additionally, it can increase user satisfaction and confidence. DQ is very critical for any DW, but very little research work has been taken up for quantifying it. DQ checks may be carried out at two levels viz. (i) at various stages of data flow (e.g., validation or checks at source system, staging area, DW repository and dissemination) including metadata (data structure) and (ii) at data content level (e.g., accuracy, timeliness, consistency, etc.). The data quality checks and metadata quality are more important from the DW point of view, users can understand DW contents and how it can be accessed. In this paper the entire value chain of data flow from source (who is the data originator) to DW and dissemination to end user (the consumer of the data) has been analysed to find out possible stages of data flow where there is scope of data quality improvement. An attempt has been made to explore an appropriate approach for preparation of benchmark (ideal DW in respect of DQ) and quantifying Data Quality Index (DQI) of any typical DW. The existing DW system of the Bank (i.e. Database on Indian Economy (DBIE)) is used as a typical DW and analysed its data quality using proposed methodology. In the paper, suggestions have also been provided to enhance DQ of typical DW. Index terms - Data Quality (DQ), Data Warehouse (DW).