An Empirical Investigation of the Impact of Data Quality and its Antecedents on Data Warehousing

Data warehousing is a topic of great interest in the business community, due to increasing business intelligence demands, coupled with increased data availability and processing capability. Despite large financial backing of data warehousing implementations, many fail. Little research has been conducted pertaining to data warehousing success. Traditional system success models (DeLone and McLean, 1992; Seddon, 1997) may be extensible to data warehousing, provided both infrastructure and business application aspects of the implementation are carefully considered, and provided increased attention is paid to the antecedents of data and system quality. Wixom and Watson (2001) conducted an empirical study examining the antecedents to data and system quality to data warehousing success, but found no statistically significant support for the data quality antecedents proposed. This paper reviews system success and data quality literature and proposes a new model for data warehousing success. The new model extends traditional system success models to data warehousing, but proposes a new set of data quality antecedents, which can be empirically examined.