Abstract
Poor data quality is one of the fundamental obstacles to effective use of data warehouse approaches. Complete editing of all corporate archived data is impractical so approaches to data applications that minimize the effect of data errors are important. This paper explores the use of robust data analysis techniques to reduce the impact of data quality problems on the business decision results.
Recommended Citation
Schwarzkopf, A., "Managing Data Quality: Robust and Resistance Tools for a Data Warehousing Environment" (1998). AMCIS 1998 Proceedings. 324.
https://aisel.aisnet.org/amcis1998/324
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