A Comparative Study of Data Cleaning Tools

Samson Oni, Zhiyuan Chen, Susan Hoban and Onimi Jademi
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Samson Oni: University of Maryland Baltimore County, USA
Zhiyuan Chen: University of Maryland Baltimore County, USA
Susan Hoban: University of Maryland, Baltimore County, USA
Onimi Jademi: University of Maryland, Baltimore County, USA

International Journal of Data Warehousing and Mining (IJDWM), 2019, vol. 15, issue 4, 48-65

Abstract: In the information era, data is crucial in decision making. Most data sets contain impurities that need to be weeded out before any meaningful decision can be made from the data. Hence, data cleaning is essential and often takes more than 80 percent of time and resources of the data analyst. Adequate tools and techniques must be used for data cleaning. There exist a lot of data cleaning tools but it is unclear how to choose them in various situations. This research aims at helping researchers and organizations choose the right tools for data cleaning. This article conducts a comparative study of four commonly used data cleaning tools on two real data sets and answers the research question of which tool will be useful based on different scenario.

Date: 2019
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