Data quality in ETL process: A preliminary study

Manel Souibgui · Faten Atigui · Saloua Zammali · Samira Si-Saïd Cherfi · Sadok Ben Yahia

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Data Quality and Management Big Data and Business Intelligence Web Data Mining and Analysis

Abstract

The accuracy and relevance of Business Intelligence & Analytics (BI&A) rely on the ability to bring high data quality to the data warehouse from both internal and external sources using the ETL process. The latter is complex and time-consuming as it manages data with heterogeneous content and diverse quality problems. Ensuring data quality requires tracking quality defects along the ETL process. In this paper, we present the main ETL quality characteristics. We provide an overview of the existing ETL process data quality approaches. We also present a comparative study of some commercial ETL tools to show how much these tools consider data quality dimensions. To illustrate our study, we carry out experiments using an ETL dedicated solution (Talend Data Integration) and a data quality dedicated solution (Talend Data Quality). Based on our study, we identify and discuss quality challenges to be addressed in our future research.

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References

2012Business Intelligence and Analytics: From Big Data to Big Impact 1996Beyond Accuracy: What Data Quality Means to Data Consumers 2008Building a Data Warehouse 2000Data Cleaning: Problems and Current Approaches.2009Methodologies for data quality assessment and improvement 1998The impact of poor data quality on the typical enterprise 2008Swoosh: a generic approach to entity resolution 2009A Survey of Extract–Transform–Load Technology

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