Data Wrangling in Database Systems: Purging of Dirty Data

Otmane Azeroual
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Otmane Azeroual: German Center for Higher Education Research and Science Studies (DZHW), Schützenstraße 6a, Berlin 10117, Germany

Data, 2020, vol. 5, issue 2, 1-9

Abstract: Researchers need to be able to integrate ever-increasing amounts of data into their institutional databases, regardless of the source, format, or size of the data. It is then necessary to use the increasing diversity of data to derive greater value from data for their organization. The processing of electronic data plays a central role in modern society. Data constitute a fundamental part of operational processes in companies and scientific organizations. In addition, they form the basis for decisions. Bad data quality can negatively affect decisions and have a negative impact on results. The quality of the data is crucial. This includes the new theme of data wrangling, sometimes referred to as data munging or data crunching, to find the dirty data and to transform and clean them. The aim of data wrangling is to prepare a lot of raw data in their original state so that they can be used for further analysis steps. Only then can knowledge be obtained that may bring added value. This paper shows how the data wrangling process works and how it can be used in database systems to clean up data from heterogeneous data sources during their acquisition and integration.

Keywords: information systems; data management systems; heterogeneous data; data integration; dirty data identification; data quality; data curation; data management; data wrangling; data munging; data crunching (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
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