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.
Highlights
MotivationThe quality of the evidence and the benefits of the resulting decisions depend heavily on the quality of the underlying data
The present paper examines how the new topic of data wrangling can be used to solve data quality problems
Not maintained attributes; Abuse of attributes for additional information; Incorrect data caused by incorrect input, including, e.g., wrong reading, etc.; Typing error; Inaccurate data; Missing data; Redundant and inconsistent data; Various incorrect formats; Duplicate records; Outdated information. These data quality problems are based on a compilation of cases discussed in the literature [8,11,12]
Summary
The quality of the evidence and the benefits of the resulting decisions depend heavily on the quality of the underlying data. The new term data wrangling is designed to handle a variety of complex data of any size This requires a highly structured database or information system in which the basis for further analysis and visualization is stored [7]. The analysis and elimination of causes for identified problems is the decisive basis for sustainable success Against this background, the present paper examines how the new topic of data wrangling can be used to solve data quality problems. The aim was to introduce the importance of data quality in the databases (such as research data management) to the reader and to illuminate the handling of incorrect data in the process of data wrangling with a practical tool and to show approaches.
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