Authors:
Daniel Staegemann
1
;
Matthias Volk
1
;
Akanksha Saxena
1
;
Matthias Pohl
1
;
Abdulrahman Nahhas
1
;
Robert Häusler
1
;
Mohammad Abdallah
2
;
Sascha Bosse
1
;
Naoum Jamous
1
and
Klaus Turowski
1
Affiliations:
1
Magdeburg Research and Competence Cluster Very Large Business Applications, Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
;
2
Department of Software Engineering, Al-Zaytoonah University of Jordan, Amman, Jordan
Keyword(s):
Big Data, Data Quality, Data Acquisition, Data Management, Literature Review.
Abstract:
In the recent years, the term big data has attracted a lot of attention. It refers to the processing of data that is characterized mainly by 4Vs, namely volume, velocity, variety and veracity. The need for collecting and analysing big data has increased manifolds these days as organizations want to derive meaningful information out of any data that is available and create value for the business. A challenge that comes with big data is inferior data quality due to which a lot of time is spent on data cleaning. One prerequisite for solving data quality issues is to understand the reasons for their occurrence. In this paper, we discuss various issues that cause reduced quality of the data during the acquisition and management. Furthermore, we extend the research to categorize the quality of data with respect to the identified issues.
