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Analysis and Improvement of Data Quality. Improvement of Data Quality for Business Purposes
Author(s) -
Mihai Alexandru Drăgan,
Daniel Metz
Publication year - 2017
Publication title -
virgil madgearu review of economic studies and research
Language(s) - English
Resource type - Journals
ISSN - 2069-0606
DOI - 10.24193/rvm.2017.10.09
Subject(s) - data quality , dispose pattern , computer science , quality (philosophy) , context (archaeology) , quality assurance , enterprise resource planning , data governance , risk analysis (engineering) , quality management , enterprise data management , data science , process (computing) , process management , knowledge management , business , operations management , engineering , management system , enterprise software , metric (unit) , philosophy , epistemology , paleontology , external quality assessment , biology , programming language , operating system
Data presents nowadays a high potential for innovation. Its usage is seen as the foundation of the digitalization process that became a trend in the past few years. For an effective use of the huge amount of data there is also a very important factor to be taken into consideration: the uncertainty of data quality. In the digitalization context, poor data quality can be seen as a threat to the value generated by the amount of data we dispose of. Moreover, poor data quality isn’t only a threat for automated business processes but also an important factor for significant cost increase. Consistent and accurate data is mandatory for successful software. The aim of this paper is on the one hand, to outline the challenges related to the process of data quality assurance and data quality management and on the other hand, to put up for discussion an approach of dealing with data via ERP (Enterprise Resource Planning) Systems with the goal of improvement and optimization of the data quality for an efficient use of the business data.

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