Year: 2019
Type: article
Abstract: Nowadays, the importance of achieving and maintaining a high standard of data quality is widely recognized by both practitioners and researchers. Based on its impact on businesses, the quality of data is commonly viewed as a valuable asset. The literature comprises various techniques for defining, assessing, and improving data quality. However, requirements for data and their quality vary between organizations. Due to this variety, choosing suitable methods that are advantageous for the data quality of an organization or in a particular context can be challenging. This paper surveys data quality frameworks in a comparative way regarding the definition, assessment, and improvement of data quality with a focus on methodologies that are applicable in a wide range of business environments. To aid the decision process concerning the suitability of these methods, we further provide a decision guide to data quality frameworks. This guidance aims to help narrow down possible choices for data quality methodologies based on a number of specified criteria.
Source: IEEE Access
Authors: Corinna Cichy, Stefan Rass
Institution: University of Klagenfurt
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,750
OpenAlex ID: W2911299907