Measuring data quality with weighted metrics. (3rd April 2019)

• Record Type: Journal Article
• Authors: Vaziri, Reza; Mohsenzadeh, Mehran; Habibi, Jafar
• Is Part Of: Total quality management & business excellence. Volume 30:Number 5/6(2019)
• Page Start: 708
• Page End: 720
• Publication Date: 2019-04-03
• Subjects: data quality -- information quality -- metrics -- weighted metrics -- methodology; Total quality management -- Periodicals; Quality control -- Periodicals; Quality assurance -- Periodicals; Production management -- Quality control -- Periodicals; 658.401305
• DOI: 10.1080/14783363.2017.1332954
• Languages: English
• ISSNs: 1478-3363

Abstract

Abstract : Data quality (DQ) has been defined as 'fitness for use'. In order to measure and improve DQ, various methodologies have been defined. A DQ methodology is a set of guidelines and techniques that define a rational process to measure and improve the quality of data. In order to make DQ measurement and improvement more organised, DQ dimensions have been defined. A dimension is a single aspect of DQ, such as accuracy, completeness, timeliness, and relevancy . In order to measure dimensions, special tools have been developed. These are called metrics . In most organisations, some data are more significant than others. In other words, some data carry more 'weight'. Hence, they must play a more important role in DQ measurement. Most metrics developed so far do not take into account data weights . In this paper, new metrics based on data weights are defined in order to make them more practical. The effectiveness of the new 'weighted metrics' is tested in a case study. The case study shows that the DQ measurements by weighted metrics more closely reflect the opinion of data users.