The development of data quality metrics using thematic analysis

Abstract

Data quality management remains a challenge in every organization in which high quality data needed to help in decision making. Poor data quality management has a negative impact that can result in financial loss, loss of privacy, business process failure and inefficiencies, creates legal and security risks and loss of reputation. Much research has been conducted on data quality metrics and related information such as selecting data quality dimensions but most of the studies on data quality metrics are less than fit to help in decision making[1]–[8]. There is also lack of standardization regarding usage of each dimension. These reasons provide the rationale for the objectives of this study which are (1) To investigate the appropriateness of dimensions used in existing data quality metrics that used in assessing data quality and (2) To propose a data quality metrics to assess quality of data. The study conducted an extensive literature review to address the objectives. We used thematic analysis to determine the theme studied by each paper. Then, a data quality metrics is suggested with dimensions that are discussed. The study found that key to importance is accuracy, completeness, timeliness and scope of data.