The Development of Data Quality Metrics using Thematic Analysis
Puteri Nor Ilya Nadia Zulkiffli1, Emelia Akashah P. Akhir2, Nor Shakirah Aziz3, Karl Cox4

1Puteri Nor Ilya Nadia Zulkiffli, Department of Computer and Information Science, University Technology Petronas, Perak, Malaysia.

2Emelia Akashah P. Akhir, Positive Computing, University Technology Petronas, Perak, Malaysia.

3Nor Shakirah Aziz, Center for Research in Data Science, University Technology Petronas, Perak, Malaysia.

4Karl Cox, School of Computing, Engineering and Mathematics, University of Brighton, East Sussex, United Kingdom, South Coast  England.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript Published on 19 June 2019 | PP: 304-310 | Volume-8 Issue-8S June 2019 | Retrieval Number: H10510688S19/19©BEIESP

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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.

Keywords: Data Quality, Data Quality Dimension, Accuracy, Completeness, Timeliness.
Scope of the Article: Big Data Quality Validation