A Thematic Review on Data Quality Challenges and Dimension in the Era of Big Data

Abstract

Data quality is the primary concern faced by most of the organizations due to improper maintenance in the database. Data obtained from the various resources are dirty, affecting the accuracy of predicted results. There are a lot of challenges when handling Big Data because it requires well-defined and precise measurement processes. The challenges are in the characteristics of big data itself where the V’s play an important role in measuring and determining data quality. Although the issue has been discussed over 20 years, there is no guideline in identifying the important dimension of data quality being proposed to adhere with the context of Big Data. Therefore, the purpose of this systematic review is to review literature on the issue, challenges, and dimension of data quality in the era of Big Data using thematic review. This review included journal and conference proceeding papers from ACM Digital Library, Scopus, and Science Direct published between 2016 until 2020. Inclusion and exclusion processes have filtered out 21 final articles for the review. A systematic review on these 21 articles focuses on the issue, challenges, and dimension of data quality. The results of this study benefit the future study on the development of data quality dimensions and can be a guideline for the researcher to design the data quality assessment framework.