Co-dependence relationship between master data management and data quality: A review

Abstract

Master Data Management refers to the consolidation, integration and standardization of master data from multiple data sources into a centralized system to support data quality improvement in an organization. Nevertheless, while Master Data Management came into prominence in the information systems field of study, there is a lack of review papers for this topic have been published. Hence, this paper reports the results of a systematic literature review on the Master Data Management research topic. It aims to summarize the research progress of Master Data Management since 2000 to July 2016 and to review the association of Master Data Management and Data Quality. Search strategies with relevant keywords were used to identify literature from seven prestigious academic databases, namely 1) ACM Digital Library; 2) Emerald; 3) IEEE; 4) Science Direct; 5) Scopus; 6) Springer Link; 7) Web of Science, and one industry research database, namely Gartner. Additionally, the study made use of Google Scholar to find more related literature on the MDM research topic. From the review, 777 articles were found during the initial search and 347 relevant articles were filtered out for the analysis of MDM research progress. Then, out of the relevant articles, 49 were selected to discuss the association of MDM and Data Quality. This paper is a first academic systematic literature review on the progress of Master Data Management and its association with Data Quality. The result of the review shows that Master Data Management came into prominence from 2009 in parallel with the Big Data movement. Most researchers describe Master Data Management as a means to resolve data quality issues encountered during the management of multiple data sources. It ensures better data quality in the organization by combining a set of processes, data governance, and technology implementations.