Open AccessImproving Data Quality and Data Governance Using Master Data Management: A Review
Author(s) -
Sanny Hikmawati,
Paulus Insap Santosa,
Indriana Hidayah
Publication year - 2021
Publication title -
ijitee (international journal of information technology and electrical engineering)
Language(s) - English
Resource type - Journals
ISSN - 2550-0554
DOI - 10.22146/ijitee.66307
Subject(s) - data governance , master data , data quality , corporate governance , process management , stakeholder , data management , quality (philosophy) , knowledge management , process (computing) , computer science , control (management) , business process , business , data mining , political science , public relations , marketing , finance , work in process , metric (unit) , philosophy , epistemology , operating system , artificial intelligence
Master data management (MDM) is a method of maintaining, integrating, and harmonizing master data to ensure consistent system information. The primary function of MDM is to control master data to keep it consistent, accurate, current, relevant, and contextual to meet different business needs across applications and divisions. MDM also affects data governance, which is related to establishing organizational actors’ roles, functions, and responsibilities in maintaining data quality. Poor management of master data can lead to inaccurate and incomplete data, leading to lousy stakeholder decision-making. This article is a literature review that aims to determine how MDM improves the data quality and data governance and assess the success of MDM implementation. The review results show that MDM can overcome data quality problems through the MDM process caused by data originating from various scattered sources. MDM encourages organizations to improve data management by adjusting the roles and responsibilities of business actors and information technology (IT) staff documented through data governance. Assessment of the success of MDM implementation can be carried out by organizations to improve data quality and data governance by following the existing framework.