Factors Influencing Master Data Quality: A Systematic Review

Abstract

Master data refers to the data that represents the core business of the organization, shared among different applications, departments, and organizations and most valued as the important asset to the organization. Despite the outward benefit of master data mainly in decision making and organization performance, the quality of master data is at risk. This is due to the critical challenges in managing master data quality the organization may expose. Hence the primary aim of this study is to identify factors influencing master data quality from the lens of total quality management while adopting the systematic literature review method. The study proposed 19 factors that inhibit the quality of master data namely data governance, information system, data quality policy and standard, data quality assessment, integration, continuous improvement, teamwork, data quality vision and strategy, understanding of the systems and data quality, data architecture management, personnel competency, top management support, business driver, legislation, information security management, training, change management, customer focus, and data supplier management that can be categorized to five components which are organizational, managerial, stakeholder, technological, and external. Another important finding is the identification of the differences for factors influencing master data compared to other data domain which are business driver, organizational structure, organizational culture, performance evaluation and rewards, evaluate cost/benefit tradeoffs, physical environment, risk management, storage management, usage of data, internal control, input control, staff participation, middle management's commitment, the role of data quality and data quality manager, audit, and personnel relation. It is expected that the findings of this study will contribute to a deeper understanding of the factors that will lead to an improved master data quality.

A Data Quality Model for Master Data Repositories

“…transactional) data is a good starting point for studying the quality of master data, but taking into consideration the unique features of master data shown in Section 2.1. However, even when several works have dealt explicitly with how master data management has helped organizations to solve data quality issues (Silvola et al, 2011), only a few works address the evaluation of the quality of the master data repositories (Ibrahim et al, 2021;Vilminko-Heikkinen and Pekkola, 2017).…”

“…However, dealing with poor data quality in the master data repositories is still one of the biggest challenges in master data management (Benkherourou and Bourouis, 2022;Ibrahim et al, 2021;Silvola et al, 2011). Several works stated that master data quality issues go beyond consistency and deduplication (Cleven and Wortmann, 2010;Fan et al, 2013).…”

Data Management Risks: A Bane of Construction Project Performance

“…Taleb et al [36] stress that a good data quality assessment includes data refinery (filtering, cleansing, compression, and format conversion) and valuation. In other words, data quality ensures data fitness before usage [37]. Document and content management is another data management approach.…”