Master Data Quality in the Era of Digitization - Toward Inter-organizational Master Data Quality in Value Networks: A Problem Identification

Schäffer, Thomas; Leyh, Christian

doi:10.1007/978-3-319-58801-8_9

Cited by 15 publications

(43 citation statements)

References 22 publications

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“…Concerning the improvement of master data quality, understanding factors influencing the quality of the data is a pre-requisite. Even though literature focusing on factors influencing master data quality is scarce, partial contribution by a few scholars such as [1], [21], [46], [47] providing a good starting point.…”

Section: B Master Data Qualitymentioning

confidence: 99%

“…A more specific perspective has been adopted by [1] that explored the challenges and requirements in managing master data quality in the context of digitalization. The author has adopted the SLR approach in getting a deeper insight into the current state of master data quality study and further validated the finding using 33 semi-structured interviews.…”

Section: B Master Data Qualitymentioning

confidence: 99%

“…The evolution of digital transformation and a data-driven economy requires the formulation of new strategies to ensure the organization stays relevant and competitive. An organization is expected to face various issues as the effect of development that requires proactive management action [1]. Taking into account that data is an important element for every organization [2]- [4], the massive amount of data that are created and stored in response to digitalization possess new challenges in the management of data quality.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the organization is normally held responsible to manage a few types of data namely master data, transaction data, and reference data, to name a few. Master data is ranked as having the highest priority to be managed due to the valuable information it holds about the organization [5] and should be considered as an important asset to the organization [1], [6]. Master data represents the organization's core business objects that form the foundation of the main business process and must therefore be used unambiguously across the entire related application, department, and organization.…”

Section: Introductionmentioning

confidence: 99%

“…According to [20] poor master data quality is one of the biggest challenges faced by the organization in managing the complexity of digitalization apart from standardization and governance. www.ijacsa.thesai.org Furthermore, according to [1], 80% of companies acknowledged the impact of poor master data quality to be high or very high for their performance, 82% of the company engaged in data quality initiative but not using the systematic or established method and only 15% of the companies know the established method for improving master data quality.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Factors Influencing Master Data Quality: A Systematic Review

Ibrahim¹,

Mohamed²,

Safie³

2021

IJACSA

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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.

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