Changes in roles, responsibilities and ownership in organizing master data management

Vilminko-Heikkinen, Riikka; Pekkola, Samuli

doi:10.1016/j.ijinfomgt.2018.12.017

Cited by 28 publications

(26 citation statements)

References 38 publications

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“…The systematic review process produced 15 related studies as presented in Table III. Regarding the credibility of the source, eight studies are from indexed journals [1], [14], [21], [59]- [63], four studies are from established conferences [13], [64]- [66], and three studies are from book publications [45], [67], [68]. In the case of present study, four articles were published in 2019 [14], [62], [63], [66], two articles in 2018 [13], [65], four articles in 2017 [1], [59]- [61], two articles in 2016 [21], [64] [24, 86], and three articles in 2015 [45], [67], [68].…”

Section: Resultsmentioning

confidence: 99%

“…In this case, a total of 11 studies were found focusing on an organizational component in improving master data quality. The discussed factors are data governance [14], [21], [68], [45], [59], [61]- [65], [67], teamwork [59], [61], [64], [67], data quality vision and strategy [45], [62], [63], [67], training [59], and change management [64].…”

Section: )mentioning

confidence: 99%

“…Factor Author Organizational Data governance [14], [21], [68], [45], [59], [61]- [65], [67] Organizational Teamwork [59], [61], [64], [67] Organizational Data quality vision and strategy [45], [62], [63], [67] Organizational Training [59] Organizational Change management [64] Managerial Data quality policy and standard [14], [21], [59], [61], [62], [65], [67], [68] Managerial Data quality assessment [21], [45], [59], [62], [64], [65], [68] Managerial Continuous improvement [21], [45], [59], [62], [64]…”

Section: Componentmentioning

confidence: 99%

“…Understanding of the systems and data quality [45], [59], [61], [64] Managerial Data architecture management [45], [65]- [67] Managerial Information security management [14], [68] Stakeholder Personnel competency [14], [59], [64], [66] Stakeholder Top management support [14], [64], [66] Stakeholder Customer focus [62] Stakeholder Data supplier management [1] Technological Information system [1], [13], [14], [21], [45], [62], [63], [66]- [68] Technological Integration [13], [62], [63], [66]- [68] External Business driver [14], [67], [68] External Legislation [14], [61], [67] www.ijacsa.thesai.org a) Data governance: Data governance involves the establishment of an organizational structure for managing master data quality that can be either a newly formed committee or reoccupied existing formal organizational structure. The latter is preferred to avoid any bureaucracy [45].…”

Section: Managerialmentioning

confidence: 99%

“…Another strategy in defining roles and responsibilities in managing master data quality is through the concept of ownership [61]- [64]. Vilminko-Heikkinen and Pekkola [63] further explained the approach using three-level of master data quality management which are managerial level that involves the concept owner role responsible for the whole master data quality management initiative, support function role involving technical task and data domain level role consist of data owner task responsible for the data domain as a whole.…”

Section: Managerialmentioning

confidence: 99%

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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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