Critical Success Factors for Corporate Data Quality Management

Lucas, A.

doi:10.1007/978-3-030-16181-1_60

Cited by 2 publications

(16 citation statements)

References 13 publications

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“…In contrary to the previous studies, [27], [28] explored factors related to data quality management regardless of specific data domain, where the findings support a higher www.ijacsa.thesai.org generalization. Also rooted in TQM theory based on the study by [33], [27] suggested information quality management (IQM) framework which consists of 11 interdependent factors which are 1) IQM governance, 2) continuous IQM improvement, 3) training, 4) information quality requirements management, 5) information quality risk management, 6) information quality assessment/monitoring, 7) continuous information quality improvement, 8) information product lifecycle management, 9) storage management, 10) information security management, and 11) information architecture management.…”

Section: A Data Qualitycontrasting

confidence: 90%

“…Also rooted in TQM theory based on the study by [33], [27] suggested information quality management (IQM) framework which consists of 11 interdependent factors which are 1) IQM governance, 2) continuous IQM improvement, 3) training, 4) information quality requirements management, 5) information quality risk management, 6) information quality assessment/monitoring, 7) continuous information quality improvement, 8) information product lifecycle management, 9) storage management, 10) information security management, and 11) information architecture management. Furthermore, [28] enriched the work of [24], [25], [27] by suggesting the top three factors influencing data quality management, namely, 1) data governance, 2) management commitment and leadership, and 3) continuous data quality management improvement.…”

Section: A Data Qualitymentioning

confidence: 99%

“…Besides, data quality can be considered as a subdiscipline of TQM. Several researchers show the advancement in discussing factors influencing data quality in various contexts [24]- [28]. Based on the theory of TQM, factors influencing data quality can be classified into five components which are organizational, managerial, stakeholder, technological, and external [25], [43], [44].…”

Section: A Data Qualitymentioning

confidence: 99%

“…Sufficient communication, understanding, and involvement between technical and business employees across the department are the provision of effective teamwork [24]- [26], [28]. The effective teamwork can be facilitated using business friendly approach [69].…”

Section: B) Teamworkmentioning

confidence: 99%

“…d) Training: Effective and adequate training is essential in ensuring the employees are equipped with sufficient knowledge and skills in managing master data quality [24], [25], [27], [28], [70]. According to [59], in the case of the industry that deals with specific technical data such as the health field, employees need to have sufficient training not only on how to properly perform data entry and data processing, but capable to perform data quality checking especially involving semantic data quality checking to ensure the quality of the master data.…”

Section: B) Teamworkmentioning

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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Section: A Data Qualitycontrasting

confidence: 90%

Section: A Data Qualitymentioning

confidence: 99%

Section: A Data Qualitymentioning

confidence: 99%

Section: B) Teamworkmentioning

confidence: 99%

Section: B) Teamworkmentioning

confidence: 99%

See 3 more Smart Citations

Factors Influencing Master Data Quality: A Systematic Review

Ibrahim¹,

Mohamed²,

Safie³

2021

IJACSA

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The Maturity Model of Data Quality Management in Banking Industry: PT XYZ Core System Customer Data

Mulyadi

Ruldeviyani²,

Alfiany³

et al. 2023

JKKI

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PT XYZ, engaged in the financial industry, has a target to become a leading company in Southeast Asia and has been supported by more than 200 million customer data in its core system. This huge amount of data is expected to create business opportunities, build a risk-aware culture, and increase supremacy in the business strategy of PT XYZ. These things can be achieved if the data used is of good quality data. In fact, found anomalies in a large number of customer data. To get recommendations for improving the quality of customer data, it is necessary to assess the quality of customer data. The customer data quality assessment in this study uses the method introduced by Loshin (2011). Loshin’s Data Quality Management Model (DQMM) adopts a capability maturity level model in building its characteristic matrix. Maturity levels obtained are 3.6 (expectations), 3.6 (dimensions), 4.4 (policy), 3.8 (procedures), 4.2 (governance), 3.8 (standardization), 4, 2 (technology), and 3.8 (performance management). Regarding the expectation that senior management can achieve the highest level of data quality, 9 strategic recommendations were produced 9 strategy recommendations were submitted to PT XYZ is the result of mapping between criteria that have not been met with data quality management activity in Data Management Body of Knowledge (DMBOK) version 2.0. Measurement and monitoring of good data quality is the most influential recommendation for PT XYZ.

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Critical Success Factors for Corporate Data Quality Management

Cited by 2 publications

References 13 publications

Factors Influencing Master Data Quality: A Systematic Review

Factors Influencing Master Data Quality: A Systematic Review

The Maturity Model of Data Quality Management in Banking Industry: PT XYZ Core System Customer Data

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