Measurement of Export Data Quality Using Task-Based Data Quality (TBDQ): Case Study of the Directorate General of Customs and Excise

Pradnyana, I Nyoman Prama; Pradipta, Dhea Junestya; Ruldeviyani, Yova

doi:10.1109/icitee49829.2020.9271755

Cited by 3 publications

(6 citation statements)

References 8 publications

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“…Regarding DQM, the authors pinpoint the research gap among previous studies: lack of practical, applicable approaches [39], [40], [42], [48]- [54]. Previous studies tend to stop their research after providing recommendations from known best practices.…”

Section: ) Problem Analysismentioning

confidence: 99%

“…For instance, this study employs the same method to remedy ED4 and DM1 using DQM5. Notably, Pradnyana et al [39] evaluated data quality but overlooked establishment of quality management procedures, which serve as the foundation for DQM [7]. Based on these findings, the authors have mapped recommendations for each component's gap, following DMBOK guidelines, as shown in Table 4.…”

Section: ) Organisational Maturity Levelmentioning

confidence: 99%

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A Practical Approach to Enhance Data Quality Management in Government: Case Study of Indonesian Customs and Excise Office

Nugraha,

Wibowo,

Genia

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: The exponential data growth emphasises the importance of efficient information flow in organisations, especially in the financial sector. Data quality significantly influences decision-making, necessitating reliable Data Quality Management (DQM) frameworks. Previous studies propose DQM to maintain data quality through regulation, technology, measurement, evaluation, and improvement. Researchers highlight high-quality data benefits in private organisations but note the lack of improvement in data utilisation in public organisations. In Indonesia, data accuracy and quality are crucial for financial policies, with frequent reports of data inaccuracies in the Directorate General of Customs and Excise (DJBC), demanding standardised DQM practices. However, However, prior studies have yet to provide comprehensive and practical solutions to improve DQM practices. This study therefore aims to measure the DQM maturity, provide recommendations based on best practices, and formulate a practical strategy for improvements along with indicators tailored to the organisation, a topic that previous research has not explored. Methods: This study falls under a mixed method approach (a quantitative study followed by a qualitative study) and employs a three-stage methodology. The authors conduct maturity assessment using Loshin model through an assisted enumeration from 5 key stakeholders followed by recommendations based on the Data Management Body of Knowledge (DMBOK) and strategy formulation from internal documents and interview. Results: The data analysis yielded a DQM maturity score of 3.10, indicating a "defined to managed" level of maturity. Among eight components, only one receives a Managed level, two components are in the Defined level and the rest belongs to a Repeatable level. This study also proposes three strategies to bolster DQM by targeting 49 weak points, which will be progressively and sequentially implemented over a three-year period, using twelve possible solutions. Conclusion: The study highlights the importance of efficient data flow, particularly in the financial sector, and suggests DQM for maintaining data quality. DJBC's import DQM level is assessed using Loshin's measurements, revealing areas for improvement through key DMBOK activities. Recommendations include data governance, strategic planning, and sequential DQM implementation. The study concludes by formulating a practical approach to be applied in a three-year span with ten indicators to measure success. Keywords: Data Quality Management, Data Quality Maturity Model, Data Quality Strategy, Loshin, DMBOK

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Section: ) Problem Analysismentioning

confidence: 99%

Section: ) Organisational Maturity Levelmentioning

confidence: 99%

A Practical Approach to Enhance Data Quality Management in Government: Case Study of Indonesian Customs and Excise Office

Nugraha,

Wibowo,

Genia

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

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“…1) Task-based Data Quality Method (TBDQ) TBDQ is a process-driven data quality method that specifically helps organizations where employees play an essential role, directly or indirectly, in creating or manipulating information. TBDQ focuses on structured data, and it has two main phases, namely assessment (planning and evaluation) and improvement (evolution and execution) [7]. TBDQ uses a questionnaire survey and a simple ratio to measure data quality [22].…”

Section: Data Quality Measurementmentioning

confidence: 99%

“…The research also includes some strict criteria, such as the national identity number that must be validated with the web service. Next, for consistency, the expected criteria are similar to the rules used in previous research [7], stating that data need to have the same format and should be compatible with previous ones. The checking process for inconsistency is carried out by comparing the data field format with the determined one [5].…”

Section: A Define Phasementioning

confidence: 99%

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Strategies to Improve Data Quality Management Using Total Data Quality Management (TDQM) and Data Management Body of Knowledge (DMBOK): A Case Study of M-Passport Application

Rahmawati¹,

Ruldeviyani²,

Abdullah³

et al. 2023

CommIT (Communication and Information Technology) Journal

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M-Passport is a mobile application developed for Indonesians to request for passport online. The applicants independently input all required data using this application, so the quality of data entered must be considered to ensure the passport’s validity as an official state document. However, input errors increase the time needed for the interview process and make the data verification procedure inefficient. The research aims to assess the data quality of M-Passport for organizations to take deliberate actions to enhance the data quality. The research applies the Total Data Quality Management (TDQM) method and the Data Management Body of Knowledge (DMBOK). Six data quality dimensions are used. It consists of completeness, validity, accuracy, timeliness, uniqueness, and consistency. The measure phase is carried out on 17 entities in the M-Passport database through a query process in the production environment. Then, the analysis phase observes the problems based on the pre-determined dimensional classification groups. The result indicates that the average values of completeness, validity, accuracy, consistency, timeliness, and uniqueness are 99.20%, 99.41%, 100%, 90.68%, 78.52%, and 99.98%, respectively. According to the findings, timeliness and consistency are the lowest dimensions in fulfilling business rules. It indicates that organizations need to focus more on improving data quality in these dimensions. Then, based on the DMBOK, the research also generates recommendations for resolving technical and operational issues.

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Enhancing Data Quality Management: A Case Study of Screening and Handling Stunting Toddlers in Big Data Applications

Riatma,

Roshinta,

Safi'ie

et al. 2023

2023 6th International Conference of Computer and Informatics Engineering (IC2IE)

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Measurement of Export Data Quality Using Task-Based Data Quality (TBDQ): Case Study of the Directorate General of Customs and Excise

Cited by 3 publications

References 8 publications

A Practical Approach to Enhance Data Quality Management in Government: Case Study of Indonesian Customs and Excise Office

A Practical Approach to Enhance Data Quality Management in Government: Case Study of Indonesian Customs and Excise Office

Strategies to Improve Data Quality Management Using Total Data Quality Management (TDQM) and Data Management Body of Knowledge (DMBOK): A Case Study of M-Passport Application

Enhancing Data Quality Management: A Case Study of Screening and Handling Stunting Toddlers in Big Data Applications

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