DATA QUALITY IMPROVEMENT: CASE STUDY FAST PAYMENT SYSTEM INFRASTRUCTURE

Jefree W.L.H Manurung; Yova  Ruldeviyani

doi:10.46764/teknimedia.v7i1.379

Jefree W.L.H Manurung Universitas Indonesia
Yova Ruldeviyani Universitas Indonesia

DOI: https://doi.org/10.46764/teknimedia.v7i1.379

Abstrak

This study aims to assess the data quality of the Fast Payment System infrastructure application. The assessment employs a specialized framework for financial data, namely the QAFD (Quality Assessment Framework for Data), which focuses on five data quality dimensions. Given the high transaction volume and operational criticality of fast payment systems, ensuring reliable data quality is essential to support reporting, analysis, and policy communication. Data quality assessment was conducted through two approaches: objective and subjective assessment, applied to 34 variables. The objective assessment was based on quantitative measurements of the data, while the subjective assessment involved user or stakeholder perceptions of data quality. The objective assessment results showed that the Accuracy dimension reached 99.99%. The Completeness dimension for mandatory data variables was recorded at 84.41%. For the Uniqueness dimension, the variable sending_customers_id_number_hash achieved 87.34%, while receiving_customers_id_number_hash reached 99.96%. Meanwhile, both the Currency and Timeliness dimensions achieved a 100% rate. A comparison between the objective and subjective assessment results indicated discrepancies in the Completeness and Uniqueness dimensions, while the other dimensions were aligned. These findings indicate that data quality challenges extend beyond technical processing aspects and reflect the importance of continuously improving metadata clarity and business rule alignment governing variable usage and identifier relationships. This study contributes by providing empirical evidence of QAFD implementation in fast payment operational data and emphasizes the value of proactive data governance through metadata enhancement and strengthened validation mechanisms to support reliable reporting and institutional credibility.

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