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All Journal JT-IBSI (Jurnal Teknik Ibnu Sina) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Aprizal Y
Universitas Ibnu Sina
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Data Quality Management dalam Data Warehouse: Tinjauan Literatur Tiara Aziza; David Saro; Nofri Yudi Arifin; Romiko Afriantoni; Aprizal Y; Atman Lucky Fernandes
Jurnal Responsive Teknik Informatika Vol 9 No 02 (2025): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v9i02.1476

Abstract

This study presents a systematic literature review of Data Quality Management (DQM) in data warehouse environments, aiming to map key dimensions, processes, and architectural/technological enablers, and to identify research gaps. Searches were conducted across Scopus, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar (as a complement) for the period 2009–2025, following PRISMA 2020. Of 200 initial records, 133 were excluded during the first screening, 67 underwent further assessment, and 6 studies met the inclusion criteria for in-depth analysis. Thematic synthesis indicates that effective DQM rests on four integrated pillars: (1) standardized quality dimensions and metrics (accuracy, completeness, consistency, timeliness, and traceability), (2) prevention–detection–correction processes embedded along the ETL/ELT pipeline (including consistent SCD policies and handling of late-arriving data), (3) architectural/technological support (automated data tests within CI/CD, catalogs/metadata, data lineage, observability, and data contracts), and (4) governance that clarifies roles and accountability (data owners/stewards) with incident-response procedures. Practically, organizations should start from critical data elements and high-priority consumption paths, translating SLA/SLI into executable rules. Limitations include the small number of included studies and contextual heterogeneity, motivating further work on cross-domain metric standardization, open DQM benchmarks, cost–benefit evaluations of observability/contract enforcement, and the impact of data quality on analytic/AI performance in near real-time settings.
Co-Authors Abdul Rohmad Basar Al Rusman Ali Basriadi Andry Fratama Andry Fratama David Saro Deo Bagus Prayoga Devega Dirgantara Devega, Army Trilidia Didin Setyawan Fernandes, Atman Lucky Ghea Paulina Suri Hanafi Hanafi Indah Kusuma Dewi, Indah Kusuma Indra Sufian Ismail Ismail Larisang Muhammad Ropianto Nofri Yudi Arifin Novi Widiana Putri Revi Illya Badri Romiko Afriantoni Rozali Rozali Sabtu Sabtu Taslimahudin Taslimahudin Tiara Aziza Weni Lestari Putri Willy Rizki Perdana