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"Dalam rangka mendukung pengambilan keputusan yang tepat bagi pimpinan berbasis data (data driven organization), Kemenkeu menyusun inisiatif strategis optimalisasi Sistem Layanan Data Kementerian Keuangan (SLDK) dan pengembangan proyek data analytics. Dalam pengembangan data analytics, terdapat permasalahan rendahnya kualitas data sehingga data driven organization belum terwujud dengan optimal. Penelitian ini meggunakan metode kualitatif dengan melalui proses wawancara dan observasi. Pengukuran kualitas data dan tingkat kematangan kualitas data menggunakan kerangka kerja Loshin’s Data Quality, DAMA-Data Management Book of Knowledge (DMBoK), dan Government Data Qualiaty (GDQ). Hasil pengukuran kualitas data menunjukkan terdapat permasalahan data tidak akurat dan tidak lengkap dan tingkat kematangan kualitas data Kemenkeu berada pada level Repeatable. Menyusun strategi kualitas data, ketentuan teknis, tim kualitas data, dan prosedur pengelolaan kualitas data; identifikasi harapan dan aturan kualitas data; mengukur, memantau, dan melaporkan kualitas data; mengelola aturan, knowledge base, dan metadata; meningkatkan kesadaran; melakukan pelatihan; menyediakan tools, menerapkan aturan dan menangani permasalahan; memutakhirkan SLA; mengelola kinerja kualitas data; dan melakukan audit kualitas data merupakan strategi peningkatan kualitas data yang dilaksanakan dalam empat tahap pada Tahun 2022-2023.

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To support the right decision making for data-driven organizations, the Ministry of Finance (MoF) has developed a strategic initiative for optimizing the MoF's Data Service System (SLDK) and developing a data analytics project. In the development of data analysis, there is a problem of low data quality so that data-driven organizations have not been realized optimally. This study uses a qualitative method through interview and observation. Measurement of data quality and maturity level of data quality uses the Loshin's Data Quality framework, DAMA-Data Management Book of Knowledge (DMBoK), and Government Data Quality (GDQ). The results of the measurement of data quality indicate that there are problems with inaccurate and incomplete data and the MoF's data quality level is at the Repeatable level. Develop a data quality strategy, technical provisions, data quality team, and data quality management procedures; identification of data quality expectations and rules; measure, monitor, and report on data quality; manage rules, knowledge base, and metadata; raise awareness; conduct training; provide tools, apply rules and carry out problem solving; updating SLAs; manage data quality performance; and conducting data quality audits is a data quality improvement strategy implemented in four stages in 2022-2023.

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"Dalam mendukung misi terkait nilai kekayaan negara, Direktorat Jenderal Kekayaan Negara (DJKN) diharapkan memiliki data penilaian aset yang dapat diandalkan. Dalam pelaksanaannya, ditemukan jika masih belum optimalnya kualitas data DJKN dalam mewujudkan Data Driven Organization. Penelitian ini bertujuan untuk mengevaluasi kualitas data dan menyusun rekomendasi peningkatan kualitas data penilaian aset. Penelitian ini menggunakan metode kualitatif dengan teknik wawancara dan observasi. Pengukuran profiling kualitas data dan tingkat kematangan kualitas data menggunakan kerangka kerja Loshin's Data Quality dan Data Management Book of Knowledge (DMBoK). Hasil pengukuran profiling kualitas data menunjukkan jika masih terdapat permasalahan kualitas terkait keakuratan, kelengkapan, ketepatan waktu, dan keabsahan data. Sementara untuk tingkat kematangan manajemen kualitas data, DJKN berada pada level dua yaitu repeatable. Dari hasil pengukuran, dilakukan analisis akar penyebab permasalahan dan analisis kesenjangan untuk menjadi bahan penyusunan rekomendasi. Sebanyak 19 rekomendasi disusun mengacu pada aktivitas manajemen kualitas data DMBoK dan dipetakan ke dalam empat tahapan plan-do-check-act dari Shewhart/Deming. Rekomendasi tersebut meliputi koordinasi, harapan dan aturan, prosedur, master data, katalog layanan, dampak bisnis, kelompok kerja, perbaikan teknis, sentralisasi dokumentasi, knowledge sharing, evaluasi dan pemantauan, knowledge base, metadata, data cleansing, sertifikasi dan continous improvement.

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In supporting the mission related to the value of state assets, the Directorate General of State Assets Management (DJKN) is expected to have reliable asset valuation data. The fact is, it was found that the quality of DJKN data was still not optimal in realizing a Data Driven Organization. This study aims to evaluate the quality of data and develop recommendations to improve the quality of asset valuation data. This study uses a qualitative method with interview and observation techniques. Data quality profiling and data quality maturity measurements were made using Loshin's Data Quality and Data Management Book of Knowledge (DMBoK) frameworks. The results of data quality profiling measurements show that there are still quality problems related to the accuracy, completeness, timeliness, and validity of data. Meanwhile, for the maturity level of data quality management, DJKN is at level two, which is repeatable. From the measurement results, a root cause analysis of the problem and a gap analysis were carried out to become material for strategy preparation. A total of 19 recommendations were developed referring to DMBoK's data quality management activities and mapped into four stages of Shewhart/Deming's plan-do-check-act. The strategy includes coordination, expectations and rules, procedures, master data, service catalog, business impact, working groups, technical improvements, documentation centralization, knowledge sharing, evaluation and monitoring, knowledge base, metadata, data cleansing, certification and continuous improvement."