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Kementerian Pendidikan Nasional Direktorat Jenderal Pendidikan Tinggi. “Toward Understanding Outcomes Associated with Data Quality Improvement,” International Journal of Production Economics (193:August), Elsevier Ltd, pp. “Material Data Matter - Standard Data Format for Engineering Materials,” Technological Forecasting and Social Change (101), Elsevier Inc., pp. “Dual Assessment of Data Quality in Customer Databases,” ACM Journal of Data and Information Quality (1:3), 15:1-15:29. “Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi Menggunakan CALDEA Dan EVAMECAL,” Seminar Nasional ReSaTek II-2012 (November 2012), TI-G.1-TI-G.11.Įven, A., and Shankaranaryanan, G. “Information Systems Success: The Quest for the Dependent Variable,” Information Systems Research (3:1), pp. A Case Study on Assessing the Organizational Maturity of Data Management, Data Quality Management, and Data Governance by Means of MAMD, (October).ĭeLone, W. “Data Quality Assessment and Improvement: A Vrije Universiteit Brussel Case Study,” Procedia Computer Science (106:June 2016), The Author(s), pp. Peraturan Badan Pusat Statistik Nomor 87 Tahun 2018 Tentang Statuta Politeknik Statistika STIS, p. Peraturan Badan Akreditasi Nasional Perguruan Tinggi Nomor 59 Tahun 2018.īadan Pusat Statistik. The activities needed to be carried out are developing and promoting awareness of data quality defining data quality requirements profiling, analyzing, and evaluating data quality define business rules for data quality, establish, and evaluate the data quality services levels, manage problems related to data quality, design and implement operational procedures for data quality management, and monitor operations and performance of data quality management procedures.īadan Akreditasi Nasional. Thus, recommendations have been proposed based on the DAMA-DMBOK framework.
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Only the data quality dimensions component has achieved the expected target. Data quality management maturity has been measured using Loshin’s Data Quality Maturity Model which result is in level 1 to level 2 of maturity. Data quality assessment result indicates that educational data in Statistics Polytechnic did not meet completeness, validity, accuracy, and currency criteria. The educational data is used for implementing higher-education quality assurance system and formulating policies related to universities and majors in Indonesia. Every varsity in Indonesia is responsible for ensuring the completeness, the validity, the accuracy, and the currency of its educational data.