DATA QUALITY ASSESSMENT: A CASE STUDY ON ASSET VALUATION COMPARISON DATA
Abstract
To realize a data-driven organization, good data quality is needed as a foundation for solving various problems related to data management. The case study used in this research is asset valuation comparison data. The purpose of this research is to define dimensions, measure and analyze data quality on asset valuation comparison data. There are three dimensions used in measuring data quality in this study which are adjusted based on existing regulations at Ministry X, namely accuracy, completeness, and validity. This research uses the stages in the Total Data Quality Management (TDQM) framework to measure data quality. The results of measuring all dimensions, 29 out of 58 business rules cannot be fulfilled completely. The business rules that can be fulfilled in each dimension are 47.06% in the completeness dimension, 60% in the validity dimension, and 44.44% in the accuracy dimension. The main factor causing the existence of data attributes that have not met the data quality business rules is because the asset valuation comparison data comes from various data sources. In addition, there are methods or standards for recording data from data source units that are not uniform, so an evaluation of the uniformity of data standardization and the implementation of data governance is needed. The results of this study can be used as material for organizational consideration to be more aware of the current state of data quality. In addition, it can be used by organizations to design strategies and steps to improve data quality so that it can support leaders in making the right decisions.
Downloads
References
H. Li, B. Tang, H. Lu, M. A. Cheema, and C. S. Jensen, “Spatial Data Quality in the IoT Era: Management and Exploitation,” in Proceedings of the 2022 International Conference on Management of Data, New York, NY, USA: ACM, Jun. 2022, pp. 2474–2482. doi: 10.1145/3514221.3522568.
Y. Zhang, “Human resource data quality management based on multiple regression analysis,” in Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, New York, NY, USA: ACM, Dec. 2020, pp. 465–470. doi: 10.1145/3444370.3444614.
S. K. Pradhan, H.-M. Heyn, and E. Knauss, “Identifying and managing data quality requirements: a design science study in the field of automated driving,” Software Quality Journal, May 2023, doi: 10.1007/s11219-023-09622-8.
J. H. Buelvas P., F. E. Avila B., N. Gaviria G., and D. A. Munera R., “Data Quality Estimation in a Smart City’s Air Quality Monitoring IoT Application,” in 2021 2nd Sustainable Cities Latin America Conference (SCLA), IEEE, Aug. 2021, pp. 1–6. doi: 10.1109/SCLA53004.2021.9540154.
Thomas C. Redman, Getting In Front On Data : Who Does What. Technics Publication, 2016.
D. E. Irawan, Y. Ulfa, A. Pamumpuni, I. A. Dinata, T. T. Putranto, and H. Siswoyo, “Reusable data is the new oil,” E3S Web of Conferences, vol. 317, p. 05023, Nov. 2021, doi: 10.1051/e3sconf/202131705023.
Antara and Kodrat Setiawan, “Jokowi: Data Adalah New Oil, Bahkan Lebih Berharga dari Minyak - Bisnis Tempo.co,” Tempo.co. Accessed: Oct. 06, 2023. [Online]. Available: https://bisnis.tempo.co/read/1299253/jokowi-data-adalah-new-oil-bahkan-lebih-berharga-dari-minyak
Dody Dharma Hutabarat, Canrakerta, Lazuardi Zulfikar, Dimas Rahadian, and Lysa Novita Sirait, Membangun Budaya Data di Kementerian Keuangan. Jakarta: Central Transformation Office, Sekretariat Jenderal, Kementerian Keuangan, 2021.
Kementerian Keuangan, “Keputusan Menteri Keuangan Nomor 618/KMK.01/2020 tentang Grand Design Sistem Layanan Data Kementerian Keuangan.” Kementerian Keuangan, Jakarta, 2020.
Kementerian Keuangan, “Keputusan Menteri Keuangan Nomor 269/KMK.01/2021 tentang Tata Kelola Data di Lingkungan Kementerian Keuangan.” Kementerian Keuangan, Jakarta, 2021.
Kementerian Keuangan, “Peraturan Menteri Keuangan Nomor 118/PMK.01/2021 tentang Organisasi dan Tata Kerja Kementerian Keuangan.” Kementerian Keuangan, Jakarta, 2021.
Direktorat Jenderal Kekayaan Negara, “Peraturan Direktur Jenderal Kekayaan Negara Nomor 7/KN/2022 tentang Petunjuk Teknis Penilaian Tanpa Survei Lapangan.” Kementerian Keuangan, Jakarta, 2022.
Y. Setiadi, A. N. Hidayanto, F. Rachmawati, and A. Y. L. Yohannes, “Data Quality Management Maturity Model : A Case Study in Higher Education’s Human Resource Department,” 7th International Conference on Computing, Engineering and Design, ICCED 2021, pp. 1–5, 2021, doi: 10.1109/ICCED53389.2021.9664881.
W. A. Bowo, A. Suhanto, M. Naisuty, S. Ma’mun, A. N. Hidayanto, and I. C. Habsari, “Data Quality Assessment: A Case Study of PT JAS Using TDQM Framework,” in 2019 Fourth International Conference on Informatics and Computing (ICIC), IEEE, Oct. 2019, pp. 1–6. doi: 10.1109/ICIC47613.2019.8985896.
S. D. Rahmawati and Y. Ruldeviyani, “Data Quality Management Strategy to Improve the Quality of Worker’s Wage and Income Data: A Case Study in BPS-Statistics Indonesia, 2018,” in 2019 Fourth International Conference on Informatics and Computing (ICIC), IEEE, Oct. 2019, pp. 1–6. doi: 10.1109/ICIC47613.2019.8985803.
R. Ji, H. Hou, G. Sheng, and X. Jiang, “Data Quality Assessment for Electrical Equipment Condition Monitoring,” in 2022 9th International Conference on Condition Monitoring and Diagnosis (CMD), IEEE, Nov. 2022, pp. 1–4. doi: 10.23919/CMD54214.2022.9991385.
S. Cho, C. Weng, M. G. Kahn, and K. Natarajan, “Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study,” JMIR Mhealth Uhealth, vol. 9, no. 12, 2021, doi: 10.2196/31618.
R. Rahmawati, Y. Ruldeviyani, P. P. Abdullah, and F. M. Hudoarma, “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,” CommIT (Communication and Information Technology) Journal, vol. 17, no. 1, pp. 27–42, Mar. 2023, doi: 10.21512/commit.v17i1.8330.
M. Nedjat-Haiem and J. E. Cooke, “Student strategies when taking open-ended test questions,” Cogent Education, vol. 8, no. 1, Jan. 2021, doi: 10.1080/2331186X.2021.1877905.
DAMA International, Data Management Body of Knowledge (DAMA-DMBOK2). New Jersey: Technics Publications, 2017.
Copyright (c) 2024 I Gusti Ngurah Adi Wicaksana, Achmad Nizar Hidayanto, Handini Mekkawati, Rizha Febriyanti
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
