Data Quality Management Maturity Measurement of Government-Owned Property Transaction in BMKG

Authors

  • Septian Bagus Wibisono Faculty of Computer Science, University of Indonesia
  • Achmad Nizar Hidayanto Faculty of Computer Science, University of Indonesia
  • Widijanto Satyo Nugroho Faculty of Computer Science, University of Indonesia

DOI:

https://doi.org/10.21512/commit.v12i2.4470

Keywords:

Data Quality Management Maturity, Government-Owned Property (GOP), Indonesian Agency for Meteorological, Climatological, and Geophysics

Abstract

Government-Owned Property (GOP) management, including the bookkeeping of GOP transaction, is part of GOP Officer responsibility to ensure the quality of transaction data. This responsibility also applies to GOP Officer in Indonesian Agency for Meteorological, Climatological and Geophysics ‘Badan Meteorologi, Klimatologi, dan Geofisika’ (BMKG). GOP data as the source for the Central Government Financial Report is expected to be well-maintained. It must be presented as accurate as possible, although there are still inaccurate data presented in the latest BMKG GOP Report. This qualitative research using document study and some interview sessions aims to measure how well the Data Quality Management (DQM) maturity of GOP transaction in BMKG using Loshin’s Data Quality Maturity model. Thus, the result of maturity assessment is analyzed to recommend and implement DQM activities from the Data Management Body of Knowledge (DMBOK). The purpose is to improve GOP DQM. The research shows that the level of DQM maturity is at a repeatable level to defined level. Moreover, 52 maturity characteristics need to be followed through with DQM activities.

Dimensions

Plum Analytics

References

M. Yousif, “The rise of data capital,” IEEE Cloud Computing, vol. 2, no. 2, p. 4, 2015.

E. Brynjolfsson, L. M. Hitt, and H. H. Kim, “Strength in numbers: How does data-driven decision making affect firm performance?” SSRN, 2011.

P. Brous, P. Herder, and M. Janssen, “Governing asset management data infrastructures,” Procedia Computer Science, vol. 95, pp. 303–310, 2016.

R. Jugulum, “Importance of data quality for analytics,” in Quality in the 21st Century. Springer, 2016, pp. 23–31.

J. G. Geiger, “Data quality management, the most critical initiative you can implement,” in SUGI 29 Proceedings, Montreal, Canada, May 9–12, 2004, pp. 1–14.

M. Mosley, The DAMA dictionary of data management. Technics Publications, LLC, 2008.

J. Wild, K. W. Shaw, and B. Chiappetta, Fundamental accounting principles. McGraw-Hill Higher Education, 2010.

K.-S. Ryu, J.-S. Park, and J.-H. Park, “A data quality management maturity model,” ETRI Journal, vol. 28, no. 2, pp. 191–204, 2006.

M. Ofner, B. Otto, and H. O¨ sterle, “A maturity model for enterprise data quality management,” Enterprise Modelling and Information Systems Architectures-An International Journal, vol. 8, no. 2, pp. 8–22, 2013.

DataFlux Corporation, The data governance maturity model: Establishing the people, policies and technology that manage enterprise data, DataFlux Corporation, 2007.

D. Loshin, The practitioner’s guide to data quality improvement. Elsevier, 2010.

C. Batini, C. Cappiello, C. Francalanci, and A. Maurino, “Methodologies for data quality assessment and improvement,” ACM Computing Surveys (CSUR), vol. 41, no. 3, p. 16, 2009.

L. Du, “Financial decision support system research based on data warehouse,” in International Conference on Information Management, Innovation Management and Industrial Engineering, vol. 2. Xi’an, China: IEEE, Dec. 26–27, 2009, pp. 23–26.

M. Francisco, S. N. Alves-Souza, E. G. Campos, and L. S. De Souza, “Total data quality management and total information quality management applied to costumer relationship management,” in Proceedings of the 9th International Conference

on Information Management and Engineering. Barcelona, Spain: ACM, Oct. 09–11, 2017, pp. 40–45.

T. Dai, H. Hu, Y. Wan, Q. Chen, and Y. Wang, “A data quality management and control framework and model for health decision support,” in 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). Zhangjiajie, China: IEEE, Aug. 15–17, 2015, pp. 1792–1796.

S. Malange, E. K. Ngassam, S. Ojo, and I. Osunmakinde, “Methodology for improving data quality management in South African government departments,” in IST-Africa Conference. Lilongwe, Malawi: IEEE, May 6–8, 2015, pp. 1–8.

International Organization for Standardization, “ISO 80000-61 data quality – part 61: Data quality management: Process reference model,” Online, International Organization for Standardization, 2016.

D. Loshin, “Rule-based data quality,” in Proceedings of the Eleventh International Conference on Information and Knowledge Management. McLean, Virginia, USA: ACM, Nov. 4–9, 2002, pp. 614–616.

R. Silvola, J. Harkonen, O. Vilppola, H. Kropsu-Vehkapera, and H. Haapasalo, “Data quality assessment and improvement,” International Journal of Business Information Systems, vol. 22, no. 1, pp. 62–81, 2016.

P. H. S. Panahy, F. Sidi, L. S. Affendey, and M. A. Jabar, “The impact of data quality dimensions on business process improvement,” in Fourth World Congress on Information and Communication Technologies (WICT). Melaka, Malaysia: IEEE, Dec. 8–11, 2014, pp. 70–73.

A. Vetr`o, L. Canova, M. Torchiano, C. O. Minotas, R. Iemma, and F. Morando, “Open data quality measurement framework: Definition and application to open government data,” Government Information Quarterly, vol. 33, no. 2, pp. 325–337, 2016.

J. Du and L. Zhou, “Improving financial data quality using ontologies,” Decision Support Systems, vol. 54, no. 1, pp. 76–86, 2012.

B. Otto, K. Wende, A. Schmidt, and P. Osl, “Towards a framework for corporate data quality management,” in 18th Australasian Conference on Information Systems (ACIS). Toowoomba: The University of Southern Queensland, Dec. 5–7, 2007, pp. 916–926.

M. H. Ofner, B. Otto, and H. O¨ sterle, “Integrating a data quality perspective into business process management,” Business Process Management Journal, vol. 18, no. 6, pp. 1036–1067, 2012.

T. O’Brien, “Accounting for data quality in enterprise systems,” Procedia Computer Science, vol. 64, pp. 442–449, 2015.

B. H. P. J. Vermeer, “How important is data quality for evaluating the impact of EDI on global supply chains?” in Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. Washington, USA: IEEE, Jan. 4–7, 2000, pp. 1–10.

S. Juddoo, “Overview of data quality challenges in the context of big data,” in International Conference on Computing, Communication and Security (ICCCS). Pamplemousses, Mauritius: IEEE, Dec. 4–5, 2015, pp. 1–9.

Downloads

Published

2018-11-01
Abstract 2141  .
PDF downloaded 844  .