Framework for Prioritizing Solutions in Overcoming Data Quality Problems Using Analytic Hierarchy Process (AHP)

Document Type : Research Paper

Authors

1 Department of Information System, Faculty of Computer Science, Universitas Indonesia, Depok, West Java.

2 Prof., Department of Information System, Faculty of Computer Science, Universitas Indonesia, Depok, West Java.

3 MSc., Department of Information System, Faculty of Computer Science, Universitas Indonesia, Depok, West Java.

4 MSc., STMIK BIna Insani, Bekasi, Jawa Barat.

Abstract

The Central Statistics Agency (BPS) is a government institution that has the authority to carry out statistical activities in the form of censuses and surveys, to produce statistical data needed by the government, the private sector and the general public, as a reference in planning, monitoring, and evaluation of development results. Therefore, providing quality statistical data is very decisive because it will have an impact on the effectiveness of decision making. This paper aims to develop a framework to determine priority of solutions in overcoming data quality problems using the Analytic Hierarchy Process (AHP). The framework is built by conducting interviews and Focus Group Discussion (FGD) on experts to get the interrelationship between problems and solutions. The model that has been built is then tested in a case study, namely the Central Jakarta Central Bureau of Statistics (BPS). The results of the study indicate that the proposed model can be used to formulate solutions to data problems in BPS.

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References

Brackett, M., & Earley, P. S. (2009). The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide).
Chengalur-Smith, I.N., Ballou, D.P., & Pazer, H.L. (1999). The impact of data quality information on decision making: an exploratory analysis, IEEE Transactions on Knowledge and Data Engineering 11 (6), 853–864.
Duvier, C., Neagu, D., Oltean-dumbrava, C., & Dickens, D. (2018). Data quality challenges in the UK social housing sector. International Journal of Information Management, 38(1), 196–200.
Efraim, T. (2011). Decision support and business intelligence systems. Pearson Education India.
English, L.P. (1999). Improving Data Warehouse and Business Information Quality. John Wiley & Sons, New York, NY.
Eppler, M., & Helfert M. (2004). A classification and analysis of data quality costs, in: Proceedings of the 9th International Conference on Information Quality (ICIQ04), 2004, pp. 311–325.
Fisher, C.W., & Kingma, B.R. (2001). Criticality of data quality as exemplified in two disasters. Information & Management, 39 (2), 109–116.
Ge, M., & Helfert, M. (2008). Effects of information quality on inventory management. International Journal of Information Quality, 2 (2), 177–191.
Groot, M. (2017). A Primer in Financial Data Management. Massachusetts, Amerika: Academic Press (Elsevier).
Gürdür, D., El-khoury, J., & Nyberg, M. (2018). Methodology for linked enterprise data quality assessment through information visualizations. Journal of Industrial Information Integration, (March), 0–1. https://doi.org/10.1016/j.jii.2018. 11.002.
Haegemans, T., Snoeck, M., & Lemahieu, W. (2019). A theoretical framework to improve the quality of manually acquired data. Information & Management, 56(1), 1–14.
Haug, A., & Arlbjørn, J. S. (2011). Barriers to master data quality. Journal of Enterprise Information Management, 24(3), 288–303. https://doi.org/10.1108/17410391111122862
Jung, W., Olfman, L., Ryan, T., & Park, Y.T. (2005). An experimental study of the effects of representational data quality on decision performance, in: AMCIS 2005 Proceedings, p. 298.
Keller K. L., & Staelin, R. (1987). Effects of quality and quantity of information on decision effectiveness. Journal of Consumer Research, 14 (2), 200–213.
Mendes, S. D. F., Dong, C., & Sampaio, P. (2015). Expert Systems with Applications DQ 2 S – A framework for data quality-aware information management. Expert Systems With Applications, 42(21), 8304–8326. https://doi.org/10.1016/j.eswa.2015.06.050.
Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decision Support Systems 26 (4), 275–286.
Redman, T.C. (1998). The impact of poor data quality on the typical enterprise. Communications of the ACM, 41 (2), 79–82.
Redman, T. C. (2008). Data driven: profiting from your most important business asset. Harvard Business Press.
Shankaranarayanan, G., & Cai, Y. (2006). Supporting data quality management in decision making. Decision Support Systems, 42 (1), 302–317.
Slone, J.P. (2006). Information Quality Strategy: An Empirical Investigation of the Relationship Between Information Quality Improvements and Organizational Outcomes. Doctoral Thesis, Capella University, Minneapolis, MN, USA.
Umar, A., Karabatis, G., Ness, L., & Horowitz, B. (1999). Enterprise data quality: a pragmatic approach. Information Systems Frontiers, 1(3), 279-301.
Wang, Y., Yan, X., Zhou, Y., & Li, X. (2015). Using AHP for evaluating travel mode competitiveness in long-distance travel. 2015 International Conference in Transportation Information and Safety (ICTIS), pp. 213-218.
Wibowo, W. C., Dayanti, I. S., Hidayanto, A. N., Etivani, I., & Phusavat, K. (2018). Prioritizing solutions for overcoming knowledge transfer barriers in software development using the fuzzy analytic hierarchy process. Knowledge Management & E-Learning: An International Journal (KM&EL), 10(2), 217-249.
Xu, H., Nord, J.H., Brown, N., & Nord, G.D. (2002). Data quality issues in implementing an ERP. Industrial Management & Data Systems, 102(1), 47-58.
Yeganeh, N. K., Sadiq, S., & Sharaf, M. A. (2014). A framework for data quality aware query systems. Information Systems, 46, 24 – 44. https://doi.org/10.1016/j.is.2014.05.005.
Journal of Information Technology Management
Volume 10, Issue 4
2018
Pages 27-40

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