Data Quality Assessment of Company’s Maintenance Reporting: A Case Study
Manik Madhikermi, Sylvain Kubler, Andrea Buda, Kary Främling
2015
Abstract
Businesses are increasingly using their enterprise data for strategic decision-making activities. In fact, information, derived from data, has become one of the most important tools for businesses to gain competitive edge. Data quality assessment has become a hot topic in numerous sectors and considerable research has been carried out in this respect, although most of the existing frameworks often need to be adapted with respect to the use case needs and features. Within this context, this paper develops a methodology for assessing the quality of enterprises' daily maintenance reporting, relying both on an existing data quality framework and on a Multi-Criteria Decision Making (MCDM) technique. Our methodology is applied in cooperation with a Finnish multinational company in order to evaluate and rank different company sites/office branches (carrying out maintenance activities) according to the quality of their data reporting. Based on this evaluation, the industrial partner wants to establish new action plans for enhanced reporting practices.
References
- Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15):2787- 2805.
- Batini, C., Cappiello, C., Francalanci, C., and Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys (CSUR), 41(3):16.
- Berndt, D. J., Fisher, J. W., Hevner, A. R., and Studnicki, J. (2001). Healthcare data warehousing and quality assurance. Computer, 34(12):56-65.
- Buda, A., Kubler, S., Borgman, J., Främling, K., Madhikermi, M., and Mirzaeifar, S. (2015). Data supply chain in industrial internet. In Proceedings of the 11th IEEE World Conference on Factory Communication Systems.
- Dunn, S. (1998). Reinventing the maintenance process: towards zero downtime. In Queensland Maintenance Conference Proceedings, Queensland, Australia.
- Figueira, J., Greco, S., and Ehrgott, M. (2005). Multiple criteria decision analysis: state of the art surveys, volume 78. Springer Science & Business Media.
- Främling, K. (1996). Modélisation et apprentissage des préférences par réseaux de neurones pour l'aide à la décision multicritère. PhD thesis, INSA Lyon.
- Främling, K., Holmström, J., Loukkola, J., Nyman, J., and Kaustell, A. (2013). Sustainable PLM through intelligent products. Engineering Applications of Artificial Intelligence, 26(2):789-799.
- Jarke, M. and Vassiliou, Y. (1997). Data warehouse quality: A review of the dwq project. In IQ, pages 299-313.
- Kahn, B. K., Strong, D. M., and Wang, R. Y. (2002). Information quality benchmarks: product and service performance. Communications of the ACM, 45(4):184- 192.
- Knight, S.-A. and Burn, J. M. (2005). Developing a framework for assessing information quality on the world wide web. Informing Science: International Journal of an Emerging Transdiscipline, 8(5):159-172.
- Krogstie, J., Lindland, O. I., and Sindre, G. (1995). Defining quality aspects for conceptual models. Proceedings of the IFIP8.1 Working Conference on Information Systems Concepts: Towards a Consolidation of Views, 1995:216-231.
- Lindland, O. I., Sindre, G., and Solvberg, A. (1994). Understanding quality in conceptual modeling. Software, IEEE, 11(2):42-49.
- Mardani, A., Jusoh, A., and Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications-two decades review from 1994 to 2014. Expert Systems with Applications.
- Mobley, R. K. (2002). An introduction to predictive maintenance. Butterworth-Heinemann.
- Peabody, J. W., Luck, J., Jain, S., Bertenthal, D., and Glassman, P. (2004). Assessing the accuracy of administrative data in health information systems. Medical care, 42(11):1066-1072.
- Price, R. J. and Shanks, G. (2009). A semiotic information quality framework: Theoretical and empirical development.
- Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resources allocation. New York: McGraw.
- Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1):9-26.
- Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process, volume 4922. RWS publications Pittsburgh.
- Tzeng, G.-H. and Huang, J.-J. (2011). Multiple attribute decision making: methods and applications. CRC Press.
- Wang, R. Y. and Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of management information systems, pages 5-33.
- Zheng, G., Zhu, N., Tian, Z., Chen, Y., and Sun, B. (2012). Application of a trapezoidal fuzzy ahp method for work safety evaluation and early warning rating of hot and humid environments. Safety Science, 50(2):228- 239.
Paper Citation
in Harvard Style
Madhikermi M., Kubler S., Buda A. and Främling K. (2015). Data Quality Assessment of Company’s Maintenance Reporting: A Case Study . In Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-103-8, pages 162-172. DOI: 10.5220/0005518401620172
in Bibtex Style
@conference{data15,
author={Manik Madhikermi and Sylvain Kubler and Andrea Buda and Kary Främling},
title={Data Quality Assessment of Company’s Maintenance Reporting: A Case Study},
booktitle={Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2015},
pages={162-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005518401620172},
isbn={978-989-758-103-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Data Quality Assessment of Company’s Maintenance Reporting: A Case Study
SN - 978-989-758-103-8
AU - Madhikermi M.
AU - Kubler S.
AU - Buda A.
AU - Främling K.
PY - 2015
SP - 162
EP - 172
DO - 10.5220/0005518401620172