Munawar Munawar



Many articles on data warehouse development have been written, however no clearly defined standards have been formulated that is applicable to all type of organizations. The rapid growth in data volumes has given rise to new problems for institutions: data quality, which is a critical issue when data are transferred from one system to another. Lack of data quality provided by data warehouse can lead to bad strategic decisions and indicates a significant failure rate. Thus, data quality in data warehouse needs to be assured. It has been widely accepted that data quality issues can emerge at any stage of data warehouse development. However, yet little work is done for formulating data quality that should be considered in the entirety of data warehouse development. This study was achieved through qualitative method by reviewing the most common practices data warehouse development in five organisations that is applicable to all type of organizations and then tried to confirm whether data quality in data warehouse from literature review is practiced in the five organisations followed by confirmation from the experts in order to determine specific data quality dimensions that correlated with data warehouse development. Based on the similirities in the development stages, identification of common practices for DW development can be obtained: requirements analysis, conceptual design, logical design, ETL, and physical design. There are sixteen dimensions of data quality that should be considered in the development of data warehouse.

Keywords : data warehouse, data quality, common practices.


Daftar Pustaka

Agosta, L. (2004). Data Warehousing Lessons Learned: A Time of Growth for Data Warehousing, in DM Review Magazine, 2004, pp. Retrieved on 29/3/2011, from http://www.dmreview.com/article_sub.cfm?articleId=1012461.

Akbar, K., Krishna, S.M and Reddy, V.R. (2013). ETL Process Modeling In DWH Using Enhanced Quality Techniques. International Journal of Database Theory and Application Vol. 6, No. 4, August, 2013.

Al-Debei, M.M. (2011). Data Warehouse as a Backbone for Business Intelligence. European Journal of Economics, Finance, and Administrative Sciences, 33, 153-166.

Alshawi, S., Saez-Pujol, I. and Irani, Z. (2003). Data warehousing in decision support for pharmaceutical R & D supply chain. International Journal of Information Management, 23, 259-268. http://dx.doi.org/10.1016/S0268-4012(03)00028-8

Amin, M.R and Arefin, M.T. (2010). The Empirical Study on the Factors Affecting Data Warehousing Success. International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) Volume 1, Issue 2, December 2010

Babbie, E. R. (2009). The Practice of Social Research, 12th edition, Wadsworth Publishing, 2009, ISBN 0-495-59841-0, pp. 436–440

Ballou, D. P., and Tayi, G.K. 1999. Enhancing Data Quality in Data Warehouse Environments. Communications of the ACM January 1999/Vol. 42, No. 1. pp 73-78

Bilal Ali, Y. A. (2014). Challenges in the Successful Implementation of Data Warehouse. Journal of Management Research. ISSN 1941-899X. 2014. Vol 6, No 3

Celko, J., & McDonald, J. 1995. Don’t warehouse dirty data.Datamation, 41(19), 42–53.

Chenoweth, T., Corral, K. and Demirkan, H. (2006). Seven key interventions for data warehouse success, Communications of the ACM, vol. 49, pp. 114-119.

Conner, D. (2003). Data warehouse failures commonplace, Network World, vol. 20, p. 24.

Cowie, J. and Burstein, F. (2007). Quality of data model for supporting mobile decision making. Decision Support Systems 43, 1675–1683

Coleho, P.S. and Esteves, S.P. (2007). The choice between a 5-point and 10-point scale in the framework of customer satisfaction measurement. International Journal of Market Research, Vol. 49. No. 3, 2007, pp.313-345

Dakrory, S.B, Mahmoud, T.M., and Ali, A.A. (2015). Automated ETL Testing on the Data Quality of a Data Warehouse. International Journal of Computer Applications 131(16):9-16, December 2015. Published by Foundation of Computer Science (FCS), NY, USA

Eckerson, W (2002) Data Quality and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data. The Data Warehousing Institute, Seattle, WA.

Eppler, M. J. (2006). Managing Information Quality: Increasing the Value of Information in Knowledge-Intensive Products and Processes (2nd ed.): Springer.

Flyvberg, B. (2006). Five misunderstandings about case study research. Qualitative Inquiry 12, 2, 219-245

Gosain, A. and Heena. (2015). Literature Review of Data model Quality metrics of Data Warehouse. International Conference on Computer, Communication and Convergence (ICCC 2015). doi: 10.1016/j.procs.2015.04.176

Griffin, R. K. (1998). Data Warehousing. Cornell Hospitality Quarterly, 39, 28-35.

Hwang, H. G., Ku, C. Y., Yen, D. C. and Cheng, C. C. (2004). Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan, Decision Support Systems, vol. 37, pp. 1-21.

Hwang, M. I., & Xu, H. (2008). A Structural Model of Data Warehousing Success. Journal of Computer Information Systems, 49(1).

Hudicka, J. 2003. Bumpy Ride - Data Migration Projects Still Plagued by Problems. Intelligent Enterprise, 10

Haug, A., Zachariassen, F and Van Liempd, D. (2011). The Costs of Poor Data Quality. Journal of Industrial Engineering and Management (JIEM 2011), 4(2): 168-193

Hwang, H. G., Ku, C. Y., Yen, D. C. and Cheng, C. C. (2004). Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan, Decision Support Systems, vol. 37, pp. 1-21.

Hayen, R. L., Rutashobya, C. D. and Vetter, D. E. (2007). An Investigation of the Factors Affecting Data Warehousing Success, International Association for Computer Information Systems (IACIS), vol. 8, pp. 547-553.

Inmon, W. H. (2005). Building the data warehouse (4th ed.): John Wiley & Sons, Inc. New York, NY, USA.

Jaklic, J., Coelho, P. S. And Popovic, A. (2009). Information Quality Improvement as a Measure of Business Intelligence System Benefits. WSEAS Transactions on Business and Economics. Issue 9. Volume 6. September 2009. ISSN. 1109-9526

Jarke, M., Jeusfeld, M., Quix, C., Vassiliadis, P. (1999). Architecture and Quality in Data Warehouses: An Extended Repository Approach. Information Systems 24(3), 229–253

Johnson, L. K. (2004). Strategies for Data Warehousing, MIT Sloan Management Review, vol. 45, p. 9.

Joseph, M.V. (2013). Significance of Data Warehousing and Data Mining in Business Applications. International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-1, March 2013

Kimball, R., Ross, M., Thornthwaite, W., Mundy, J. and Becker, B. (2008). The Data Warehouse Life Cycle Toolkit: Practical Techniques for Building Data Warehouse and Business Intelligent Systems. Second Edition. Wiley Publishing, Inc. IN 46256

Kumar, V. and Thareja, R. (2013). A Simplified Approach for Quality Management in Data Warehouse. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.3, No.5, September 2013

Lindlof and Taylor. (2002). Qualitative Communication Research Methods, 2nd Edition. Thousands Oaks: SAGE. ISBN 978-0-7619-2494-4

Loshin, D. (2008). The Data Quality Business Case: Projecting Return on Investment, Informatica White paper. Retrieved July 15, 2010 from http://www.melissadata.com/enews/articles/1007/2.htm

Mukherjee, D. (2003). An empirical investigation of critical factors that influence data warehouse implementation success.

MajidZaman, E., Quadri, S.M.K., and But, M. A. (2012). Information Integration for Heterogeneous Data Sources. IOSR Journal of Engineering Apr. 2012, Vol. 2(4) pp: 640-643. ISSN: 2250-3021

Munawar, Salim, N., Ibrahim, R. (2011). Toward Data Quality Integration into the Data Warehouse Development. In: Ninth IEEE International Conference on Dependable, Autonomic and Secure Computing. IEEE Computer Sociaty, 978-0-7695-4612-4/11, doi:10.1109/DASC.2011.194

Nemoni, R. R and Konda, R. 2009. A Framework for Data Quality in Datawarehouse. In J. Yang et. Al (Eds): UNISCON 2009, LNBIP 20, pp 292 – 297. Springer-Verlag Berlin Heidelberg 2009

Paim, F. R. S., and Castro, J. B. (2003). DWARF: An Approach for Requirements Definition and Management of Datawarehouse Systems. In Proceedings of the 11th IEEE International Conference on Requirement Engineering (pp 75-78)

Pandey, A. and Mishra, S. (2014). Moving from Traditional Data Warehouse to Enterprise Data Management : a Case Study. Issues in Information Systems Volume 15, Issue II, pp. 133-140, 2014

Prakash, N., Singh, Y., Gosain, A. (2004). Informational Scenarios for Data Warehouse Requirements Elicitation. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 205–216. Springer, Heidelberg

Pighin and Leronutti, (2008). A Methodology Supporting the Design and Evaluating the Final Quality of Data Warehouses. IGI Global

Pipino, L., Lee, Y., Wang, R. (2002). Data Quality Assessment. Commun. ACM 45, 4

Punch, K. F. (2005). Introduction to Social Research: quantitative and qualitative approaches, London: SAGE.

Ramamurthy, K., Sen, A., and Sinha, A. P. (2008). An empirical investigation of the key determinants of data warehouse adoption, Decision Support Systems, vol. 44, pp. 817-841.

Que, W. T. (1988). Marketing Research. Marketing Research of Singapore, 1988. ISBN 981-00-0610-1, pp. 385-388

Rahman, N., Marz, J., and Akhter, S. (2012). An ETL Metadata Model for Data Warehousing. Journal of Computing and Information Technology - CIT 20, 2012, 2, 95–111. doi:10.2498/cit.1002046

Rainardi, Vincent. (2008). Building a data warehouse: with examples in sql server. A Press.

Ranjit Singh and Kawaljeet Singh. (2010). A Descriptive Classification of Causes of Data Quality Problems in Data Warehousing. IJCSI International Journal of Computer Science Issues. Vol. 7, Issue 3, No 2 May 2010. ISSN : 1694-0784

Redman, T.C. (2001). Data Quality: The field guide. Digital Press, Boston.

Richards J. Heur Jr and Randolph H. Pherson (2014). Structured Analytic Techniques for Intelligent Analysis. CQ Press.

Rizzi, S., Abelló, A., Lechtenbörger, J., & Trujillo, J. 2006. Research in data warehouse modeling and design: Dead or alive? In Proceedings of the 9th ACM Int. Workshop on Data Warehousing and OLAP (DOLAP ‘06), (pp. 3-10) ACM Press

Rizzi, S. (2009). Conceptual Modelling Solutions for the Data Warehouse. IGI Global

Schiefer, J., List, B. and Bruckner, R.M. (2002). A Holistic Approach for Managing Requirements of Data Warehouse Systems. Eight Americas Conference on Information Systems.

Shahzad, M.K. (2012). Improving Business Processes using Processoriented Data Warehouse. Doctoral Dissertation in Computer and Systems Sciences School of Information and Communication Technologies KTH - Royal Institute of Technology, Stockholm, Sweden

Silberschatz, Avi., Hank Korth, S. Sudarshan. (2006). Database System Concepts, Fifth Edition. New York : McGraw-Hill.

Stake, R. E. (1995). The Art of Case Study Research, London: SAGE.

Summer, E., & Ali, D. (1996). A Practical Guide for Implementing Data Warehousing. Computers Ind. Engng, 31, 307-310. http://dx.doi.org/10.1016/0360-8352(96)00137-4

Tellis, W. (1997). Introduction to case study. The Qualitative Report, 3 (2), 1-11.

Vaisman, A. (2007). Data Quality-Based Requirements Elicitation for Decision Support Systems. in Data Warehouse and OLAP : Concepts, Architectures and Solutions. pp 58-86. M. Gordon Hunter (University of Lethbridge, Canada). DOI: 10.4018/978-1-60566-090-5

Verma, D, Tyagi, A., and Sharma D. (2014). Data Quality Problems in Data Warehousing. International Journal of Innovative Research in Technology. Volume 1 Issue 7. ISSN: 2349-6002

Wang, R.Y. and Strong, D.M. (1996). Beyond accuracy: What data quality means to data consumers, Journal of Management Information Systems. 12(4): 5-34.

Watson, H.J. and Haley, B.J. (1997). Data warehousing: a framework and survey of current practices, Journal of Data Warehousing 2 (1) (1997) 10–17.

Watson, H. J., Goodhue, D. L. and Wixom, B. H. (2002). The benefits of data warehousing: why some organizations realize exceptional payoffs. Information and Management, vol. 39, pp. 491-502.

Watson, H. and Haley, B. (1998). A Structural Model of Data Warehousing Success. Journal of Data Warehousing, 2, 10-17.

Winter, R and Strauch, B. (2003). A method for demand-driven information requirements analysis in data warehousing. In Proceedings of Hawaii International Conference on System Sciences, Hawaii, 1359-1365

Wixom, B. H. and Watson, H. J. (2001). An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly, vol. 25, pp. 17-41

Teks Lengkap: PDF


  • Saat ini tidak ada refbacks.