Prioritizing Barriers and Strategies Mapping in Business Intelligence Projects Using Fuzzy AHP TOPSIS Framework in Developing Country

Ika Chandra Hapsari, Rayhan Anandya, Achmad Nizar Hidayanto, Nur Fitriah Ayuning Budi, Kongkiti Phusavat


Business Intelligence (BI) is an essential technology in an increasingly competitive landscape since it helps make decisions more accurately. To achieve an effective BI implementation, the organization must formulate the right strategy to overcome its challenges. This research aimed to develop a framework to map barriers into strategies using qualitative and quantitative methods. The qualitative approach is driven by interviewing BI experts to validate the barriers and strategies previously obtained. Based on the interview, there are 19 barriers and 9 strategies that could be used. The quantitative approach compiles a priority list of the most significant barriers and the most effective strategies to overcome these barriers using fuzzy AHP TOPSIS, an MCDM method to eliminate inconsistencies during ranking. The results indicate that the lack of collaboration between the IT and BI departments, the BI implementation demands to be done quickly, and low data quality are the main barriers that hinder BI's success. This research also found that business people's involvement in a BI project is the best strategy to overcome the obstacles. The chances of a successful BI implementation will increase by having good cooperation between IT and business units within the company.


Doi: 10.28991/ESJ-2022-06-02-010

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Business Intelligence; BI Barriers; BI Strategic; BI Implementation; MCDM; Fuzzy AHP TOPSIS.


Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International Journal of Information Management, 46, 93–103. doi:10.1016/j.ijinfomgt.2018.11.020.

Wieder, B., & Ossimitz, M. L. (2015). The Impact of Business Intelligence on the Quality of Decision Making - A Mediation Model. Procedia Computer Science, 64, 1163–1171. doi:10.1016/j.procs.2015.08.599.

Dehghan, A., Mehrabi, A., & Fotouhi, N. (2013). The necessity of establishing Business Intelligence competency centers for achievement of BI projects. In IKT 2013 - 2013 5th Conference on Information and Knowledge Technology, 242–247. doi:10.1109/IKT.2013.6620072.

Ain, N. U., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review. Decision Support Systems, 125(June 2019), 113113. doi:10.1016/j.dss.2019.113113.

Cognini, R., Corradini, F., Polzonetti, A., & Re, B. (2014). Five factors that make pervasive business intelligence a winning wager. In IEEE International Conference on Industrial Engineering and Engineering Management, Vols. 2015-January, 617–621. doi:10.1109/IEEM.2014.7058712.

Gartner. (2015). Gartner Survey Shows More Than 75 Percent of Companies Are Investing or Planning to Invest in Big Data in the Next Two Years. Gartner, 1–2.

Bordeleau, F. E., Mosconi, E., & de Santa-Eulalia, L. A. (2020). Business intelligence and analytics value creation in Industry 4.0: a multiple case study in manufacturing medium enterprises. Production Planning and Control, 31(2–3), 173–185. doi:10.1080/09537287.2019.1631458.

Kursan, I., & Mihić, M. (2010). Business intelligence: The role of the internet in marketing research and business decision-making. Management : Journal of Contemporary Management Issues, 15(1), 69–86.

Caseiro, N., & Coelho, A. (2019). The influence of Business Intelligence capacity, network learning and innovativeness on startups performance. Journal of Innovation and Knowledge, 4(3), 139–145. doi:10.1016/j.jik.2018.03.009.

Williams, S., & Williams, N. (2007). The Profit Impact of Business Intelligence. In The Profit Impact of Business Intelligence. doi:10.1016/B978-0-12-372499-1.X5000-5.

Kwak. Y. H. (2002). Critical Success Factors in International Development Project Management. CIB 10th International Symposium Construction Innovation & global Competitiveness, September 9-13, Cincinnati, Ohio, USA.

Stamford, C. (2014). Gartner Says Worldwide Business Intelligence and Analytics Software Market Grew 8 Percent in 2013. In Gardner. Available online: (accessed on December 2021).

Llave, M. R. (2017). Business Intelligence and Analytics in Small and Medium-sized Enterprises: A Systematic Literature Review. Procedia Computer Science, 121, 194–205. doi:10.1016/j.procs.2017.11.027.

Tutunea, M. F. (2015). Business Intelligence Solutions for Mobile Devices – An Overview. Procedia Economics and Finance, 27(15), 160–169. doi:10.1016/s2212-5671(15)00985-5.

Peter Mesároš, Štefan Čarnický, & Tomáš Mandičák. (2015). Key Factors and Barriers of Business Intelligence Implementation. US-China Law Review, 12(2). doi:10.17265/1548-6605/2015.02.006.

Williams, S. (2011). 5 Barriers to BI Success and how to overcome them. Strategic Finance, Montvale, 93(1), 27-33.

Kusumawardani, R. P., & Agintiara, M. (2015). Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process. Procedia Computer Science, 72, 638–646. doi:10.1016/j.procs.2015.12.173.

Rajak, M., & Shaw, K. (2019). Evaluation and selection of mobile health (mHealth) applications using AHP and fuzzy TOPSIS. Technology in Society, 59, 101186. doi:10.1016/j.techsoc.2019.101186.

Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers and Industrial Engineering, 117, 303–318. doi:10.1016/j.cie.2018.01.015.

Saghafian, S., & Hejazi, S. R. (2005). Multi-criteria group decision making using a modified fuzzy TOPSIS procedure. In Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA and Conference on Intelligent Agents, Web Technologies and Internet Vol. 2, 215–220. doi:10.1109/cimca.2005.1631471.

Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. doi:10.1016/j.eswa.2010.04.066.

Grublješič, T., Coelho, P. S., & Jaklič, J. (2019). The shift to socio-organizational drivers of business intelligence and analytics acceptance. Journal of Organizational and End User Computing, 31(2), 37–62. doi:10.4018/JOEUC.2019040103.

Ferrissa, W. (2017). 500 Perusahaan Terdaftar sebagai e-Commerce Terpercaya di Kominfo. Kementerian Komunikasi dan Informatika RI, Indonesia. Available online: (accessed on December 2021).

The World Bank. (2021). The World Bank In Indonesia, Available online: overview#1 (accessed on November 2021).

The World Bank. (2020). Individuals using the Internet (% of population). Available online: /indicator/ IT.NET.USER.ZS?locations=ID (accessed on November 2021).

World Economic Situation and Prospects (2014). Country classification: Data sources, country classifications and aggregation methodology. Available online: classification.pdf (accessed on December 2021). (2008). Solusi Business Intelligence Rambah Sektor Publik. Available online: (accessed on December 2021).

Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of Computer Information Systems, 50(3), 23–32. doi:10.1080/08874417.2010.11645404.

Olszak, C. M., & Ziemba, E. (2012). Critical success factors for implementing business intelligence systems in small and medium enterprises on the example of Upper Silesia, Poland. Interdisciplinary Journal of Information, Knowledge, and Management, 7, 129–150. doi:10.28945/1584.

Fortune, J., & White, D. (2006). Framing of project critical success factors by a systems model. International Journal of Project Management, 24(1), 53–65. doi:10.1016/j.ijproman.2005.07.004.

Wise, L. (2007). Five Steps to Business Intelligence Project Success. Technology Evaluation Center, Quebec, Canada.

Dawson, L., & Van Belle, J.-P. (2013). Critical success factors for business intelligence in the South African financial services sector. SA Journal of Information Management, 15(1). doi:10.4102/sajim.v15i1.545.

Wayne W. Eckerson. (2002). Data Quality and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data. The data Warehouse Institute (TDWI), California, United States.

Boyer, J., Frank, B., Green, B., & Harris, T. (2010). A Practical Guide for Achieving BI Excellence. MC Press Online, Idaho, United States.

Ali Khan, A. M., Amin, N., & Lambrou, N. (2010). Drivers and Barriers to Business intelligence adoption: A case of Pakistan. In Proceedings of the European, Mediterranean and Middle Eastern Conference on Information Systems: Global Information Systems Challenges in Management, EMCIS 2010.

Lennerholt, C., Van Laere, J., & Söderström, E. (2020). User-Related Challenges of Self-Service Business Intelligence. Information Systems Management, 38(4), 309–323. doi:10.1080/10580530.2020.1814458.

Jalil, N. A., & Hwang, H. J. (2019). Technological-centric business intelligence: Critical success factors. International Journal of Innovation, Creativity and Change, 5(2), 1499–1516.

Jahantigh, F. F., Habibi, A., & Sarafrazi, A. (2019). A conceptual framework for business intelligence critical success factors. International Journal of Business Information Systems, 30(1), 109–123. doi:10.1504/IJBIS.2019.097058.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. doi:10.1016/S0019-9958(65)90241-X.

Dernoncourt, F. (2013). Introduction to fuzzy logic. Massachusetts Institute of Technology (MIT), Massachusetts, United States.

Ordoobadi, S. M. (2009). Development of a supplier selection model using fuzzy logic. Supply Chain Management, 14(4), 314–327. doi:10.1108/13598540910970144.

Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Systems with Applications, 41(2), 679–693. doi:10.1016/j.eswa.2013.07.093.

Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. doi:10.1016/0377-2217(90)90057-I.

Kahraman, C., Ruan, D., & Doǧan, I. (2003). Fuzzy group decision-making for facility location selection. In Information Sciences 157(1–4), 135–153. doi:10.1016/S0020-0255(03)00183-X.

Chen, J. F., Hsieh, H. N., & Do, Q. H. (2015). Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Applied Soft Computing Journal, 28, 100–108. doi:10.1016/j.asoc.2014.11.050.

Baylan, E. B. (2020). A Novel Project Risk Assessment Method Development via AHP-TOPSIS Hybrid Algorithm. Emerging Science Journal, 4(5), 390–410. doi:10.28991/esj-2020-01239.

Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin, Germany. doi:10.1007/978-3-642-48318-9.

Mamaghani, N. D., Samizadeh, R., & Saghafi, F. (2010). Customized Knowledge Management Success Factors for Iranian Organizations. Proceedings of Knowledge Management 5th International Conference 2010, Bucharest, Romania, 335–341.

Kabra, G., Ramesh, A., & Arshinder, K. (2015). Identification and prioritization of coordination barriers in humanitarian supply chain management. International Journal of Disaster Risk Reduction, 13, 128–138. doi:10.1016/j.ijdrr.2015.01.011.

Eckerson, W. (2005). The Keys to Enterprise Business Intelligence: Critical Success Factors. TDWI Report, 1–15. Available online: (accessed on December 2021).

Passlick, J., Guhr, N., Lebek, B., & Breitner, M. H. (2020). Encouraging the use of self-service business intelligence–an examination of employee-related influencing factors. Journal of Decision Systems, 29(1), 1–26. doi:10.1080/12460125.2020.1739884.

Nidhra, S., Yanamadala, M., Afzal, W., & Torkar, R. (2013). Knowledge transfer challenges and mitigation strategies in global software development—A systematic literature review and industrial validation. International Journal of Information Management, 33(2), 333–355. doi:10.1016/j.ijinfomgt.2012.11.004.

Scannapieco, M. (2006). Data Quality: Concepts, Methodologies and Techniques. Data-Centric Systems and Applications. Springer-Verlag, Berlin, Germany.

Teixeira, A., Oliveira, T., & Varajão, J. (2019). Evaluation of Business Intelligence Projects Success - a Case Study. Business Systems Research, 10(1), 1–12. doi:10.2478/bsrj-2019-0001.

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DOI: 10.28991/ESJ-2022-06-02-010


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