Literature Study on the Role of Business Intelligence in Supporting Data-Driven Decision Making
Keywords:
Business Intelligence, Data-Driven Decision Making, Decision Support System, Business Analytics, Data AnalyticsAbstract
The increasing volume of organizational data has driven the need for technology capable of transforming raw data into meaningful information. Business Intelligence (BI) has emerged as a strategic solution that enables organizations to collect, process, analyze, and visualize data to support evidence-based decision-making. Recent studies have shown that BI technology significantly contributes to organizational performance, operational efficiency, and competitive advantage in the digital era. This study aims to examine the role of Business Intelligence in supporting data-driven decision-making and identify the benefits and challenges associated with its implementation in organizations. This study uses a literature review method by analyzing and synthesizing findings from scientific journals, conference proceedings, and academic publications related to Business Intelligence and data-driven decision-making. The selected literature was published between 2020 and 2025 to ensure the relevance and novelty of the findings. The study results indicate that Business Intelligence improves the quality of decision-making by providing accurate, timely, and integrated information through dashboards, reporting systems, and data analytics tools. Organizations implementing BI report increased operational efficiency, faster decision-making processes, better data accessibility, and more effective strategic planning. In addition, BI applications also support performance monitoring, trend analysis, and forecasting activities. However, several challenges identified include data quality issues, high implementation costs, the complexity of system integration, and limited human resources with specialized expertise. The findings of this study indicate that Business Intelligence plays a crucial role in supporting data-driven decision-making and improving organizational effectiveness. This study successfully identified the benefits and challenges of BI implementation. Further research is recommended to focus on integrating Business Intelligence with emerging technologies, such as Artificial Intelligence and Big Data Analytics, to optimize organizational decision-making capabilities.
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[1] N. Ashal and A. Morshed, “Balancing data-driven insights and human judgment in supply chain management: The role of business intelligence, big data analytics, and artificial intelligence,” J. Infrastructure, Policy Dev., vol. 8, no. 6, 2024, doi: 10.24294/jipd.v8i6.3941.
[2] R. Chaudhuri, S. Chatterjee, D. Vrontis, and A. Thrassou, “Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture,” Ann. Oper. Res., vol. 339, no. 3, pp. 1757–1791, 2024, doi: 10.1007/s10479-021-04407-3.
[3] Abayomi Abraham Adesina, Toluwalase Vanessa Iyelolu, and Patience Okpeke Paul, “Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights,” World J. Adv. Res. Rev., vol. 22, no. 3, pp. 1927–1934, 2024, doi: 10.30574/wjarr.2024.22.3.1961.
[4] Chidera Victoria Ibeh, Onyeka Franca Asuzu, Temidayo Olorunsogo, Oluwafunmi Adijat Elufioye, Ndubuisi Leonard Nduubuisi, and Andrew Ifesinachi Daraojimba, “Business analytics and decision science: A review of techniques in strategic business decision making,” World J. Adv. Res. Rev., vol. 21, no. 2, pp. 1761–1769, 2024, doi: 10.30574/wjarr.2024.21.2.0247.
[5] N. A. Siddiqui, “Optimizing Business Decision-Making Through Ai-Enhanced Business Intelligence Systems: A Systematic Review Of Data-Driven Insights In Financial And Strategic Planning,” Strateg. Data Manag. Innov., vol. 2, no. 01, pp. 202–223, 2025, doi: 10.71292/sdmi.v2i01.21.
[6] C. V. Suresh Babu, S. Adhithya, M. Mohamed Hathil, V. K. N. Srivathsan, and R. Gokul, “Enhancing data-driven decision-making: The role of decision tree algorithm at the intersection of AI and business intelligence,” Intersect. AI Bus. Intell. Data-Driven Decis., pp. 53–88, 2024, doi: 10.4018/979-8-3693-5288-5.ch003.
[7] M. S. Hosen et al., “Data-Driven Decision Making: Advanced Database Systems for Business Intelligence,” Nanotechnol. Perceptions, vol. 20, no. S3, pp. 687–704, 2024, doi: 10.62441/nano-ntp.v20iS3.51.
[8] L. J. Basile, N. Carbonara, R. Pellegrino, and U. Panniello, “Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making,” Technovation, vol. 120, 2023, doi: 10.1016/j.technovation.2022.102482.
[9] O. O. Olaniyi, A. I. Abalaka, and S. O. Olabanji, “Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company,” J. Sci. Res. Reports, vol. 29, no. 9, pp. 64–72, 2023, doi: 10.9734/jsrr/2023/v29i91785.
[10] M. Schmitt, “Automated machine learning: AI-driven decision making in business analytics,” Intell. Syst. with Appl., vol. 18, 2023, doi: 10.1016/j.iswa.2023.200188.
[11] H. W. Lo, “A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing,” Adv. Eng. Informatics, vol. 56, 2023, doi: 10.1016/j.aei.2023.101998.
[12] V. Charles, P. Garg, N. Gupta, and M. Agarwal, “Data Analytics and Business Intelligence,” Data Anal. Bus. Intell., 2023, doi: 10.1201/9781003189640.
[13] H. Shafa, “Artificial Intelligence-Driven Business Intelligence Models for Enhancing Decision-Making in U.S. Enterprises,” ASRC Procedia Glob. Perspect. Sci. Scholarsh., vol. 01, no. 01, pp. 771–800, 2025, doi: 10.63125/b8gmdc46.
[14] L. Pratt, C. Bisson, and T. Warin, “Bringing advanced technology to strategic decision-making: The Decision Intelligence/Data Science (DI/DS) Integration framework,” Futures, vol. 152, 2023, doi: 10.1016/j.futures.2023.103217.
[15] R. Chen, B. Gu, and Z. Ye, “Design and Implementation of Big Data-Driven Business Intelligence Analytics System,” Adv. Transdiscipl. Eng., vol. 75, pp. 1219–1227, 2025, doi: 10.3233/ATDE250877.
[16] Preethi Rajan, “Integrating IoT Analytics into Marketing Decision Making: A Smart Data-Driven Approach,” Int. J. Data Informatics Intell. Comput., vol. 3, no. 1, pp. 12–22, 2024, doi: 10.59461/ijdiic.v3i1.92.
[17] R. Ribeiro, A. Pilastri, C. Moura, J. Morgado, and P. Cortez, “A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics,” Neural Comput. Appl., vol. 35, no. 23, pp. 17375–17395, 2023, doi: 10.1007/s00521-023-08596-9.
[18] R. Bogue, “The role of machine learning in robotics,” Ind. Rob., vol. 50, no. 2, pp. 197–202, 2023, doi: 10.1108/IR-11-2022-0279.
[19] U. Mangal, S. Mogha, and S. Malik, “Data-Driven Decision Making: Maximizing Insights Through Business Intelligence, Artificial Intelligence and Big Data Analytics,” 2024 Int. Conf. Adv. Comput. Res. Sci. Eng. Technol. ACROSET 2024, 2024, doi: 10.1109/ACROSET62108.2024.10743399.
[20] A. Al-Okaily, A. P. Teoh, and M. Al-Okaily, “Evaluation of data analytics-oriented business intelligence technology effectiveness: an enterprise-level analysis,” Bus. Process Manag. J., vol. 29, no. 3, pp. 777–800, 2023, doi: 10.1108/BPMJ-10-2022-0546.
[21] A. R. H. Tawil, M. Mohamed, X. Schmoor, K. Vlachos, and D. Haidar, “Trends and Challenges towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises,” Big Data Cogn. Comput., vol. 8, no. 7, 2024, doi: 10.3390/bdcc8070079.
[22] M. H. Zamil, “Ai-Driven Business Analytics for Financial Forecasting: a Systematic Review of Decision Support Models in Smes,” Rev. Appl. Sci. Technol., vol. 04, no. 02, pp. 86–117, 2025, doi: 10.63125/gjrpv442.
[23] A. Mahmood, A. Al Marzooqi, M. El Khatib, and H. AlAmeemi, “How Artificial Intelligence can leverage Project Management Information system (PMIS) and data driven decision making in project management,” Int. J. Bus. Anal. Secur., vol. 3, no. 1, pp. 180–191, 2023, doi: 10.54489/ijbas.v3i1.215.
[24] Emmanuel Osamuyimen Eboigbe, Oluwatoyin Ajoke Farayola, Funmilola Olatundun Olatoye, Obiageli Chinwe Nnabugwu, and Chibuike Daraojimba, “Business Intelligence Transformation Through Ai and Data Analytics,” Eng. Sci. Technol. J., vol. 4, no. 5, pp. 285–307, 2023, doi: 10.51594/estj.v4i5.616.
[25] et al., “Data-Driven Healthcare: The Role of Business Intelligence Tools in Optimizing Clinical and Operational Performance,” Am. J. Appl. Sci., vol. 7, no. 08, pp. 50–73, 2025, doi: 10.37547/tajas/volume07issue08-06.
[26] S. Samsidar, “Integration of Big Data Analytics in Management Information Systems for Consumer Behavior Prediction,” Sist. Informasi, Manajemen, dan Bisnis Adapt., vol. 1, no. 1, 2025, doi: 10.63985/simba.v1i1.7.
[27] A. Elragal and N. Elgendy, “A data-driven decision-making readiness assessment model: The case of a Swedish food manufacturer,” Decis. Anal. J., vol. 10, 2024, doi: 10.1016/j.dajour.2024.100405.
[28] M. M. Islam, “Precision Medicine and AI: How AI Can Enable Personalized Medicine Through Data-Driven Insights and Targeted Therapeutics,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 11, pp. 1267–1276, 2023, doi: 10.17762/ijritcc.v11i11.11359.
[29] U. Haldar et al., “AI-Driven Business Analytics for Economic Growth Leveraging Machine Learning and MIS for Data-Driven Decision-Making in the U.S. Economy,” J. Posthumanism, vol. 5, no. 4, 2025, doi: 10.63332/joph.v5i4.1178.
[30] S. Singh, S. Suman Rajest, S. Hadoussa, A. J. Obaid, and R. Regin, Data-driven decision making for long-term business success. books.google.com, 2023. doi: 10.4018/979-8-3693-2193-5.
[31] B. Singh, C. Kaunert, R. Malviya, S. Lal, and M. K. Arora, “Scrutinizing consumer sentiment on social media and data-driven decisions for business insights: Fusion of artificial intelligence (AI) and business intelligence (BI) foster sustainable growth,” Intersect. AI Bus. Intell. Data-Driven Decis., pp. 183–210, 2024, doi: 10.4018/979-8-3693-5288-5.ch007.
[32] Mojeed Dayo Ajegbile, Janet Aderonke Olaboye, Chikwudi Cosmos Maha, Geneva Tamunobarafiri Igwama, and Samira Abdul, “Integrating business analytics in healthcare: Enhancing patient outcomes through data-driven decision making,” World J. Biol. Pharm. Heal. Sci., vol. 19, no. 1, pp. 243–250, 2024, doi: 10.30574/wjbphs.2024.19.1.0436.
[33] M. Al-Okaily and A. Al-Okaily, “Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality,” J. Model. Manag., vol. 20, no. 2, pp. 301–321, 2025, doi: 10.1108/JM2-08-2023-0183.
[34] M. Žilka, Z. T. Kalender, J. Lhota, V. Kalina, and R. Pinto, “Tools to support managerial decision - building competencies in data driven decision making in manufacturing SMEs,” Procedia Comput. Sci., vol. 232, pp. 416–425, 2024, doi: 10.1016/j.procs.2024.01.041.
[35] Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen, “Integrating business intelligence and predictive analytics in banking: A framework for optimizing financial decision-making,” Financ. Account. Res. J., vol. 6, no. 8, pp. 1517–1530, 2024, doi: 10.51594/farj.v6i8.1505.
[36] V. Pantović, D. Vidojević, S. Vujičić, S. Sofijanić, and M. Jovanović-Milenković, “Data-Driven Decision Making for Sustainable IT Project Management Excellence,” Sustain. , vol. 16, no. 7, 2024, doi: 10.3390/su16073014.
[37] S. Kapoor, “AI-Driven Decision Support Systems in Sports Project Management: Enhancing Strategic Planning,” Int. J. Artif. Intell. Data Sci. Mach. Learn., vol. 2, pp. 1–11, 2021, doi: 10.63282/3050-9262.ijaidsml-v2i3p101.
[38] O. Kabadurmus, Y. Kayikci, S. Demir, and B. Koc, “A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks,” Socioecon. Plann. Sci., vol. 85, 2023, doi: 10.1016/j.seps.2022.101417.
[39] M. Jiménez-Partearroyo, A. Medina-López, and S. Rana, “Business intelligence and business analytics in tourism: insights through Gioia methodology,” Int. Entrep. Manag. J., vol. 20, no. 3, pp. 2287–2321, 2024, doi: 10.1007/s11365-024-00973-7.
[40] M. Azmi, A. Mansour, and C. Azmi, “A Context-Aware Empowering Business with AI: Case of Chatbots in Business Intelligence Systems,” Procedia Comput. Sci., vol. 224, pp. 479–484, 2023, doi: 10.1016/j.procs.2023.09.068.
[41] M. Maghsoudi and N. Nezafati, “Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach,” Telemat. Informatics Reports, vol. 11, 2023, doi: 10.1016/j.teler.2023.100070.
[42] A. Erica, L. Gantari, O. Qurotulain, A. Nuche, and O. Sy, “Optimizing Decision-Making: Data Analytics Applications in Management Information Systems,” APTISI Trans. Manag., vol. 8, no. 2, pp. 115–122, 2024, doi: 10.33050/atm.v8i2.2202.
[43] N. Azoury, S. Subrahmanyam, and N. Sarkis, “The Influence of a Data-Driven Culture on Product Development and Organizational Success through the Use of Business Analytics,” J. Wirel. Mob. Networks, Ubiquitous Comput. Dependable Appl., vol. 15, no. 2, pp. 123–134, 2024, doi: 10.58346/JOWUA.2024.I2.009.
[44] R. Sultana, “Ai-Powered Bi Dashboards in Operations: a Comparative Analysis for Real-Time Decision Support,” ASRC Procedia Glob. Perspect. Sci. Scholarsh., vol. 03, no. 91, pp. 62–93, 2023, doi: 10.63125/wqd2t159.
[45] F. P. Eka Putra, F. Muslim, N. Hasanah, Holipah, R. Paradina, and R. Alim, “Analisis Komparasi Protokol Websocket dan MQTT Dalam Proses Push Notification,” J. Sistim Inf. dan Teknol., pp. 63–72, 2024, doi: 10.60083/jsisfotek.v5i4.325.
[46] Á. Szukits and P. Móricz, “Towards data-driven decision making: the role of analytical culture and centralization efforts,” Rev. Manag. Sci., vol. 18, no. 10, pp. 2849–2887, 2024, doi: 10.1007/s11846-023-00694-1.
[47] Fauzan Prasetyo Eka Putra, Yogi Setiawan, Samsul Arifin, and Wahyu Hidayatullah, “Peran VPN dalam Menjaga Privasi Pengguna Jaringan Publik,” J. Inform. Dan Tekonologi Komput., vol. 5, no. 1, pp. 27–33, 2025, doi: 10.55606/jitek.v5i1.5834.
[48] Q. Hossain, F. Yasmin, T. R. Biswas, and N. B. Asha, “Data-Driven Business Strategies: A Comparative Analysis of Data Science Techniques in Decision-Making,” Sch. J. Econ. Bus. Manag., vol. 11, no. 09, pp. 257–263, 2024, doi: 10.36347/sjebm.2024.v11i09.002.
[49] F. P. Eka Putra, Amir Hamzah, W. Agel, and R. O. Firmansyah Kusuma, “Impelementasi Sistem Keamanan Jaringan Mikrotik Menggunakan Firewall Filtering dan Port Knocking,” J. Sistim Inf. dan Teknol., pp. 82–87, 2024, doi: 10.60083/jsisfotek.v5i4.329.
[50] Comfort Idongesit Michael et al., “Data-driven decision making in IT: Leveraging AI and data science for business intelligence,” World J. Adv. Res. Rev., vol. 23, no. 1, pp. 472–480, 2024, doi: 10.30574/wjarr.2024.23.1.2010.
[51] F. Prasetyo Eka Putra, S. R. Sutarsih, S. Sofiyulloh, P. Permana, and M. Umar Mansyur, “Optimalisasi Perancangan Aplikasi Manajemen Data Koloman, Di Desa Pulau Mandangin Sampang – Madura Berbasis Website,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 9, no. 2, pp. 285–294, 2024, doi: 10.36341/rabit.v9i2.4840.
[52] Fauzan Prasetyo Eka Putra, Noviyani Dwi Saputri, Fathur Rosi, and Rohilia Loati, “Optimalisasi Infrastruktur Cloud Networking melalui Integrasi SDN, NFV, dan Multi-Cloud,” J. Inform. Dan Tekonologi Komput., vol. 5, no. 1, pp. 118–125, 2025, doi: 10.55606/jitek.v5i1.6099.
[53] L. Hollingworth, A. M. Sullivan, C. Condon, M. Bhatt, and W. C. Brandt, “Data-Driven Decision Making in Higher Education,” J. Res. Leadersh. Educ., vol. 7, no. 1, pp. 78–97, 2012, doi: 10.1177/1942775112440632.
[54] F. P. E. Putra, R. M. Ilhamsyah, S. A. Efendy, and A. Rizki, “Implementation And Evaluation Of Zerotier-Based Virtual Network For Device Connectivity,” Brill. Res. Artif. Intell., vol. 5, no. 1, pp. 281–290, 2025, doi: 10.47709/brilliance.v5i1.5966.
[55] F. P. E. Putra, M. Irfan, M. Aziz, and R. N. Saputra, “Wireless Network Design at Pamekasan Regency Public Library,” Brill. Res. Artif. Intell., vol. 5, no. 1, pp. 144–150, 2025, doi: 10.47709/brilliance.v5i1.5876.
[56] S. Singh, S. S. Rajest, S. Hadoussa, A. J. Obaid, and R. Regin, Data-driven intelligent business sustainability. books.google.com, 2023. doi: 10.4018/979-8-3693-0049-7.
[57] A. Al-Okaily, A. P. Teoh, M. Al-Okaily, M. Iranmanesh, and M. A. Al-Betar, “The efficiency measurement of business intelligence systems in the big data-driven economy: a multidimensional model,” Inf. Discov. Deliv., vol. 51, no. 4, pp. 404–416, 2023, doi: 10.1108/IDD-01-2022-0008.
[58] F. P. E. Putra, U. Ubaidi, R. N. Saputra, F. M. Haris, and S. N. R. Barokah, “Application of Internet of Things Technology in Monitoring Water Quality in Fishponds,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 356–361, 2024, doi: 10.47709/brilliance.v4i1.4231.
[59] F. P. E. Putra, U. Ubaidi, A. Hamzah, W. A. Pramadi, and A. Nuraini, “Systematic Literature Review: Security Gap Detection On Websites Using Owasp Zap,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 348–355, 2024, doi: 10.47709/brilliance.v4i1.4227.
[60] L. Hurbean, F. Militaru, M. Muntean, and D. Danaiata, “The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance,” Sci. Ann. Econ. Bus., vol. 70, no. S1, pp. 43–54, 2023, doi: 10.47743/saeb-2023-0012.
[61] Fauzan Prasetyo Eka Putra, Maktsuful Ghummah, Moh. Amrullah, and Rafli Hidayatullah, “Studi Kinerja Mesh Network untuk Penerapan Internet of Things (IoT) di Lingkungan Perkotaan,” J. Inform. Dan Tekonologi Komput., vol. 5, no. 1, pp. 63–73, 2025, doi: 10.55606/jitek.v5i1.5895.
[62] A. Collins, O. Hamza, A. Eweje, and G. O. Babatunde, “Integrating 5G Core Networks with Business Intelligence Platforms: Advancing Data-Driven Decision-Making,” Int. J. Multidiscip. Res. Growth Eval., vol. 5, no. 1, pp. 1082–1099, 2024, doi: 10.54660/.ijmrge.2024.5.1.1082-1099.
[63] A. Solanki, K. Jain, and S. Jadiga, “Building a Data-Driven Culture: Empowering Organizations with Business Intelligence,” Int. J. Comput. Trends Technol., vol. 72, no. 2, pp. 46–55, 2024, doi: 10.14445/22312803/ijctt-v72i2p109.
[64] M. N. H. Mamun, “Role of Ai and Data Science in Data-Driven Decision Making for It Business Intelligence: a Systematic Literature Review,” ASRC Procedia Glob. Perspect. Sci. Scholarsh., vol. 01, no. 01, pp. 564–588, 2025, doi: 10.63125/n1xpym21.
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