Applied IT & Engineering
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Applied IT & Engineering

Volume 2 Number 1 2024

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 2 (1)

Transforming Business Operations Through IT-Enabled Predictive and Prescriptive Analytics: Challenges, Opportunities, and Best Practices

Ispita Jahan1*, Niladry Chowdhury2, Md Sakib Mia2, Md Iqbal Hossain3, Anik Biswas4

+ Author Affiliations

Applied IT & Engineering 2 (1) 1-7 https://doi.org/10.25163/engineering.2110381

Submitted: 08 October 2024 Revised: 02 December 2024  Published: 04 December 2024 

Abstract

Background: The worldwide business environment experienced fast digital transformation which makes organizations implement data-driven decision strategies for enhancing their performance and market position. Modern business environments leverage advanced IT infrastructures to deliver predictive and prescriptive analytics which function as transformative tools. The analytical process of predictive analytics utilizes past and present data to forecast future patterns while prescriptive analytics generates optimal action advice.

Methods: The research conducted secondary literature analysis combined with multinational enterprise case studies and a 210-manager survey from IT services and finance and manufacturing sectors through a mixed-methods research design. Quantitative data received analysis through descriptive statistics while qualitative case studies together with semi-structured interviews revealed detailed information about adoption patterns and best practices and challenges.

Results: The study reveals that 74% of organizations which implement predictive analytics experience enhanced forecasting precision and 68% of those using prescriptive models report cost reduction benefits. The sectoral study demonstrates that IT services lead with 81% adoption rate while finance follows at 69% and manufacturing stands at 59%. Organizations face critical challenges from two main sources: 62% of them struggle with insufficient skilled professionals and 54% need to address high implementation expenses.

Conclusion: Modern business operations undergo transformation through predictive and prescriptive analytics because these methods deliver both agility and efficiency as well as competitiveness. The sustainable adoption of these methods needs organizations to solve problems related to skill shortages together with governance gaps and cost hurdles.

Keywords: Predictive analytics, Prescriptive analytics, IT-enabled transformation, Business operations, Data-driven decision-making

References

Andriosopoulos, D., Doumpos, M., Pardalos, P. M., & Zopounidis, C. (2019). Computational approaches and data analytics in financial services: A literature review. Journal of the Operational Research Society, 70(10), 1581–1599. https://doi.org/10.1080/01605682.2019.1595193

Atuahene, B. T., Kanjanabootra, S., & Gajendran, T. (2022). Transformative role of big data through enabling capability recognition in construction. Construction Management and Economics, 41(3), 208–231. https://doi.org/10.1080/01446193.2022.2132523

Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1), 111–138. https://doi.org/10.1007/s42786-020-00020-3

Bose, R., & Luo, X. (2011). Integrative framework for assessing firms’ potential to undertake Green IT initiatives via virtualization – A theoretical perspective. The Journal of Strategic Information Systems, 20(1), 38–54. https://doi.org/10.1016/j.jsis.2011.01.003

Broby, D. (2022). The use of predictive analytics in finance. The Journal of Finance and Data Science, 8, 145–161. https://doi.org/10.1016/j.jfds.2022.05.003

Carvalho, H., Azevedo, S. G., & Cruz-Machado, V. (2012). Agile and resilient approaches to supply chain management: influence on performance and competitiveness. Logistics Research, 4(1–2), 49–62. https://doi.org/10.1007/s12159-012-0064-2

Coombs, C. R. (2014). When planned IS/IT project benefits are not realized: a study of inhibitors and facilitators to benefits realization. International Journal of Project Management, 33(2), 363–379. https://doi.org/10.1016/j.ijproman.2014.06.012

Cornwell, N., Bilson, C., Gepp, A., Stern, S., & Vanstone, B. J. (2022). The role of data analytics within operational risk management: A systematic review from the financial services and energy sectors. Journal of the Operational Research Society, 74(1), 374–402. https://doi.org/10.1080/01605682.2022.2041373

Doumpos, M., Lemonakis, C., Niklis, D., & Zopounidis, C. (2018). Analytical techniques in the assessment of credit risk. In EURO advanced tutorials on operational research. https://doi.org/10.1007/978-3-319-99411-6

Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1–21. https://doi.org/10.1080/23270012.2015.1020891

Ghosh, B., & Scott, J. E. (2011). Antecedents and Catalysts for developing a healthcare analytic Capability. Communications of the Association for Information Systems, 29. https://doi.org/10.17705/1cais.02922

Grover, V., Chiang, R. H., Liang, T., & Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388–423. https://doi.org/10.1080/07421222.2018.1451951

Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. The Annals of Family Medicine, 13(6), 554–561. https://doi.org/10.1370/afm.1865

Gunasekaran, A., Lai, K., & Edwincheng, T. (2007). Responsive supply chain: A competitive strategy in a networked economy?. Omega, 36(4), 549–564. https://doi.org/10.1016/j.omega.2006.12.002

Ismail, H. S., Poolton, J., & Sharifi, H. (2011). The role of agile strategic capabilities in achieving resilience in manufacturing-based small companies. International Journal of Production Research, 49(18), 5469–5487. https://doi.org/10.1080/00207543.2011.563833

Matovic, N., & Ovesni, K. (2021). Interaction of quantitative and qualitative methodology in mixed methods research: integration and/or combination. International Journal of Social Research Methodology, 26(1), 51–65. https://doi.org/10.1080/13645579.2021.1964857

Mazzarol, T. (2014). Research review: A review of the latest research in the field of small business and entrepreneurship. Small Enterprise Research, 21(1), 2–13. https://doi.org/10.1080/13215906.2014.11082073

Morr, C. E., Jammal, M., Ali-Hassan, H., & Ei-Hallak, W. (2022). Machine learning for practical decision making. In International series in management science/operations research/International series in operations research & management science. https://doi.org/10.1007/978-3-031-16990-8

Östlund, U., Kidd, L., Wengström, Y., & Rowa-Dewar, N. (2010). Combining qualitative and quantitative research within mixed method research designs: A methodological review. International Journal of Nursing Studies, 48(3), 369–383. https://doi.org/10.1016/j.ijnurstu.2010.10.005

Poornima, S., & Pushpalatha, M. (2020). A survey on various applications of prescriptive analytics. International Journal of Intelligent Networks, 1, 76–84. https://doi.org/10.1016/j.ijin.2020.07.001

Raj, P., Raman, A., Nagaraj, D., & Duggirala, S. (2015). The High-Performance Technologies for big and fast data analytics. In Computer communications and networks (pp. 25–66). https://doi.org/10.1007/978-3-319-20744-5_2

Reference

Sanchez, R. (1997). Preparing for an uncertain future. International Studies of Management and Organization, 27(2), 71–94. https://doi.org/10.1080/00208825.1997.11656708

Sarker, I. H. (2021). Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN Computer Science, 2(5). https://doi.org/10.1007/s42979-021-00765-8

Sassanelli, C., & Terzi, S. (2022). The D-BEST Reference Model: a flexible and sustainable support for the digital transformation of small and medium enterprises. Global Journal of Flexible Systems Management, 23(3), 345–370. https://doi.org/10.1007/s40171-022-00307-y

Sharma, A. K., Sharma, D. M., Purohit, N., Rout, S. K., & Sharma, S. A. (2021). Analytics techniques: descriptive analytics, predictive analytics, and prescriptive analytics. In EAI/Springer Innovations in Communication and Computing (pp. 1–14). https://doi.org/10.1007/978-3-030-82763-2_1

Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433–441. https://doi.org/10.1057/ejis.2014.17

Teece, D. J. (2014). The Foundations of Enterprise Performance: Dynamic and Ordinary capabilities in an (Economic) Theory of Firms. Academy of Management Perspectives, 28(4), 328–352. https://doi.org/10.5465/amp.2013.0116

Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639. https://doi.org/10.1016/j.ejor.2017.02.023

Yang, B., Burns, N. D., & Backhouse, C. J. (2004). Management of uncertainty through postponement. International Journal of Production Research, 42(6), 1049–1064. https://doi.org/10.1080/00207540310001631601


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