Information and engineering sciences | Online ISSN 3068-0115
RESEARCH ARTICLE   (Open Access)

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) 1-8 https://doi.org/10.25163/engineering.2110381

Submitted: 10 January 2024  Revised: 02 December 2024  Published: 04 December 2024 

The research demonstrates how IT-powered predictive and prescriptive analytics improve operational efficiency and resilience in contemporary organizational operations.

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

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