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Experimental Result Paper | Computer Engineering | Volume 15 Issue 4, April 2026 | Pages: 308 - 312 | India
A Unified Framework Linking Business Strategy to Analytics and Machine Learning Outcomes
Abstract: To achieve a competitive advantage, organizations are now increasingly relying on analytics and machine learning to make data-driven decisions in modern data-driven environments. Nevertheless, there is a critical gap between top-level business strategy and the implementation of analytical models at the operational level. The paper presents a single framework, which serves to systematically align business goals with analytics processes and machine learning results. The framework combines some of the most important elements such as defining the strategic goals, data acquisition, feature engineering, model selection, and performance measurement in terms of business-related metrics such as accuracy, precision, recall, and return on investment. It focuses on the feedback-driven lifecycle whereby there is constant monitoring and refinement of models as the business needs change. The proposed framework will help to improve the interpretability, accountability, and value realization of analytics efforts by bridging the disconnection between the business stakeholders and technical teams.. The framework is flexible and scalable in that the applicability of the framework can be seen across various fields, including finance, retail, and risk management. The work has value to the academia and the industry through offering a systematic method of converting the strategic intent into quantifiable analytical outputs.
Keywords: Business Strategy, Machine Learning, Data Analytics, Unified Framework, Decision-Making, KPIs, MLOps
How to Cite?: Bhagyashree Gadekar, Dr. Pravin Haribhau Ghosekar, "A Unified Framework Linking Business Strategy to Analytics and Machine Learning Outcomes", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 308-312, https://www.ijsr.net/getabstract.php?paperid=SR26403125914, DOI: https://dx.dx.doi.org/10.21275/SR26403125914