From Data Insight to Strategic Execution: A Systematic Framework for Business Analytics Application

DOI: https://doi.org/10.62517/jbm.202509611

Author(s)

Ying Xu

Affiliation(s)

Westcliff University, 17877 Von Karman Ave, #400 Irvine, CA, 92614, USA

Abstract

Business analytics has emerged as a critical capability for organizations seeking to leverage data for strategic advantage. While considerable research focuses on technical methods-such as predictive modeling, machine learning, and optimization-there remains a persistent gap between analytical insights and actionable decision-making. This review proposes a systematic framework that connects data foundations, analytical insight generation, actionable decision modeling, strategic execution, and closed-loop feedback. The framework emphasizes how insights can be operationalized, integrated into business processes, and aligned with organizational objectives. Cross-industry applications in marketing, supply chain, and finance illustrate the versatility and impact of analytics across domains. The paper further discusses current implementation challenges, including fragmented data environments, organizational culture, model deployment difficulties, and regulatory constraints, and highlights emerging trends such as automated analytics, real-time decision intelligence, large language model integration, and causal simulation. By providing a structured perspective, this review offers both scholars and practitioners guidance for bridging the insight–execution gap and realizing measurable business value.

Keywords

Business Analytics; Data-Driven Decision Making; Actionable Insights; Strategic Execution; Predictive and Prescriptive Analytics; Closed-Loop Feedback

References

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