Leveraging Data Driven Decision Making Models for Enhancing Strategic Performance in Modern Business Organizations

Most modern businesses are thinking strategically, performing business analysis, and optimizing resource use thanks to Data-driven Decision-Making (DDDM) models. On the other hand, businesses that do not use this model experience inconsistent business outcomes, ineffective resource use, and lengthy decision-making when analyzing the business. This paper examines how DDDM models work and the overall business-strategic performance of modern businesses through business analysis, predictive modeling, and performance evaluations. 120 managers and operatives from a mix of medium- and large-scale enterprises were included in this research, which used secondary data as the basis for analysis. Buffered data were primarily used to evaluate the framework, with correlation analysis and regression used to supplement it. Organizations implementing data-driven strategic models achieved 89.8% strategic planning accuracy, 86.5% operational efficiency, and 83.3% risk reduction capability. Improvement of 29.1% and 22.3% in productivity and client satisfaction was also noted. Regression analysis revealed a strong positive relationship between analytics adoption and organizational performance, with an R² value of 0.87 and a significance level of p < 0.01. This paper shows that DDDM models have improved growth prospects, business performance, and streamlined overall business and organizational functions. The prospective framework provides pragmatic answers for those aiming to integrate analytics-based modern business performance with long-range competitiveness and performance improvement for the firm.