Integrated Business Analytics Framework for Improving Strategic Decision-Making and Performance Optimization in Modern Organizations

Abstract

Data is expensive and valuable to organizations, but too often, they don't use it to improve strategic decision-making or performance. This study comes up with an Integrated Business Analytics Framework (IBAF), presents the five layers of data foundation, analytics engine, decision integration, performance optimization, and governance and culture, with three explicit feedback loops connecting them, and evaluates it. The resource-based view and information processing logic lead to six hypotheses, which are grounded in the balanced performance measurement approach. The hypotheses are explored in a simulated set of 312 organizations using multi-item latent variables, OLS path analysis, and bootstrapped mediation and moderation analyses. The psychometric properties of the measurement were satisfactory, Cronbach's alpha was from 0.86 to 0.90, Composite Reliability varied from 0.90 to 0.92, and Average Variance Extracted was from 0.65 to 0.73. The data foundation also strongly supported the analytics engine (β = 0.44), which, through indirect effect, decision quality (indirect effect = 0.17, 95% CI [0.11, 0.24]) was a strong predictor of organizational performance (β = 0.47). In addition, governance and culture greatly strengthened the positive relationship between decision integration and decision quality (interaction β = 0.13), and the operational feedback loops positively predicted performance improvement (β = 0.37). Stratification analyses showed that decisions took 32% less time and 69% of decisions were documented by the organizations in the highest integration tercile as opposed to 48% in the lowest. These practical results show that the value of organizations lies more in the integrated use of the system, and not in any one analytic tool, and provide practitioners with a diagnostic structure, key performance indicators, and a roadmap for phased implementation.