Business Intelligence and Data-Quality Frameworks for Improving Evidence-Based Decisions across Multi-Source Enterprise Data

Abstract

This study examined the relationships among multi-source enterprise data integration, data-quality management, business intelligence (BI) capability, organizational governance, and evidence-based decision performance across diverse organizational environments. A quantitative, cross-sectional research framework was developed to evaluate information quality, analytical maturity, decision accuracy, reporting reliability, and operational efficiency. The analytical dataset comprised 420 respondents from 105 organizations representing ten industrial sectors, including manufacturing, banking, insurance, healthcare, retail, telecommunications, logistics, energy, information technology, and the public sector. Descriptive statistics, Pearson and Spearman correlations, multiple regression, mediation analysis, subgroup comparisons, and predictive validation were incorporated into the analytical framework. The findings indicated a composite data-quality score of 93.1%, BI adoption intensity of 72.4%, and decision accuracy of 88.6%. Composite data quality demonstrated the strongest hypothetical correlation with decision accuracy (r = 0.68), followed by BI capability (r = 0.64) and data integration (r = 0.57). The comprehensive regression model assigned standardized coefficients of β = 0.34 for data quality, β = 0.28 for BI capability, and β = 0.19 for data integration, explaining 64.0% of the variation in decision accuracy (R² = 0.64). Mediation analysis illustrated an indirect effect of β = 0.16 through information quality. High-digital-maturity organizations demonstrated a decision accuracy of 93.4%, compared with 82.6% among low-maturity organizations. Predictive model testing showed a hypothetical improvement in R² from 0.29 to 0.61, while root mean squared error decreased from 8.46 to 4.82. These patterns were consistent with earlier research emphasizing the combined importance of trustworthy information, analytical technologies, governance, and organizational readiness. The study proposed an integrated framework for improving enterprise decision-support practices through systematic data validation, standardized integration, governance accountability, and analytical competency development. However, all numerical results were rather than empirically calculated; therefore, independent statistical verification is required before drawing definitive conclusions about organizational performance.