Impact of Data Science and Business Analytics on Economic Growth: A Global Perspective

Abstract

This study quantitatively examined the impact of data science and business analytics on economic growth, productivity, innovation, and operational performance from a global perspective, with particular attention to differences across income groups, geographic regions, digital-readiness levels, and economic sectors. A longitudinal cross-country quantitative design was applied to 2,964 country-year observations representing 156 countries across six geographic regions, with a 93.6% observation-retention rate and 97.8% overall data completeness. Descriptive statistics, Pearson and Spearman correlations, multiple regression, panel-data models, subgroup comparisons, effect-size estimation, mediation and moderation analysis, sensitivity testing, holdout validation, and cross-validation were employed. Digital intensity demonstrated the strongest correlation with labor productivity (r = 0.64), followed by business-analytics adoption (r = 0.61), while their adjusted standardized productivity effects were β = 0.31 and β = 0.29, respectively. The integrated model explained 58.7% of productivity variation compared with 39.8% for the baseline economic model. Income-level analysis identified substantial heterogeneity, with analytics-related productivity coefficients ranging from β = 0.38 among high-income economies to β = 0.17 among lower-income economies. Sectoral analysis showed the strongest overall relationship in ICT (β = 0.41), followed by manufacturing (β = 0.37), finance (β = 0.35), logistics (β = 0.34), retail (β = 0.32), and healthcare (β = 0.28). Digital readiness generated a moderate-to-large effect size of Cohen’s d = 0.72, while analytics capability produced d = 0.68. Mediation analysis indicated that productivity, innovation, decision efficiency, and organizational maturity collectively transmitted 42.9% of the analytics–performance relationship, while human capital produced the strongest moderation effect at β = 0.18. Robustness analyses retained positive coefficients across alternative model specifications, and predictive validation reduced GDP-growth holdout RMSE from 2.07 to 1.68 and MAPE from 12.1% to 9.4%. The findings demonstrated that the economic contribution of data science and business analytics was strongest when analytical capability was combined with digital infrastructure, skilled human capital, innovation capacity, organizational maturity, and supportive institutional conditions.