Business Analytics and Data-Driven Decision Making in the Era of Big Data

Abstract

In light of the growing use of big data and analytics, the process of making business decisions is evolving towards being informed by evidence and efficiency. The purpose of this paper is to identify how such factors like descriptive, predictive and prescriptive analytics, application of AI and ML technologies and use of big data platforms influence the quality of decisions and organisational performance. Applying a quantitative methodology, data were collected from 250 managers in various industries including manufacturing, IT services, retail and finance. Regression analysis and SEM model were used to identify relationships. As the results show, predictive analytics (β = 0.42, p < 0.01) and prescriptive analytics (β = 0.36, p < 0.05) have the most effect on decision quality, while SEM confirms a strong relationship between Analytics Capabilities → Decision Quality (β = 0.71, p < 0.001) and Decision Quality → Organisational Performance (β = 0.63, p < 0.001). Moreover, an indirect effect from analytics capabilities on organisational performance (β = 0.29, p < 0.05) was identified. The results provide support for the assertion that companies with greater analytics maturity make more accurate and evidence-based decisions leading to higher levels of operational efficiency and innovation. Implications for managers are that there is a need for emphasis on analytics infrastructure, training and development, as well as data governance to enhance the potential for gains from adopting analytics. The limitations of this study are that it is cross-sectional, based on self-report data, and limited to a particular industry.