The impact of the integration of Big Data and Artificial Intelligence in Management Control: Systematic Literature Review

Abstract

In a context marked by digitalization and the emergence of Data Science, this research examines the future of management control in light of advancements in Big Data and Artificial Intelligence (AI). Through a systematic literature review conducted according to the PRISMA protocol, covering 62 articles, the study explores three main axes. First, it analyzes the specificities of Big Data, characterized by the 7 Vs (Volume, Velocity, Variety, Variability, Veracity, Value, and Visualization), which fundamentally transform the nature and scale of the data that organizations must process. Next, it examines the contributions of Data Science and Artificial Intelligence to Big Data processing, particularly through the four dimensions of Business Analytics (descriptive, diagnostic, predictive, and prescriptive). Finally, it assesses the implications of these technologies for the management control function. The results show that the integration of Big Data and AI does not render the management controller obsolete; rather, it encourages a reinvention of the role toward a “Strong Controller”, capable of managing multidimensional and overall performance through real-time analyses, more reliable forecasts, and better integration of ESG criteria. However, this transformation faces several major challenges: resistance to change, insufficient technical skills, data quality issues, and legal constraints. Collaboration between the management controller, managers, employees, and data specialists thus becomes essential to overcoming these challenges. This research demonstrates that, by mastering these innovative technologies while maintaining strategic vision and professional expertise, the management controller can not only ensure their own sustainability but also enhance their contribution to corporate governance and sustainable organizational performance.

Classification JEL : M41

Paper type : Theoretical Research