Strategic Decision-Making in Uncertain Markets: A Data-Analytics-Driven Managerial Perspective

Abstract

Faster technological change, geopolitical shocks, supply-chain upheaval, and behavioral volatility have led to uncertainty in the market that has seen traditional forecasting and intuitive strategy becoming more vulnerable. This paper argues that modern strategic decision-making must combine (1) advanced data analytics (machine learning, time-series models, Bayesian updating), (2) probabilistic and scenario-based methods (Monte Carlo simulation, scenario planning, robust decision making), and (3) managerial processes that translate analytics into actions (decision rules, dashboards, contingency playbooks). We introduce a methodology of analytics integration in the process of strategy formation, provide examples of statistical tools using normative cases, and describe how organisations can institutionalise analytic thinking without falling into the most frequent traps (overfitting, false precision, cultural resistance). The paper contains text citations and a short quantitative illustration for the way managers may translate incertitude into efficient probabilities. The paper also ends with practical suggestions on how managers can future-proof strategy in turbulent environments.