A Review of Integrated Data Management Systems for Enhancing Financial Forecasting Accuracy

Financial forecasting is increasingly critical for strategic decision-making in organizations, yet its accuracy is often compromised by fragmented data sources, inconsistent reporting, and analytical inefficiencies. Integrated Data Management Systems (IDMS) have emerged as a solution to these challenges by consolidating data repositories, enabling real-time analytics, and enhancing predictive modeling capabilities. This study reviews the current state of IDMS implementations, focusing on their impact on financial forecasting accuracy. Through a systematic analysis of academic literature, industry reports, and case studies, the study identifies key features, benefits, and challenges of IDMS adoption. Findings indicate that integration of data sources, automation of workflows, and implementation of advanced analytics substantially improve forecast precision, reduce operational risks, and support informed decision-making. The study provides a comprehensive framework for understanding how IDMS contribute to enhanced forecasting performance in diverse organizational contexts.