Developing an Integrated Data Visualization Model for Continuous Business Performance Monitoring and Optimization

In the era of data-driven decision-making, organizations face the challenge of transforming vast, heterogeneous datasets into actionable insights for continuous performance improvement. This review examines the development of an integrated data visualization model designed to enable real-time business performance monitoring and optimization. The study explores the convergence of data integration, visualization analytics, and business intelligence frameworks to support dynamic decision environments. It highlights how visual analytics—when coupled with machine learning and predictive modeling—facilitates anomaly detection, trend forecasting, and KPI tracking across multiple business domains. Furthermore, the paper reviews the role of interactive dashboards, cross-platform interoperability, and automated data pipelines in enhancing situational awareness and reducing decision latency. By synthesizing findings from recent advances in visualization technologies, the review identifies best practices for designing scalable, adaptive, and user-centric visualization models. It also discusses the integration of human–computer interaction (HCI) principles and visual storytelling techniques for improved interpretability and executive communication. Ultimately, this paper emphasizes that effective visualization models not only enhance business transparency but also create a foundation for data-driven cultural transformation and sustainable organizational performance.