The rapid digitization of global infrastructure has necessitated the development of sophisticated frameworks for the collection, analysis, and interpretation of heterogeneous data streams. This research article explores the convergence of information systems across two critical domains: healthcare informatics and e-commerce analytics. By synthesizing health data collection methodologies across European Union member states with predictive consumer behavior models, the study identifies universal principles of reproducible, ethical, and collaborative research. The investigation delves into the implementation of Electronic Health Records (EHR) and the utilization of novel epidemiological tools like the Data Extraction for Epidemiological Research (DExtER) system to automate clinical studies. Concurrently, the paper examines the moderator effects of gender and personality traits on digital consumption, particularly during the COVID-19 pandemic, and evaluates the efficacy of machine learning in optimizing Customer Acquisition Cost (CAC) through automated cohort analysis. Through an extensive theoretical elaboration on Business Intelligence (BI) and Open Science design, the research argues that the future of evidence-based policy and commercial sustainability depends on the standardization of Key Performance Indicators (KPIs) and the mitigation of cybersecurity risks. The findings suggest that while the context of data-whether clinical or commercial-varies, the underlying requirements for data integrity, reproducibility, and dynamic behavioral analysis remain constant. This article provides a comprehensive synthesis for researchers and practitioners aiming to navigate the complexities of big data management in the 21st century.