DATA QUALITY AND REAL-TIME DECISION-MAKING ACROSS HEALTHCARE, FINANCE, AND RETAIL ENTERPRISES

Abstract

The study examines how quality real-time information influences the decision-making processes within the health, financial, and retail industries. It discusses the operational efficiency and rapid decision-making systems that operational efficiency and operationalized data pipelines bring. Another significance that is raised in the study is on the value of the quality of data in these industries, where accurate, dependable, and timely data is central in making efficient and timely decisions. Among the key findings, there should be a 20-30% higher speed of decision-making, accuracy, and overall speed of operation after high-quality data solution implementation. The healthcare sector has reduced the time spent diagnosing patients by 30% using AI algorithms and real-time patient data analytics, and cut down fraud by 20% with AI algorithms and real-time fraud detection in the financial industry. In the retail sector, the use of real-time, personalized recommendations has already led to a 10% increase in sales by 10%. The other problem addressed in the research is the data quality, integration of the system, and adoption of the technology, particularly in the areas yet to adopt the fully integrated and modernized systems. The study supports the application of cloud-native platforms, microservices, and machine learning to enable the development of scalable, efficient real-time processing of data. The study concludes with practical suggestions of how organizations may enhance their data systems and a future research on the capacity to apply across industries, 5G integration, and data privacy. The real-time data solutions are not only comprehensive to improve operational outcomes, besides augment customer satisfaction and innovate within industries.