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Background: The advent of big data has revolutionized various sectors, enabling the extraction of valuable insights and the development of predictive analytics models. The integration of large datasets, advanced statistical methods, and machine learning techniques has paved the way for significant advancements in predictive analytics. Objective: This article aims to examine the current trends and future directions in the application of big data in predictive analytics. It explores the statistical methods employed and the impact of these advancements on various industries. Methods A comprehensive literature review was conducted, focusing on peer-reviewed articles, industry reports, and case studies from 2015 to 2024. Statistical techniques such as regression analysis, time series forecasting, and machine learning algorithms were analyzed. Descriptive statistics and inferential methods were utilized to summarize the findings and draw meaningful conclusions. Results: The analysis reveals a substantial increase in the adoption of big data technologies across sectors such as healthcare, finance, and marketing. Predictive models leveraging big data have shown improvements in accuracy and efficiency. For instance, in healthcare, predictive analytics has improved patient outcomes by 15%, while in finance, it has reduced fraudulent activities by 18%. In marketing, customer retention rates have increased by 12% due to more accurate targeting. Conclusion: Big data plays a crucial role in predictive analytics, driving innovation and efficiency. The future of predictive analytics lies in the continuous development of sophisticated statistical methods and the integration of real-time data processing. These advancements will further refine predictive models, offering more precise and actionable insights across various domains.
Jasim et al. (Tue,) studied this question.