AI-Driven Data Analytics: A Systematic Review of Methods and Applications

Abstract

AI-based data analytics combines both artificial intelligence methods with data analysis to derive valuable insights out of big and complex data. The following review examines its history, important techniques such as machine learning, deep learning and natural language processing, and important processes such as data cleaning and feature engineering. It emphasizes various usage in healthcare, finance, retail, transportation and cybersecurity. Other topics that have been addressed in the study include performance evaluation measures, issues such as data quality, scalability and interpretability, and ethical issues such as bias, privacy and transparency. Lastly, it also summarizes the future research directions which include explainable AI, automation, privacy keeping, and real time analytics to enhance better decision-making systems.