International Journal of Science, Engineering and TechnologyAuthors: Shelke Rutvik Santosh, Prof. S. R. Borhade, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: The volume, speed and variety of data produced by digital systems, sensors and online platforms have outgrown traditional analytical methods. Artificial intelligence enables organizations to extract insights from large, complex and unstructured data with greater speed and accuracy. This paper reviews how AI techniques, particularly machine learning, deep learning and natural language processing, are applied across the data analysis pipeline: automated data cleaning and preparation, automated machine learning for model and feature selection, pattern recognition and predictive modelling with ensemble methods, anomaly detection in transactions and sensor streams, and natural-language interfaces that translate questions into database queries so that non technical users can explore data conversationally. Applications in healthcare, finance and retail are discussed. The review also examines explainable AI methods that attribute predictions to input features, and the problems of data quality, bias and fairness. A five-layer AI-assisted analytics architecture comprising data ingestion, preparation, modelling, explanation and conversational insight layers is proposed, together with research gaps, objectives, a comparison of traditional and AI-driven analysis and challenges such as privacy, compute cost and skill shortages. The review concludes that AI augments rather than replaces human analysts, freeing them for interpretation and strategic decisions.
DOI: https://doi.org/10.5281/zenodo.24216130
