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@article{208424,
        author = {Dr. K. Vijayalakshmi and J. N. Nareen Karthik and V. S. Mahaananth and V. Manikandan and Sheetal Shevkari},
        title = {AutoInsight: An AI-Based Automated Exploratory Data Analysis and Data Storytelling Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {73-83},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208424},
        abstract = {Exploratory Data Analysis (EDA) forms a foundational stage of the data analytics workflow, allowing practitioners to surface patterns, trends, relationships, anomalies, and other defining characteristics of a dataset. In practice, however, carrying out EDA by hand calls for considerable subject-matter familiarity, coding skill, and time, which puts effective data analysis out of reach for many non-technical users. This paper introduces AutoInsight, an AI-driven framework built to streamline and speed up both exploratory data analysis and the communication of its results through data storytelling. The system automatically cleans and prepares incoming data, flags data-type and quality problems, computes descriptive statistics, chooses fitting visualizations, and surfaces notable patterns and anomalies across structured datasets. 
AutoInsight further applies AI-based natural language generation to convert these analytical outputs into short, easy-to-read narratives, helping users grasp not just what the data shows but why certain trends or relationships matter. 
By pairing automated analytical methods with visualization and language-driven storytelling, the framework delivers a complete, start-to-finish data exploration experience. Cutting down on repetitive manual work and presenting findings in an approachable form allows AutoInsight to raise analytical efficiency, interpretability, and accessibility for technical and non-technical users alike. The proposed approach illustrates how AI-assisted EDA combined with automated storytelling can serve as a practical foundation for data-driven decision-making.},
        keywords = {Artificial Intelligence, Exploratory Data Analysis, Data Science, Data Visualization, Automated Data Analysis, Data Storytelling, Anomaly Detection, Natural Language Generation.},
        month = {September},
        }