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@article{206719,
        author = {Ananya and Deepthi P Dsouza and Amritha and D Rajeev and Prabhu A Angadi},
        title = {Smart Data Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {234-241},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206719},
        abstract = {This project Smart Data Analysis presents an automated, end-to-end data pipeline system designed to improve data quality, streamline preprocessing, and support reliable business analytics. Modern organizations often struggle with inconsistent, incomplete, and unstructured data, which leads to inaccurate insights and poor decision-making. To address this, the proposed system integrates automated data ingestion, profiling, cleaning, version control, and analytics within a unified platform. Using a combination of heuristic rules and machine learning techniques, the system detects anomalies, resolves inconsistencies, performs intelligent cleaning, and ensures reproducibility through dataset and code versioning. It further incorporates a feature store and automated KPI-driven report generation to support data-driven decision processes. Built using technologies such as Kafka, Spark, Pandas, PySpark, DVC, Git, FastAPI, and Power BI, the pipeline ensures scalability, transparency, and continuous monitoring through observability tools. Evaluated on academic and business datasets, the system demonstrates significant improvements in data consistency, reporting accuracy, and operational efficiency, making it suitable for applications in HR analytics, finance, sales forecasting, customer feedback analysis, healthcare, and more.},
        keywords = {.},
        month = {July},
        }