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@article{180309,
        author = {Gayatri Tavva},
        title = {Data Visualization Techniques for Key Data Insights},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1123-1129},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180309},
        abstract = {As disciplines continue to thrive with complex and high-volume data, the need for robust, scalable, and interpretable data visualization techniques is essential. This review, studies the history, techniques, and challenges of current data visualization from conventional approaches to advanced approaches, from interaction models and cognitive aspects to analytic results. Architecture models, experimental findings, and comparative evaluations that impact a given application to the effectiveness of visual encoding are discussed. Drawing from a review of visualization frameworks in a number of application domains, including healthcare, business intelligence, and machine learning, this study identifies key performance factors and knowledge gaps. Lastly, the perspective on emerging directions to further automate, personalize, and seamlessly integrate visual analytics into AI workflows was provided.},
        keywords = {Cognitive Load, Dashboard Evaluation, Data Visualization, Encoding Strategies, Human-Centered Design, Insight Extraction, Interaction Design, Temporal Data, Visualization Literacy, Visual Analytics.},
        month = {June},
        }