Data visualization is an essential component of Big Data analytics, enabling stakeholders to interpret complex data insights effectively. Visualization tools help in identifying trends, patterns, and anomalies that might be overlooked in raw data.

Tableau and Microsoft Power BI are leading business intelligence platforms that offer powerful data visualization capabilities. They allow users to create interactive dashboards and reports with drag-and-drop interfaces, making data accessible to non-technical users. These tools support real-time data connections, enabling dynamic dashboards that update automatically as new data arrives.

For more customizable and detailed visualizations, Python libraries such as Matplotlib, Seaborn, and Plotly are commonly used in data science workflows. Matplotlib provides a foundation for creating static, animated, and interactive visualizations. Seaborn, built on top of Matplotlib, simplifies the creation of complex statistical plots with aesthetically pleasing default styles. Plotly offers interactive graphing capabilities, allowing for zooming, panning, and real-time data updates in web applications.

In AI, visualization tools are not limited to data exploration but also play a critical role in model interpretation. Techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) provide visual insights into model predictions, helping data scientists understand the contribution of each feature to the final output.