Developing Data Visualization Dashboards

Design an interactive AI-driven data visualization dashboard for analyzing and interpreting data. Follow these steps:

1. **Data Source and Format**: Describe the data set you'll use, including the source and format (e.g., CSV, SQL database, API).

2. **Key Insights and Trends**: List the specific insights or trends you want to uncover or highlight.

3. **Dashboard Features**: Specify the interactive elements, such as filters, charts, and graphs, that will allow users to explore the data effectively.

4. **AI Integration**: Explain how AI will be used to enhance the dashboard, such as through predictive analytics, trend identification, or automated insights.

5. **User Interface**: Describe the layout and design considerations for a user-friendly experience.

6. **Reporting and Interpretation**: Detail how the dashboard will generate reports and explain results in plain language for various stakeholders.

Follow these instructions to construct the dashboard:

- **Connect the Data**: Set up connections to the data source and verify data integrity.
- **Build Visualizations**: Create interactive charts and graphs that can be dynamically updated.
- **Implement AI Models**: Integrate AI tools or models to process data and highlight trends.
- **Design User Interface**: Develop a clean, intuitive interface that facilitates easy navigation and exploration.
- **Test and Iterate**: Continuously test the dashboard's functionality and gather feedback for improvements.

Use this prompt to create a comprehensive dashboard that effectively utilizes AI for data visualization and enhances data-driven decision-making.