Grafana Assistant streamlines the process of analyzing and visualizing Controller Area Network (CAN) data, particularly for engineers who may not have a strong data science background. CSS Electronics, which produces CAN bus data loggers, highlights how their users often deal with vast amounts of data—sometimes terabytes—collected from various devices. Traditional methods of data analysis can be tedious and time-consuming, but Grafana Assistant simplifies this by allowing users to query large data lakes without needing to write complex SQL queries. The assistant offers several key features, such as zero setup if a Grafana integration is already in place, the ability to analyze large datasets seamlessly, and the capability to create custom dashboards quickly. Instead of manually uploading small files as required by ChatGPT, Grafana Assistant can directly access and analyze data already stored in cloud servers. Users can interact with the assistant through natural language prompts, making data exploration intuitive. For instance, engineers can ask about the structure of their data and receive organized summaries, or they can request specific visualizations, like average speed over time, which the assistant can generate instantly. Real-world examples show how effective Grafana Assistant can be. Users can start by assessing what data is available in their CAN bus logs, then explore it interactively before creating dashboards. In one case, a user requested a new dashboard based on a broad prompt and received a functional dashboard filled with relevant panels. However, results can vary significantly with vague prompts, emphasizing the importance of clarity in instructions. More detailed prompts lead to better outcomes, demonstrating how the assistant can be a powerful tool in data visualization when used correctly.