  Exploring data is crucial in data analysis, as it helps users understand and
interpret the data more effectively. However, performing effective data
exploration requires in-depth knowledge of the dataset and expertise in data
analysis techniques. Not being familiar with either can create obstacles that
make the process time-consuming and overwhelming for data analysts. To address
this issue, we introduce InsightPilot, an LLM (Large Language Model)-based,
automated data exploration system designed to simplify the data exploration
process. InsightPilot automatically selects appropriate analysis intents, such
as understanding, summarizing, and explaining. Then, these analysis intents are
concretized by issuing corresponding intentional queries (IQueries) to create a
meaningful and coherent exploration sequence. In brief, an IQuery is an
abstraction and automation of data analysis operations, which mimics the
approach of data analysts and simplifies the exploration process for users. By
employing an LLM to iteratively collaborate with a state-of-the-art insight
engine via IQueries, InsightPilot is effective in analyzing real-world
datasets, enabling users to gain valuable insights through natural language
inquiries. We demonstrate the effectiveness of InsightPilot in a case study,
showing how it can help users gain valuable insights from their datasets.
