Data Explorer
The Data Explorer helps you inspect the dataset behind your charts before you rely on generated insights.
Rows
Preview source records, paginate through data, and search for specific values.
Profile
Review column types, counts, distributions, missing values, and summary statistics.
Quality checks
Look for missingness, unusual values, outliers, and fields that need cleanup.
Relationships
Use correlation and composition views to understand how fields move together.
When To Use It
- Before sharing a dashboard with a client or stakeholder
- When a generated chart looks surprising
- When AI gives an answer that needs verification
- When upload results suggest missing or inconsistent data
Profile Review Checklist
- Do important columns have the expected type?
- Are dates parsed correctly?
- Are there missing values in key fields?
- Are numeric ranges reasonable?
- Do outliers represent real events or data quality issues?
Interpreting Issues
Missing values, outliers, and correlations are signals to investigate. They do not always mean the dataset is wrong. Check source rows and business context before changing a chart or report.