What is data profiling?
Data profiling is the process of examining a dataset to understand its structure, content and quality, checking value distributions, ranges, null rates, uniqueness and patterns. It reveals what a table actually contains before you trust or transform it, exposing gaps, outliers and anomalies early.
Before you build a report or a model, you need to know what is really in the data. Data profiling answers that by scanning columns and summarising them.
Profiling typically reports:
- Value distributions most common and rare values.
- Ranges and extremes minimum, maximum, outliers.
- Completeness null and blank rates per column.
- Uniqueness duplicate and distinct counts.
- Patterns formats, lengths and unexpected values.
Profiling is how you discover that a 'country' column has forty spellings of one country, or that 20% of a key field is empty, before those surprises corrupt an analysis.
iDBQuery makes profiling conversational. Point it at a source and its schema introspection maps the tables and columns automatically; then you can simply ask, 'how many customers have no email?', 'what's the range of order values?', 'which product names appear more than once?', and it writes the SQL, runs it live, and cites the answer to the underlying rows. Its Analyst agent can profile an unfamiliar dataset on its own as part of investigating a question. So instead of running separate profiling tooling, you explore and quality-check your data in plain language and see exactly which rows drive each finding.
Updated 2026-06-22