How do I spot a data-entry error with iDBQuery?
Ask iDBQuery to check for data-entry errors and it surfaces outliers, impossible values, duplicates and inconsistencies, like a negative price or a date in the future, and cites the exact rows. You can catch mistakes before they distort a report, without writing a single validation query.
A single bad record (an extra zero, a wrong date, a duplicated order) can quietly skew a report and lead to a bad decision. iDBQuery helps you catch these errors by asking in plain language.
- It surfaces outliers and impossible values, like negative quantities or prices far outside the norm
- It finds duplicates and records that break expected patterns
- It flags inconsistencies between related fields or across systems
- It cites the exact rows so you can inspect and fix them
Ask *are there any orders with a negative amount, a future date or an unusually large value?* iDBQuery returns the suspect rows for review. Follow up with *does this customer appear more than once with slightly different names?* to catch duplicates that inflate counts.
Because iDBQuery queries your data in place and cites every result, you can go straight from a flagged anomaly to the exact record and correct it, rather than knowing something is off but not where. And because it works across one live model of your sources, it can catch mismatches between two systems that should agree. Finance, operations and data teams use this to validate a dataset before trusting it, clean up entry mistakes before they compound, and keep reports accurate, without hand-writing a battery of validation checks.
Updated 2026-06-22