How do I run data-quality checks with iDBQuery?

Data-quality checks catch the problems that quietly break reports: nulls, duplicates, impossible values and broken references. iDBQuery lets you run them as plain-language questions: - Connect the source you want to inspect. - Ask a check like 'Find orders with a null customer ID, a negative amount, or a ship date before the order date.' - iDBQuery writes the SQL, scans the live data, and returns the offending rows, each cited so you can fix them at the source.

A worked example: before a board report, an analyst asks 'Are there customers with duplicate emails, invoices with no line items, or amounts more than three standard deviations from the mean?' iDBQuery returns each problem set separately, so nothing dubious sneaks into the numbers.

The differentiator is that you do not need to write validation SQL or stand up a data-observability tool; you describe the rule and iDBQuery checks it against live data in seconds. Because every flagged row is cited, you can hand the list straight to whoever owns the system to correct. And because iDBQuery spans sources, you can check consistency across systems too, for instance 'find customers in the CRM with no matching record in billing.' Save the checks as a report and re-run them each period as a standing data-quality gate.