How does iDBQuery handle messy or dirty data?

iDBQuery is designed to work with real, imperfect data rather than a pristine warehouse: - Its semantic layer maps cryptic table and column names (think cust_dt, amt_2) to real business meaning, so you can ask in plain language even when the schema is obscure. - It reads inconsistent formats and can OCR scanned PDFs and folders of documents, turning them into queryable data. - It handles cleanup within the query, coalescing nulls, normalising text and casting types, instead of demanding an upfront ETL project.

A worked example: over a table with mixed date formats and stray nulls, you ask 'monthly revenue, ignoring rows with no amount,' and iDBQuery writes SQL that filters and casts appropriately, then cites what it included.

You can also point iDBQuery at the mess to inspect it: 'find rows with missing or malformed values' returns the offenders so you can fix them at the source.

The differentiator is that dirty data does not block you. A traditional BI stack needs the data modelled and cleaned before anyone can ask a question; iDBQuery lets you query it as it is, cleaning inside the query and showing the SQL so you can see exactly how it handled the mess. And because every figure is cited, you can verify that a cleaned-up number rests on the rows you would expect.