What is data quality?

Data quality is the difference between numbers you can act on and numbers that mislead. It is usually assessed across several dimensions:

• Accuracy does the value reflect reality?
• Completeness are required fields populated?
• Consistency does it agree across systems?
• Timeliness is it current enough to use?
• Validity does it conform to expected formats and rules?
• Uniqueness free of duplicate records?

Poor quality, missing values, duplicates, stale data, undermines every report built on top of it.

iDBQuery helps in two ways. First, because it queries your live sources and cites every figure back to its source row, quality issues become visible rather than hidden inside a black-box metric, you can click through and see the actual records behind a number. Second, you can use plain-language questions and its Analyst agent to run quality checks directly: find duplicate customers, count rows with missing values, spot outliers, or reconcile totals between two systems. iDBQuery does not magically clean your data, but it makes data-quality problems easy to surface and investigate, so you can trust, or fix, the numbers before you rely on them.