Data quality issues exist, but where's the evidence?

FastDQ generates evidence to help build credible cases for data quality initiatives, providing defensible analysis in a format ready for business presentations.

Business users often understand data quality problems exist but lack concrete evidence to present to decision-makers.

FastDQ bridges this gap by providing quantified, professionally-framed findings that help to highlight potential strategic data quality investment cases.

Summary:

- 24 analyses

- Conditional (column types) and universal analyses

- Weighs likelihood of false positives against the extent to which the analysis score represents a likely data quality issue to the organisation

- Results prioritised to focus attention on highest-confidence, highest-impact issues