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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.
All processing happens in your browser. Your data is never uploaded anywhere. Analysis history is encrypted and stored locally on your device.
FastDQ uses a transparent, systematic approach to data quality assessment. Every finding is scored by both confidence level (how certain we are) and severity (how bad it is).
Issues are ranked 1-9 based on the intersection of confidence and severity:
| Critical | Concerning | Minor | |
|---|---|---|---|
| Core | 1 (High) | 3 (Medium) | 6 (Medium) |
| Plausible | 2 (High) | 4 (Medium) | 7 (Low) |
| Potential | 5 (Medium) | 8 (Low) | 9 (Low) |
| Analysis | Dimension | Tier | Calculation | Thresholds (Good/Minor/Concerning) |
|---|---|---|---|---|
| Overall Completeness | Completeness | Core | (total_cells - missing) / total_cells × 100 |
>95% / >85% / >50% |
| Column Completeness | Completeness | Core | (rows - missing) / rows × 100 per column |
>95% / >85% / >50% |
| Row Completeness | Completeness | Core | rows_under_50%_complete / total_rows × 100 |
≤5% / ≤15% / ≤50% |
| Duplicate Rows | Uniqueness | Core | (rows - duplicates) / rows × 100 |
>98% / >92% / >80% |
| ID Field Uniqueness | Uniqueness | Core | unique_values / total_values × 100 (1st col) |
100% / >98% / >95% |
| Email Without @ | Validity | Core | emails_with_@ / total_emails × 100 |
100% / >99% / >90% |
| Phone With Letters | Validity | Core | phones_no_letters / total_phones × 100 |
100% / >99% / >90% |
| Negative Ages | Accuracy | Core | ages_≥0 / total_ages × 100 |
>99.5% / >98% / >95% |
| Numeric Type Violations | Validity | Core | valid_numerics / total_values × 100 |
100% / >99% / >95% |
| Whitespace Issues | Consistency | Core | values_no_whitespace / total_values × 100 |
>92% / >80% / >60% |
| Age Over 130 | Accuracy | Plausible | ages_≤130 / total_ages × 100 |
>99.5% / >98% / >95% |
| Statistical Outliers | Validity | Plausible | values_in_IQR_range / total × 100 (2.5×IQR) |
>97% / >92% / >85% |
| Inconsistent Delimiters | Consistency | Plausible | majority_pattern / total_delimited × 100 |
>97% / >90% / >75% |
| Mixed Boolean Representations | Consistency | Plausible | count(distinct_boolean_formats)Severity reduced 1 level when dominant format >90% |
≤2 / ≤2 / ≤3 formats |
| Date Format Inconsistencies | Consistency | Plausible | count(distinct_date_formats) |
1 / 1 / ≤2 formats |
| Date Sequence Issues | Accuracy | Plausible | valid_sequences / total_pairs × 100 |
>99.5% / >98% / >95% |
| Age-Birth Mismatches | Accuracy | Plausible | matching_pairs / total_pairs × 100 (±1yr tolerance) |
>99.5% / >98% / >95% |
| Negative Salary | Accuracy | Plausible | salaries_≥0 / total_salaries × 100 |
>99.5% / >98% / >95% |
| Encoding Artifacts | Validity | Plausible | clean_values / total_values × 100 |
>99.8% / >99% / >97% |
| Case Pattern Inconsistencies | Consistency | Potential | consistent_values / total_values × 100 |
>95% / >85% / >70% |
| Name Format Inconsistencies | Consistency | Potential | majority_format / total_names × 100 |
>90% / >80% / >60% |
| Placeholder Values | Completeness | Potential | (total - placeholders) / total × 100 |
>95% / >85% / >50% |
| Test Email Domains | Validity | Potential | valid_domains / total_emails × 100 |
>99% / >97% / >90% |
| Disproportionate Frequency | Uniqueness | Potential | 100 - dominant_value_percentage |
<60% / <75% / <90% dominance |