data-quality-analysis

To generate a data quality report, this Skill automates Phase 1 ingestion, performs dimension-by-dimension assessment against the ABS Data Quality Framework, and outputs a formal HTML report with a co-located appendix.

A column quality scorecard evaluates dataset accuracy by computing completeness, validity, consistency, uniqueness, timeliness, and accuracy for each column, providing a summary score to highlight specific data quality gaps.

Yes, you can produce reproducible data profiling code for raw datasets because the workflow includes optional Python-based profiling code alongside the automated HTML report generation for auditability.

This data profiling workflow supports governance reporting and data readiness audits by evaluating seven ABS dimensions—institutional environment, relevance, timeliness, accuracy, coherence, interpretability, and accessibility—to guide remediation.

The best way to assess data quality dimensions for modeling datasets is running this Skill to compute per-dimension ratings and per-column scorecards, turning messy data into trusted quality insights for data scientists.

A limitation of automated HTML report generation is that it relies on a predefined template for dimensional assessments, meaning custom output formats or non-ABS framework structures may require additional post-processing.