data-quality-analysis

Data quality assessment applies the ABS 1520.0 framework across seven dimensions: institutional environment, relevance, timeliness, accuracy, coherence, interpretability, and accessibility. It generates a structured markdown report with a per-column scorecard for completeness, validity, consistency, uniqueness, and accuracy.

Data profiling is a guided discovery workflow that identifies data quality issues before analysis. You need it when reviewing a dataset, checking fitness for purpose, or producing a data quality declaration to ensure your data meets governance and audit trail standards.

Yes, data quality report generation produces standalone Markdown or HTML documents suitable for governance and audit trails. The report includes a systematic seven-dimension assessment based on ABS 1520.0 and a per-column quality scorecard.

Yes, the framework produces a per-column quality scorecard evaluating completeness, validity, consistency, uniqueness, timeliness, and accuracy. This column-level profiling complements the broader seven-dimension dataset assessment.

Evaluating data fitness for purpose requires applying the ABS Data Quality Framework's seven dimensions to your dataset. This structured approach covers institutional environment, relevance, timeliness, accuracy, coherence, interpretability, and accessibility in a comprehensive report.

The ABS 1520.0 framework is designed for institutional data reviews and pre-analysis assessments. For non-standard datasets, the seven-dimension assessment still applies, but the guided discovery profiling phase may require manual context interpretation for dimensions like institutional environment and accessibility.