codexkit-data-quality-auditor

To audit data quality across DAMA dimensions, profile your dataset to evaluate Completeness, Accuracy, Consistency, Timeliness, Validity, and Uniqueness. This generates a scored report, an issue log with severity, and a remediation plan to enforce data governance.

A data quality issue log details identified defects with severity levels and affected records. The remediation plan provides actionable fixes, examples, and ownership assignments to resolve dataset inaccuracies and establish ongoing monitoring rules.

Establish data quality gates during ingestion by profiling new data sources against the six DAMA dimensions. This validates completeness and consistency before integration, producing a scorecard and monitoring rules to prevent bad data entry.

Yes, data profiling is used for onboarding new data sources into a governance program. It scores dataset health across six DAMA dimensions, builds an issue log, and creates a remediation plan to ensure incoming data meets quality standards.

The best way to score data completeness and consistency is to run a six-dimension DAMA audit. This profiles the dataset, assigns quality scores, logs issues with affected records, and outputs a remediation plan to support data governance.