Data Quality Tool

This tool provides a domain-focused evaluation framework for data quality within Informatica-centric environments. It is intended for data engineers, data stewards, BI analysts, and governance leads who require repeatable, auditable quality assessments before ETL runs, data cataloging, or reporting. It solves problems of inconsistent data, missing values, and rule drift across sources by delivering a quantitative quality score and a prioritized list of remediation actions.
Core logic centers on profiling, rule-template application, and a composite score across key quality dimensions such as completeness, accuracy, consistency, timeliness, validity, and integrity. Required inputs include data_source_type, sample_size, and column metadata; optional inputs include rule templates, weightings, and data lineage hints. Outputs comprise data_quality_score, issues, profiling_report, and export options in JSON or CSV for governance portals.
The approach favors explainable results, configurable thresholds, and reproducible runs so teams can track improvements over time and benchmark datasets. Data quality insights can drive data cleansing priorities and inform Informatica-based remediation plans.