data-quality-auditor

Data profiling analyzes individual columns in a dataset to understand data types, null counts, cardinality, and distributions. It serves as the foundation for detecting completeness issues, spotting anomalies, and validating data consistency before deeper analysis.

Run a Python data profiler script on the CSV file to detect outliers and analyze missing values. It applies IQR and Z-score methods for outlier identification, classifies missing data mechanisms (MCAR, MAR, MNAR), and recommends appropriate imputation strategies.

Run a Python audit script on the CSV file to generate a remediation playbook. The script computes the Data Quality Score, identifies anomalies and missing values, and outputs an exportable report with targeted, actionable fixes for each identified issue.

Yes, the audit performs cross-column checks to validate referential integrity, duplicates, and logical constraints. It evaluates these consistency rules across multiple columns and surfaces violations within an exportable report for remediation.

Yes, the profiling approach supports ongoing monitoring and targeted scans for streaming-like data profiles. You can execute continuous or focused audits on specific columns to track completeness, validity, and anomalies over time.

Python script audits are limited to static CSV datasets and streaming-like data profiles. They cannot directly query live databases without prior export, and running full audits on extremely large datasets may impact processing performance.