Data Quality Audit Engine

A Python-based suite of high-precision diagnostic tools for auditing data integrity and compliance across common file formats.

🔐 Data Security

As a localized auditing tool, Data Quality Audit ensures data privacy by design:

• Local-Only Processing: No data is transmitted externally. All audits are conducted in strict isolation on the host machine.
• Audit Logging: Comprehensive execution logs provide traceability for every diagnostic run.

🛡️ Role-Based Utilization (RBAC)

The suite is designed for specific organizational functions:

• Data Auditor: Executes the full diagnostic suite to identify anomalies and missing values.
• Data Scientist: Leverages the distribution analysis modules to validate dataset readiness for modeling.
• Compliance Officer: Reviews generated PDF/HTML reports for regulatory verification.

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🔍 Technical Deep Dive

Multi-Format Parsing Engine

The engine utilizes specialized handlers for different structural complexities:

• CSV Auditor: High-speed schema validation using pandas and numpy for flat file integrity.
• Excel Auditor: Deep-scan of multi-sheet workbooks, including cell-level metadata and formula validation.
• JSON Auditor: Recursive validation of nested structures, ensuring schema consistency across semi-structured data.

Diagnostic Capabilities

• Uniqueness & Completeness: Automated identification of duplicate records and sparse columns.
• Distribution Analysis: Statistical profiling to detect outliers and skewness.
• Referential Integrity: Checks for broken relations across multiple files (if applicable).

🚀 How to Run

1. Prerequisites: Ensure you have Python and Jupyter installed.
2. Launch Notebooks:

   jupyter notebook

3. Select Module: Open the relevant .ipynb for CSV, Excel, or JSON auditing and follow the cell-by-cell instructions.

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Data Quality: Clean data, clear insights.