Data Quality Analysis & Cleaning Tool
Automated data quality checker identifying and fixing common issues in operational datasets
The Problem
Organizations struggle with messy data—duplicate records, inconsistent formatting, missing values, and typos create reporting errors and operational inefficiencies. Manual data cleaning is time-consuming, error-prone, and doesn't scale. Teams need automated systems to identify, document, and fix data quality issues before they impact business decisions.
The Solution
I built a Python-based data quality analysis tool that automatically detects, documents, and resolves common data issues in client/case management datasets.
The tool performs:
Automated issue detection: Identifies duplicates, missing values, formatting inconsistencies, typos, and logic errors
Data standardization: Removes leading/trailing spaces, standardizes capitalization, fixes common abbreviations and typos
Database storage: Stores both original and cleaned data in SQLite for auditable before/after comparison
SQL-powered analysis: Enables filtered queries and reporting on cleaned datasets
Visual reporting: Generates charts showing data quality metrics and issue distribution
The Impact
Reduced manual cleaning time by automating identification and standardization of common data issues
Improved data reliability for reporting and analysis by fixing inconsistencies at the source
Created audit trail with before/after documentation for quality assurance and compliance
Demonstrated technical capability to build data analysis tools using Python, SQL, and visualization libraries
Tools & Skills
Tools: Python, SQL, Google Colab
Skills: Data analysis, data cleaning, database design, data visualization, quality assurance, automation scripting