Advanced Data Analysis and Database Cleaning

Context

Databases grow over time without anyone defining quality standards. The inevitable result: inconsistencies between systems, duplicates generated by different teams, and formats that vary depending on who entered the data. What was functional at some point becomes an unreliable asset.

The challenge

• Duplicate records generated from multiple sources and systems
• Fields with inconsistent formats (dates, names, references)
• Incomplete data or incorrect values difficult to detect manually
• Reports with contradictory figures depending on which table was queried
• No auditability: no way to know which data was reliable

The solution

AI-powered audit

Automatic analysis of the initial state of databases: quality profiling, anomaly detection, and generation of a diagnostic report per table.

Intelligent deduplication

Fuzzy matching algorithms combined with AI to identify duplicate records even when they have variations in name, format, or encoding.

Normalization and enrichment

Format standardization, completion of missing fields through inference, and cross-table validation to detect inconsistencies.

Reproducible pipeline

The process is not a one-time cleanup. It remains as an automated pipeline that can be run periodically to maintain data quality.

Results

• 94% accuracy in data after the cleaning and normalization process
• Millions of records processed automatically
• 60% reduction in duplicates across main tables
• Business reports are now reliable and consistent with each other

Business impact

Aspect	Before	After
Data reliability	Low, unknown	High, measured
Duplicates	Widespread	Eliminated
Inconsistent reports	Common	Eliminated
Quality audit	Non-existent	Automated

Data is a company’s most valuable asset when it’s reliable. A quality pipeline transforms chaos into infrastructure.

Status: IN PRODUCTION