Data Sanitization

Technical Definition

Data Sanitization is the systematic cleaning and净化 process applied to raw extracted data before it enters analytical pipelines or storage systems. This encompasses multiple operations: PII detection and removal (names, addresses, phone numbers, emails, IDs), format standardization (phone numbers to E.164, dates to ISO 8601), encoding normalization (handling character encoding edge cases), whitespace cleanup, and HTML/Markdown stripping. Effective sanitization balances compliance requirements (especially DPDP Act 2023 and GDPR) against data utility—removing personal identifiers while preserving business-critical information like product attributes or pricing data.

Business Use Case

Market research companies sanitize scraped review data to analyze sentiment without retaining personally identifiable reviewer information. They strip names and profile URLs but preserve review text, ratings, and timestamps. Healthcare data aggregators must heavily sanitize clinical trial data, removing patient identifiers while preserving drug names, dosages, and outcome metrics. Financial data providers similarly process scraped SEC filings, extracting structured financial metrics while removing personally identifiable information about filing preparers.

Pro-Tip

Implement multi-pass sanitization with regex patterns for common PII formats combined with NLP-based Named Entity Recognition (NER) for unstructured text. Start with pattern matching (emails, phones, SSNs), then apply NER to catch names and addresses that slip through regex. Store sanitization audit logs showing what was removed and why—this is crucial for DPDP/GDPR compliance when demonstrating data minimization practices.