The term "data substitution" refers to the targeted replacement of data fields or content within a system or dataset with alternative values. The goal is to anonymize or pseudonymize data for privacy protection, correct inaccurate or incomplete records, or improve overall data quality. Data substitution can be performed manually or automatically and is commonly used in contexts such as data migration, data masking, software testing, or regulatory compliance.
Rule-based Data Replacement: Substitution of data fields based on predefined rules (e.g., replacing all phone numbers with a placeholder number).
Data Masking: Replacing sensitive information with structurally similar but fictional values, typically for testing purposes.
Anonymization and Pseudonymization: Removal or transformation of personal data to protect individual privacy in compliance with GDPR.
Automated Data Matching: Identification and substitution of outdated or incorrect data with up-to-date or accurate values.
Context-sensitive Substitution: Replacing data based on its usage context (e.g., substituting a delivery address only in test orders).
Substitution Logging: Comprehensive documentation of all replacements performed to meet compliance standards.
A test system replaces all real customer names with fictitious names to comply with data protection policies.
During a data migration, outdated product codes are substituted with current item numbers.
A company substitutes actual IBANs in a database with synthetically generated test IBANs for internal training systems.
A marketing database is cleaned by replacing invalid email addresses with valid placeholders.
To comply with GDPR, archived customer names are replaced with anonymized tokens.