The Impact of Artificial Intelligence on Customer Experience and Personalization

Abstract

In the age of big data, securing sensitive information is critical for maintaining privacy and protecting organizational assets. Anonymization and data masking are key methods for safeguarding data while retaining its usefulness for analysis, testing, and regulatory compliance. Data masking conceals original information by replacing it with realistic but fictional data, protecting sensitive details without losing functionality. Anonymization removes or modifies identifiable information to ensure privacy, making the process irreversible.These techniques are vital in big data analytics, where large datasets often contain personally identifiable information (PII) or other confidential data. This paper explores types of data anonymization, including pseudonymization and various masking techniques such as substitution, shuffling, and randomization. It also examines entity-based data masking to maintain referential integrity, and compares static and dynamic data masking approaches for different use cases across structured and unstructured data. Finally, it addresses challenges in applying anonymization to software testing, analytics, training, and compliance, underscoring the importance of these practices in enabling secure, privacy-conscious data insights.