Data masking is based on the premise that sensitive data can be transformed into less sensitive but still useful data. This is necessary to satisfy application testing use cases that require representative and coherent data, as well as analytics that involve the use of aggregate data for scoring, model building and statistical reporting. The market for data protection, DM included, continues to evolve with technologies designed to redact, anonymize, pseudonymize, or in some way deidentify data in order to protect it against confidentiality or privacy risk.