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.
Data security platforms (DSPs) combine data discovery, policy definition and policy enforcement across data silos. Policy enforcement capabilities include format-preserving encryption, tokenization and dynamic data masking. These capabilities can be delivered through connectors, agents, proxies and APIs. Business requirements to leverage data and share data, for example for AI/ML use cases, require data security controls and highly-granular data access which is provisioned fast and humanly understandable. Tight-fitting data access and security controls allow you to reveal and share (leverage) more of your data. However, organizations face sufficient complexity when it comes to provisioning and rightsizing entitlements and data security controls. This extends to data privacy as well as analytics governance and ethics. The DSP delivers most of the required components critical to enabling good data governance and optimized data security controls while preventing the exponential increase of data access and policy rules.
A data and analytics governance platform is a set of integrated business and technology capabilities that help business leaders and users develop and manage a diverse set of governance policies and enforce those policies across business and data management systems. These platforms are unique from data management in that data management focuses on policy execution, whereas D&A platforms are used primarily by business roles — not only or even specifically IT roles — for policy management. Data and analytics (D&A) leaders who are investing in operationalizing and automating the work of D&A governance should evaluate this market. The work of D&A governance primarily includes policy setting and policy enforcement, and collaborates with data management (policy execution). Use cases are employed across numerous governance policy categories and multiple business scenarios and asset types (data, KPIs, analytics models). The intersection of use-case/business scenarios, policy categories and assets to be governed is then used to identify the technology capability. These capabilities may share similar names across policy categories, but may not mean the same thing, or may be used differently by various governance personas. For example, data classification in a data security implementation would be quite different from a data classification effort for creating trust models, which would be based on lineage and curation.