Data security posture management (DSPM) discovers previously unknown data across on-premises data centers and cloud service providers (CSPs). It also helps categorize and classify previously unknown and discovered unstructured and structured data. As data rapidly proliferates, DSPM assesses who has access to it to determine its security posture and exposure to privacy, security and AI-usage-related risks. DSPM is delivered as software or as a service.
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.
Gartner defines metadata management solutions as applications to enable the collection, analysis and orchestration of metadata related to organizational data assets. These solutions enable workflow and operational support to make data easy to find, use and manage. They do this by collating metadata in any form from within its own application and third-party systems, and providing the ability to search, analyze and make decisions on the collated results. They also provide transparent cross-referencing over all related metadata, and derive insights from data (such as usage patterns and performance) through analysis of metadata to support a wide range of data-driven initiatives.
Gartner defines the privacy user experience (UX) as the components of an organization’s privacy program that directly touch an individual. These components provide transparency and control over individuals’ personal data, enabling them to manage and exercise preferences and rights. Privacy UX provides organizations with a compliance-backed foundation for responsible data use by consolidating and synchronizing individual choices across all touchpoints, thus enabling robust consent management and efficient subject rights processing. Central to most privacy laws are people, and their clarity and control over their personal data. The privacy UX enables individuals to control how much personal data to expose, to whom and for what purpose. It encompasses all the touchpoints where individuals meet and engage with organizations.