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.
Legislators motivated by aggressive digitalization and increased consumer concern about the handling of personal data — especially when it comes to AI workloads and data-sharing practices — have passed laws governing consumer privacy rights.1,2,3,4 These rights have become part of consumers’ basic expectations when engaging with commercial organizations or government entities. At the heart of the SRR automation market are three key capabilities: Discovery of existing information held on individuals, and continuous monitoring for changes to data stores and new systems that are being onboarded. Maintenance of the capacity to act on that information should the data subject request modification, deletion or restriction of processing. Tracking of request workflows and holding of detailed records to gauge effectiveness and demonstrate compliance. Organizations face great challenges in sifting through structured and unstructured data stores — whether on-premises, in the cloud, or with partners and subprocessors. In addition to the discovery and retrieval requirement, organizations must redact personal data that is associated with other individuals to ensure they are not violating one user’s rights in order to respond to another. For those reasons, request fulfillment must follow a repeatable and scalable process in order to remain manageable and efficient.