Active metadata management is a set of capabilities that enables continuous access and processing of metadata that support ongoing analysis over a different spectrum of maturity, use cases and vendor solutions. Active metadata outputs range from design recommendations based upon execution results and reports of runtime steps through, and indicators of, business outcomes achieved. The resulting recommendations from those analytics are issued as design inputs to humans or system-level instructions that are expected to have a response.
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.
Gartner defines a Data and Analytics Governance Platform as a set of integrated business and technology capabilities that help business leaders and users to develop and deploy a diverse set of governance policies and monitor and enforce those policies across their organizations’ business systems. These platforms are unique from data management in that data management focuses on policy execution, whereas these platforms are used primarily by business roles — not only or even specifically IT roles.
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.