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.
Enterprise search engines are specialized search tools designed to help organizations index, search, and retrieve information stored within their internal data repositories. Unlike general web search engines that index and search the entire internet, enterprise search engines focus on the internal data of an organization, which can include documents, emails, databases, intranet sites, and other digital assets or data sources. Modern enterprise search engines often incorporate Natural Language Processing (NLP) and Machine Learning (ML) and AI-powered technologies to enhance their capabilities and improve the search experience. This type of search engine is adept at handling both structured and unstructured data, making it invaluable for diverse use cases such as knowledge management, customer support, and business intelligence. By integrating these enterprise search software capabilities, organizations can ensure that employees have quick and relevant access to the information they need, thereby improving productivity and decision-making.
Gartner defines the generative AI (GenAI) knowledge management apps/general productivity submarket as technologies that enable companies to better retrieve and contextualize information and insight from their knowledge bases, including enterprise AI search, conversational AI platforms, and productivity tools for communications and content development.
Gartner defines Insight Engines as follows: Insight engines apply relevancy methods to discover, analyze, describe and organize content and data. They enable the interactive or proactive delivery or synthesis of information to people, and data to machines, in the context of their respective business moments. Insight engines should be viewed as platforms on which applications are provided, developed or augmented by applying the capabilities listed above to specific employee and customer experience use cases. Such applications are provided out of the box by vendors (e.g., intranet or site search), developed through technical partnerships (e.g., search within third-party applications), developed with customers in-house (e.g., expert finder), or developed through integration with third-party applications (e.g., extracting data from documents to support RPA).
Knowledge Management (KM) Software helps organizations centralize, organize, and share information efficiently across teams. It provides a centralized repository for storing diverse content types—such as documents, presentations, and multimedia—making knowledge easily accessible and searchable. A robust search functionality ensures quick retrieval of relevant information, while features like file version history, access control, and content editing enhance collaboration and governance. These capabilities reduce duplication of effort, preserve institutional knowledge, and streamline workflows. KM software is widely used by customer support, product and operations, HR and training, and IT and compliance teams—any function that depends on consistent, accurate, and easily retrievable information.