Gartner defines augmented data quality (ADQ) solutions as a set of capabilities for enhanced data quality experience aimed at improving insight discovery, next-best-action suggestions and process automation by leveraging AI/machine learning (ML) features, graph analysis and metadata analytics. Each of these technologies can work independently, or cooperatively, to create network effects that can be used to increase automation and effectiveness across a broad range of data quality use cases. These purpose-built solutions include a range of functions such as profiling and monitoring; data transformation; rule discovery and creation; matching, linking and merging; active metadata support; data remediation and role-based usability.
Gartner defines the market for cloud database management systems (DBMSs) as software products that store and manipulate data and are primarily delivered as platform as a service (PaaS) in the cloud. Cloud DBMSs may optionally be capable of running on-premises or in hybrid, multicloud or intercloud configurations. They can be used for transactional and/or analytical work. They typically persist data using a combination of proprietary and open components in a durable manner, enabling a full range of create, read, update and delete operations. They are used by application end users, designers, developers and operators of large database systems.
CCM software is defined as both a strategy and a market-fulfilled by applications that improve the creation, delivery, storage and retrieval of outbound and interactive communications. It supports the production of individualized customer messages, marketing collateral, new product introductions and transaction documents. It is a collection of computer programs that composes, personalizes, formats and delivers content acquired from various sources into targeted and relevant electronic and physical communications between an enterprise and its customers, prospective customers and business partners. It delivers targeted communications through a wide range of media including mobile, email, SMS, Web pages, social media sites and print. The CCM market has evolved from the convergence of document generation/composition and output management technologies. Current CCM solutions include the core elements of a design tool, a composition engine, a workflow/rule engine and multichannel output management.
The market for data integration tools consists of stand-alone software products that enable organizations to combine data from multiple sources and perform tasks related to data access, transformation, enrichment and delivery. They enable use cases such as data engineering, delivering modern data architectures, self-service data integration, operational data integration and supporting AI projects. Data management leaders procure data integration tools for their teams, including data engineers and data architects, or for other users, such as business analysts or data scientists. These products are primarily consumed as SaaS or deployed on-premises, in public or private cloud, or in hybrid configurations.
Data preparation is an iterative and agile process for finding, combining, cleaning, transforming and sharing curated datasets for various data and analytics use cases including analytics/business intelligence (BI), data science/machine learning (ML) and self-service data integration. Data preparation tools promise faster time to delivery of integrated and curated data by allowing business users including analysts, citizen integrators, data engineers and citizen data scientists to integrate internal and external datasets for their use cases. Furthermore, they allow users to identify anomalies and patterns and improve and review the data quality of their findings in a repeatable fashion. Some tools embed ML algorithms that augment and, in some cases, completely automate certain repeatable and mundane data preparation tasks. Reduced time to delivery of data and insight is at the heart of this market.
Data and Analytics refers to products and services that enable organizations to collect, integrate, analyze, and act on data to drive informed decision-making and business outcomes. This category includes markets that focus on empowering enterprises to manage data pipelines, ensure data quality and governance, extract insights through advanced analytics, and machine learning across structured and unstructured data environments.
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.
Finance refers to the products and services that support the planning, management, analysis, and optimization of financial operations across enterprises and financial institutions. This category includes markets that support core accounting, financial planning, treasury, tax, audit, compliance, investment management, and digital banking—enabling organizations to maintain financial integrity and ensure regulatory compliance.
Geospatial technology refers to a set of technologies used to acquire, manipulate and store geographic information. The geospatial information system (GIS) software market in energy and utilities is defined by buyers looking for software and applications to manage and optimize geotagged data for spatial analysis, hydrologic and water quality analysis, network models, pipeline and field planning, design, construction, and operations. GIS can support real-time design and modeling; visualize electrical, gas, and/or water and pipeline network topology; model geological and surface feature relationships; and depict the relationship between assets and the environment including network/grid, facilities, land, vehicles, equipment, employees, customers and surrounding elements.
Master data management (MDM) is a technology-enabled business discipline where business and IT organizations work together for the uniformity, accuracy, stewardship, semantic consistency and accountability of enterprises’ shared master data assets. Organizations use MDM solutions as part of an MDM strategy, which should be part of a wider enterprise information management (EIM) strategy. An MDM strategy potentially encompasses management of multiple master data domains (e.g., customer, citizen, product, “thing,” asset, person/party, supplier, location, and financial master data domains). Data and analytics (D&A) leaders procure MDM tools for data engineers or less-technical users, such as data stewards.
Master data management (MDM) of product data solutions are software products that: Support the global identification, linking and synchronization of product data across heterogeneous data sources through semantic reconciliation of master data. Create and manage a central, persisted system of record or index of record for product master data. Enable the delivery of a single, trusted product view to all stakeholders, to support various business initiatives. Support ongoing master data stewardship and governance requirements through workflow-based monitoring and corrective-action techniques. Are agnostic to the business application landscape in which they reside; that is, they do not assume or depend on the presence of any particular business application(s) to function.
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 multichannel marketing hubs (MMHs) as software applications, primarily delivered as SaaS, that orchestrate personalized campaigns and event-driven customer journeys across marketing channels. These applications leverage customer data, predictive models and real-time insights to optimize the timing, channel and content of interactions. MMHs apply advanced analytics, AI and prescriptive intelligence to help marketing and technical teams manage the end-to-end life cycle of customer journeys. Although MMHs overlap with customer data platforms (CDPs) and personalization engines, their primary focus is enabling marketing users to manage large-scale consumer interactions, particularly in owned media channels such as email and app push. Multichannel marketing hubs empower marketers to deliver personalized media and orchestrate customer journeys, thus driving revenue, engagement and loyalty. These SaaS applications unify customer data, predictive insights and real-time decision making to optimize interactions across digital channels. MMHs enable multidisciplinary teams to manage campaigns and event-driven journeys via advanced analytics, artificial intelligence/machine learning (AI/ML) and prescriptive intelligence.
Gartner defines privileged access management (PAM) as tools that provide an elevated level of technical access through the management and protection of accounts, credentials and commands, which are used to administer or configure systems and applications. PAM tools — available as software, SaaS or hardware appliances — manage privileged access for both people (system administrators and others) and machines (systems or applications). Gartner defines five distinct tool categories for PAM tools: privileged account and session management (PASM), privilege elevation and delegation management (PEDM), secrets management, cloud infrastructure entitlement management (CIEM) and remote PAM (RPAM). Privileged access is access beyond the normal level granted to both human and machine accounts. It allows users to override existing access controls, change security configurations, or make changes affecting multiple users or systems. As privileged access can create, modify and delete IT infrastructure, along with company data contained in that infrastructure, it presents catastrophic risk. Managing privileged access is thus a critical security function for every organization and requires a specific set of procedures and tools. PAM tools focus on either privileged accounts or privileged commands.