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.
Reviews for 'Application Development, Integration and Management - Others'
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. These packaged solutions help implement and support the practice of data quality assurance, mostly embedded as part of a broader data and analytics (D&A) strategy. Various existing and upcoming use cases include: 1. Analytics, artificial intelligence and machine learning development 2. Data engineering 3. D&A governance 4. Master data management 5. Operational/transactional data quality
Gartner defines the market for cloud database management systems (DBMSs) as the market for software products that store and manipulate data and that are primarily delivered as software as a service (SaaS) 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 work and/or analytical work. They may have features that enable them to participate in a wider data ecosystem. Must-have capabilities for this market include: Availability as SaaS on provider-managed public or private cloud systems; Management of data within cloud storage — that is, cloud DBMSs are not hosted in infrastructure as a service (IaaS), such as in a virtual machine or a container managed by the customer.
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 data integration tools market comprises stand-alone software products that allow organizations to combine data from multiple sources, including performing tasks related to data access, transformation, enrichment and delivery. Data integration tools enable use cases such as data engineering, operational data integration, delivering modern data architectures, and enabling less-technical data integration. Data integration tools are procured by data and analytics (D&A) leaders and their teams for use by data engineers or less-technical users, such as business analysts or data scientists. These products are 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.
Reviews for 'Data and Analytics - Others'
A D&A governance platform is a set of integrated business capabilities that helps business leaders and users evaluate and implement a diverse set of governance policies and monitor and enforce those policies across their organizations’ business systems. These platforms are unique from data management and discrete governance tools in that data management and such tools focus on policy execution, whereas these platforms are used primarily by business roles — not only or even specifically IT roles.
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.
MDM is a technology-enabled business discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, governance, semantic consistency and accountability of an enterprise’s official shared master data assets. Master data has the lowest number of consistent and uniform sets of identifiers and attributes that uniquely describe the core entities of the enterprise and are used across multiple business processes.
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 multichannel marketing hubs (MMHs) as software applications that orchestrate personalized communications to individuals in common marketing channels. MMHs optimize the timing, format and content of interactions through the analysis of customer data, audience segments and offers. MMHs are foundational for multichannel marketing, customer journey orchestration and next best action programs.
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 four distinct tool categories for PAM tools: privileged account and session management (PASM), privilege elevation and delegation management (PEDM), secrets management, and cloud infrastructure entitlement management (CIEM).