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.
Analytics and business intelligence platforms — enabled by IT and augmented by AI — empower users to model, analyze and share data. Analytics and business intelligence (ABI) platforms enable organizations to understand their data. For example, what are the dimensions of their data — such as product, customer, time, and geography? People need to be able to ask questions about their data (e.g., which customers are likely to churn? Which salespeople are not reaching their quotas?). They need to be able to create measures from their data, such as on-time delivery, accidents in the workplace and customer or employee satisfaction. Organizations need to blend modeled and nonmodeled data to create new data pipelines that can be explored to find anomalies and other insights. ABI platforms make all of this possible.
Reviews for 'Cloud Computing - Others'
Content collaboration tools provide an easy way for employees to use and share content both inside and outside the organizations. Since these tools can be used to collaborate with customers, partners and suppliers, they often provide rich security and privacy controls. Today, much of this functionality also can be found in other tools such as cloud office platforms, workstream collaboration platforms, content services platforms and content services applications. Functional differentiators in dedicated CCTs are difficult to identify.
Reviews for 'Data Center - Others'
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.
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling that support the independent use by, and collaboration between, data scientists and their business and IT counterparts through all stages of the data science life cycle. These stages include business understanding, data access and preparation, experimentation and model creation, and sharing of insights. They also support machine learning engineering workflows including creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances and/or as a fully managed cloud offering. Data science and machine learning (DSML) platforms are designed to allow a broad range of users to develop and apply a comprehensive set of predictive and prescriptive analytical techniques. Leveraging data from distributed sources, cutting-edge user experience, and native machine learning and generative AI (GenAI) capabilities, these platforms help to augment and automate decision making across an enterprise. They provide a range of proprietary and open-source tools to enable data scientists and domain experts to find patterns in data that can be used to forecast financial metrics, understand customer behavior, predict supply and demand, and many other use cases. Models can be built on all types of data, including tabular, images, video and text for applications that require computer vision or natural language processing.
Reviews for 'Data and Analytics - Others'
Gartner defines the market for data and analytics (D&A) services as consulting and system integration (C&SI) and managed services. These services manage data for all uses (operational and analytical), and analyze data to drive business processes and improve business outcomes through more effective decision making. The core capabilities for vendor solutions in the D&A services market include: D&A strategy and operating model design Data management Analytics and business intelligence (ABI) Data science and machine learning D&A governance Program management Enterprise metadata
Gartner defines file and object storage platforms as software and/or hardware platforms that offer object and distributed file system technologies for storing and managing unstructured data over NFS, SMB and Amazon S3 access protocols. File and object storage platforms store, secure, protect and scale an organization’s unstructured data with access over the network using protocols such as NFS, SMB and Amazon S3. Use cases include analytics, workload consolidation, backup and archiving, hybrid cloud, object-native applications, cloud IT operations, and high-performance files.
The global industrial IoT platform delivers multiple integrations to industrial OT assets and other asset-intensive enterprises’ industrial data sources to aggregate, curate and deliver contextualized insights that enable intelligent applications and dashboards through an edge-to-cloud architecture. The global industrial Internet of Things (IIoT) platform market exists because of the core capabilities of integrated middleware software that support a multivendor marketplace of intelligent applications to facilitate and automate asset management decision making. IIoT platforms also provide operational visibility and control for plants, infrastructure and equipment. Common use cases are augmentation of industrial automation, remote operations, sustainability and energy management, global scalability, IT/operational technology (OT) convergence, and product servitization of industrial products. The IIoT platform monitors IoT endpoints and event streams, supports and/or translates a variety of manufacturer and industry proprietary protocols, analyzes data in the platform, at the edge and in the cloud, integrates and engages IT and OT systems in data sharing and consumption, enables application development and deployment and can enrich and supplement OT functions for improved asset management life cycle strategies and processes. In some emerging use cases, the IIoT platform may obviate some OT functions.
Gartner defines ICS as a hybrid, multidomain (i.e., on-premises, colocation, edge and public cloud), consumption-based as-a-service offerings for enterprise mission-critical infrastructure such as storage, compute and networking. In this case, multidomain reflects the characteristics of a multicloud hybrid environment. ICS vendors combine their unique capabilities to provide API-centric control and vendor-managed data services planes for infrastructure onboarding, provisioning and SLA-based life cycle management and support. ICS offerings include software-defined infrastructure solutions and appliances for storage as a service (STaaS), compute as a service (CaaS), networking as a service (NaaS), and other data services offerings. Data services include backup, disaster recovery (DR) and ransomware recovery, optionally managed by IT staff and/or service providers.
Integrated systems combine server, shared storage and network devices, along with management software and support in a preintegrated stack. The integrated system market has four segments: integrated infrastructure system, integrated reference architecture, integrated stack system and hyperconverged infrastructure (HCI) segment. The overall HCI segment is further subdivided into Hyperconverged Integrated Systems (HCIS), which provides both software and hardware in an appliance model and the software only segment in which vendors provide the Hyperconverged software. This is then integrated with HW by a reseller or the end customer.
Integration means making independently designed applications and data work well together. IoT integration means making the mix of new IoT devices, IoT data, IoT platforms and IoT applications — combined with IT assets (business applications, legacy data, mobile, and SaaS) — work well together in the context of implementing end-to-end IoT business solutions. The IoT integration market is defined as the set of IoT integration capabilities that IoT project implementers need to successfully integrate end-to-end IoT business solutions.
Gartner defines Oracle Cloud Application (OCA) services as only those services associated with the products under Oracle Cloud Applications, also known as SaaS. This means consultancy, migration, implementation, ongoing services, postimplementation evolution and optimization services. To qualify, each vendor project must have an “anchoring” OCA product from at least one of the following Oracle “Fusion” solutions: - Advertising and customer experience (ACX) - Industry applications (IA) - Enterprise resource planning (ERP; includes the previous EPM applications) - Human capital management (HCM) - Supply chain management (SCM)
The services for Oracle Cloud Infrastructure (OCI) market includes consulting, implementation and ongoing management services for Oracle and non-Oracle workloads hosted on OCI. Service providers in this market combine expertise in Oracle solutions and OCI with skills in managing private infrastructure, hybrid IT, multicloud, sovereign and distributed cloud to provide strategic and operational assistance as clients define and realize their cloud goals and business outcomes with OCI.
The primary storage platform (PSP) market addresses the need of I&O leaders to operate and support standardized enterprise storage products, along with platform-native service capabilities to support structured data applications. PSP products like primary enterprise storage arrays provide mandatory and common enterprise-class primary storage features and capabilities needed to support the platform. Platform-native services like storage as a service (STaaS) and ransomware protection, with PSP product capabilities, are required to support platform-native services. The PSP market has emerged at the convergence of two major enterprise storage market developments: the evolution of the PSP product market in conjunction with the demand for hybrid, multidomain platform-native storage services, extending on-premises services to public cloud, edge and colocation environments.