Analytics and business intelligence (ABI) platforms enable less technical users, including businesspeople, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). ABI platforms may optionally include the ability to create, modify or enrich a semantic model including business rules.
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'
The market for data integration tools includes vendors that offer software products to enable the construction and implementation of data access and data delivery infrastructure for a variety of data integration scenarios. For vendors, the demand for traditional data integration capabilities alongside the demand for innovative solutions requires robust, consistent delivery of highly developed solutions. Similarly, data integration tools interoperate and integrate with master data tools, data governance tools and data quality tools. Examples of this type of interoperability include: • Support for governance and management of data assets • Data acquisition for analytics and business intelligence (BI) and data • Sourcing and delivery of master data in support of master data management (MDM) • Data consistency between operational applications • Interenterprise data sharing.
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.
This market evaluates vendors of data science and machine-learning platforms. These are software products that data scientists use to help them develop and deploy their own data science and machine-learning solutions. More precisely, Gartner defines a data science and machine-learning platform as: A cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products. Machine learning is a popular subset of data science that warrants specific attention when evaluating these platforms.
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 distributed file systems and object storage as software and hardware solutions that are based on “shared nothing architecture” and that support object and/or scale-out file technology to address requirements for unstructured data growth. A shared-nothing architecture is a distributed computing architecture in which there is no single point of contention across the system. Distributed file system storage uses a single parallel file system to cluster multiple storage nodes together, presenting a single namespace and a storage pool to provide high-bandwidth data access for multiple hosts in parallel. Data is distributed over multiple nodes in the cluster to deliver data availability and resilience in a self-healing manner, and to provide high throughput and scale capacity linearly. Object storage refers to devices and software that house data in structures called “objects,” and serve clients data via RESTful HTTP APIs, such as Amazon Simple Storage Service (S3) and OpenStack Swift.
Gartner defines the market for industrial Internet of Things (IIoT) platforms as a set of integrated software capabilities. These capabilities span efforts to improve asset management decision making, as well as operational visibility and control for plants, depots, infrastructure and equipment within asset-intensive industries. These efforts also occur within related operating environments of those industries. The IIoT platform may be consumed as a technology suite or as an open and general-purpose application platform, or both in combination. The platform is engineered to support the requirements of safety, security and mission criticality associated with industrial assets and their operating environments. The IIoT platform software that resides on devices — such as, controllers, routers, access points, gateways and edge computing systems — is considered part of a distributed IIoT platform.
The integrated systems market consists of four segments: Integrated Infrastructure Systems (IIS) - Server, storage and network hardware are integrated to provide shared compute infrastructure. Integrated Reference Architecture (IRA) - These are products in which a specification for a logical set of hardware and/or software components for an integrated system are certified by two or more vendors. Ideally, these components have a common source of service and support. Integrated Stack Systems (ISS) - Server, storage and network hardware are integrated with application software to provide appliance or appliancelike functionality. Hyperconverged Integrated Systems (HCIS) - Compute, network and storage hardware are tightly coupled, eliminating the need for a traditional storage area network (SAN). Storage management functions, compute provisioning and additional, optional capabilities are delivered via the management software layer and/or hardware.
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 the Oracle Cloud Application Services as only those services associated with the Oracle SaaS products or with Oracle PaaS products being built or supported in conjunction with an Oracle SaaS product. To qualify each project must have an 'anchoring' Oracle SaaS product from at least one of the products / pillars identified in the Oracle Cloud Excellence Implementer program on the Oracle Partner Network website. Currently this is version 3787137 (as below) and consists of the SaaS pillars: CX / EPM / ERP / HCM / SCM.
Oracle Cloud Infrastructure Professional and Managed Services focuses on services for Oracle Cloud Infrastructure (OCI), including 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 and distributed cloud to provide strategic and operational assistance as clients define and realize their cloud goals with OCI.
Primary storage covers vendors that offer dedicated products or product lines for solid-state arrays (SSAs) or hybrid storage arrays (or both), and software-defined storage (SDS) software. Hybrid storage arrays include solid-state drive (SSD) and hard-disk drive (HDD) configurations. SSA products are 100% solid-state, technology-based systems that cannot be combined or expanded with HDDs. SSAs and hybrid storage arrays must have a dedicated product name and an associated model number. SDS products are designed to operate on industry-standard, commodity hardware on-premises or in the public cloud. A primary storage product’s foremost purpose is to support response time and input/output per second (IOPS)-sensitive structured data workloads. Typical use cases include mission-critical workloads, such as IBM Db2, Microsoft SQL, Microsoft Exchange and SharePoint, Oracle Databases and applications, SAP HANA, and in-house-developed transactional applications.
Gartner defines Public Cloud IT Transformation Services as solutions designed to deliver transformational IT outcomes via cloud-native professional and managed application services built exclusively from public hyperscale cloud infrastructure and platform services. Providers that offer professional services and managed services related to the infrastructure operations for one of the hyperscale public cloud providers (Amazon Web Service (AWS), Google Cloud or Microsoft Azure), with the appropriate transformational competency(ies) of the partner program would be considered as part of this market.