Technology and service providers (TSPs) who are able to offer an end-to-end service for 4G/5G private mobile network (PMN) include: CSPs, Network equipment providers (NEPs), Systems integrators (SIs) and Hyperscalers. PMNs are used exclusively by a given enterprise client, providing higher security and reliability than public cellular networks. PMN offerings can include voice, video, messaging and broadband data, as well as specific critical communications features (such as lone worker protection [LWP] or push-to-talk over cellular [PTToC]). PMNs can then support use cases around HD video, data, artificial intelligence (AI)/machine learning (ML) and Internet of Things (IoT), and run on-site or in a cloud, or a multiaccess edge computing data center (MEC DC).
Gartner defines AI code assistants as tools that assist in generating and analyzing software code and configuration. The assistants use foundation models such as large language models (LLMs) that have been optionally fine-tuned for code, or program-understanding technologies, or a combination of both. Software developers prompt the code assistants to generate, analyze, debug, fix, and refactor code, to create documentation, and to translate code between languages. Code assistants integrate into developer tools like code editors, command-line terminals and chat interfaces. Some can be customized to an organization’s specific codebase and documentation. AI code assistants can enhance a software developer’s experience by boosting efficiency, accelerating application development, minimizing cognitive overload, amplifying problem-solving skills, accelerating learning pace, fostering creativity and maintaining state of flow.
Gartner defines the application programming interface (API) management market as the market for software to manage, govern and secure APIs. Organizations use APIs to modernize their architectures; APIs provide access to systems, services, partners and data services. API management software enables organizations to plan, deploy, secure, operate, version control and retire APIs, regardless of their size, region or industry.
Gartner defines access management (AM) as platforms that include an identity provider (IdP) and establish, manage and enforce runtime access controls to at least cloud, modern standards-based web and classic web applications. AM’s purpose is to enable single sign-on (SSO) access for people (workforce, consumer and other users) and machines into protected applications in a streamlined and consistent way that enhances user experience. AM is also responsible for providing security controls to protect the user session in runtime, enforcing authentication (with multifactor authentication [MFA]) and authorization using adaptive access. Lastly, AM can provide identity context for other cybersecurity tools to enable identity-first security.
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.
The application delivery controller is a key component within enterprise and cloud data centers to improve availability, security and performance of applications. Application delivery controllers (ADCs) provide functions that optimize delivery of enterprise applications across the network. ADCs provide functionality for both user-to-application and application-to-application traffic, and effectively bridge the gap between the application and underlying protocols and traditional packet-based networks. This market evolved from the load-balancing systems that were developed in the latter half of the 1990s to ensure the availability and scalability of websites. Enterprises use ADCs today to improve the availability, scalability, end-user performance, data center resource utilization, security of their applications.
Reviews for 'Application Development, Integration and Management - Others'
Application platforms provide runtime environments for application logic. They manage the life cycle of an application or application component, and ensure the availability, reliability, scalability, security and monitoring of application logic. They typically support distributed application deployments across multiple nodes. Some also support cloud-style operations (elasticity, multitenancy and self-service).
Blockchain as a Service is a cloud-based service that enables users to build, host, and use their own blockchain apps, functions, and smart contracts without the need to maintain and setup the underlying infrastructure themselves. It's similar to how software as a service (SaaS) works, offering blockchain technology on a subscription basis, making it easier and more accessible for businesses, financial institutions and developers to leverage blockchain technology for various features like scaling up the blockchain solutions, secure transactions, and decentralization, without the complexity and cost of developing and managing a blockchain infrastructure on their own.
Gartner defines cloud AI developer services (CAIDS) as cloud-hosted or containerized services and products that enable software developers who are not data science experts to use artificial intelligence (AI) models via APIs, software development kits (SDKs) or applications. Core capabilities include automated machine learning (autoML) including automated data preparation, automated feature engineering and automated model building, and model management and operationalization for language, vision and tabular use cases. Optional and important complementary capabilities include AI code models and assistants. Cloud AI developer services help organizations embed intelligence, such as AI and ML insights, into their applications. While that is what cloud AI developer services offer, it is more important to note how they accomplish this. These services democratize and increase the availability of AI and ML to software engineers through the automation and features offered. Traditional activities regarding data acquisition, data quality, feature engineering, algorithm selection and model training are augmented by the technology. Cloud AI developer services open up a world of possibilities for software engineers to build AI and ML production capabilities and features for enterprise-built applications.
Gartner defines cloud application platforms as those that provide managed application runtime environments for applications and integrated capabilities to manage the life cycle of an application or application component. They typically enable distributed application deployments and support cloud-style operations — such as elasticity, multitenancy and self-service — without requiring infrastructure provisioning or container management. Cloud application platforms are designed to facilitate the deployment, runtime execution, and management of modern cloud-native or cloud-optimized applications (e.g., web-based apps, back-end services with/without APIs, etc.) without the need to manage any underlying compute infrastructure. Also, they are designed to enhance developer productivity, accelerate development and deployment cycles, and increase operational effectiveness by making it easier to scale on demand.
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.
Cloud development environments (CDEs) provide remote, ready-to-use access to a cloud-hosted development environment with minimal effort for setup and configuration. This decoupling of the development workspace from the physical workstation enables a low-friction, consistent developer experience. CDEs offer built-in integrated development environment (IDE) capabilities such as code editing, debugging, code review and code collaboration, but also integrate with artificial intelligence (AI) code assistants and DevOps tools such as source code and artifact repositories. CDE users include but are not limited to software engineers, data scientists and AI engineers. CDEs provide consistent, secure developer access to preconfigured remote development workspaces. This frees developers from setting up their own local environments, eliminating the need to install and maintain dependencies, software development kits, security patches and plug-ins, which increasingly include AI code assistants. CDEs are prepackaged with tools to support multiple programming languages and frameworks enabling teams to write code across multiple technology stacks with standardized and templatized workflows. Developers can either access a remotely hosted IDE using a browser-based interface or use their locally installed IDE to connect to the CDE.
Gartner defines cloud WAAP as a category of security solutions designed to protect web applications irrespective of their hosted locations. Typically delivered as a service, cloud WAAP is offered as a series of security modules that provide protection from a broad range of runtime attacks. It offers protection from the Top 10 web application security risks defined by the Open Web Application Security Project (OWASP) and automated threats, provides API security, and can detect and protect against multiple sophisticated Layer 7 attacks targeted at web applications. Cloud WAAP’s core features include web application firewall (WAF), bot management, distributed denial of service (DDoS) mitigation and API protection.
Gartner defines communications platform as a service (CPaaS) as a cloud-based platform used by developers, the IT team and other nontechnical business roles to build an array of communications-related capabilities using APIs, SDKs, documentation and no-code/low-code visual builders. The CPaaS tools facilitate access to multiple communications channels spanning voice, SMS, email, messaging apps, video and conversational capabilities, along with security. The purpose of CPaaS is to enable enterprises to improve communications workflows by providing simplified access to multiple communications capabilities. CPaaS enables enterprises to shorten time to market for new products and services, personalize communications, and orchestrate customer journeys across multiple channels. It delivers digital engagement and operationalizes customer experience, while also driving business efficiencies at scale with digital service delivery. It’s modular/composable in design and can expand from initial single-use cases to many others as additional business units learn of its value. CPaaS capabilities can also be consumed in a wholesale model, powering third-party cloud vendor offerings such as contact center, CRM, multichannel marketing and ERP. There are also wholesale use cases in which CPaaS providers wholesale to each other and telcos.
Gartner defines contact center as a service (CCaaS) as solutions offering SaaS-based applications that enable customer service departments to manage multichannel customer interactions holistically from both a customer-experience and employee-experience perspective. CCaaS is a key technology platform used to support the customer service experience, whether it be self-service or assisted by customer service representatives. All organizations need to offer customer assistance. The preference is for remote support via voice and digital channels over physical presence in offices and stores, though it is common for organizations to offer multiple options.
Gartner defines container management as offerings that enable the deployment and operation of containerized workloads. Delivery methods include stand-alone software or as a service. Delivery methods include cloud, managed service and software for containers running on-premises, in the public cloud and/or at the edge. Container management automates the provisioning, operation and life cycle management of containerized workloads at scale. Centralized governance and security policies are used to manage container workloads and associated resources. Container management supports the requirements of modern applications (also refactoring legacy applications), including platform engineering, cloud management and continuous integration/continuous deployment (CI/CD) pipelines. Benefits include improved agility, elasticity and access to innovation.
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 'Customer Relationship Management - Others'
The market for distributed denial of service (DDoS) mitigation includes vendors that detect and mitigate DDoS attacks and offer it as a dedicated offering. It includes specialty vendors, whose primary focus is DDoS mitigation, as well as providers that offer DDoS mitigation as a feature of other services. These include dedicated appliance-based vendors, communication service providers (CSPs), content delivery network (CDN) vendors, hosting providers and cloud infrastructure and platform services (CIPS) vendors.
Gartner defines data integration as the discipline comprising the architectural patterns, methodologies and tools that allow organizations to achieve consistent access and delivery of data across a wide spectrum of data sources and data types to meet the data consumption requirements of business applications and end users. Data integration tools enable organizations to access, integrate, transform, process and move data that spans various endpoints and across any infrastructure to support their data integration use cases. The market for data integration tools includes vendors that offer a stand-alone software product (or products) to enable the construction and implementation of data access and data delivery infrastructure for a variety of data integration use cases.
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.
Data virtualization technology is based on the execution of distributed data management processing, primarily for queries, against multiple heterogeneous data sources, and federation of query results into virtual views. This is followed by the consumption of these virtual views by applications, query/reporting tools, message-oriented middleware or other data management infrastructure components. Data virtualization can be used to create virtualized and integrated views of data in-memory, rather than executing data movement and physically storing integrated views in a target data structure. It provides a layer of abstraction above the physical implementation of data, to simplify querying logic.
Reviews for 'Data and Analytics - Others'
Gartner defines desktop as a service (DaaS) as the provision of virtual desktops by public cloud or other service providers. DaaS provides desktop or application end-user experiences from virtual machines (VMs) accessed using a remote display protocol. DaaS vendors incorporate a fully managed control plane service into their offerings, which facilitates user connections and provides a management interface. DaaS can be delivered preconfigured as a service. Alternatively, it can be delivered as a platform, in which case the client is responsible for assembly, configuration and management. DaaS is charged for using subscription- or usage-based payment structures. DaaS solutions allow remote workers, offshore workers, third-party employees, contractors, home workers and office workers to access virtual desktops hosted in the cloud. DaaS solutions include technology that enables centralized management of all VMs. DaaS virtual desktops can be configured for a variety of use cases associated with contact center workers, process workers, information workers, and workers who require high-performance computing or rich graphics.
Gartner defines DevOps platforms as those that provide fully integrated capabilities to enable continuous delivery of software using Agile and DevOps practices. The capabilities span the development and delivery life cycle built around the continuous integration/continuous delivery (CI/CD) pipeline and include aspects such as versioning, testing, security, documentation and compliance. DevOps platforms support team collaboration, consistency, tool simplification and measurement of software delivery metrics. DevOps platforms simplify the creation, maintenance and management of the components required for the delivery of modern software applications. Platforms create common workflows and data models, simplify user access, and provide a consistent user experience (UX) to reduce cognitive load. They lead to improved visibility, auditability and traceability into the software development value stream. This end-to-end view encourages a systems-thinking mindset and accelerates feedback loops.
Gartner defines distributed hybrid infrastructure as offerings that deliver cloud-native attributes, which can be deployed and operated where the customer prefers. This is a key distinction to public cloud IaaS, which is based on a centralized approach. Offerings are software and/or integrated hardware with a unified control plane. Distributed hybrid infrastructure provides the foundation for the deployment of applications in a distributed manner that retains a cloud or cloud-inspired approach. In doing so, it improves agility and flexibility for the workloads outside of public cloud infrastructure.
Edge AI refers to the use of AI techniques embedded in IoT endpoints, gateways and edge servers, that can process and store data close to where it’s generated. While predominantly focused on AI inference, more sophisticated systems may include a local training capability to provide in-situ optimization of the AI models. This is done by constantly monitoring AI models and autoscaling them to match demands. Edge AI systems can reduce latency and data transport consumption, improve local processing capabilities thus find usage in applications ranging from autonomous vehicles to streaming analytics.
Edge Distribution Platform (EDP) is a highly distributed, edge-based, integrated network and cloud delivery infrastructure. It provides as-a-service functionalities such as edge compute and storage, web application and perimeter security, content and API acceleration, and data and analytics and AI applications. Edge distribution platform providers offer these functionalities by deploying network, compute, storage and caching nodes across geographically distributed self-owned or third party data center locations. Figure 1 shows the functionalities and potential offerings provided from an edge distribution platform
Gartner defines enterprise backup and recovery software solutions as technology that captures a point-in-time copy (backup) of enterprise data in on-premises, hybrid, multicloud and software as a service (SaaS) environments. These solutions write this data to one or more secondary storage targets for the primary purpose of recovering it in case of loss. Protecting and recovering business application data, irrespective of the underlying infrastructure type and its location, is more important than ever. As enterprises move toward more complex environments that include large and expansive amounts of business-critical data, enterprise backup and recovery software solutions protect these workloads, whether they reside in on-premises, hybrid, multicloud or software as a service (SaaS) environments. These solutions are vital to organizations’ ability to recover data following events that cause it to become inaccessible. Whether such an event is accidental, malicious or environmental, organizations use these solutions to recover and restore access to the affected data accurately and efficiently. Solutions must offer effective capabilities to simplify the management of data protection across complex enterprise environments. They must also ensure reliable recovery not just from accidental or operational errors but also from data loss arising from constantly changing threats, and expedite and orchestrate data recovery responses to traditional disaster and ransomware events.
Gartner defines the enterprise conversational AI platform market as the market for software platforms used to build, orchestrate and maintain multiple use cases and modalities of conversational automation. The enterprise conversational AI platform consists of: A capability layer providing runtime capabilities that include: Natural language understanding (NLU), Dialogue management, Channel integration, Back-end integration, Access control for platform users, Life cycle management; A tooling layer geared toward business users that includes: A no-code environment for building and maintaining, applications, Analytic tools for understanding dialogue flows, NLU intent and entity tuning tools, A/B flow testing tools.
Event brokering is a role played by middleware in facilitating event-driven application architecture. The minimum capability required to play the role of event broker is pub-sub messaging. All middleware products, including MOMs and ESBs, supporting pub-sub can play the role of an event broker and can be referred to as basic 'event brokers' when so deployed. Middleware products that additionally offer special support for event-centric use cases (for example, a persistent event ledger for analysis and event sourcing, or programmable extensibility for custom filtering and analysis) are 'advanced' event brokers.
The market for ESP platforms consists of software subsystems that perform real-time computation on streaming event data. They execute calculations on unbounded input data continuously as it arrives, enabling immediate responses to current situations and/or storing results in files, object stores or other databases for later use. Examples of input data include clickstreams; copies of business transactions or database updates; social media posts; market data feeds; images; and sensor data from physical assets, such as mobile devices, machines and vehicles.
Generative AI (GenAI) apps use generative AI capabilities for user experience and task augmentation to accelerate and assist the completion of a user’s desired outcomes. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. When embedded in the experience, generative AI offers richer contextualization for singular tasks such as generating and editing text, code, images and other multimodal output. As an emerging capability, process-aware generative AI agents can be prompted by users to accelerate workflows that tie multiple tasks together. Apart from helping save time and money, generative AI apps help improve branding of businesses by creating more engaging and effective content while also creating more engaging and immersive experiences for customers. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
Generative AI (GenAI) engineering refers to the field of engineering that focuses on the development, implementation and optimization of generative AI models. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. By developing GenAI models, engineers can create new and innovative ways to generate content. The vendors in this segment are made up by incumbent and startup vendors covering full-model life cycle management, specifically adjusted to and catering to development, refinement and deployment of generative models (e.g., LLMs) and other GenAI artifacts in production applications. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
Generative AI (GenAI) Infrastructure providers are infrastructure vendors (such as cloud platforms and hardware manufacturers) that offer underlying technology, tools and hardware that other companies and developers use to build and deploy specific generative AI applications in production. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. These providers offer scalable, reliable and cost-effective solutions for generative AI projects, which can be complex and expensive to train and deploy. Generative AI infrastructure providers focus on research and developing the foundational AI techniques, while application developers focus on building products using those foundational technologies. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
Generative AI (GenAI) model providers focus on developing and providing generative AI technologies and make them available to other developers, businesses and general public through APIs or commercial licenses. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. This layer of vendors offers access to commercial or open-source foundation models such as LLMs and other types of generative algorithms (such as GANs, genetic/evolutionary algorithms or simulations). These models can be provided for developers to embed into their applications or be used as base models for fine-tuning customized models for their software offerings or internal enterprise use cases. This helps businesses gain the benefits of advanced generative AI technologies while avoiding the high costs, expertise requirements and time needed to develop these technologies in-house. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
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.
Hadoop distributions are used to provide scalable, distributed computing against on-premises and cloud-based file store data. Distributions are composed of commercially packaged and supported editions of open-source Apache Hadoop-related projects. Distributions provide access to applications, query/reporting tools, machine learning and data management infrastructure components. First introduced as collections of components for any use case, distributions are now often delivered as part of a specific solution for data lakes, machine learning or other uses. They subsequently grow into additional, expanded roles, competing with both older technologies like database management systems (DBMSs) and newer ones like Apache Spark.
The hybrid cloud storage market comprises diverse deployment patterns with underlying technologies that address a wide range of data types. Products in this market must facilitate seamless data services across different environments, including disparate data centers, co-locations, edge locations and public cloud infrastructure. Hybrid cloud data solutions are offered through various means such as distributed hybrid infrastructure, hybrid cloud storage platforms, data transfer appliances, hyperconverged solutions, storage arrays, software-defined storage (SDS) products, and comprehensive data management software.
Infrastructure monitoring tools capture the health and resource utilization of IT infrastructure components, no matter where they reside (e.g., in a data center, at the edge, infrastructure as a service [IaaS] or platform as a service [PaaS] in the cloud). This enables I&O leaders to monitor and collate the availability and resource utilization data of physical and virtual entities — including servers, containers, network devices, database instances, hypervisors and storage. These tools collect data in real time and perform historical data analysis or trending of the elements they monitor.
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).
Integrated Development Environment software provides an interface to write code facilitating application development. IDEs provide programmers with tools to design, build, test, and debug software programs in a graphical user interface (GUI). The user can write and edit source code in the code editor. The compiler in the IDEs translates the source code into an executable language for the computer. The debugger helps examine the code to detect and solve any issues or bugs. Some of the IDEs have advanced features like refactoring, code search, data visualization, continuous integration and continuous deployment (CI/CD) tools.
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.
Machine learning Infrastructure as a Service (ML IaaS) is an infrastructure delivery model that provisions virtualized or bare-metal infrastructure resources that are performance optimized for compute-intensive ML and DNN workloads . The ML IaaS market is characterized by core capabilities, including hardware-accelerated, high-performance compute platforms, usually augmented by accelerator technologies like GPU, FPGA or custom processors like Google TPU. Due to the unique nature of hardware-involved DNN frameworks (such as TensorFlow, pyTorch, Caffe and MxNet), they need to be reconfigured and integrated with appropriate libraries to take full advantage of ML IaaS capabilities.
Gartner defines managed hybrid cloud hosting (MHCH) as a standardized, productized offering that combines a cloud-enabled system infrastructure platform — consisting of a pool of compute, network and storage hardware — with cloud infrastructure framework software to facilitate self-service and rapid provisioning. In addition to offering this service from cloud infrastructure located in its own data center, the provider must offer a choice of using a hyperscale public infrastructure as a service (IaaS) provider or an Asian country-specific, large-scale IaaS provider. The infrastructure platform should be located both in a service provider's data center for the cloud-enabled system infrastructure (CESI) platform and in an Asia country for the public IaaS platform. It also requires the use of a standardized deployment across all service provider customers and leverages a single codebase.
Meeting solutions are real-time communication services with their associated devices that support live interactions between participants for internal and external collaboration, presentations, learning, training sessions and webinars. Meeting solutions power diverse use cases, such as one-on-one meetings, remote sales engagements, board meetings, telehealth sessions, remote banking and consulting services, to name just a few. Meeting solutions enable rich information sharing and interaction by combining audio and video, in-meeting chat, content and screen sharing, and visual collaboration and whiteboarding.
Mobile back-end services deliver capabilities to mobile apps via APIs and/or software development kits (SDKs) that can be incorporated into mobile apps, and, increasingly, web apps and other digital channels. MBSs are commonly cloud-hosted services, but they can also be deployed in a virtual private cloud or even on-premises. The services are delivered as middleware between the client resident mobile apps and the enterprise systems of record, whether on-premises or in the cloud, along with any public or third-party data sources. In addition, many MBS providers also offer hosted databases for both structured and unstructured data. These hosted solutions can be independent of back-end data repositories, or they can be a buffer for the systems of record and provide a cached data source to isolate back ends from high transaction rates, which are often seen in conjunction with mobile apps.
Gartner defines the network firewall market as the market for firewalls that use bidirectional stateful traffic inspection (for both egress and ingress) to secure networks. Network firewalls are enforced through hardware, virtual appliances and cloud-native controls. Network firewalls are used to secure networks. These can be on-premises, hybrid (on-premises and cloud), public cloud or private cloud networks. Network firewall products support different deployment use cases, such as for perimeters, midsize enterprises, data centers, clouds, cloud-native and distributed offices.
Gartner defines observability platforms as products that ingest telemetry (operational data) from a variety of sources including, but not limited to, logs, metrics, events and traces. They are used to understand the health, performance and behavior of applications, services and infrastructure. Observability platforms enable an analysis of the telemetry, either via human operator or machine intelligence, to determine changes in system behavior that impact end-user experience such as outages or performance degradation. This allows for early, and even preemptive, problem remediation. Observability solutions are used by IT operations, site reliability engineers, cloud and platform teams, application developers, and product owners. Observability platforms are used by organizations to understand and improve the availability, performance and resilience of these critical applications and services. Investment in and successful deployment of observability platforms leads to revenue loss avoidance and enables faster product development cycles and improvements in brand perception.
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Public cloud storage is infrastructure as a service (IaaS) that provides block, file, object and hybrid cloud storage services delivered through various protocols. The services are stand-alone, but often used in conjunction with compute and other IaaS products. The services are priced based on capacity, data transfer and/or number of requests. The services provide on-demand storage capacity and self-provisioning capabilities. Stored data exists in a multitenant environment, and users access that data through the block, network and REST protocols provided by the services.
Public-key infrastructure (PKI) is a foundational infrastructure component used to securely exchange information using digital certificates. It is included in all the browsers to protect traffic across the public internet, and organizations use it to secure their business environment. The organizations generally use public-key cryptography and X.509 certificates for authentication and verification of the ownership of a public key. The software allows for end-to-end lifecycle management of these certificates. The certificate lifecycle management (CLM) includes enrollment, validation, deployment, revocation, and renewal of the certificates to provide uninterrupted service. Fundamentally, security and risk management technical professionals use PKI and CLM software to manage risks. The software can alert and notify the admin users if the certificates are expiring or are out of policy compliance. Further, the software also provides capabilities to discover, assign ownership, and report on the organization’s usage of certificates from multiple CAs.
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Gartner defines the service mesh market as the market for distributed computing middleware that enables, secures and optimizes communications between services running primarily in container management systems. A service mesh provides lightweight mediation, dynamic service discovery, request routing, observability, traceability and communication security. The service mesh is a technology that provides software infrastructure for communications between distributed application components deployed mainly in container management systems such as Kubernetes. This type of middleware helps manage and monitor service-to-service (east-west) communications, especially among microservices within an application domain. It also provides visibility into service interactions, enabling proactive operations and faster diagnostics. It automates complex communication concerns, thereby improving security, developer productivity and ensuring that standards and policies are enforced consistently across applications.
Gartner defines the service orchestration and automation platform (SOAP) market as encompassing solution suites that deliver capabilities enabling organizations to manage workloads, workflows, resource provisioning and data pipelines across their technology landscapes. SOAPs enable infrastructure and operations (I&O) leaders to design and implement business services. These platforms combine workflow orchestration, workload automation and resource provisioning across an organization’s hybrid digital infrastructure. Increasingly, they are central to an organization’s ability to deploy workloads and to optimize deployments as a part of cost and availability initiatives. SOAPs expand the role of traditional workload automation by adapting to use cases that deliver and extend into data pipelines, cloud-native infrastructures and application architectures. These tools complement and integrate with DevOps toolchains to provide customer-focused agility, cost savings, operational efficiency and process standardization.
Gartner defines speech-to-text (STT) platforms as business applications that process speech content, either live or in batch to produce: A transcript of the conversation Metadata about the call, the callers, attributes of call, emotional context Value-added services (e.g., biometric, legal) Workflow tools to support downstream work (e.g., intent detection, CRM updates) The capabilities of STT solutions vary. At a minimum, providers can offer a set of generic APIs with no tailored industry offering. More advanced solutions support complex deployments of edge technologies tailored to specific industries such as medical and legal. As natural language experiences are rapidly adopted by customers, users and employees, STT solutions must address a number of deployment configurations and be tailored for end-user domain knowledge to improve their accuracy.
Gartner defines strategic cloud platform services (SCPS) as standardized, automated, public cloud offerings integrating infrastructure services (e.g., computing, network and storage), platform services (e.g., application, data and value-added services such as AI/ML) and transformation services (resources to help customers adopt cloud-oriented IT delivery models). Although owned by the service provider, infrastructure and platform services may be hosted in providers’ infrastructures or customers’ data centers. Services should be elastically scalable, metered by use, and consumable via web-based interfaces and programmable APIs. Transformation programs may be delivered by automated, self-service interfaces, and managed interactions facilitated by account teams/partners.
Visual intelligence helps customers find products with relevant visual attributes using real-world images, video and text. Expanding on visual search capabilities, this helps customers identify a specific product, provides related content or detailed information, or otherwise triggers engagement. These solutions analyze catalogs to understand taxonomy and product attributes in addition to visual features, product identification using technologies like computer vision, natural language processing and machine learning. This technology helps ease the path to purchase, driving consumers in sectors like Healthcare, Sports & entertainment, manufacturing , retail etc., from awareness to conversion in an instant by presenting products with relevant visual attributes.
Gartner defines zero trust network access (ZTNA) as products and services that create an identity and context-based, logical-access boundary that encompasses an enterprise user and an internally hosted application or set of applications. The applications are hidden from discovery, and access is restricted via a trust broker to a collection of named entities, which limits lateral movement within a network.