Sunday, June 17, 2018

The Forrester Wave™: AI-Based Text Analytics Platforms Report Q2 2018

Complex Concept Extraction Is A Key Differentiator
As basic, mostly keyword-extraction-based technology becomes outdated and less effective, improved complex concept extractions (emotion, effort, intention, risk, and fraud-level tagging) will dictate which providers lead the pack. Vendors that can provide these advanced features position themselves to successfully deliver enterprisegrade text analytics solutions to their customers.

The AI-based text analytics platforms market is growing because more companies see turnkey solutions as a way to address their top text analytics challenges. This market growth is in large part due to AD&D pros increasingly preferring to buy rather than build text analytics solutions.

Forrester’s research uncovered a market in which Clarabridge, IBM, SAS, and Micro Focus lead the pack. OpenText, Attivio, Expert System, and EPAM Systems offer competitive options.

Wednesday, June 13, 2018

The Forrester Wave™: Big Data Fabric, Q2 2018

Big data platform components like data lakes, Hadoop, and NoSQL have made big data architectures more affordable, allowing companies to pursue insights-driven competitive advantage. But security concerns, complex data structures, problems involved in moving legacy data, large volumes, latency challenges, and variable speed of ingestion are significant obstacles to moving business data to these platforms, especially when dealing with data distributed across data centers. We find that most organizations are creating multiple repositories and platforms, creating more data silos rather than a unified platform for insights. But disparate technology stacks compromise timely, integrated data delivery to business users, customers, and partners.


Big data fabric, an emerging platform, accelerates insights by automating ingestion, curation, discovery, preparation, and integration from data silos.


Talend, Denodo Technologies, Oracle, IBM, And Paxata Lead The Pack in "The Forrester Wave™: Big Data Fabric, Q2 2018"

Thursday, May 31, 2018

Magic Quadrant for Industrial IoT Platforms 2018

By 2020, on-premises Internet of Things (IoT) platforms coupled with edge computing will account for up to 60% of industrial IoT (IIoT) analytics, up from less than 10% today.

By the end of 2022, the lack of compelling platforms in the market will induce 15% of manufacturers to develop or acquire IoT platforms, up from less than 1% today.

Market Definition/Description:
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. 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.

No clear leader in "Magic Quadrant for Industrial IoT Platforms"

Sunday, May 27, 2018

Magic Quadrant for Cloud Infrastructure as a Service 2018

When people think about "cloud computing," cloud IaaS is often one of the first things that comes to mind. It's the "computing" in cloud computing — on-demand compute, storage and network resources, delivered on-demand, in near-real time, as a service. The market is maturing rapidly; IaaS is on the Slope of Enlightenment on Gartner's  "Hype Cycle for Cloud Computing, 2017."

Cloud computing is a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using internet technologies. Cloud infrastructure as a service (IaaS) is a type of cloud computing service; it parallels the infrastructure and data center initiatives of IT.

Amazon AWS, Microsoft Azure and Google GCP are the leaders in “Magic Quadrant for Cloud Infrastructure as a Service : 2018”

Monday, May 21, 2018

"Gartner Magic Quadrant for the CRM Customer Engagement Center 2018"

By 2022, 70% of customer interactions will involve an emerging technology such as machine learning applications, chatbots or mobile messaging, up from 15% in 2018.

By 2022, 20% of all customer service interactions will be completely handled by AI, an increase of 400% from 2018.

Customers are demanding consistency of treatment when self-service escalates to assisted service. In turn, application leaders will demand that vendors provide channel synchronization, better use of AI, team collaboration, contextual knowledge and event-centric treatment.

Market Definition/Description
This Magic Quadrant examines the global market for customer service and support applications that enable customer service and support agents to engage customers through their preferred communication channel. It covers a wide range of customer service applications for organizations with customer engagement centers (CECs), ranging from very small (fewer than 20 agents) through average size (50 agents) to very large, distributed centers (over 10,000 agents).

SalesForce, PegaSystems, Microsoft, Oracle and Zendesk are the leaders in "Gartner Magic Quadrant for the CRM Customer Engagement Center 2018"

Tuesday, October 27, 2015

Magic Quadrant for Operational Database Management Systems 2015

Gartner defines a DBMS as a complete software system used to define, create, manage, update and query a database. A database is an organized collection of data that may be in multiple formats and may be stored in some form of storage medium (which may include hard-disk drives, flash memory, solid-state drives and/or DRAM). Additionally, according to Gartner's definition, DBMSs provide interfaces to independent programs and tools that both support, and govern the performance of, a variety of concurrent workload types. There is no presupposition that DBMSs must support the relational model or that they must support the full set of possible data types in use today. Furthermore, we do not stipulate that the DBMS must be a closed-source product; we include commercially supported open-source DBMS products in this market. Operational DBMSs must, however, include functionality to support backup and recovery, and have some form of transaction durability — although the atomicity, consistency, isolation and durability (ACID) model is not a requirement.


For this Magic Quadrant, Gartner defines operational DBMSs as systems that also support multiple structures and data types, such as XML, text, JavaScript Object Notation (JSON), audio, image and video content. They must include mechanisms to isolate workload resources and control various parameters of end-user access within managed instances of the data. For a definition of an operational DBMS workload.

Oracle, IBM, Amazon AWS, Microsoft, MongoDB, SAP, DataStax (Cassandra), EnterpriseDB, Redis, MarkLogic & InterSystems are in the leaders quadrant of ‘Magic Quadrant for Operational Database Management Systems 2015’.

Source: Gartner

Tuesday, August 18, 2015

Gartner's 2015 Hype Cycle for Emerging Technologies Identifies the Computing Innovations That Organizations Should Monitor

What is Gartner's Hype Cycle : The hype cycle is a graphical representation of the life cycle stages a technology goes through from conception to maturity and widespread adoption. The hype cycle is a branded tool created by Gartner, an information technology (IT) research and consultancy company.

Major changes in the 2015 Hype Cycle for Emerging Technologies (see Figure 1) include the placement of autonomous vehicles, which have shifted from pre-peak to peak of the Hype Cycle. While autonomous vehicles are still embryonic, this movement still represents a significant advancement, with all major automotive companies putting autonomous vehicles on their near-term roadmaps. Similarly, the growing momentum (from post-trigger to pre-peak) in connected-home solutions has introduced entirely new solutions and platforms enabled by new technology providers and existing manufacturers.

Digital Marketing (Stage 4): The digital marketing stage sees the emergence of the Nexus of Forces (mobile, social, cloud and information). Enterprises in this stage focus on new and more sophisticated ways to reach consumers, who are more willing to participate in marketing efforts to gain greater social connection, or product and service value. Enterprises that are seeking to reach this stage should consider the following technologies on the Hype Cycle: Gesture Control, Hybrid Cloud Computing, Internet of Things (IoT), Machine Learning, People-Literate Technology, Speech-to-Speech Translation.

Digital Business (Stage 5): Digital business is the first post-nexus stage on the roadmap and focuses on the convergence of people, business and things. The IoT and the concept of blurring the physical and virtual worlds are strong concepts in this stage. Physical assets become digitalized and become equal actors in the business value chain alongside already-digital entities, such as systems and apps. Enterprises seeking to go past the Nexus of Forces technologies to become a digital business should look to these additional technologies: 3D Bioprinting for Life Science R&D, 3D Bioprinting Systems for Organ Transplant, Human Augmentation, Affective Computing, Augmented Reality, Bioacoustics Sensing, Biochips, Brain-Computer Interface, Citizen Data Science, Connected Home, Cryptocurrencies, Cryptocurrency Exchange, Digital Dexterity, Digital Security, Enterprise 3D Printing, Smart Robots, Smart Advisors, Gesture Control, IoT, IoT Platform, Machine Learning, Micro Data Centers, Natural-Language Question Answering, Neurobusiness, People-Literate Technology, Quantum Computing, Software-Defined Security, Speech-to-Speech Translation, Virtual Reality, Volumetric and Holographic Displays, and Wearables.

Autonomous (Stage 6): Autonomous represents the final post-nexus stage. This stage is defined by an enterprise's ability to leverage technologies that provide humanlike or human-replacing capabilities. Using autonomous vehicles to move people or products and using cognitive systems to recommend a potential structure for an answer to an email, write texts or answer customer questions are all examples that mark the autonomous stage. Enterprises seeking to reach this stage to gain competitiveness should consider these technologies on the Hype Cycle: Autonomous Vehicles, Bioacoustic Sensing, Biochips, Brain-Computer Interface, Digital Dexterity, Human Augmentation, Machine Learning, Neurobusiness, People-Literate Technology, Quantum Computing, Smart Advisors, Smart Dust, Smart Robots, Virtual Personal Assistants, Virtual Reality, and Volumetric and Holographic Displays.