Gartner Magic Quadrant for Master Data Management Solutions 2018

Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of an enterprise’s official, shared master data assets.

MDM solutions are enterprise software products that:

o   Support the global identification, linking and synchronization of master data across heterogeneous data sources through semantic reconciliation of master data.

o   Create and manage a central, persisted system of record or index of record for master data.

o   Support the four MDM hub implementation styles, as defined by Gartner (see the Completeness of Vision section, “Offering [Product] Strategy”).

o   Enable generation and delivery of a trusted version of one or more subject areas (i.e., data domains) to all stakeholders, in support of various business initiatives.

o   Support ongoing master data stewardship and governance requirements through workflow-based monitoring and corrective-action techniques.

o   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 (aka “application-neutral”).

Informatica & Orchestra Networks lead the pack in "Gartner Magic Quadrant for Master Data Management Solutions 2018" with SAP, IBM, Stibo, Semarchy, Profisee are challenger.

The Forrester Wave™: Data Preparation Solutions Q4 2018

Data Preparation Solutions Are The Rocket Fuel For Data Activation. Key Differentiators Are Machine Learning Automation, Collaboration, And Activation.

The data prep solutions market is growing and maturing to support enterprises' critical need to move at the speed of their customers by scaling more data faster with governed self-service.

Forrester's research uncovered a market in which Trifacta, Paxata, Unifi Software, TIBCO Software, and SAP are Leaders; Oracle, ClearStory Data, and Datawatch are Strong Performers; Datameer is a Contender; and SAS is a Challenger in "The Forrester Wave™: Data Preparation Solutions Q4 2018"

The Forrester Wave™: Cloud Data Warehouse, Q4 2018

Cloud data warehouse solutions are changing the way we build and support data platforms for insights. You can provision a cloud data warehouse in minutes without requiring any technical expertise, allowing business analysts and other nontechnical users to access, store, and process large amounts of data for insights.

Forrester defines cloud data warehouse as: An on-demand, secure, and scalable self-service data warehouse that automates the provisioning, administration, tuning, backup, and recovery to accelerate analytics and actionable insights while minimizing administration requirements.

AWS (Redshift), Snowflake, Google (BigQuery), And Oracle [Autonomous Data Warehouse (ADW)..old exadata]Lead The Pack; Teradata, IBM, Hortonworks, Microsoft, and MarkLogic are Strong Performers in "The Forrester Wave™: Cloud Data Warehouse, Q4 2018"

Gartner Magic Quadrant for Operational Database Management Systems : 2018

By 2023, 75% of all databases will be on a cloud platform — a development that will drastically change the DBMS vendor landscape.

The operational database management system (OPDBMS) market is defined by relational and nonrelational database management products suitable for the traditional transactions used to support business processes. These include a broad range of enterprise-level applications — both purchased business applications, such as ERP and CRM applications, and custom-made transactional systems. Gartner defines a DBMS as a complete software system used to define, create, update, manage and query a database.

Microsoft, Oracle, AWS and SAP are the leaders in "Gartner Magic Quadrant for Operational Database Management Systems : 2018". Check out for Cassandra, Mongo, GCP & Marklogic..

The Forrester New Wave™: Enterprise Container Platform Software Suites, Q4 2018

The Forrester New Wave differs from traditional Forrester Wave™. The New Wave only evaluates emerging technologies, and this time its extreme containerization.

Enterprise container platforms provide a container-based development environment with container execution, orchestration, integration, security, and management capabilities designed to provision and control container clusters across multiple on-premises and cloud infrastructure platforms.

The report evaluated eight vendors across following categories:

ü  Runtime and orchestration

ü  Image management

ü  Operations management

ü  Security features

ü  User experience

ü  Integrations and APIs

ü  Vision

ü  Market approach

Forrester's research uncovered a market in which Docker, Red Hat, and Rancher Labs are Leaders; Pivotal, Mesosphere, and IBM are Strong Performers; and SUSE and Platform9 are Contenders in "The Forrester New Wave™: Enterprise Container Platform Software Suites, Q4 2018"

The Forrester Wave™: Omnichannel Order Management Systems, Q3 2018

Digital Strategy Pros Are Looking For Order Control And Business Optimization. Order Orchestration, Store Fulfillment, And Advanced Analytics Are Key Differentiators. As omni-channel customer journeys become more complex, improved order orchestration, store fulfillment, and advanced analytics will dictate which providers will lead the pack.

 

Forrester defines omnichannel commerce as: “The coordination of traditional channels (marketing, selling, and fulfillment) and supporting systems to create a seamless and consistent customer experience.”

Enterprise-level inventory visibility, Distributed order management (DOM), Customer service & Store fulfillment are the key.

 

Manhattan Associates And IBM Lead The Pack in "The Forrester Wave™: Omnichannel Order Management Systems, Q3 2018" ; Oracle NetSuite, Oracle, Aptos, and Magento are Strong Performers; Digital River, Radial, and Jagged Peak are Contenders; and Kibo is a Challenger.

“The Forrester Wave™: Multimodal Predictive Analytics And Machine Learning Solutions, Q3 2018.”

“Machine learning is an elemental core competency that every enterprise must have. The reasons are many. Machine learning gives enterprises the power to predict. It is a fundamental building block to AI.”

 

Forrester defines enterprise PAML (Predictive Analytics And Machine Learning) as: Software that provides enterprise data scientist teams and stakeholders with

1) Tools to analyze data;

2) Workbench tools to build predictive models using statistical and machine learning algorithms;

3) A platform to train, deploy, and manage analytical results and models; and

4) Collaboration tools for extended enterprise teams, including businesspeople, data engineers, application developers, DevOps, and AI engineers.

 

SAS, IBM and RapidMiner Lead The Pack in “The Forrester Wave™: Multimodal Predictive Analytics And Machine Learning Solutions, Q3 2018.”. Check out for KNIME, SAP, Datawatch, TIBCO Software, and Dataiku..

Magic Quadrant for Metadata Management Solutions - 2018

“By 2020, most data and analytics use cases will require connecting to distributed data sources, leading enterprises to double their investments in metadata management.”

 

Market Definition/Description: Metadata management is about an organization’s management of its data and information assets. Metadata describes the various facets of an information asset that can improve its usability throughout its life cycle. Enterprise metadata management (EMM) encompasses the roles, responsibilities, processes, organization and technology necessary to ensure that the metadata across the enterprise adds value to that enterprise’s data.

 

Informatica, Collibra, Alation, Smartlogic, Datum, IBM, Oracle, Alex Solution and ASG Technologies are the leaders in “Magic Quadrant for Metadata Management Solutions - 2018”

Gartner Magic Quadrant for Data Integration Tools 2018

Gartner estimates that the data integration tools market generated more than $2.9 billion in software revenue (constant currency) at the end of 2017, and the market is expected to approach $4.5 billion in 2022.

 

(Data Integration Tools) Market Definition/Description: The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types. This integration takes place in the enterprise and beyond the enterprise — across partners and third-party data sources and use cases — to meet the data consumption requirements of all applications and business processes. Enterprise, 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.

 

Informatica, IBM, SAP, SAS, Talend, Oracle are the leaders in "Gartner Magic Quadrant for Data Integration Tools 2018". Check out for Denodo, Attunity.

The Forrester Wave™: Machine Learning Data Catalogs, Q2 2018

MLDCs (Machine Learning Data Catalogs) Are The Stepping Stone For The Intelligent Business

Data Pros Are Looking For Data Understanding That Everyone Can Access: The MLDC market is growing because firms want to scale data to the masses through selfservice. However, back-end data management technology can’t support tribal knowledge, provide a good user experience (UX) for data consumers, and scale across a highly federated data ecosystem. MLDCs solve this and scale elastically by leveraging their machine learning (ML) capabilities. Machine Learning, Collaboration, And Activation Are Key Differentiators

 

IBM, Reltio, Unifi Software, Alation, And Collibra Lead The Pack in "The Forrester Wave™: Machine Learning Data Catalogs, Q2 2018". Check out Informatica, Waterline, Horton..etc

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.

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"

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"

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”

"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"