“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"