Gartner Magic Quadrant for Cloud Database Management Systems 2020

 

By 2022, 75% of all databases will be deployed or migrated to a cloud platform, with only 5% ever considered for repatriation to on-premises.

 Gartner defines the cloud database management system (DBMS) market as being that for products from vendors that supply fully provider-managed public or private cloud software systems that manage data in cloud storage. Data is stored in a cloud storage tier (such as a cloud object store, a distributed data store or other proprietary cloud storage infrastructure), and may use multiple data models — relational, nonrelational (document, key-value, wide-column, graph), geospatial, time series and others.

 AWS, Microsoft, Google, Oracle, IBM, SAP, Teradata & Alibaba Cloud are the leaders in “Gartner Magic Quadrant for Cloud Database Management Systems 2020".

Gartner Magic Quadrant for Metadata Management Solutions 2020

 Metadata management is a core aspect of an organization’s ability to manage its data and information assets. The term “metadata” describes the various facets of an information asset that can improve its usability throughout its life cycle.

 Metadata supports understanding of an organization’s data assets, how those data assets are used and their business value. Metadata management initiatives deliver business benefits such as improved compliance and corporate governance, better risk management, better shareability and reuse, and better assessments of the impact of change within an enterprise, while creating opportunities and guarding against threats.

 Informatica, IBM, Collibra, Erwin, Alex Solution, Alation, SAP, Oracle, Smatlogic and ASG are the leaders in "Gartner Magic Quadrant for Metadata Management Solutions 2020".

Gartner’s Top Nine Strategic Tech Trends For 2021

 The top nine strategic technology trends for 2021 are:

1.      Internet of Behaviors

2.      Total experience

3.      Privacy-enhancing computation

4.      Distributed cloud

5.      Anywhere operations

6.      Cybersecurity mesh

7.      Intelligent composable business

8.      AI engineering

9.      Hyperautomation

https://www.forbes.com/sites/peterhigh/2020/10/26/gartners-top-nine-strategic-tech-trends-for-2021/?sh=71316f1521f6 

The Forrester Wave™: Machine Learning Data Catalogs, Q4 2020

 Machine learning data catalogs (MLDCs) are more than a metadata management tool and marketplace. Standalone tools provide an enterprise hub across the ecosystem and solution- and platform-based catalog and metadata repositories. This hub combines a traditional data management business glossary, data stewardship, data preparation, and data marketplaces for a central platform to serve contextualized data. Machine learning (ML) is the glue that makes this happen. ML automates the mundane aspects of understanding the data and applying policies, business rules, tags, and classifications. It provides introspection and inferencing to identify and anticipate error and conflict impacts. And it speeds up collaboration, data curation, and remediation with embedded intelligence and behavioral learning from a social media user experience (UX).

Collaboration, Lineage, And Data Variety Are Key Differentiators

Alation, Collibra, Alex Solutions, And IBM Lead The Pack; data.world, Informatica, Io-Tahoe, and Hitachi Vantara are Strong Performers in "The Forrester Wave™: Machine Learning Data Catalogs, Q4 2020"

The Forrester Wave™: Multimodal Predictive Analytics And Machine Learning, Q3 2020

 As the demand for AI skyrockets, vendors must provide data science and extended AI teams with more automation to increase productivity, model operations for smooth deployment, and a product roadmap that makes breakneck machine learning innovations accessible. Automation, ModelOps, And Product Roadmap Should Factor Prominently In Buy Decisions.

IBM, SAS, RapidMiner, Dataiku, And TIBCO Software Lead The Pack in "The Forrester Wave™: Multimodal Predictive Analytics And Machine Learning, Q3 2020"

Magic Quadrant for Cloud Infrastructure and Platform Services

 Gartner's Definition/Description

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 and platform services (CIPS) are defined as standardized, highly automated offerings, in which infrastructure resources (e.g., compute, networking and storage) are complemented by integrated platform services. These include managed application, database and functions as-a-service offerings. The resources are scalable and elastic in near-real time and are metered by use. Self-service interfaces are exposed directly to the customer, including a web-based user interface (UI) and an API. The resources may be single-tenant or multitenant, and can be hosted by a service provider or on-premises in the customer’s data center.

AWS, Azure and GCP are the leaders in "Magic Quadrant for Cloud Infrastructure and Platform Services 2020".

Gartner Magic Quadrant for Data Integration Tools 2020

 "By 2023, organizations utilizing data fabrics to dynamically connect, optimize and automate data management processes will reduce time to integrated data delivery by 30%. Through 2025, over 80% of organizations will use more than one cloud service provider (CSP) for their data and analytics use cases, making it critical for them to prioritize an independent and CSP-neutral integration technology to avoid vendor lock-ins."

15th straight year, Informatica has been named a Leader in the Gartner Magic Quadrant for Data Integration Tools.

Informatica, IBM, SAP, Oracle, Talend, SAS and Denodo are the leaders in "Gartner Magic Quadrant for Data Integration Tools 2020"

Magic Quadrant for Data Quality Solutions 2020

 By 2022, 60% of organizations will leverage machine-learning-enabled data quality technology for suggestions to reduce manual tasks for data quality improvement.

Market Definition: The term “data quality” relates to the processes and technologies for identifying, understanding and correcting flaws in data that support effective data and analytics governance across operational business processes and decision making. The packaged solutions available include a range of critical functions, such as profiling, parsing, standardization, cleansing, matching, enrichment, monitoring and collaborating.

Informatica, IBM, SAP, SAS, Talend & Precisely are the leaders in "Magic Quadrant for Data Quality Solutions 2020".

The Forrester Wave™: Enterprise Data Fabric, Q2 2020

Data fabric focuses on automating the process integration, transformation, preparation, curation, security, governance, and orchestration to enable analytics and insights quickly for business success. It minimizes complexity by automating processes, workflows, and pipelines, generating code and streamlining data to accelerate various use cases such as customer 360, data science, fraud detection, internet-of-things (IoT) analytics, risk analytics, and healthcare insights.

AI/ML, Self-Service, And Graph Engine Are Key Differentiators

Oracle, Talend, Cambridge Semantics, SAP, Denodo, And IBM Lead The Pack in "The Forrester Wave™: Enterprise Data Fabric, Q2 2020".

2020 Magic Quadrant for the CRM Customer Engagement Center

By 2023, 30% of customer service organizations will deliver proactive customer service using artificial intelligence (AI), process orchestration and continuous intelligence.

Gartner’s Definition: The current reshaping of the customer engagement center (CEC) market is characteristic of the consolidation of the customer service technology market. Customer service and support organizations must deliver consistent, effortless, intelligent and personalized customer service to their customers. The ability to orchestrate customer requests with assisted service, as well as with self-service, is one of the four pillars of great customer service within a leading customer service and support operation.

The four pillars of great customer service are:

1.    Getting connected

2.    Process orchestration

3.    Resource management

4.     Analytics and insights

Salesforce, Pegasystems, Servicenow, Microsoft, Oracle & Zendesk are the leaders in 2020 Magic Quadrant for the CRM Customer Engagement Center

2020 Magic Quadrant for Multichannel Marketing Hubs

Digital marketing leaders use multichannel marketing hubs to orchestrate contextually relevant experiences across complex journeys. Marketing leaders remain focused on driving more relevant digital experiences across the customer journey. 

Gartner defines the multichannel marketing hub (MMH) as a technology that orchestrates a company’s communications with and offers to customer segments across multiple channels. These include websites, mobile, social, direct mail, call centers, digital advertising and email. MMH capabilities also may extend to integrating marketing offers and leads with sales for execution in both B2B and B2C environments.

Adobe, Salesforce, SAS, Acoustic, SAP are the leaders in “2020 Magic Quadrant for Multichannel Marketing Hubs”.

2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights.

The DSML (Data Science and Machine Learning Platforms)  platform offers a mixture of basic and advanced functionality essential for building DSML solutions (primarily predictive and prescriptive models). The platform also supports the incorporation of these solutions into business processes, surrounding infrastructure, products and applications. It supports variously skilled data scientists in multiple tasks across the data and analytics pipeline, including all of the following areas:

• Data ingestion
• Data preparation
• Data exploration
• Feature engineering
• Model creation and training
• Model testing
• Deployment
• Monitoring
• Maintenance
• Collaboration

SAS, Databricks, Tibco, DataIku, Alteryx and MathWorks are the leaders in "2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms"

Magic Quadrant for Analytics and Business Intelligence Platforms 2020

Augmented capabilities are becoming key differentiators for analytics and BI platforms, at a time when cloud ecosystems are also influencing selection decisions.

By 2022, augmented analytics technology will be ubiquitous, but only 10% of analysts will use its full potential.

Modern analytics and business intelligence (ABI) platforms are characterized by easy-to-use functionality that supports a full analytic workflow — from data preparation to visual exploration and insight generation — with an emphasis on self-service and augmentation.

Microsoft (PowerBI), Tableau, Qlik and ThoutSpot are the leaders in "Magic Quadrant for Analytics and Business Intelligence Platforms 2020"

Forrester Wave™: Data Management For Analytics, Q1 2020

While traditional data warehouses often took years to build, deploy, and reap benefits from, today's organizations want simple, agile, integrated, cost-effective, and highly automated solutions to support insights. In addition, traditional architectures are failing to meet new business requirements, especially around high-speed data streaming, real-time analytics, large volumes of messy and complex data sets, and self-service. 

DMA (Data Management For Analytics) is a modern architecture that minimizes the complexity of messy data and hides heterogeneity by embodying a trusted model and integrated policies and by adapting to changing business requirements. It leverages metadata, in-memory, and distributed data repositories, running on-premises or in the cloud, to deliver scalable and integrated analytics. 

Oracle, SAP, IBM, Teradata and Google are the leaders in “Forrester Wave™: Data Management For Analytics, Q1 2020” Check out for Snowflake, AWS, Microsoft, Mongo…