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…