Gartner Information Technology Research

Critical Capabilities for Data Science and Machine Learning Platforms

Published: 02 March 2020

ID: G00391146

Analyst(s): Pieter den Hamer , Alexander Linden , Carlie Idoine , Erick Brethenoux , Jim Hare , Svetlana Sicular , Farhan Choudhary , Peter Krensky


The functions and features of data science and machine learning platforms are evolving quickly to keep pace with a highly innovative space. This research helps data and analytics leaders to evaluate 16 of these platforms across 15 critical capabilities.

Table Of Contents

What You Need to Know


  • Critical Capabilities Use-Case Graphics
  • Vendors
    • Altair
    • Alteryx
    • Anaconda
    • Databricks
    • Dataiku
    • DataRobot
    • Domino
    • Google
    • IBM
    • KNIME
    • MathWorks
    • Microsoft
    • RapidMiner
    • SAS
    • TIBCO Software
  • Context
  • Product/Service Class Definition
  • Critical Capabilities Definition
    • Data Access
    • Data Preparation
    • Data Exploration and Visualization
    • Augmentation (Automation)
    • User Interface
    • Machine Learning
    • Other Advanced Analytics
    • Flexibility and Openness
    • Performance and Scalability
    • Delivery
    • Platform and Project Management
    • Model Management
    • Precanned Solutions
    • Collaboration
    • Coherence
  • Use Cases
    • Business Exploration
    • Advanced Prototyping
    • Production Refinement
    • Augmented Data Science and Machine Learning
  • Vendors Added and Dropped
    • Added
    • Dropped

Inclusion Criteria

  • Exclusion Criteria
  • Critical Capabilities Rating


  • Honorable Mentions

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