Gartner Research

Critical Capabilities for Data Science Platforms

Published: 07 June 2017

ID: G00326671

Analyst(s): Alexander Linden , Svetlana Sicular, Jim Hare, Peter Krensky, Shubhangi Vashisth, Carlie Idoine

Summary

The data science market is being transformed by disruptive technologies and high expectations for artificial intelligence. Data and analytics leaders should closely monitor market conditions for 2017, which dictate new critical capabilities and use-case scenarios for data science platforms.

Table Of Contents

What You Need to Know

Analysis

  • Critical Capabilities Use-Case Graphics
  • Vendors
    • Alpine Data
    • Alteryx
    • Angoss
    • Dataiku
    • Domino Data Lab
    • FICO
    • H2O.ai
    • IBM
    • KNIME
    • MathWorks
    • Microsoft
    • Quest
    • RapidMiner
    • SAP
    • SAS (EM)
    • SAS (VAS)
    • Teradata
  • Context
  • Product/Service Class Definition
  • Critical Capabilities Definition
    • Data Access
    • Data Preparation
    • Data Exploration and Visualization
    • 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
    • Production Refinement
    • Business Exploration
    • Advanced Prototyping
    • Added
    • Dropped

Inclusion Criteria

  • Gate 1
  • Gate 2
  • Gate 3
  • Critical Capabilities Rating

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