Hype Cycle for Data Science and Machine Learning, 2018

Published: 23 July 2018 ID: G00340329

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The hype around data science and machine learning continues to defy gravity and soar to ever-higher levels. Data and analytics leaders should use this Hype Cycle to understand technologies generating excitement and inflated expectations, as well as significant movements in adoption and maturity.

Table of Contents

  • Analysis
    • What You Need to Know
    • The Hype Cycle
    • The Priority Matrix
    • Off the Hype Cycle
    • On the Rise
      • Artificial General Intelligence
      • Conversational Chatbots for Analytics
      • Continuous Intelligence
      • Guided Analytics
      • Human-in-the-Loop Crowdsourcing
      • API Marketplaces
      • Embedded Analytics
      • IoT Edge Analytics
    • At the Peak
      • Machine Learning-Enabled Data Management
      • Advanced Anomaly Detection
      • Citizen Data Science
      • Optimization
      • Augmented Analytics
      • AutoML
      • Decision Management
      • Digital Ethics
      • Graph Analytics
      • Prescriptive Analytics
      • Deep Neural Nets (Deep Learning)
      • Machine Learning
      • Event Stream Processing
      • Cognitive Computing
      • Notebooks
    • Sliding Into the Trough
      • Data Preparation
      • Data Lakes
      • Predictive Analytics
      • Spark
      • Python
      • Speech Analytics
    • Climbing the Slope
      • Text Analytics
      • Model Management
      • Video/Image Analytics
    • Entering the Plateau
      • Ensemble Learning
    • Appendixes
      • Hype Cycle Phases, Benefit Ratings and Maturity Levels
  • Gartner Recommended Reading
© 2018 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartners research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.

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