Hype Cycle for Advanced Analytics and Data Science, 2015


Archived Published: 06 July 2015 ID: G00277762

Analyst(s):

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Summary

As this year's Hype Cycle clearly indicates, the field of advanced analytics and data science is extremely innovative. Some important infrastructure concepts — including analytic marketplaces — allow the use of advanced analytics to scale much better, but much more development in this area is needed.

Table of Contents

  • Analysis
    • What You Need to Know
    • The Hype Cycle
    • • The Priority Matrix
    • Off the Hype Cycle
    • On the Rise
      • Analytics Marketplaces
      • Chief Analytics Officer
      • Natural Language Generation
      • Model Factory
      • Smart Data Discovery
      • Crowdsourcing of Microwork
      • Hadoop-Based Data Discovery
      • Graph Analysis
      • In-DBMS Analytics
      • Data Lakes
      • Deep Learning
      • PMML
      • Uplift Modeling
      • Citizen Data Science
      • Real-time Analytics
      • Self-Service Data Preparation
      • Optimization
    • At the Peak
      • Emotion Detection/Recognition
      • Prescriptive Analytics
      • Event Stream Processing
      • Geospatial and Location Intelligence
      • Spark
      • Predictive Analytics
      • Machine Learning
    • Sliding Into the Trough
      • Natural-Language Question Answering
      • Model Management
      • Speech Analytics
      • Linked Data
      • R
      • Text Analytics
      • Video Analytics
    • Climbing the Slope
      • Ensemble Learning
      • Simulation
      • Load Forecasting
    • Appendixes
      • Hype Cycle Phases, Benefit Ratings and Maturity Levels
  • Gartner Recommended Reading
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