Gartner Research

Boost Your Training Data for Better Machine Learning

Published: 26 July 2019

Summary

Not having access to enough quality training data is one of the biggest showstoppers for machine learning projects. Data and analytics leaders responsible for machine learning initiatives can overcome this situation by following the nine techniques described here.

Included in Full Research

  • Key Challenges
  • Scrutinize Your Current Data Collection Strategy
    • Technique 1: Move Past Legacy Data Collection Requirements to Collect More Data
    • Technique 2: Spend More Time on Data Preprocessing
  • Acquire More Data
    • Technique 3: Incorporate External Datasets to Enrich Your Own Dataset
    • Technique 4: Exploit Crowdsourcing to Generate New Data and Labels
    • Technique 5: Obtain More Data From Peer Organizations With Data Pooling and Sharing
  • Synthesize Additional Data
    • Technique 6: Augment Data Using Domain-Specific Transformations
    • Technique 7: Simulations Can Also Generate New Labelled Event Data
  • Use Advanced Machine Learning Techniques
    • Technique 8: Minimize Expensive Real-World Sampling With Active Learning
    • Technique 9: Use Transfer Learning to Utilize Data That You Don’t Even Have Access To

Analysts:

Akif Khan,  Alexander Linden,  Anthony Mullen

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