Trends Report

Synthetic Data Delivers Real Value To Fuel The Future Of AI

Explore The Weird, Wonderful, And As-Yet Unseen Use Case Landscape

September 7th, 2022
Rowan Curran, Senior Analyst
Rowan Curran Senior Analyst
Jeremy Vale, Researcher
Jeremy Vale Researcher
With contributors:
Srividya Sridharan , Mike Gualtieri , Brandon Purcell , Michele Goetz , Stephanie Liu , Enza Iannopollo , Natalie Schibell , Alex Shlyankevich , Kyle Rybarczyk , Karsten Monteverde

Summary

AI applications demand an ever-growing breadth and depth of data to power the models behind intelligence — and enterprises are struggling to get enough of the right data to train them. Technology executives and AI team leaders must understand synthetic data so that they can support their teams with the data they need. Synthetic data for AI duplicates, mimics, or extrapolates real-world data — encompassing everything from transfigured spreadsheets to full-blown simulated worlds. This data allows teams to train models for use cases and circumstances that would otherwise be unattainable due to poor quality data, privacy concerns, security restrictions, or lack of existing data.

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