Synthetic Data Generation Market Snapshot: Feb. 2022

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Machine learning training data is not always readily available. In many cases, “ground truth” data is unavailable, can be difficult to collect, or is considered private, making its use difficult. Synthetic data is often used in scenarios where there isn’t sufficient training data available to be used in machine learning algorithms, especially in supervised machine learning. Synthetic data approaches can generate image, video, text, audio, sensor fusion, and structured data that can be used to train machine learning systems as well as provide some basic privacy protection for real-world data used in training machine learning models. In this latest snapshot of Cognilytica Market Intelligence, Cognilytica evaluates synthetic data solutions that provide needed annotations for machine learning training. The vendor landscape for Synthetic Data continues to expand with 76 vendors tracked in this snapshot, with a market size over $110M in 2021 growing to $1.15B by the end of 2027. Most decision factors for synthetic data hinge on the type of data to be generated, the method for data generation, and specific considerations for the range of acceptable data and accuracy rates.

In this Research Snapshot of Cognilytica’s Market intelligence, we cover:

  • The Synthetic Data Generation Market Overview
  • Synthetic Data Generation Forecasts and Trends across all market segments
  • Synthetic Data Generation Vendor overview covering 75 vendors with detailed profiles on “established” vendors
  • Decision Factors for Buyers of Synthetic Data Generation Solutions
  • Guided Questions to help Buyers interact with Synthetic Data Generation vendors
Statement of Opinion & Terms and Conditions of Sale
Although Cognilytica believes that the results, conclusions, and analysis produced in support of this report are well informed, comprehensive, and reasonable, Cognilytica cannot guarantee future results, accuracy of market predictions, or applicability of conclusions to report purchaser or reader’s business. Moreover, Cognilytica does not assume responsibility for the accuracy and completeness of such statements. The information derived in this report are statements of opinion only, and Cognilytica shall not be held liable in any manner for any conclusions or actions taken pursuant to this report. The information contained herein has been obtained from sources believed to be reliable. Cognilytica shall have no liability for errors, omissions, or inadequacies in the information contained herein or for interpretations thereof. Report purchaser and/or reader assumes sole responsibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice. Cognilytica does not make open its research methods, underlying data, sources, or means and methods of analysis for inquiry, evaluation, or examination.

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