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.
Snapshot Meta: 28 Pages, 76 Vendors, 2 tables, 9 charts / graphs, 2 images
- Table of Contents
- Executive Summary3
- Key Findings 3
- Synthetic Data Markets Overview 4
- Synthetic Data Classification in Data Infrastructure 4
- Synthetic Data: An aspect of Data Generation vs. Data Augmentation 5
- Simulated vs. Synthetic Data 5
- Synthetic Data Types and Capabilities 6
- Synthetic Image Data 6
- Synthetic Video Data 6
- Synthetic Text Data 6
- Synthetic Audio Data 7
- Synthetic Sensor Fusion / Point Cloud Data 7
- Synthetic Structured Data 7
- Synthetic Data Applications 7
- Augmentation of Human-Based Data Annotation 7
- Avoiding the Use of Personally Identifiable Information or Sensitive Information 8
- Government and Industry Applications of Synthetic Data 8
- Challenges with Synthetic Data 9
- Synthetic Data Market Segmentation 9
- Synthetic Data Market Forecast & Trends 10
- Market Observations 11
- Vendor Analysis 11
- Synthetic Data Market Trends 12
- Synthetic Data Displacing Ground-Truth Real World Data for Labeling 12
- Synthetic Data Market Shows Rapid Growth Potential 13
- Synthetic Data Market Currently Segmented by Data Type and Data Generation Method 13
- Privacy-First Synthetic Data 14
- Synthetic Data and the Metaverse 14
- Synthetic Data Market Map & Key Vendor Profiles 15
- Criteria for Determining Established and Emerging Vendors 15
- Vendor Profiles in this Snapshot and Online 15
- Synthetic Data Market Map & Vendor Profiles 16
- Established Synthetic Data Profiles 17
- Datagen 17
- Gretel.ai 18
- Synthetic Data: Decision Factors 19
- Synthetic Data Solution Provider Considerations 19
- Ground Truth Data vs. Synthetically Generated Data 19
- Structured vs. Unstructured Synthetic Data Generation and Data Type Identification 19
- Programmatic vs. AI-generated vs. Simulated Data Generation 20
- Privacy-First Considerations 20
- Synthetic Data Solution Vendor Requirements 21
- Decision Factors to Guide Vendor Selection 22
- Synthetic Data: Guided Questions 24
- Questions for Vendors on Synthetic Data Capabilities 24
- Questions for Vendors on Quality Control 24
- Questions for Vendors on Privacy 24
- Questions for Vendors on Synthetic Image Data Capabilities 24
- Questions for Vendors on Synthetic Video Data Capabilities 25
- Questions for Vendors on Synthetic Text Data Capabilities 25
- Questions for Vendors on Synthetic Audio Data Capabilities 26
- Questions for Vendors on Synthetic Sensor Fusion & Point Cloud Data Capabilities 26
- Questions for Synthetic Data Vendors Cloud vs. On-Premise 26
- Questions for Vendors on Pricing and Support 27
- Related Research 28
- About Cognilytica 28
