Enterprise IT / AI
Synthetic Training Data - Tabular
What is Synthetic Training Data - Tabular?
Companies in this market identify key patterns in tabular datasets, such as patient health records or customer purchasing history, to generate anonymous synthetic datasets that retain the key properties of the original data. The anonymity of synthetic data enables secure and compliant collaboration.
Selling a product or service?
Reach more buyers.
Your future customers are researching their next tech solution on CB Insights. Make sure they can find you.
Claim your CB Insights ProfileLeaders
Leaders are the most established service providers in the market and possess the breadth to address various customer needs.
See why these companies made it as leaders for Synthetic Training Data - Tabular. These companies include Facteus and Diveplane.
United States / Founded Year: 2010
Facteus is a provider of actionable insights from financial data. Through its synthetic data process, Facteus safely transforms raw financial transaction data from legacy technologies into actionable information, which can be used for machine learning, artificial intelligence, data monetization, and other strategic use cases, without compromising data privacy. The company’s data products have been gathered directly from over 1,000 financial institutions, payment companies, fintechs, and payment card programs, giving business and investment executives access to the “truth” of actual consumer financial transactions, not just general trends.
United States / Founded Year: 2017
Diveplane develops technology that helps businesses and government organizations understand AI with a trainable, interpretable and auditable. GEMINAI by Diveplane creates a verifiable synthetic ‘twin’ dataset with the same statistical properties of the original data, but without including the real-world confidential or personal information.
Known Partners
Known Customers
virginia department of behavioral health and developmental services, Alder Hey Children's Hospital, University of Edinburgh
Key People
Michael Capps, Christopher Hazard, Michael Resnick, and 2 more
Highfliers
Highfliers are the most innovative service providers in the market and possess the resources to address evolving customer needs.
See why these companies made it as highfliers for Synthetic Training Data - Tabular. These companies include Mostly AI and Tonic.
Austria / Founded Year: 2017
Mostly AI develops and offers a synthetic data engine that provides organizations the ability to generate realistic and representative synthetic users and data, retaining structure and variation while preserving privacy.
Known Partners
Key People
Michael Platzer, Klaudius Kalcher, Tobias Hann, and 2 more
United States / Founded Year: 2018
Tonic automatically creates mock data that preserves characteristics of secure datasets so that developers, data scientists, and salespeople can work without breaching privacy.
Known Partners
Key People
Andrew Colombi, Ian Coe, Karl Hanson
Outperformers
Outperformers are the most specialized service providers in the market and possess the expertise to address unique customer needs.
See why these companies made it as outperformers for Synthetic Training Data - Tabular. These companies include .
Challengers
Challengers are the most promising service providers in the market and possess the agility to address emerging customer needs.
See why these companies made it as challengers for Synthetic Training Data - Tabular. These companies include Ydata and Syntho.
Portugal / Founded Year: 2019
Ydata is an AI enabled data privacy tool. It creates synthetic data sets similar to confidential internal data sets that care be safely shared, and synthetic data sets for forecasting.
Known Partners
Key People
Gonçalo Ribeiro
Netherlands / Founded Year: 2020
Syntho provides AI based privacy enhancing technology (PET) to generate anonymous synthetic data that preserves all the value of the original data.
Key People
Wim Janssen, Simon Brouwer
What is Synthetic Training Data - Tabular?
Companies in this market identify key patterns in tabular datasets, such as patient health records or customer purchasing history, to generate anonymous synthetic datasets that retain the key properties of the original data. The anonymity of synthetic data enables secure and compliant collaboration.
Selling a product or service?
Reach more buyers.
Your future customers are researching their next tech solution on CB Insights. Make sure they can find you.
Claim your CB Insights Profile