Synthetic data, artificially generated data that mimics real-world data, is a technology that has undergone significant transformation in recent years. Since the dawn of data-driven synthetic data generation with generative models,...
Photo by Roman Kraft on Unsplash As machine learning continues to evolve, the use of Large Language Models (LLMs) has become increasingly prevalent, particularly in complex tasks requiring deep understanding and generation of human-like...
Photo by Isaac Smith on Unsplash Synthetic data bootstrap In the dynamic landscape of organizations high-quality data is a requirement for the development of many solutions - from software testing and validation all the way to Artificial...
Cover Photo by Avery Evans on Unsplash Dive into data management with our latest whitepaper, which presents an in-depth Gap analysis among YData Fabric, Alation, and Informatica—three solutions in the realm of data catalogs. These...
While allowing for meaningful data behavior, it is crucial that synthetic data safeguards individual privacy. Therefore, ensuring the efficacy of synthetic data applications also requires a strong assessment of re-identification risks....
One of the most valuable and unique characteristics of synthetic data is that it keeps the properties and behavior of original data without a one-to-one link with the real events, thus fostering data privacy and enabling secure data...
A merry data analysis for all As the holiday season approaches, it's not just about decorating trees and sharing gifts; it's also a time to give back to the community and spread joy. This year, why not celebrate the season of giving by...
An Advent to explore Generative AI and Synthetic Data Holidays are approaching and you are feeling like you want to explore something new - synthetic data might just be it! Options are always great, and data profiling is always a good...
Cover Photo by Avery Evans on Unsplash In the dynamic landscape of software development, the significance of effective Test Data Management (TDM) cannot be overstated. Traditional approaches, such as IBM InfoSphere Optim, have long been...
The Power of Data Quality, Orchestration, Profiling, and Synthetic Data Retail is not only a fast-paced but also a highly competitive landscape, demanding from the players to be always ahead of the competition. The adoption of AI and...
One of the most important applications of synthetic data is its use in developing machine learning solutions – to train and test machine learning models – when real data is hard to collect or sensitive to share. For that reason, it is...
Synthetic data, designed to mimic real-world datasets, must be able to provide the same answers as real data to be valuable. For instance, when determining the average of customers that buy certain products, the result returned by the...
