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...
As organizations increasingly rely on synthetic data to improve their machine learning models, ensuring that the relations like pairwise distributions and correlations are kept in synthetic data is part of the fidelity assessment whenever...
When generating synthetic data, it is key that new data mimics the distribution of the original data to ensure that the synthetic dataset is a realistic representation of real-world data. In that sense, evaluating how the synthetic data...
Photo by Conny Schneider on Unsplash In the dynamic landscape of Data-Centric AI, data quality is crucial for the success of any analytics or machine learning initiative. Data profiling is an essential process that provides insights into...
As Synthetic Data becomes a must-have for the future of AI, guaranteeing its quality becomes indispensable. Fidelity, one of the main pillars of synthetic data evaluation, is crucial in ensuring that synthetic datasets accurately represent...
High-dimensional datasets are at the heart of many business applications and domains, from financial services to telecommunications, retail, and healthcare. These datasets, characterized by a large number of columns — sometimes hundreds or...
