Synthetic Data
Synthetic Data vs Real Data: Benefits, Challenges in 2025
Synthetic data is widely used across various domains, including machine learning, deep learning, generative AI (GenAI), large language models, and data analytics. According to Gartner, by 2030, synthetic data use will outweigh real data in AI models.
Top 20 Synthetic Data Use Cases & Applications in 2025
Synthetic data offers solutions to common challenges in data science, including data privacy concerns and limited dataset sizes. Synthetic data is gaining widespread popularity and applicability across industries, including machine learning, deep learning, generative AI (GenAI), large language models, and finance.
12+ Data Augmentation Techniques for Data-Efficient ML
Data augmentation techniques generate different versions of a real dataset artificially to increase its size. Computer vision and natural language processing (NLP) models use data augmentation strategy to handle with data scarcity and insufficient data diversity. Data-centric AI/ML development practices such as data augmentation can increase accuracy of machine learning models.
Synthetic Data Generation Benchmark & Best Practices ['25]
We benchmarked 7 publicly available synthetic data generators sourced from 4 distinct providers, utilizing a holdout dataset comprising 70,000 samples, with 4 numerical and 7 categorical features, to evaluate their performance in replicating real-world data characteristics. Below, you can see the benchmark results where we statistically compare the synthetic data generators.