Synthetic data is rapidly gaining attention as a solution to the worldwide data shortage, allowing businesses to utilize data characteristics without directly using original data. Defined as artificial data generated by algorithms, synthetic data has applications in statistical analysis and analytics. Its production has evolved from using Generative Adversarial Networks (GANs) to now include large language models (LLMs), which can create synthetic datasets that replicate vital characteristics of real datasets, enhancing data-driven decision-making even with limited organic data.
Synthetic data emerges as a powerful solution to the data shortage crisis, enabling businesses to leverage characteristics of user-generated data without needing to use original data.
Historically, GANs generated synthetic data, but now large language models can also effectively create synthetic data that mirrors the characteristics of existing data.
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