Despite its benefits, synthetic data also comes with challenges:
Validation: Ensuring that synthetic data accurately reflects real-world scenarios can be difficult. Rigorous validation processes are essential. Complexity: The generation of high-quality synthetic data often requires sophisticated algorithms and computational resources. Bias: Synthetic data can inadvertently perpetuate biases present in the original dataset, leading to skewed results.
