FEATURE
Why synthetic data works
Synthetic data refers to information that is artificially generated, often through simulations or algorithmic processes, rather than collected from real-world environments. The answer lies in control and precision. While collecting realworld data is slow, expensive and increasingly encumbered by legal and ethical constraints, synthetic data can be created at scale. It is tailored to specific use cases, cleaned of potential noise or bias and can be generated faster and at a fraction of the cost of collecting real-world data.
Synthetic data can be generated through multiple methods including simulations and digital twins, Generative Adversarial Networks( GANs), Variational Autoencoders( VAEs) and rule-based modelling with statistical sampling. Importantly, it can be generated in ways that real-world data cannot. If data is needed that models rare edge cases in financial fraud detection or unusual interactions in a driverless car system, synthetic data provides that capability. These are scenarios where real data is sparse or non-existent. Synthetic data can model one-in-a-million events for self-driving cars in weeks rather than years.
Real data often falls short because rare or dangerous events are difficult to capture, privacy restrictions block sharing, licensing adds costs and delays and datasets can suffer from inconsistent quality or missing values. In contrast, simulations can generate thousands of plausible variations quickly, giving models exposure to situations they may only encounter once in a million miles on actual roads. In financial crime detection, where fraud patterns may be statistically too rare, synthetic data allows algorithms to repeatedly‘ practise’ recognising them before they appear in live transactions. www. intelligentcxo. com
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