According to Gartner, 85% of Data Science projects fail (and are predicted to do so through 2022). I suspect the failure rates are even higher, as more and more organizations today are trying to utilize the power of data to improve their services or create new revenue streams. Not having the “right” data continues to prevent businesses from making the best choices. But live production data is also a massive liability, as it requires regulatory governance. Hence, many organizations are now turning towards using synthetic data – aka fake data – to train their machine learning models.
