Synthetic data: Fueling a data-centric approach to AI


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Despite increased interest in and adoption of Artificial Intelligence (AI) and Machine Learning (ML) in the enterprise, studies suggest that 85-96 percent of projects never make it to production. These numbers are even more astonishing given the growth of AI and ML in recent years, begging the question, what accounts for such a high failure rate?

Data may be the new “oil,” but it’s often the catalyst to the downfall of many AI and ML projects. The process of collecting, cleaning, and organizing raw data is long and arduous, and translating it to accurate, functioning AI certainly adds to the complexity. Like oil, data for AI and ML needs to be refined and put into a functioning engine in order to be useful. 

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