Synthetic data is an ecosystem for perfect data, showing promise in creating more capable and ethical AI models
“Is there data, and is it of sufficient diversity and quality to address my specific need?”
This is the question that many of today’s data and technology leaders have when creating a modern data architecture to support their company’s digital and AI transformations. While data may be the foundation for any AI project, there isn’t a clear-cut answer for how much of it you need to ensure a target performance. The difficulties associated with enterprise adoption could pose significant barriers to realizing the benefits of AI.
A single dataset may contain tens of millions of elements. With traditional approaches to AI projects, organizations are tasked with manually collecting and labeling data of this magnitude, which is time-consuming and costly, not to mention prone to human errors. This method has major disadvantages, as humans cannot label all the attributes a company may be interested in or need to power their AI project. Aside from the above limitations, real-world data presents a growing issue surrounding ethical use and privacy.