Organizations committed to data driven decision making often share common concerns about privacy, data integrity, and a lack of sufficient data. Synthetic data allows companies to share data and create algorithms more easily. This interesting information came to us from MIT’s Sloan School of Management in their article, “What is synthetic data — and how can it help you competitively?“
Synthetic data is annotated information that computer simulations or algorithms generate as an alternative to real-world data. Synthetic data is created in digital worlds rather than collected from or measured in the real world.
Typically, developers need large, carefully labeled datasets to train neural networks. More diverse training data generally makes for more accurate artificial intelligence (AI) models. The trade-off is that gathering and labeling these datasets, containing a rich few thousand up to tens of millions of elements, becomes time-consuming and often prohibitively expensive.
Synthetic data aims to solve these problems by giving software developers and researchers something that resembles real data but isn’t. It can be used to test machine learning models or build and test software applications without compromising real, personal data.
Data should be findable, and it is best to use a strong, standards-based taxonomy. Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies and associated rule bases for machine-assisted indexing.
Melody K. Smith
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