Although data scientists can gain great insights from large data sets—and can ultimately use these insights to tackle major challenges—accomplishing this is much easier said than done. Many such efforts are stymied from the outset, as privacy concerns make it difficult for scientists to access the data they would like to work with.

The paper, “The Synthetic Data Vault” presented at the IEEE International Conference on Data Science and Advanced Analytics, describes a system that builds machine learning models out of real databases to create artificial or synthetic data. Synthetic data can be used to develop and test algorithms and models that enable science efforts that may otherwise be thwarted due to lack of real data.

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