Synthetic data is gaining traction within the machine learning domain. However, some experts recommend DevOps teams choose data masking techniques over synthetic data techniques because production datasets contain complex relationships that make it hard to manufacture an accurate representation quickly and cheaply. Artificially generated data can be plugged into a process without taking authentic data out of production.
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Synthetic financial datasets can be found on Kaggle, a crowdsourced platform that hosts predictive modeling and analytics competitions.ĭevOps teams use synthetic data for software testing and quality assurance. Data scientists can use synthetic data to test or evaluate fraud detection systems as well as develop new fraud detection methods. In the financial sector, synthetic datasets such as debit and credit card payments that look and act like typical transaction data can help expose fraudulent activity.
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This enables data professionals to use and share data more freely.įor example, synthetic data enables healthcare data professionals to allow public use of record-level data but still maintain patient confidentiality. The techniques can be used to manufacture data with similar attributes to actual sensitive or regulated data. real dataįinancial services and healthcare are two industries that benefit from synthetic data techniques. The benefits of using synthetic data include reducing constraints when using sensitive or regulated data, tailoring the data needs to certain conditions that cannot be obtained with authentic data and generatingįor software testing and quality assurance purposes for DevOps teams.ĭrawbacks include inconsistencies when trying to replicate the complexity found within the original dataset and the inability to replace authentic data outright, as accurate authentic data is still required to produce useful synthetic examples of the information.
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Synthetic data is created algorithmically, and it is used as a stand-in for test datasets of production or operational data, to validate mathematical models and, increasingly, to train machine learning models. Synthetic data is information that's artificially manufactured rather than generated by real-world events.