Industries that can benefit from synthetic data:īusiness functions that can benefit from synthetic data include: Though synthetic data first started to be used in the ’90s, an abundance of computing power and storage space of the 2010s brought more widespread use of synthetic data. However, especially in the case of self-driving cars, such data is expensive to generate in real life. Training data is needed for machine learning algorithms.Data is needed for testing a product to be released however such data either does not exist or is not available to the testers.when privacy requirements limit data availability or how it can be used.This can be useful in numerous cases such as Synthetic data is important because it can be generated to meet specific needs or conditions that are not available in existing (real) data. Synthetic data is a type of data augmentation. It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI model training. Synthetic data, as the name suggests, is data that is artificially created rather than being generated by actual events. Figure 1: Synthetic data over real data in the future What is synthetic data? It’s estimated that by 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated. Generating synthetic data is inexpensive compared to collecting large datasets and can support AI/deep learning model development or software testing without compromising customer privacy. Collecting sufficient data to develop ML models to predict fraudulent transactions is challenging because fraudulent transactions are rare.Īs a result, businesses are turning to data-centric approaches to AI/ML development, including synthetic data to solve these problems. Bank fraud, on the other hand, is an example of a rare event. For instance, collecting data representing the variety of real-world road events for an autonomous vehicle may be prohibitively expensive. Some types of data are costly to collect, or they are rare.For this reason, privacy regulations such as GDPR and CCPA restrict the collection and use of personal data and impose fines on companies that violate them. Collecting and using sensitive data raises privacy concerns and leaves businesses vulnerable to data breaches. Many business problems that AI/ML models could solve require access to sensitive customer data such as Personally Identifiable Information (PII) or Personal Health Information (PHI).However, collecting such data is challenging because: Developing successful AI and ML models requires access to large amounts of high-quality data.
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