Synthetic Data – What Is It and What You Need to Know About It
Synthetic data has become an emerging topic in the artificial intelligence (AI) world.?
More than ever, organizations turn to advanced analytics and AI to optimize their operational process, enhance customer experience or innovate new products and services. But to stay competitive on today’s landscape using these capabilities, organizations need bigger access to internal and external data. A resource that is not always available, hard to find or maybe tricky to use. Especially customer data. Given the increased emphasis on data protection and AI ethics in Europe, many organizations have started deescalating their AI innovation efforts. They wonder how to maintain competitiveness in the algorithmic economy when data is abundant but can not be used to train models.?
That is why most professionals agree that synthetic data has value to address these issues. To better understand the value of synthetic data, we’ll open some of the fundamental questions in this article: What is synthetic data? How is it generated? Who benefits the most? And are there any valuable examples from everyday life?
What Is Synthetic Data and How Is It Generated?
Synthetic data is data that is artificially created by ML algorithms instead of generated by actual events. It can be used for a wide range of activities, such as test data for new products and tools or to add more complexity in AI training models.?
Many sources identify different types of synthetic data for various purposes. One article by?Statice?explained the three common types:
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Synthetic data is typically created via a generative model from the original dataset that produces synthetic copies resembling accurate data. The main?generative models?for synthetic data are Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive models.?
If it needs to be simplified, synthetic data can be generated when there is not some or many real data. If?there is no actual data, but a broad understanding of data set distribution, the random sample of any distribution can be created. The synthetic data’s quality depends on the engineer’s grasp of a specific data environment. Where real data does not exist, synthetic data can be the right solution. When?there is real data,?synthetic data is generated by a best-fit distribution. A hybrid synthetic data generation approach is used when only?some real data exists?where part of the dataset is generated from assumed distributions and other parts from actual data.
What Are The Advantages and Disadvantages of Synthetic Data?
Check Out Everyday Examples of Synthetic Data Usage
Read the full article at Hyperight.com.