This can be avoided if companies invested in synthetic data, which can be quickly generated and help develop reliable machine learning models. Moreover, human-annotated data is a costly and time-consuming process. Hence, minimizing privacy concerns is a top reason why companies invest in synthetic data and synthetic data generation techniques.įor entirely new products, any previous data remains unavailable. Any leakage and sharing of personally identifiable customer information can lead to expensive lawsuits that affect the brand image. Most data privacy laws restrict businesses in the way they handle sensitive data.
Synthetic data can be an asset to businesses for three main reasons: privacy concerns, faster turnaround for product testing, and training machine learning algorithms. Read this article to understand the key reasons for the increasing demand for synthetic data in the artificial intelligence field, followed by the benefits offered, challenges faced, the techniques employed for generating synthetic data to solve business problems, and some real-life applications. It confirms that synthetic data is here to stay in the coming years. According to a report published by Gartner on synthetic data in June 2021, it is predicted that by the year 2030, most of the data required for AI needs will be synthetic data that is artificially generated through rules, statistical models, and/or simulations. Synthetic data promises to meet this demand for massive volumes of data.
It leads us to the question- Is there a solution to satisfactorily meet this demand? How can data scientists obtain the data they want at the scale they require while maintaining quality, balance, accuracy, and low costs? Simple, synthetic data is the answer. Acquiring fresh data is time-consuming, expensive, or sometimes just impossible. Such an ever-increasing demand for additional data poses a challenge. Data scientists from several organizations require access to large volumes of data to train and deploy cutting-edge machine learning or deep learning models for solving challenging problems. There is an undeniable need for more data around the world. Data continues to be an integral part of the world today, from the perspective of daily interactions between humans and machines.
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