Post by account_disabled on Jan 27, 2024 1:54:18 GMT -5
Synthetic data, as the name suggests, is something that is made artificially by AI programs. It can be anything from text, images, sound and even video footage. Now the real question – Why not just use real data? The reason is the lack of control over the data. Amazon alone generates over 1,000 petabytes of data every day. Many other tech or social media giants generate massive amounts of user data. But control of this real data is only under a handful of tech giants. Smaller companies or startups, however, don't have access to such abundance. Therefore, synthetic data can be a profitable opportunity to train prototypes and create models.
Also, the fact that digitization has paved the way for companies to capture C Level Executive List our data to train their ML models. This is not a problem for us as long as they use our data to generate revenue. But the big problem happens when a hacker breaks into a system and can get sensitive data. Using traditional anonymization techniques is another problem. The technique uses pseudonymization, row and column shuffling, directory substitution, and encryption. Although it sounds promising, Studies reveal that 80% of credit card holders' identities can be re-identified from the last 3 transactions and 87% of them are at risk if their date of birth, gender and zip code are exposed. To overcome this problem, companies are now switching to synthetic data generation tools. While they provide an alternative way to capture real-world data, the processed data remains uncompromised.
What Is Synthetic Data Generation? The generation of synthetic data is a mathematical and statistical process performed by machine learning models that have been trained using objects, people and real environment. However, the output data does not contain sensitive data, but preserves the behavioral features of the real data. Synthetic data versus real data statistics Synthetic data generation is not just an innovation, but a solution for accurate, secure and cost-effective data modeling. According to Gartner, synthetic data will eclipse real data by 2030. Moreover, the impact is already visible where several startups are taking advantage of this innovation. What Are The Benefits Of Synthetic Data? Synthetic data generation is a secure, fast and scalable solution compared to traditional anonymization tools. It saves time and cost by automating manual and mundane data preparation.
Also, the fact that digitization has paved the way for companies to capture C Level Executive List our data to train their ML models. This is not a problem for us as long as they use our data to generate revenue. But the big problem happens when a hacker breaks into a system and can get sensitive data. Using traditional anonymization techniques is another problem. The technique uses pseudonymization, row and column shuffling, directory substitution, and encryption. Although it sounds promising, Studies reveal that 80% of credit card holders' identities can be re-identified from the last 3 transactions and 87% of them are at risk if their date of birth, gender and zip code are exposed. To overcome this problem, companies are now switching to synthetic data generation tools. While they provide an alternative way to capture real-world data, the processed data remains uncompromised.
What Is Synthetic Data Generation? The generation of synthetic data is a mathematical and statistical process performed by machine learning models that have been trained using objects, people and real environment. However, the output data does not contain sensitive data, but preserves the behavioral features of the real data. Synthetic data versus real data statistics Synthetic data generation is not just an innovation, but a solution for accurate, secure and cost-effective data modeling. According to Gartner, synthetic data will eclipse real data by 2030. Moreover, the impact is already visible where several startups are taking advantage of this innovation. What Are The Benefits Of Synthetic Data? Synthetic data generation is a secure, fast and scalable solution compared to traditional anonymization tools. It saves time and cost by automating manual and mundane data preparation.