![]() It can ensure data fairness by fair distribution of data while following privacy policies.Rebalancing features may help mitigate inaccuracies and missing information to provide a comprehensive and quality dataset.Without compromising sensitive data, marketers can create a customer persona that resembles a real customer journey and behavior.With the help of synthetic data, researchers can model scenarios that may not exist that could foster innovation.It provides complete control over the data where developers can adjust parameters to adapt to changing circumstances.Synthetic data removes such skewed behavior and provides a diverse outlook on possibilities. Real-world data is highly biased towards particular outcomes or categories.It saves time and costs by automating the manual and mundane preparation of data.Synthetic data generation is a secure, fast, and scalable solution as compared to traditional anonymization tools.Moreover, the impact is already visible where some startups are capitalizing on this innovation. Synthetic data generation is not just an innovation but a solution for accurate, secure, and cost-effective data modeling.Īccording to Gartner, synthetic data is going to overshadow real data by 2030. However, the output data doesn’t carry any sensitive data but preserves the behavioral features of real data. Synthetic data generation is a mathematical and statistical process performed by machine learning models that are trained using real objects, people, and the environment. What are the benefits of synthetic data?. ![]() While they offer an alternative way to capture real-world data, processed data stays uncompromised. To overcome this problem, companies are now shifting to synthetic data generation tools. Though it looks promising, studies reveal that the identity of 80% of credit card holders can be re-identified from the last 3 transactions and 87% of them are at risk if their birth date, gender, and postcode are exposed. ![]() The technique uses pseudonymization, row and column shuffling, directory replacement, and encryption. Using traditional anonymization techniques is yet another problem. This isn’t an issue for us as long as they use our data to generate revenue.īut the big problem occurs when the hacker breaks into a system and can retrieve sensitive data. And therefore, synthetic data can be a profitable opportunity to train prototypes and create models.Īlso, the fact that digitization has paved the way for companies to capture our data to train their ML models. The smaller companies or startups, however, don’t have access to such abundance. But control of these real data is under a handful of tech giants only. Many other tech or social media giants generate massive amounts of user data. The reason is the lack of control over data.Īmazon alone generates over 1000 petabytes of data every day. Now the real question – Why not simply use real data? ![]() It can be everything from text, images, voice, and even video footage. Synthetic data, as the name says, is something that is artificially made by AI programs. ![]()
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