Synthetic Data can help insurance companies to improve their underwritting, claims management and customer experience while unlocking the potential of data that have been hidden by data privacy regulations.
5 min read
Companies now have access to more employee data than ever before, and, as data collection becomes easier, it is more important than ever for HR to take position on ethical data use, privacy and security.
4 min read
Synthetic data has received considerable attention as a method of protecting patient privacy, diversifying datasets and augmenting clinical research, hence accelerating the application of Artificial Intelligence across different hospitals and institutions.
3 min read
Data anonymization is key to applying the best data-driven strategies in the banking industry. The correct implementation of this strategy based on synthetic data will allow extracting all the knowledge and insights from customer financial data.
The banking industry faces a wide variety of challenges when it comes to anonymization and data privacy. Thanks to synthetic data, banks can tackle data challenges more effectively and unlock the full potential of their data assets.
The techniques that have always been used to anonymize data do not have the guarantee of ensuring the total anonymity of the data. These are techniques that have flaws when it comes to ensuring data privacy and utility.
Without any doubt, synthetic data is one of the key technologies in the present and future of data science and artificial intelligence, but what is synthetic data? How do they work? And why is it key for data-driven companies?
The applications and benefits that synthetic data can bring in the development of data-centric strategies for companies are huge and growing in relevance for data science teams.
Throughout this article, we will work with a Kaggle dataset, making a brief demonstration of how synthetic data is generated and how it preserves the statistical properties of the original data.
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