Description: Synthetic data is data that is artificially generated rather than obtained through direct measurements. This data is created through algorithms and mathematical models, allowing for the simulation of situations and behaviors that may not be available in real datasets. The main characteristic of synthetic data is that it can replicate the statistical properties of original data, making it useful for training machine learning models and conducting analyses without compromising the privacy of sensitive information. Additionally, synthetic data can be used to fill gaps in datasets, improve model robustness, and facilitate research in areas where real data is scarce or difficult to obtain. Its relevance has grown in various fields, including computer vision and predictive analytics, where it is used to train models and forecast trends and future behaviors. In summary, synthetic data is a powerful tool in the Big Data era, enabling the generation of valuable information without the limitations of real data.
History: The concept of synthetic data began to gain attention in the 1990s when researchers started exploring the generation of artificial data to improve the quality of machine learning models. As technology advanced, especially with the rise of Big Data and artificial intelligence in the 2010s, interest in synthetic data skyrocketed. In 2014, several studies were published demonstrating the effectiveness of synthetic data in enhancing predictive models and preserving privacy. Since then, various techniques and tools have been developed to generate synthetic data, leading to its adoption across multiple industries.
Uses: Synthetic data is used in various applications, including training machine learning models, simulating scenarios in research and development, and creating datasets for software testing. It is also valuable in sectors such as healthcare, where synthetic data is generated to train models without compromising patient privacy. Additionally, it is used in the automotive industry to simulate driving conditions and in creating virtual environments for various applications.
Examples: An example of synthetic data is the use of artificially generated images to train facial recognition models, where faces that do not belong to real people are created. Another case is the generation of simulated financial transaction data to test fraud detection systems. In the healthcare sector, synthetic datasets have been created that simulate medical histories to research treatments without using real patient data.
