One of the biggest challenges for IT, Data, and Development departments in organizations is the privacy, security, and availability of quality data for decision-making in various processes. This is where synthetic data comes into play.
Synthetic data allows companies to overcome these obstacles and make the most of their data assets.
Let’s start by explaining that synthetic data is artificially generated data that mimics the characteristics and distributions of real data, without containing any personal or sensitive information. They are like “test data” used for research and new developments.
According to AWS, these data are created using advanced algorithms and artificial intelligence techniques, ensuring that they maintain the statistical properties of the original data.
On the other hand, IBM explains that this dataset can be collected from real people, events, or objects through computer simulations or algorithms. One way to generate synthetic data is through open-source data generation tools, which can be purchased or acquired.
As a result, this allows for modeling real-world information while ensuring data protection.
Evidently, this brings several benefits when implemented, mainly reflecting real-world events on a mathematical and statistical basis, which enables the training of machine learning models.
In addition, synthetic data contributes:
- Privacy protection of information: This type of data eliminates the risk of exposing sensitive data, complying with privacy regulations, and safeguarding individuals’ confidentiality, reducing the risk of exposing real data.
- Unlimited data generation: As new developments are deployed, data can be generated on demand and at scale, facilitating the availability of data for testing, development, and analysis without restrictions.
- Cost reduction: They are a cost-effective alternative to the manual collection and labeling of data, allowing companies to save time and resources.
- Innovation and development: With synthetic data, complex and customized tests can be performed for the development of new products and services without compromising data security.
As mentioned earlier, synthetic data can have various applications, including: AI training, software testing, research and development of new products, implementation of new practices with real data, information confidentiality, and quality evaluation, among others.
Synthetic data represents a powerful tool for IT managers and directors, offering a secure and efficient solution for managing business data. By adopting this technology, companies can protect privacy, reduce costs, and foster innovation, positioning themselves at the forefront in an increasingly data-driven world.
Let us know if you’d like to learn more about this topic.
Reference:
AWS. ¿Qué son los datos sintéticos? Tomado de ¿Qué son los datos sintéticos? – Explicación de los datos sintéticos – AWS
IBM. ¿Qué son los datos sintéticos? Tomado de ¿Qué son los datos sintéticos? | IBM
