In the current landscape of artificial intelligence and big data, synthetic data is emerging as a crucial solution to many challenges facing businesses. This data not only helps protect privacy, but also facilitates innovation and improves the quality of AI model training. In this blog, we will explore how synthetic data is created, why businesses need it, the challenges associated with its creation, and how advanced tools can help in this process.
What is synthetic data?
Synthetic data is data artificially generated using advanced algorithms and techniques to mimic the statistical properties of real data without including sensitive or identifiable information. This data is used to train, validate, and test AI models, and is especially useful when real data is difficult to obtain, subject to legal restrictions, or contains sensitive information.
How to create synthetic data
Creating synthetic data involves several steps and techniques, including:
- Original data modeling
Statistical or machine learning models are built based on the real data available. These models capture the essential properties and patterns of the original data.
- Generation of new data
Using the constructed models, new data are generated that mimic the characteristics of the original data. This process may include techniques such as simulation, permutation, and interpolation.
- Quality evaluation
The generated synthetic data is evaluated to ensure that it maintains the integrity and statistical properties of the original data. Consistency and validity tests are performed to confirm that the synthetic data is realistic and useful.
- Fit and refinement
Based on the evaluation, the models and generated data can be adjusted and refined to improve the quality and accuracy of the synthetic data.
Why do companies need to create synthetic data?
- Privacy protection
Synthetic data eliminates the risk of exposure to personally identifiable information (PII), helping to comply with privacy regulations such as GDPR and CCPA.
- Availability and accessibility
Synthetic data can be generated in large volumes and made available immediately, making it easy to train and continuously validate AI models without the limitations of real data.
- Innovation and development
They allow companies to experiment and develop new products and services without the risks and restrictions associated with real data.
- Improving data quality
Synthetic data can be designed to be more diverse and balanced than real data, improving the robustness and generalization of AI models.
Challenges in creating synthetic data
- Technical complexity
Creating synthetic data requires advanced knowledge in statistical modeling and machine learning techniques, which can be a challenge for many organizations.
- Quality guarantee
Ensuring that synthetic data is of high quality and maintains the properties of real data can be complicated and requires a rigorous validation process.
- Startup costs
Implementing systems to generate synthetic data can involve significant upfront costs in terms of infrastructure and human resources.
How to overcome challenges
To overcome these challenges, companies can turn to advanced tools that make it easier to create and manage synthetic data. These tools automate the data generation process, provide evaluation and validation capabilities, and ensure regulatory compliance, all while reducing technical complexity and associated costs.
For example, Nymiz offers anonymization and pseudonymization solutions that enable the generation of high-quality synthetic data, while maintaining privacy and complying with data protection regulations. These solutions not only facilitate the creation of synthetic data, but also identify and anonymize sensitive data, ensuring robust and efficient protection.
Conclusion
Creating synthetic data is an essential strategy for companies seeking to protect privacy, improve the quality of AI model training, and foster innovation. Although it presents challenges, with the support of advanced solutions, organizations can overcome these obstacles and benefit greatly from synthetic data. Adopting this technology not only ensures regulatory compliance but also drives operational efficiency and market competitiveness.
