Synthetic Data vs. Real Data: Which one would be better to use?(11/13)

Synthetic Data vs. Real Data: Which one would be better to use?(11/13)

Synthetic Data

With the rapid growth of the generative AI market, the demand for training data is skyrocketing. High-quality data is crucial to enhance AI model performance, yet obtaining the right data often poses challenges. This brings up a question many data scientists and businesses face today: should they rely on synthetic data or real data for their AI needs? This post explores when and why synthetic data or real data is best used.

---

1. Understanding the Basics of Synthetic Data and Real Data

Before diving into specifics, it’s essential to understand the core differences between synthetic data and real data. Synthetic data is artificially generated data that replicates real-world conditions. Unlike real data, which comes from actual observations, synthetic data is designed to imitate similar statistical patterns. It’s often generated using AI algorithms, making it possible to quickly produce large datasets without privacy concerns. This can be particularly useful in scenarios where data is limited or too sensitive to be shared.

For example, let’s consider autonomous vehicles. Training these AI models requires simulating thousands of unique road scenarios—many of which, like severe accidents, may be too dangerous or rare to capture as real data. Synthetic data provides a way to safely and efficiently generate these situations in a virtual environment, giving models the experience they need without real-world risks.

Real data, however, is often the gold standard due to its authenticity. Real-world data is collected from actual user behavior, sensors, or other observational methods, capturing the inherent complexity of real scenarios. Although reliable, collecting real data can be expensive, time-consuming, and often limited by privacy concerns or legal restrictions.

2. When to Use Synthetic Data in Different Fields

Synthetic data has become an invaluable resource across industries, especially in fields with high regulatory oversight, limited data availability, or specialized requirements.

For instance, in the financial industry, fraud detection models often suffer from an imbalance between abundant legitimate transactions and rare fraudulent cases. By generating synthetic fraud cases, financial institutions can create a balanced dataset, allowing their models to learn the nuanced patterns that may indicate fraudulent behavior.

In healthcare, strict regulations often restrict the use and sharing of patient data. Synthetic medical records allow researchers to develop and test AI models without accessing actual patient information, safeguarding privacy. By using it to training AI, healthcare models can learn from a variety of scenarios without risking patient confidentiality.

In the case of the retail industry, customer behavior data is sensitive and often protected by privacy laws, especially with regulations like GDPR. Synthetic data allows companies to generate large datasets that mimic customer trends and preferences without exposing individual details. As a result, businesses can refine their recommendation algorithms and customer segmentations safely and efficiently.

3. Select the appropriate solution in CUBIG

CUBIG’s soultion like DTS(Data Transform System) will help. With DTS, companies can generate private synthetic data that maintains 99% of the performance of their original data on a local server without having to transfer the original data to an external server. In other words, it can easily generate high-quality synthetic data for training AI model. Companies, also, can safely use data that meets the necessary requirements while still adhering to legal regulations.

---

Conclusion

As AI continues to advance, finding the right data to train models becomes both more complex and more essential. While real data is invaluable for its authenticity, synthetic data provides an efficiency, privacy-compliant alternative that can grow the performance of AI learning. Using CUBIG’s solutions like DTS, companies can leverage accessing data that supports their unique needs in a private, effective way.

Interested in using this private synthetic data solution, DTS? Visit the site below!

Cubig Homepage: https://azoo.ai/

Cubig Blog: https://azoo.ai/blogs/