Synthetic Data

What it is

Synthetic data refers to data created digitally instead of being collected from real-world events or direct customer interactions. Unlike data gathered by sensors, bank transactions, or forms, this is generated by mathematical models or neural networks trained to mimic the patterns, correlations, and statistical structure of an original dataset.

For an SME, this means having access to information that behaves exactly like its sales, logistics, or customer behavior data, but contains no real names, Tax IDs, or addresses. It is, essentially, a "digital twin" of the information, useful when real data is scarce, confidential, or too expensive to obtain.

How it works

The process of creating synthetic data is based on learning the statistical distribution of a source dataset. There are three main methods currently in use:

1. Generative Models (GANs and VAEs): These are neural networks trained to create new examples that the human eye (or other algorithms) cannot distinguish from the originals. One network tries to create fake data and another tries to detect the forgery, forcing the system to become extremely accurate.
2. Rule-Based Generation: Uses predefined business logic to create scenarios. For example, if we know that 20% of customers at an auto shop in Portugal request an oil change every 15,000km, the system generates thousands of fictitious records that respect that proportion.
3. LLMs (Large Language Models): Recently, models like GPT-4 have been used to generate high-quality synthetic text, such as product reviews or technical descriptions, based on specific instructions.

The secret lies in preserving analytical utility. If real data shows that customers who buy Product A tend to buy Product B after 3 days, the synthetic data must replicate that exact same trend, even if the customer "João Silva" becomes customer "ID_X942".

When to use

There are four main scenarios where synthetic data solves critical business problems:

• Privacy and Compliance (GDPR): If you need to share data with an external consultant or test new software in a development environment, using real customer data is a massive legal risk. Synthetic data allows you to test everything without ever touching actual personal data.
• AI Model Training: Often, AI needs thousands of examples of errors or failures that rarely happen in reality. We can generate thousands of images of defective parts or fraudulent transactions to teach the model how to detect them.
• Data Balancing: If you have a database where 99% of customers pay on time and 1% fail to pay, any AI will struggle to learn how to predict defaults. Creating synthetic data for the minority group helps balance the model.
• Scenario Simulation: Do you want to predict what happens if raw material prices rise by 30% and demand drops by 10%? You can generate synthetic data that simulates this future to test the resilience of your operation.

Common errors

One of the most frequent errors is Overfitting. If the synthetic data generator is too rigid, it ends up copying the real data exactly instead of learning the pattern. This not only defeats the purpose of privacy (potentially revealing real data by accident) but also makes the model useless for generalization.

Another error is the Loss of Subtle Correlations. A synthetic data file might look perfect on the surface but fail to capture complex relationships between variables (e.g., the relationship between humidity in a warehouse and the return rate of a specific textile product). If these correlations are lost, decisions made based on that data will be wrong.

Finally, there is the risk of Statistical Hallucination. Without rigorous validation (Evals), the system may generate data that is mathematically possible but physically impossible in the business context, such as a customer making 50 purchases in the same second.

Practical example for an SME

Imagine a footwear factory in São João da Madeira that wants to implement an AI solution to predict which shoe models will be most successful next season. However, the company only has structured digital data from the last two years — which is insufficient to train a robust model.

The factory uses a synthetic data generation tool. Based on the 5,000 real orders it has, it generates 50,000 new synthetic orders that respect seasonal trends, color preferences by region, and price fluctuations. During this process, they remove any identification of the retailers, protecting their trade secrets. With this expanded database, they are able to train a forecasting algorithm that is 25% more accurate than if they used only the limited real data available.

Frequently Asked Questions

Q: Is synthetic data the same as anonymized data? A: No. Anonymization tries to mask real data (e.g., deleting names), but often allows re-identification through data cross-referencing. Synthetic data is created from scratch; there is no real person behind that specific record.

Q: Will AI quality be worse if using synthetic data? A: It depends on the generation quality. In many cases, the AI becomes superior because synthetic data allows for "cleaning" biases or focusing on rare cases that real data ignores.

Q: Do I need customer permission to create synthetic data from theirs? A: According to the GDPR, if the synthesis process is irreversible and does not allow for the identification of individuals, the final result is not considered personal data, greatly facilitating legal compliance.

Q: Is it expensive to generate this data? A: For an SME, the cost of generating synthetic data is almost always lower than the cost of collecting new data manually or the risk of a fine for a data privacy breach.

• 01Generating 10,000 fictitious credit histories to train a risk model without using real customer data.
• 02Creating product images in varied environments to train computer vision without organizing photo shoots.
• 03Producing simulated chatbot conversations to test the resilience of a customer support system.
• 04Simulating industrial machine maintenance logs to predict rare failures that have not yet occurred in the factory.

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