How Synthetic Relational Data is Shaping New Frontiers in Data Utilization (08/05)

Relational databases are widely used across various domains to structure and manage complex data. However, these databases often contain sensitive information, leading to privacy and security concerns. Additionally, some data can be rare or costly to collect, making access difficult.

To address these challenges, synthetic relational databases have gained attention. Synthetic relational data is generated to preserve the characteristics and structure of real data, providing a powerful alternative for data analysis, modeling, and system testing without using actual data. The potential applications are vast, particularly in industries such as finance, healthcare, and IT.

Differences from General Tabular Synthetic Data

Synthetic relational data differs from general synthetic data in terms of data structure and complexity. General synthetic data typically consists of independent data points, making it suitable for simple data generation and analysis. In contrast, synthetic relational data must maintain complex relationships and dependencies across multiple tables, emphasizing data integrity and consistency. This is especially important in scenarios that require complex data models.

Advantages

An Alternative to Rare or Expensive Data: Synthetic relational data provides a valuable alternative to rare or costly data. Real data can be difficult to access or expensive to collect. Synthetic data addresses these shortages, enabling experimentation and analysis across various scenarios. This is particularly useful for training and testing machine learning models.

Challenges in the Generation of Synthetic Relational Data

1. Complexity of Relationships and Dependencies: One of the biggest challenges in generating synthetic relational data is accurately reflecting the relationships and dependencies between tables. It is essential to accurately mimic the relationships among various entities within a database, which requires sophisticated algorithms and models. This complexity is a key factor in determining the realism and utility of the data.
2. Difficulties in Evaluation and Verification: Evaluating and verifying the quality of synthetic relational data is a challenging task. It is necessary to establish criteria to determine how well the generated data reflects real data. Methods to assess the statistical properties, relationship realism, and usefulness of the data need to be developed, as they are crucial for ensuring the reliability of the data.

Examples of Potential Applications

Finance: Risk Management and Simulation

In the financial sector, synthetic relational data can be effectively used to assess and simulate various risk scenarios. For example, realistic datasets can be generated to train models for market volatility, credit risk, and fraud detection. This allows financial institutions to develop better risk management strategies and safely test new financial products.

Relational papers: link

Relational posts: link