Synthetic data quality

Data utility refers to how well a dataset meets the needs of its intended use. It encompasses accuracy, completeness, consistency, reliability, and relevance. High-quality data is accurate and free from errors, inconsistencies, or duplications, ensuring it can be effectively used for analysis, decision-making, and operational purposes.

Synthetic data quality pertains to how closely synthetic datasets mimic real-world data’s statistical properties and characteristics. It evaluates the fidelity of the generated data, including its accuracy, reliability, and relevance, ensuring that synthetic data is a valid substitute for actual data in various applications.

It is a synthetic data quality evaluation displayed in quality assurance and demonstrates the accuracy, privacy, and speed of the synthetic data compared to the original data. It provides a detailed analysis of the synthetic dataset, including metrics for accuracy, privacy, and performance, ensuring the data meets high standards.

At Syntho, we understand the importance of reliable and accurate synthetic data. That’s why we provide a comprehensive quality assurance report for every synthetic data run. Our quality report includes various metrics such as distributions, correlations, multivariate distributions, privacy metrics, and more. This way, you can easily assess that the synthetic data we provide is of the highest quality and can be used with the same level of accuracy and reliability as your original data.

Our quality assurance report evaluates:

• Accuracy: How closely the synthetic data matches the statistical properties of the original data.
• Privacy: Measures taken to ensure sensitive information is protected and not disclosed.
• Speed: The efficiency of the synthetic data generation process and its performance in real-time applications.

Synthetic data privacy metrics are crucial because they asses if generated data does not reveal sensitive or personally identifiable information.

• Maintaining Data Fidelity: Ensuring that synthetic datasets accurately reflect the statistical properties of real-world data.
• Balancing Privacy and Utility: Generating data that is both useful for analysis and secure from privacy risks.
• Handling Complex Data Relationships: Accurately modeling intricate relationships and dependencies in the data.
• Performance and Scalability: Efficiently generating large volumes of high-quality data in a timely manner.

High-quality synthetic data offers several benefits:

• Enhanced Privacy: Protects sensitive information while providing valuable insights.
• Improved Accuracy: Provides a reliable alternative to real data for testing and training data for machine learning models.
• Cost Efficiency: Reduces the need for extensive data collection and management.
• Increased Flexibility: Allows for the creation of diverse datasets tailored to specific requirements or scenarios.

• Statistical Comparisons: Evaluating how well the synthetic data replicates the statistical properties of the original data.
• Privacy Metrics: Assessing the effectiveness of privacy protection measures.
• Utility Testing: Determining how well the synthetic data performs in real-world applications, such as training data for machine learning models.

• Quality Assessment: Regularly evaluate synthetic datasets using statistical properties and privacy metrics to ensure accuracy and reliability.
• Robust Generation Techniques: Employ advanced algorithms and methods in the synthetic data generation process to maintain fidelity and relevance.
• Continuous Improvement: Regularly update and refine synthetic data generation techniques to address emerging challenges and enhance the quality of the synthetic data.
• Validation with Existing Data: Compare synthetic data against actual data to verify its accuracy and usefulness in practical scenarios.