How Can I Ensure the Quality of My Synthetic Data?

The quality of synthetic data depends on a variety of factors. Managing these factors can help ensure that your data is accurate, reliable, and up to industry standards.

Synthetic data is algorithmically generated data that mimics original data structured in tabular form (rows and columns). This can be a great solution for organizations that lack access to real data or are subject to strict privacy regulations.

Test data management

The quality of test data management is critical for effective testing. This is especially important when performing performance or security tests. In addition, it is critical for regulatory compliance. Using a reputable AI-powered synthetic data generator is an ideal solution to ensure high-quality test data without privacy concerns. It retains all of the characteristics of the production data and also supports relational structures.

A streamlined process for self service test data provisioning is essential to accelerate software development and improve quality. This is where a comprehensive test data management (TDM) tool like GenRocket comes in. The GenRocket TDM platform enables teams to provision the right data in the right place at the right time to maximize test coverage. The process is based on 6 dimensions of test data quality.

Model audits

The use of synthetic data allows organizations to leverage the benefits of a privacy-preserving and scalable dataset for machine learning modeling and other purposes. However, generating synthetic data requires careful consideration of the privacy, utility, and fidelity of the dataset. These considerations include the selection of variables, the distribution of values, and the relationships between features.

A model audit is an important step in the process of creating a synthetic dataset. The audit helps identify inaccuracies or errors in the source data that may have a negative impact on the quality of the resulting dataset.

This approach can also help improve the performance of AI models by enabling them to learn more about the world around them. For example, synthetic data enables autonomous vehicles to simulate rare edge cases or unpredictable pedestrian behavior that cannot be reproduced in real-world testing.

Data quality checks

Data quality is a key element of synthetic data, as it must replicate the statistical properties of an original dataset. It also needs to be consistent and accurate over time. A good synthetic data generation solution can ensure that this is the case by performing a variety of checks on the generated datasets.

In addition to field distribution stability, it should also maintain key correlations and deep structure. For other use cases, such as vendor evaluations or assessments, higher level agreement may be less critical.

In addition, the generated synthetic data must be consistent and follow business rules. For example, if you are creating test data for a claims processing application, the generated data must adhere to all relevant diagnostic procedure codes. GenRocket has a unique patented process that can guarantee this consistency and referential integrity for all permutations of potential inputs in a controlled manner.

Using multiple data sources

For some use cases such as machine learning-evaluation, internal software testing, education, training and hackathons and vendor assessment higher level agreement between the original and synthetic data sets may not be critical. Nevertheless, it is still important that the synthetic dataset captures higher level data structure, maintains key correlations and has high accuracy, which GenRocket’s Synthetic Data Quality Score measures.

Measuring the quality of synthetic data involves assessing its fidelity, utility and privacy. While traditional privacy enhancing technologies like anonymization perform well on the privacy front they may not be optimal for data utility. However, techniques that balance fidelity and privacy are maturing quickly. They require a combination of technical and organizational measures that are specific to each use case. This is where specialized synthetic data generation solutions can help.

Measuring the quality of generated synthetic data

In the context of synthetic data, data quality is an important measure. It is essential that the data accurately replicates the statistical properties of its source dataset and maintains consistency over time. This is necessary to ensure that the insights generated by the data are valid and useful for decision-making.

Using multiple data sources can help improve the accuracy of synthetic data by reducing biases in the data. This is because different data sources have unique characteristics that may not be reflected in other datasets.

Another important metric is the number of duplicate rows in the synthetic data. A high number of duplicate rows can cause the model to misclassify and reduce performance. Finally, it is important to conduct a privacy assurance assessment to determine whether the synthetic data contains real data.